2024-03-29T14:41:10Zhttps://deepblue.lib.umich.edu/dspace-oai/requestoai:deepblue.lib.umich.edu:2027.42/1564142021-07-28T23:47:33Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2020-09-02T14:57:19Z
urn:hdl:2027.42/156414
Investigating drivers’ trust in autonomous vehicles’ decisions of lane changing events
Ayoub, Jackie
Zhou, Feng
University of Michigan, Dearborn
Dearborn
2020-09-02T14:57:19Z
2020-09-02T14:57:19Z
2020-10
Conference Paper
https://hdl.handle.net/2027.42/156414
the 64th International Annual Meeting of the Human Factors and Ergonomics Society
en_US
HFES
oai:deepblue.lib.umich.edu:2027.42/288172019-01-29T20:38:01Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2006-04-10T13:53:52Z
urn:hdl:2027.42/28817
Integrated decision support and expert systems in a computer integrated manufacturing environment
Chang, Chia-Hao
Lin, Jimming T.
Dept. of Industrial & Systems Eng. University of Michigan-Dearborn, Dearborn, Michigan, USA.
Faculty of Administration, University of Ottawa, Ottawa, Ontario, Canada.
In a computer integrated manufacturing (CIM) environment, well planning, control and operational process require both expert knowledge of the area, and powerful decision support capabilities. This paper discusses the features of decision support systems and expert systems, and their integration to support the major functions from marketing and strategic level considerations to manufacturing operational planning and process. From the hierarchical structure of information flow in a company, this paper attempts to find the best way of combining decision support systems with expert systems in enhancing the planning, control and operational functions in a CIM environment.
2006-04-10T13:53:52Z
2006-04-10T13:53:52Z
1990
Article
Chang, Chia-hao, Lin, Jimming T. (1990)."Integrated decision support and expert systems in a computer integrated manufacturing environment." Computers & Industrial Engineering 19(1-4): 140-144. <http://hdl.handle.net/2027.42/28817>
http://www.sciencedirect.com/science/article/B6V27-47WTRP6-12/2/37e855d523067b13bae66c18852ef91c
http://hdl.handle.net/2027.42/28817
http://dx.doi.org/10.1016/0360-8352(90)90093-2
Computers & Industrial Engineering
en_US
IndexNoFollow
Elsevier
oai:deepblue.lib.umich.edu:2027.42/1660812021-09-02T01:49:13Zcom_2027.42_13913col_2027.42_146790
2021-01-27T04:28:54Z
urn:hdl:2027.42/166081
Predicting Driver Fatigue in Automated Driving with Explainability
Zhou, Feng
Alsaid, Areen
Blommer, Mike
Curry, Reates
Swaminathan, Radhakrishnan
Kochhar, Dev
Talamonti, Walter
Tijerina, Louis
University of Michigan-Dearborn
Ford Motor Company
Dearborn
Driver fatigue prediction, explainability, automated driving, physiological measures
Research indicates that monotonous automated driving increases the incidence of fatigued driving. Although many prediction models based on advanced machine learning techniques were proposed to monitor driver fatigue, especially in manual driving, little is known about how these black-box machine learning models work. In this paper, we proposed a combination of eXtreme Gradient Boosting (XGBoost) and SHAP (SHapley Additive exPlanations) to predict driver fatigue with explanations due to their efficiency and accuracy. First, in order to obtain the ground truth of driver fatigue, PERCLOS (percentage of eyelid closure over the pupil over time) between 0 and 100 was used as the response variable. Second, we built a driver fatigue regression model using both physiological and behavioral measures with XGBoost and it outperformed other selected machine learning models with 3.847 root-mean-squared error (RMSE), 1.768 mean absolute error (MAE) and 0.996 adjusted $R^2$. Third, we employed SHAP to identify the most important predictor variables and uncovered the black-box XGBoost model by showing the main effects of most important predictor variables globally and explaining individual predictions locally. Such an explainable driver fatigue prediction model offered insights into how to intervene in automated driving when necessary, such as during the takeover transition period from automated driving to manual driving.
2021-01-27T04:28:54Z
2021-01-27T04:28:54Z
2021-01-26
Article
https://hdl.handle.net/2027.42/166081
https://dx.doi.org/10.7302/4
https://orcid.org/0000-0001-6123-073X
Zhou, Feng; 0000-0001-6123-073X
en_US
oai:deepblue.lib.umich.edu:2027.42/1083812019-03-20T15:57:52Zcom_2027.42_13913col_2027.42_146790col_2027.42_13914
NO_RESTRICTION
urn:hdl:2027.42/108381
Driver distraction from cell phone use and potential for self-limiting behavior
Flannagan, Carol A. C.
Bao, Shan
Klinich, Kathleen D.
This project consists of three parts. The first is a review of the literature on driver distraction that primarily focuses on cell phone use. The second two parts involve analysis of an existing field operational test (FOT) database to examine: 1) self-limiting behavior on the part of drivers who use cell phones, and 2) eye glance patterns for drivers involved in cell phone conversations and visual-manual tasks (e.g., texting) as compared to no-task baseline driving. The literature review discusses the apparent contradiction between results of case-crossover and simulator studies that show increases in instantaneous risk due to talking on a cell phone and results of crash-data analyses that show no substantial increase in crashes associated with increases in cell phone use in vehicles. The first data analysis shows some evidence of self-limiting behavior in cell phone conversations. Drivers initiate calls when on slower roads and at slower speeds, often when stopped. However, they call more at night, which is a higher-risk time to drive. The second analysis showed that eye glances when talking on the phone are fixated on the road for longer periods of time than in baseline driving. In contrast, on-road eye glances when engaged in a visual-manual (VM) task are short and numerous. Eye glances on and off the road are about equal in length, and the average total off-road gaze time for a five-second interval is about 2.8 secs, or 57% of the time. Average off-road gaze time out of five seconds in baseline driving is about 0.8 sec, or 16% of the time. Results show the differences in distraction mechanism between cell-phone conversations and texting. Ramifications for potential interventions are discussed.
2014-09-05T20:38:56Z
NO_RESTRICTION
2014-09-05T20:38:56Z
2012-12
Technical Report
UMTRI-2012-36
http://hdl.handle.net/2027.42/108381
University of Michigan, Ann Arbor, Transportation Research Institute
oai:deepblue.lib.umich.edu:2027.42/1096012019-02-05T15:07:04Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2014-12-09T16:53:46Z
urn:hdl:2027.42/109601
4.2.3 Systems Engineering Approach for Modeling An Organizational Structure
Rushton, Gary
Zakarian, Armen
Grigoryan, Tigran
An organization is just another type of system. Why not use systems engineering techniques for modeling and development of the organizational structure. Within every organization there are required tasks/functions that interact with each other. Therefore, one may use system engineering techniques to define what the organization is required to do and then develop an organizational structure using some basic design principals, e.g., integration analysis technique to minimize coupling and maximize cohesion between various organizational tasks and functions. In this paper, we illustrate how systems engineering design principles can be used for modeling and analysis of an organization structure.
2014-12-09T16:53:46Z
2014-12-09T16:53:46Z
2002-08
Article
Rushton, Gary; Zakarian, Armen; Grigoryan, Tigran (2002). "4.2.3 Systems Engineering Approach for Modeling An Organizational Structure." INCOSE International Symposium 12(1): 263-273.
2334-5837
2334-5837
http://hdl.handle.net/2027.42/109601
10.1002/j.2334-5837.2002.tb02469.x
INCOSE International Symposium
Hatley, D. and Pirbhai, I., Strategies for Real‐Time System Specifications. Dorset House, New York, 1987.
Rushton, G. and Zakarian, A., “ Modular Vehicle Architectures: A Systems Approach ”, Proceedings of the Tenth Annual International Symposium of the International Council on Systems Engineering, Minneapolis, MN, July 16‐20, 2000, pp 29 – 35.
Richardson, G. P. and Pugh, A. L. ( 1981 ), Introduction to system dynamics modeling with DYNAMO. Productivity Press, Cambridge, MA.
Porras, J. I. ( 1990 ), Stream analysis: A powerful way to diagnose and manage organizational change. Addison Wesley, Menlo Park, CA.
Hatley, D., Hruschka, P., and Pirbhai, I., Process For System Architecture and Requirements Engineering. Dorset House, New York, 2000.
Zakarian, A. and Rushton, G. J., “ Development of Modular Electrical Systems,” IEEE/ASME Transactions on Mechatronics, Vol. 6 No. 4, pp. 507 – 520, 2001.
Sneat, P. H. and Sokal, R. R., Numerical Taxonomy. San Francisco, CA. W. H. Freeman, 1973.
IndexNoFollow
Wiley Periodicals, Inc.
Dorset House
oai:deepblue.lib.umich.edu:2027.42/301742019-01-29T21:20:24Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2006-04-10T15:18:33Z
urn:hdl:2027.42/30174
Machinery condition monitoring by inverse filtering and statistical analysis
Chen, Yubao
Department of Industrial and Systems Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, U.S.A
Data used for machinery condition monitoring contains mainly the same information as that obtained under normal operation conditions. The traditional practice of feature extraction, which uses such data directly, suffers from low signal-to-noise ratio. This paper presents a method that uses an inverse filter to separate the information contents of the data, so that the feature extraction can be done by statistical analysis algorithms, which would otherwise be difficult. It is shown that the inverse filtering process is equivalent to that of prediction error estimation based on a signal model in the form of an autoregressive moving-average (ARMA) model. The construction of the inverse filter can therefore be carried out by ARMA modeling. An application example of this method for the monitoring of a paper handling system is also given.
2006-04-10T15:18:33Z
2006-04-10T15:18:33Z
1992-03
Article
Chen, Yubao (1992/03)."Machinery condition monitoring by inverse filtering and statistical analysis." Mechanical Systems and Signal Processing 6(2): 177-189. <http://hdl.handle.net/2027.42/30174>
http://www.sciencedirect.com/science/article/B6WN1-494T7XM-3Y/2/847e40378cc5c818f72be0bf9763715e
http://hdl.handle.net/2027.42/30174
http://dx.doi.org/10.1016/0888-3270(92)90064-P
Mechanical Systems and Signal Processing
en_US
IndexNoFollow
Elsevier
oai:deepblue.lib.umich.edu:2027.42/418592021-07-30T00:45:49Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2006-09-08T19:42:50Z
urn:hdl:2027.42/41859
Analysis of Process Models: A Fuzzy Logic Approach
Zakarian, Armen
The University of Michigan-Dearborn, Department of Industrial and Manufacturing Systems Engineering, Dearborn, USA, US,
Dearborn
2006-09-08T19:42:50Z
2006-09-08T19:42:50Z
2001-04
Article
Zakarian, A.; (2001). "Analysis of Process Models: A Fuzzy Logic Approach." International Journal of Advanced Manufacturing Technology 17(6): 444-452. <http://hdl.handle.net/2027.42/41859>
0268-3768
https://hdl.handle.net/2027.42/41859
http://dx.doi.org/10.1007/s001700170162
International Journal of Advanced Manufacturing Technology
en_US
Springer-Verlag London Limited
oai:deepblue.lib.umich.edu:2027.42/295512019-01-29T21:00:08Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2006-04-10T14:52:10Z
urn:hdl:2027.42/29551
Data flow model of a total service quality management system
Chang, Chia-Hao
Lin, Jimming T.
Dept. of Industrial & Systems Eng. University of Michigan-Dearborn, Dearborn, Michigan, USA
Faculty of Administration University of Ottawa, Ottawa, Ontario, Canada
Dept. of Industrial & Systems Eng. University of Michigan-Dearborn, Dearborn, Michigan, USA
Faculty of Administration University of Ottawa, Ottawa, Ontario, Canada
A total service quality management model was developed in this paper. The design integrated the pre-service delivery stage, service delivery stage and post-service delivery stage into one single model, similar to the structure of a total manufacturing quality management system. The design specification was described by means of data flow diagrams (DFD). The model can be used as the base for further development toward a computer integrated service quality management information system.
2006-04-10T14:52:10Z
2006-04-10T14:52:10Z
1991
Article
Chang, Chia-hao, Lin, Jimming T. (1991)."Data flow model of a total service quality management system." Computers & Industrial Engineering 21(1-4): 117-121. <http://hdl.handle.net/2027.42/29551>
http://www.sciencedirect.com/science/article/B6V27-4817CJP-T/2/aee764d29050058a407b7ae3eb6c3918
http://hdl.handle.net/2027.42/29551
http://dx.doi.org/10.1016/0360-8352(91)90074-G
Computers & Industrial Engineering
en_US
IndexNoFollow
Elsevier
oai:deepblue.lib.umich.edu:2027.42/1537972020-03-03T22:46:58Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2020-02-18T18:34:45Z
urn:hdl:2027.42/153797
Otto: An Autonomous School Bus System for Parents and Children
Ayoub, Jackie
Mason, Brian
Morse, Kamari
Kirchner, Austin
Tumanyan, Naira
Zhou, Feng
University of Michigan, Dearborn
Dearborn
Autonomous bus; Trust; Human-centered design; Child transportation
Technological advances in autonomous transportation systems have brought them closer to road use. However, little research is reported on children’s behavior in autonomous buses (ABs) under real road conditions and on improving parents’ trust in leaving their children alone in ABs. Thus, we aim to answer the research question: “How can we design ABs suitable for unaccompanied children so that the parents can trust them?” We conducted a study using a Wizard-of-Oz method to observe children’s behavior and interview both parents and children to examine their needs in ABs. Using an affinity diagram, we grouped children’s and parents’ needs under the following categories: entertainment, communication, personal behavior, trust and desires. Using an iterative human-centered design process, we created an Otto system, a smartphone app for parents to communicate with their children and a tablet app for children to entertain during the ride.
2020-02-18T18:34:45Z
2020-02-18T18:34:45Z
2020-02-18
Conference Paper
http://hdl.handle.net/2027.42/153797
CHI Late Breaking In
https://orcid.org/0000-0001-6123-073X
Zhou, Feng; 0000-0001-6123-073X
en_US
ACM CHI
oai:deepblue.lib.umich.edu:2027.42/309322019-03-18T18:52:14Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2006-04-10T15:51:39Z
urn:hdl:2027.42/30932
Impending failure detection for a discrete process
Chen, Yubao
Department of Industrial and Systems Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, U.S.A.
Signals from a discrete process contain a strong modulation as a result of the discrete events in the process, such as paper passage in a recirculating document feeder (RDF). This paper presents a study of the methodology of process monitoring for a RDF system. A fault tree has been established that shows the cause-and-effect relationship regarding possible malfunctions of a RDF system. Critical components of the RDF system have been identified for condition monitoring. The signature from the measurements of position, vibration, vacuum pressure, and drive motor current have been analysed. A data separation scheme was used in signal processing to demodulate the strong signal component associated with paper passage. Unique index extraction algorithms based on time series analysis and modeling have been developed to detect failures of these components. A decision-making scheme based on multiple voting has been implemented.
2006-04-10T15:51:39Z
2006-04-10T15:51:39Z
1993-03
Article
Chen, Yubao (1993/03)."Impending failure detection for a discrete process." Mechanical Systems and Signal Processing 7(2): 121-132. <http://hdl.handle.net/2027.42/30932>
http://www.sciencedirect.com/science/article/B6WN1-45P6972-10/2/d8961f01c455253637029f46166a25c5
http://hdl.handle.net/2027.42/30932
http://dx.doi.org/10.1006/mssp.1993.1002
Mechanical Systems and Signal Processing
en_US
IndexNoFollow
Elsevier
oai:deepblue.lib.umich.edu:2027.42/1539622021-07-30T02:05:16Zcom_2027.42_13913col_2027.42_146790
2020-02-25T19:33:22Z
urn:hdl:2027.42/153962
Analyzing Customer Needs of Product Ecosystems Using Online Product Reviews
Ayoub, Jackie
Zhou, Feng
Yang, Jessie
Xu, Qianli
Dearborn
It is necessary to analyze customer needs of a product ecosystem in order to increase customer satisfaction and user experience, which will, in turn, enhance its business strategy and profits. However, it is often time-consuming and challenging to identify and analyze customer needs of product ecosystems using traditional methods due to numerous products and services as well as their interdependence within the product ecosystem. In this paper, we analyzed customer needs of a product ecosystem by capitalizing on online product reviews of multiple products and services of the Amazon product ecosystem with machine learning techniques. First, we filtered the noise involved in the reviews using a fastText method to categorize the reviews into informative and uninformative regarding customer needs. Second, we extracted various customer needs related topics using a latent Dirichlet allocation technique. Third, we conducted sentiment analysis using a valence aware dictionary and sentiment reasoner method, which not only predicted the sentiment of the reviews, but also its intensity. Based on the first three steps, we classified customer needs using an analytical Kano model dynamically. The case study of Amazon product ecosystem showed the potential of the proposed method.
2020-02-25T19:33:22Z
2020-02-25T19:33:22Z
2019-11-25
Conference Paper
https://hdl.handle.net/2027.42/153962
ASME
https://orcid.org/0000-0003-0274-492X
https://orcid.org/0000-0001-6071-0387
https://orcid.org/0000-0001-6123-073X
Ayoub, Jackie; 0000-0003-0274-492X
Yang, X. Jessie; 0000-0001-6071-0387
Zhou, Feng; 0000-0001-6123-073X
en_US
DETC2019-97642, V02AT03A002
oai:deepblue.lib.umich.edu:2027.42/1096202021-07-30T21:45:41Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2014-12-09T16:53:54Z
urn:hdl:2027.42/109620
5.1.1 Modular Vehicle Architectures: A Systems Approach
Rushton, Gary J.
Zakarian, Armen
Modular systems provide the ability to achieve product variety through the combination and standardization of components. In this paper, a methodology that combines the Hatley/Pirbhai system model, integration analysis, and optimization techniques for development of modular electrical/ electronic vehicle systems is presented. The approach optimizes integration and interactions of the electrical/ electronic system elements and creates functional and physical modules for the vehicle. The paper illustrates importance of system modeling in developement of modular products. Discussion on how to make the system modeling more attractive to the industry is also presented.
2014-12-09T16:53:54Z
2014-12-09T16:53:54Z
2000-07
Article
Rushton, Gary J.; Zakarian, Armen (2000). "5.1.1 Modular Vehicle Architectures: A Systems Approach." INCOSE International Symposium 10(1): 27-33.
2334-5837
2334-5837
https://hdl.handle.net/2027.42/109620
10.1002/j.2334-5837.2000.tb00353.x
INCOSE International Symposium
Iri M. ( 1968 ), “ On the Synthesis of the Loop Cut Set Matrices and the Related Problem ”, RAAG Memoirs, Vol. 4, pp. 376 – 410.
Hatley D. J. and Pirbhai I. A. ( 1987 ), “ Strategies for Real‐Time Specification ”, Dorset House, New York.
Ulrich K. and Tung K. ( 1991 ), “ Fundamentals of Product Modularity ”, DE‐Vol. 39, Issues in Design Manufacture/Integration, ASME.
Ullman D. G., ( 1992 ), “ The Mechanical Design Process ”, McGraw‐Hill, New York, NY.
Pimmler T. U. and Eppinger S. D. ( 1994 ), “ Integration Analysis of Product Decomposition ”, Design Theory and Methodology – DTM, DE‐Vol 68, ASME.
Kusiak A. ( 1998 ), “ Group Technology ”, University of Iowa, Working Paper Version 8–25.
Kusiak A. and Chow W. S. ( 1987 ), “ Efficient Solving of the Group Technology Problem ”, Journal of Manufacturing Systems, Vol. 6, No. 2, pp. 117 – 124.
IndexNoFollow
Wiley Periodicals, Inc.
Dorset House
oai:deepblue.lib.umich.edu:2027.42/936342021-07-30T22:04:01Zcom_2027.42_13913col_2027.42_146790col_2027.42_13914
NO_RESTRICTION
urn:hdl:2027.42/93634
Road safety in New York and Los Angeles: U.S. megacities compared with the nation
Sivak, Michael
Bao, Shan
This study examined road safety in the two U.S. megacities, New York and Los Angeles.
Patterns of fatal and all crashes in these megacities were compared with those for the entire U.S. (Also included were data for the two respective states, New York and California.) The data on fatal crashes came from the Fatal Analysis Reporting Systems, and the data on all crashes from the General Estimates System and the states of New York and California. The period examined was 2002 through 2009.
The results indicate that crashes in the two megacities tend to differ in numerous aspects
from typical crashes in the U.S. These included aspects related to when and where these crashed occur, nature of crashes, weather, light, involved persons, and driver actions.
2012-09-24T20:04:34Z
NO_RESTRICTION
2012-09-24T20:04:34Z
2012-09
Technical Report
UMTRI-2012-24
https://hdl.handle.net/2027.42/93634
University of Michigan, Ann Arbor, Transportation Research Institute
oai:deepblue.lib.umich.edu:2027.42/269142021-07-30T22:09:01Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2006-04-07T20:01:02Z
urn:hdl:2027.42/26914
Dual database strategy and implementation
Chang, Chia-Hao
Steiner, Thomas G.
Dept. of Industrial & Systems Eng. University of Michigan, Dearborn, Michigan, USA
Comprehensive Computer Consulting, Northville, Michigan, USA
In expanding from an application-oriented hierarchical database model information system to a system integrated with information-oriented relational database model, IBM offers its IMS/DB2 dual database strategy. There are also other non-IBM database technologies challenging DB2 as the only alternative for IMS installation. Automobile industries themselves are going through such a transition in developing their fourth-generation information systems. A couple major automobile corporations with IMS-based information systems are brought up as examples. These corporations have developed their design strategies and system architectures. Such an integration has influence upon the operating environment and the decision support for the end-user-driven information retrieval applications.
2006-04-07T20:01:02Z
2006-04-07T20:01:02Z
1987
Article
Chang, Chia-hao, Steiner, Thomas G. (1987)."Dual database strategy and implementation." Computers & Industrial Engineering 13(1-4): 208-212. <http://hdl.handle.net/2027.42/26914>
http://www.sciencedirect.com/science/article/B6V27-4802TH0-6K/2/ebc8b344de6587dab50f34bb631cc970
https://hdl.handle.net/2027.42/26914
http://dx.doi.org/10.1016/0360-8352(87)90083-0
Computers & Industrial Engineering
en_US
IndexNoFollow
Elsevier
oai:deepblue.lib.umich.edu:2027.42/1637722021-07-30T22:09:15Zcom_2027.42_13913col_2027.42_146790
2020-12-25T16:42:35Z
urn:hdl:2027.42/163772
Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability
Ayoub, Jackie
Yang, X. Jessie
Zhou, Feng
University of Michigan, Dearborn, Ann Arbor
Dearborn
Trust prediction, XGBoost, SHAP explainer, Feature importance, Automated vehicles
Technological advances in the automotive industry are bringing automated driving closer to road use. However, one of the most important factors affecting public acceptance of automated vehicles (AVs) is the public’s trust in AVs. Many factors can influence people’s trust, including perception of risks and benefits, feelings, and knowledge of AVs. This study aims to use these factors to predict people’s dispositional and initial learned trust in AVs using a survey study conducted with 1175 participants. For each participant, 23 features were extracted from the survey questions to capture his/her knowledge, perception, experience, behavioral assessment, and feelings about AVs. These features were then used as input to train an eXtreme Gradient Boosting (XGBoost) model to predict trust in AVs. With the help of SHapley Additive exPlanations (SHAP), we were able to interpret the trust predictions of XGBoost to further improve the explainability of the XGBoost model. Compared to traditional regression models and black-box machine learning models, our findings show that this approach was powerful in providing a high level of explainability and predictability of trust in AVs, simultaneously.
2020-12-25T16:42:35Z
2020-12-25T16:42:35Z
2020-12-23
Article
https://hdl.handle.net/2027.42/163772
0000-0001-6123-073X
Zhou, Feng; 0000-0001-6123-073X
en_US
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
oai:deepblue.lib.umich.edu:2027.42/227362021-07-30T22:11:50Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2006-04-07T17:05:36Z
urn:hdl:2027.42/22736
An integrated approach to the development of continuous simulations
Burns, James R.
Ulgen, Onur
Department of Industrial and Systems Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, U.S.A.
Department of Industrial and Systems Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, U.S.A.
An integrated approach to the development of Forrester-style simulation models is described. The approach incorporates the concept of an interaction matrix to assist in the development of causal loop diagrams and Dynamo flow diagrams. The interaction matrix is derived from the fundamental notions of system dynamics. Premised upon the presumption that a computer-aided procedure for model formulation can expedite, systematize, and operationalize the model formulation process, the integrated approach utilizes the interaction matrix as a data structure within the computer. An algorithm designed to interface with a remote terminal (such as a CRT display) determines the interaction matrix by interrogating a user until sufficient information about the problem of interest has been obtained. This algorithm is also described in the paper. The interrogations both motivate and facilitate the determination of quantities to be included as well as couplings between the quantities. When a quantity or coupling is designated by a user, the algorithm "knows" its identity at the time of user origination. Both algorithm and matrix are illustrated through recourse to a text-book example and the paper concludes with a summarizing discussion of the possible contribution of such an approach.
2006-04-07T17:05:36Z
2006-04-07T17:05:36Z
1978
Article
Burns, James R., Ulgen, Onur (1978)."An integrated approach to the development of continuous simulations." Socio-Economic Planning Sciences 12(6): 313-327. <http://hdl.handle.net/2027.42/22736>
http://www.sciencedirect.com/science/article/B6V6Y-45BC2F2-3D/2/12ded5219c8eb8c189601f22078c25e0
https://hdl.handle.net/2027.42/22736
http://dx.doi.org/10.1016/0038-0121(78)90036-8
Socio-Economic Planning Sciences
en_US
IndexNoFollow
Elsevier
oai:deepblue.lib.umich.edu:2027.42/1539592021-07-30T22:23:44Zcom_2027.42_13913col_2027.42_146790
2020-02-25T19:14:59Z
urn:hdl:2027.42/153959
From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI
Ayoub, Jackie
Zhou, Feng
Bao, Shan
Yang, Xijessie
Dearborn
automated driving, autoui review, manual driving
This paper gives an overview of the ten-year devel- opment of the papers presented at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) from 2009 to 2018. We categorize the topics into two main groups, namely, manual driving-related research and automated driving-related re- search. Within manual driving, we mainly focus on studies on user interfaces (UIs), driver states, augmented reality and head-up displays, and methodology; Within automated driv- ing, we discuss topics, such as takeover, acceptance and trust, interacting with road users, UIs, and methodology. We also discuss the main challenges and future directions for AutoUI and offer a roadmap for the research in this area.
2020-02-25T19:14:59Z
2020-02-25T19:14:59Z
2019-09-21
Conference Paper
https://hdl.handle.net/2027.42/153959
AutomotiveUI '19
https://orcid.org/0000-0003-0274-492X
https://orcid.org/0000-0002-0768-5538
https://orcid.org/0000-0001-6123-073X
https://orcid.org/0000-0001-6071-0387
Bao, Shan; 0000-0002-0768-5538
Zhou, Feng; 0000-0001-6123-073X
Ayoub, Jackie; 0000-0003-0274-492X
Yang, X. Jessie; 0000-0001-6071-0387
en_US
oai:deepblue.lib.umich.edu:2027.42/458892021-07-30T22:47:20Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2006-09-11T16:35:24Z
urn:hdl:2027.42/45889
Data mining algorithm for manufacturing process control
Zakarian, Armen
Sadoyan, Hovhannes
Mohanty, Pravansu
Department of Mechanical Engineering, University of Michigan – Dearborn, Dearborn, MI, 48128, U.S.A.
Department of Industrial and Manufacturing Systems Engineering, University of Michigan – Dearborn, USA
Department of Industrial and Manufacturing Systems Engineering, University of Michigan – Dearborn, USA
Dearborn
Ann Arbor
In this paper, a new data mining algorithm based on the rough sets theory is presented for manufacturing process control. The algorithm extracts useful knowledge from large data sets obtained from manufacturing processes and represents this knowledge using “if/then” decision rules. Application of the data mining algorithm developed in this paper is illustrated with an industrial example of rapid tool making (RTM). RTM is a technology that adopts rapid prototyping (RP) techniques, such as spray forming, and applies them to tool and die making. A detailed discussion on how to control the output of the manufacturing process using the results obtained from the data mining algorithm is also presented. Compared to other data mining methods, such decision trees and neural networks, the advantage of the proposed approach is its accuracy, computational efficiency, and ease of use.
2006-09-11T16:35:24Z
2006-09-11T16:35:24Z
2006-03
Article
Sadoyan, Hovhannes; Zakarian, Armen; Mohanty, Pravansu; (2006). "Data mining algorithm for manufacturing process control." The International Journal of Advanced Manufacturing Technology 28 (3-4): 342-350. <http://hdl.handle.net/2027.42/45889>
0268-3768
1433-3015
https://hdl.handle.net/2027.42/45889
http://dx.doi.org/10.1007/s00170-004-2367-1
The International Journal of Advanced Manufacturing Technology
en_US
Springer-Verlag
oai:deepblue.lib.umich.edu:2027.42/1750332022-10-30T22:00:22Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2022-10-30T21:38:29Z
urn:hdl:2027.42/175033
Design a Sustainable Micro-mobility Future: Trends and Challenges in the US and EU
Avetisyan, Lilit
Zhang, Chengxin
Bai, Sue
Pari, Ehsan Moradi
Feng, Fred
Bao, Shan
Zhou, Feng
Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn
University of Michigan Transportation Research Institute
Honda Research Institute USA, Inc.
Dearborn
Micro-mobility, Social media, Sustainability, Natural Language Processing
Micro-mobility is promising to contribute to sustainable cities with its efficiency and low cost. To better design such a sustainable future, it is necessary to understand the trends and challenges. Thus, we examined people's opinions on micro-mobility in the US and the EU using Tweets. We used topic modeling based on advanced natural language processing techniques and categorized the data into seven topics: promotion and service, mobility, technical features, acceptance, recreation, infrastructure and regulations. Furthermore, using sentiment analysis, we investigated people's positive and negative attitudes towards specific aspects of these topics and compared the patterns of the trends and challenges in the US and the EU. We found that 1) promotion and service included the majority of Twitter discussions in the both regions, 2) the EU had more positive opinions than the US, 3) micro-mobility devices were more widely used for utilitarian mobility and recreational purposes in the EU than in the US, and 4) compared to the EU, people in the US had many more concerns related to infrastructure and regulation issues. These findings help us design and prioritize micro-mobility to improve their safety and experience across the two areas for designing a more sustainable micro-mobility future.
2022-10-30T21:38:29Z
2022-10-30T21:38:29Z
2022-10-30
Article
https://hdl.handle.net/2027.42/175033
10.1080/09544828.2022.2142904
https://dx.doi.org/10.7302/6581
Journal of Engineering Design
en_US
CJEN 2142904
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 International
oai:deepblue.lib.umich.edu:2027.42/1719052022-03-11T01:00:20Zcom_2027.42_13913col_2027.42_146790
2022-03-11T00:56:22Z
urn:hdl:2027.42/171905
Predicting Driver Takeover Time in Conditionally Automated Driving
Ayoub, Jackie
Du, Na
Yang, X. Jessie
Zhou, Feng
Dearborn
Takeover time prediction, takeover control, explainable machine learning models
It is extremely important to ensure a safe takeover transition in conditionally automated driving. One of the critical factors that quantifies the safe takeover transition is takeover time. Previous studies identified the effects of many factors on takeover time, such as takeover lead time, non-driving tasks, modalities of the takeover requests, and scenario urgency. However, there is a lack of research to predict takeover time by considering these factors all at the same time. Toward this end, we used eXtreme Gradient Boosting (XGBoost) to predict the takeover time using a dataset of 129 previous studies. In addition, we used SHAP (SHapley Additive exPlanation) to analyze and explain the effects of the predictors on takeover time. We identified seven most critical predictors that resulted in the best prediction performance. Their main effects and interaction effects on takeover time were examined. The results showed that the proposed approach provided both good performance and explainability. Our findings have implications on the design of in-vehicle monitoring and alert systems to facilitate the interaction between the drivers and the automated vehicle.
2022-03-11T00:56:22Z
2022-03-11T00:56:22Z
2022-03-10
Article
https://hdl.handle.net/2027.42/171905
https://dx.doi.org/10.7302/4206
en_US
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
oai:deepblue.lib.umich.edu:2027.42/1159272019-03-18T15:07:47Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2017-01-03T16:21:17Z
urn:hdl:2027.42/115927
A Stimulation Method to Assess the Contractile Status of the Lumbar Extensors in a Seated Posture
Jia, Bochen
Nussbaum, Maury A.
Agnew, Michael J.
The purpose of present study was to develop and evaluate methods to assess stimulation responses of the lumbar extensors, as part of a longer‐term goal of detecting fatigue during prolonged sitting. Three stimulation frequencies (2, 5, and 8 Hz) were tested in separate stages, which include 3 stimulation trains and 4 sampling blocks. Repeated measures analyses of variance were used to determine whether any significant differences in mean stimulation responses occurred with respect to stimulation frequency, sampling block, and stimulation train. Reliability of measured stimulation responses was assessed within and between sampling blocks using intraclass correlation coefficients. Stimulation frequencies significantly affected the stimulation responses and time‐to‐potentiation differed between the 3 stimulation frequencies; it was highest for 2 Hz stimulation. All 3 stimulation frequencies resulted in excellent reliability within and between sampling blocks. Use of the current protocol at 2 Hz is recommended as appropriate to measure the lumbar extensors status during prolonged sitting.
2015-11-12T21:03:55Z
2017-01-03T16:21:17Z
2015-11
Article
Jia, Bochen; Nussbaum, Maury A.; Agnew, Michael J. (2015). "A Stimulation Method to Assess the Contractile Status of the Lumbar Extensors in a Seated Posture." Human Factors and Ergonomics in Manufacturing & Service Industries 25(6): 674-684.
1090-8471
1520-6564
http://hdl.handle.net/2027.42/115927
10.1002/hfm.20584
Human Factors and Ergonomics in Manufacturing & Service Industries
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IndexNoFollow
Rancho Rehabilitation Engineering Program, Rancho Los Amigos Medical Center
Wiley Periodicals, Inc.
oai:deepblue.lib.umich.edu:2027.42/288672019-01-29T20:39:21Zcom_2027.42_13913col_2027.42_85215col_2027.42_146790col_2027.42_21621
2006-04-10T13:55:49Z
urn:hdl:2027.42/28867
A microcomputer based decision support tool for assigning dock doors in freight yards
Tsui, Louis Y.
Chang, Chia-Hao
Department of Industrial and Systems Engineering, University of Michigan-Dearborn, Dearborn, Michigan 48128, USA
Department of Industrial and Systems Engineering, University of Michigan-Dearborn, Dearborn, Michigan 48128, USA
This paper addresses issues at the shipping/receiving dock of a shipping company, where trucks come in from venders, get their shipments unloaded and reloaded onto trucks going to the customers. The assignment of dock doors to incoming and outgoing trucks determines the efficiency of the dock operation. A microcomputer based tool based on a bilinear program is proposed for recognition of the shipping pattern, and the assignment of the dock doors.
2006-04-10T13:55:49Z
2006-04-10T13:55:49Z
1990
Article
Tsui, Louis Y., Chang, Chia-Hao (1990)."A microcomputer based decision support tool for assigning dock doors in freight yards." Computers & Industrial Engineering 19(1-4): 309-312. <http://hdl.handle.net/2027.42/28867>
http://www.sciencedirect.com/science/article/B6V27-47WTRP6-29/2/f38ffbdcab8d7f6fd7e5c2d3d51ad524
http://hdl.handle.net/2027.42/28867
http://dx.doi.org/10.1016/0360-8352(90)90128-9
Computers & Industrial Engineering
en_US
IndexNoFollow
Elsevier
oai:deepblue.lib.umich.edu:2027.42/297542021-08-01T22:31:00Zcom_2027.42_13913col_2027.42_85215col_2027.42_146790col_2027.42_21621
2006-04-10T15:00:57Z
urn:hdl:2027.42/29754
An optimal solution to a dock door assignment problem
Tsui, Louis Y.
Chang, Chia-Hao
Department of Industrial and Systems Engineering University of Michigan-Dearborn, Dearborn, Michigan 48128, USA
Department of Industrial and Systems Engineering University of Michigan-Dearborn, Dearborn, Michigan 48128, USA
This paper addresses issues at the shipping/receiving dock of a shipping company, where trucks arrive from vendors to have their shipments unloaded, sorted, and reloaded onto trucks going to the customers. The assignment of dock doors to incoming and outgoing trucks determines the efficiency of the dock operation. A bilinear programming model is proposed to solve the assignment problem.
2006-04-10T15:00:57Z
2006-04-10T15:00:57Z
1992-11
Article
Tsui, Louis Y., Chang, Chia-Hao (1992/11)."An optimal solution to a dock door assignment problem." Computers & Industrial Engineering 23(1-4): 283-286. <http://hdl.handle.net/2027.42/29754>
http://www.sciencedirect.com/science/article/B6V27-4817D49-B2/2/535d4370d1c711424cd5bff188be7161
https://hdl.handle.net/2027.42/29754
http://dx.doi.org/10.1016/0360-8352(92)90117-3
Computers & Industrial Engineering
en_US
IndexNoFollow
Elsevier
oai:deepblue.lib.umich.edu:2027.42/1730442022-08-02T14:51:57Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2022-07-30T17:01:41Z
urn:hdl:2027.42/173044
Investigating Explanations in Conditional and Highly Automated Driving: The Effects of Situation Awareness and Modality
Avetisyan, Lilit
Ayoub, Jackie
Zhou, Feng
Dearborn
Explanations, Situation awareness, Modality, Automated driving
With the level of automation increases in vehicles, such as conditional and highly automated vehicles (AVs), drivers are becoming increasingly out of the control loop, especially in unexpected driving scenarios. Although it might be not necessary to require the drivers to intervene on most occasions, it is still important to improve drivers' situation awareness (SA) in unexpected driving scenarios to improve their trust in and acceptance of AVs. In this study, we conceptualized SA at the levels of perception (SA L1), comprehension (SA L2), and projection (SA L3), and proposed an SA level-based explanation framework based on explainable AI. Then, we examined the effects of these explanations and their modalities on drivers' situational trust, cognitive workload, as well as explanation satisfaction.
A three (SA levels: SA L1, SA L2 and SA L3) by two (explanation modalities: visual, visual + audio) between-subjects experiment was conducted with 340 participants recruited from Amazon Mechanical Turk. The results indicated that by designing the explanations using the proposed SA-based framework, participants could redirect their attention to the important objects in the traffic and understand their meaning for the AV system. This improved their SA and filled the gap of understanding the correspondence of AV’s behavior in the particular situations which also increased their situational trust in AV. The results showed that participants reported the highest trust with SA L2 explanations, although the mental workload was assessed higher in this level.
The results also provided insights into the relationship between the amount of information in explanations and modalities, showing that participants were more satisfied with visual-only explanations in the SA L1 and SA L2 conditions and were more satisfied with visual and auditory explanations in the SA L3 condition. Finally, we found that the cognitive workload was also higher in SA L2, possibly because the participants were actively interpreting the results, consistent with a higher level of situational trust.
These findings demonstrated that properly designed explanations, based on our proposed SA-based framework, had significant implications for explaining AV behavior in conditional and highly automated driving.
2022-07-30T17:01:41Z
2022-07-30T17:01:41Z
2022-07-28
Article
https://hdl.handle.net/2027.42/173044
10.1016/j.trf.2022.07.010
https://dx.doi.org/10.7302/4875
Transportation Research Part F: Psychology and Behaviour
https://orcid.org/0000-0003-4206-6385 , https://orcid.org/0000-0003-0274-492X, https://orcid.org/0000-0001-6123-073X
Zhou, Feng; 0000-0001-6123-073X
en_US
http://creativecommons.org/licenses/by-nd/4.0/
Attribution-NoDerivatives 4.0 International
Elsevier
oai:deepblue.lib.umich.edu:2027.42/1539652021-08-01T23:00:09Zcom_2027.42_13913col_2027.42_146790
2020-02-25T20:06:43Z
urn:hdl:2027.42/153965
A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems
Zhou, Feng
Ayoub, Jackie
Yang, X. Jessie
Xu, Qianli
Dearborn
machine learning, customer needs analysis, product ecosystems, kano model, design automation, design for X, design theory and methodology, product design
Creating product ecosystems has been one of the strategic ways to enhance user experience and business advantages. Among many, customer needs analysis for product ecosystems is one of the most challenging tasks in creating a successful product ecosystem from both the perspectives of marketing research and product development. In this paper, we propose a machine-learning approach to customer needs analysis for product ecosystems by examining a large amount of online user-generated product reviews within a product ecosystem. First, we filtered out uninformative reviews from the informative reviews using a fastText technique. Then, we extract a variety of topics with regard to customer needs using a topic modeling technique named latent Dirichlet allocation. In addition, we applied a rule-based sentiment analysis method to predict not only the sentiment of the reviews but also their sentiment intensity values. Finally, we categorized customer needs related to different topics extracted using an analytic Kano model based on the dissatisfaction-satisfaction pair from the sentiment analysis. A case example of the Amazon product ecosystem was used to illustrate the potential and feasibility of the proposed method.
2020-02-25T20:06:43Z
2020-02-25T20:06:43Z
2019-10-03
Article
1528-9001
https://hdl.handle.net/2027.42/153965
ASME
https://orcid.org/0000-0001-6071-0387
https://orcid.org/0000-0001-6123-073X
https://orcid.org/0000-0003-0274-492X
Yang, X. Jessie; 0000-0001-6071-0387
Zhou, Feng; 0000-0001-6123-073X
Ayoub, Jackie; 0000-0003-0274-492X
en_US
MD-19-1275
oai:deepblue.lib.umich.edu:2027.42/275402021-08-01T23:29:35Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2006-04-07T20:31:25Z
urn:hdl:2027.42/27540
The structure of quality information system in a computer integrated manufacturing environment
Chang, Chia-Hao
University of Michigan-Dearborn, USA
2006-04-07T20:31:25Z
2006-04-07T20:31:25Z
1988
Article
Chang, Chia-hao (1988)."The structure of quality information system in a computer integrated manufacturing environment." Computers & Industrial Engineering 15(1-4): 338-343. <http://hdl.handle.net/2027.42/27540>
http://www.sciencedirect.com/science/article/B6V27-4817CW9-66/2/8dae6d4a55426616cca7e11265b5a92f
https://hdl.handle.net/2027.42/27540
http://dx.doi.org/10.1016/0360-8352(88)90108-8
Computers & Industrial Engineering
en_US
IndexNoFollow
Elsevier
oai:deepblue.lib.umich.edu:2027.42/1670032022-06-08T02:20:52Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2021-03-27T02:45:21Z
urn:hdl:2027.42/167003
Using Eye-tracking Data to Predict Situation Awareness in Real Time during Takeover Transitions in Conditionally Automated Driving
Zhou, Feng
Yang, X. Jessie
de Winter, Joost
University of Michigan, Ann Arbor
University of Michigan, Dearborn
Delft University of Technology
Dearborn
Real-time situation awareness prediction, takeover, automated driving, eye-tracking measures, explainability
Situation awareness (SA) is critical to improving takeover performance during the transition period from automated driving to manual driving. Although many studies measured SA during or after the driving task, few studies have attempted to predict SA in real time in automated driving. In this work, we propose to predict SA during the takeover transition period in conditionally automated driving using eye-tracking and self-reported data. First, a tree ensemble machine learning model, named LightGBM (Light Gradient Boosting Machine), was used to predict SA. Second, in order to understand what factors influenced SA and how, SHAP (SHapley Additive exPlanations) values of individual predictor variables in the LightGBM model were calculated. These SHAP values explained the prediction model by identifying the most important factors and their effects on SA, which further improved the model performance of LightGBM through feature selection. We standardized SA between 0 and 1 by aggregating three performance measures (i.e., placement, distance, and speed estimation of vehicles with regard to the ego-vehicle) of SA in recreating simulated driving scenarios, after 33 participants viewed 32 videos with six lengths between 1 and 20 s. Using only eye-tracking data, our proposed model outperformed other selected machine learning models, having a root-mean-squared error (RMSE) of 0.121, a mean absolute error (MAE) of 0.096, and a 0.719 correlation coefficient between the predicted SA and the ground truth. The code is available at https://github.com/refengchou/Situation-awareness-prediction. Our proposed model provided important implications on how to monitor and predict SA in real time in automated driving using eye-tracking data.
2021-03-27T02:45:21Z
2021-03-27T02:45:21Z
2021-03-26
Article
https://hdl.handle.net/2027.42/167003
https://dx.doi.org/10.7302/799
IEEE Transactions on Intelligent Transportation Systems
0000-0001-6123-073X
Zhou, Feng; 0000-0001-6123-073X
en_US
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
IEEE
oai:deepblue.lib.umich.edu:2027.42/1625932021-08-02T00:25:54Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2020-09-29T16:01:44Z
urn:hdl:2027.42/162593
Psychophysiological responses to takeover requests in conditionally automated driving
Du, Na
Yang, X. Jessie
Zhou, Feng
University of Michigan, Dearborn
University of Michigan, Ann Arbor
Dearborn
Human-automation interaction, Automated driving, Transition of control, Psychophysiological measures
In SAE Level 3 automated driving, taking over control from automation raises significant safety concerns because drivers out of the vehicle control loop have difficulty negotiating takeover transitions. Existing studies on takeover transitions have focused on drivers' behavioral responses to takeover requests (TORs). As a complement, this exploratory study aimed to examine drivers' psychophysiological responses to TORs as a result of varying non-driving-related tasks (NDRTs), traffic density and TOR lead time. A total number of 102 drivers were recruited and each of them experienced 8 takeover events in a high fidelity fixed-base driving simulator. Drivers' gaze behaviors, heart rate (HR) activities, galvanic skin responses (GSRs), and facial expressions were recorded and analyzed during two stages.
First, during the automated driving stage, we found that drivers had lower heart rate variability, narrower horizontal gaze dispersion, and shorter eyes-on-road time when they had a high level of cognitive load relative to a low level of cognitive load. Second, during the takeover transition stage, 4s lead time led to inhibited blink numbers and larger maximum and mean GSR phasic activation compared to 7s lead time, whilst heavy traffic density resulted in increased HR acceleration patterns than light traffic density. Our results showed that psychophysiological measures can indicate specific internal states of drivers, including their workload, emotions, attention, and situation awareness in a continuous, non-invasive and real-time manner. The findings provide additional support for the value of using psychophysiological measures in automated driving and for future applications in driver monitoring systems and adaptive alert systems.
2020-09-29T16:01:44Z
2020-09-29T16:01:44Z
2020-09-23
Article
https://hdl.handle.net/2027.42/162593
Accident Analysis & Prevention
https://orcid.org/0000-0001-6123-073X
Zhou, Feng; 0000-0001-6123-073X
en_US
http://creativecommons.org/licenses/by/4.0/
Attribution 4.0 International
oai:deepblue.lib.umich.edu:2027.42/1095912021-08-03T00:57:26Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2014-12-09T16:53:41Z
urn:hdl:2027.42/109591
6.3.1 Modular Design Approach for Development of Electrical, Electronic, and Software System Architectures for Multiple Product Platforms
Rushton, Gary
Zakarian, Armen
Grigoryan, Tigran
Modular systems provide the ability to achieve product variety through the combination and standardization of components. Modular design approaches used in the development of electrical, electronic, and software (EES) systems allow sharing of architectures/modules between different product lines. Modular products provide economies of scale, reduced development time, reduced order lead‐time, and easier product diagnostics, maintenance and repair. In this paper, new optimization algorithms and software tools are presented that allow EES system design engineers to develop architectures/modules that can be shared across product platforms (for OEMs) and across OEMs (for suppliers). Approaches presented in this paper use matrix clustering and graph based techniques. The application of the approach is illustrated with an example from the automotive industry on the development of a modular EES system that can be shared across multiple vehicle platforms.
2014-12-09T16:53:41Z
2014-12-09T16:53:41Z
2003-07
Article
Rushton, Gary; Zakarian, Armen; Grigoryan, Tigran (2003). "6.3.1 Modular Design Approach for Development of Electrical, Electronic, and Software System Architectures for Multiple Product Platforms." INCOSE International Symposium 13(1): 517-527.
2334-5837
2334-5837
https://hdl.handle.net/2027.42/109591
10.1002/j.2334-5837.2003.tb02637.x
INCOSE International Symposium
King, J. R. ( 1980 ), “ Machine‐Component Group Formation in Production Flow Analysis: An Approach Using a Rank Order Clustering Algorithm ”, International Journal of Production Research, vol. 18, no. 2, pp. 213 – 232.
Kusiak, A. ( 2000 ), “ Computational Intelligent in Design and Manufacturing ”, Wiley, New York, NY.
Kusiak, A. and Chow, W. S. ( 1987 ), “ Efficient Solving of the Group Technology Problem ”, Journal of Manufacturing Systems, vol. 6, no. 2, pp. 117 – 124.
Kusiak, A. and Chow, W. ( 1988 ), “ Decomposition of Manufacturing Systems,” IEEE Journal of Robotics and Automation, Vol. 4, No. 5, pp. 457 – 471.
Kusiak, A. and Huang, C. C. ( 1996 ), “ Development of Modular Products ”, IEEE Transactions on Components, Packaging, and Manufacturing Technology – Part A, vol. 19, no. 4, pp. 523 – 538.
Kusiak, A. and Huang, C. C. ( 1998 ), “ Modularity in Design of Products and Systems ”, IEEE Transactions on Systems, Man, and Cybernetics‐Part A: Systems and Humans, vol. 28, no. 1, pp. 66 – 77.
Mahajan, V. and Jain, A. K. ( 1978 ), “ An Approach to Normative Segmentation ”, Journal of Market Research, Vol. 15, 338 – 345.
McAuley, J. ( 1972 ), “ Machine Grouping for Efficient Production ”, The Production Engineer, February, pp. 53 – 57.
Ng, S. M. ( 1991 ), “ Bond Energy, Rectilinear Distance and Worse‐case Bound for the Group Technology Problem,” J. of Operations Research, Vol. 42, No. 7, pp. 571 – 578.
Ni, L. M. and Jain, A. K. ( 1985 ), “ A VLSI Systolic Architecture for Pattern Clustering ”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 7, pp. 80 – 89.
O'Grady, P. ( 1999 ), “ The Age of Modularity: Using the New World of Modular Products to Revolutionize Your Corporation ”, Wiley, New York, NY.
Pimmler, T. U. and Eppinger, S. D. ( 1994 ), “ Integration Analysis of Product Decomposition ”, Design Theory and Methodology – DTM, DE‐vol. 68, ASME.
Sneat, P. H. and Sokal, R. R., “ Numerical Taxonomy. San Francisco ”, CA. W. H. Freeman, 1973.
Tambouratzis, G. ( 2002 ), “ Improving the Clustering Performance of the Scanning n‐Tuple Method by Using Self Supervised Algorithms to Introduce Subclasses,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 6, pp. 722 – 733.
Tarjan, R. ( 1972 ), “ Depth‐First Search and Linear Graph Algorithms ”, SIAM Journal of Computing, vol. 1, no. 2, pp. 146 – 160.
Ulrich, K. and Tung, K. ( 1991 ), “ Fundamentals of Product Modularity ”, DE‐vol. 39, Issues in Design Manufacture/Integration, ASME.
Zakarian, A. and Rushton, G. ( 2001 ), “ Development of Modular Electrical Systems ”, IEEE/ASME Transactions of Mechatronics, Vol. 6, No. 4, December 2001.
Birnbaum, P. H., ( 1977 ), “ Assessment of Alternative Measurements Forms in Academic Interdisciplinary Research Projects ”, Management Science, Vol. 24, pp. 272 – 284.
Cheng, H. D. and Tong, C. ( 1991 ), “ Clustering Analyzer ”, IEEE Transactions on Circuits and Systems, Vol. 38, No. 1, pp. 124 – 128.
Jain, A. K. and Dubes, R. C., “ Algorithms for Clustering Data ”, Englewood Cliffs, New Jersey, Prentice Hall, 1988.
IndexNoFollow
Englewood Cliffs
Wiley Periodicals, Inc.
oai:deepblue.lib.umich.edu:2027.42/1663192022-06-08T01:53:36Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2021-03-01T05:08:35Z
urn:hdl:2027.42/166319
Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models
Ayoub, Jackie
Yang, "X. Jessie
Zhou, Feng
PhD student
Assistant professor
Assistant professor
Dearborn
COVID-19, misinformation detection, trust, BERT, DistilBERT, SHAP
Misinformation of COVID-19 is prevalent on social media as the pandemic un- folds, and the associated risks are extremely high. Thus, it is critical to detect and combat such misinformation. Recently, deep learning models using natural language processing techniques, such as BERT (Bidirectional Encoder Represen- tations from Transformers), have achieved great successes in detecting misinfor- mation. In this paper, we proposed an explainable natural language processing model based on DistilBERT and SHAP (Shapley Additive exPlanations) to com- bat misinformation about COVID-19 due to their efficiency and effectiveness. First, we collected a dataset of 984 claims about COVID-19 with fact checking. By augmenting the data using back-translation, we doubled the sample size of the dataset and the DistilBERT model was able to obtain good performance (accuracy: 0.972; areas under the curve: 0.993) in detecting misinformation about COVID-19. Our model was also tested on a larger dataset for AAAI2021 - COVID-19 Fake News Detection Shared Task and obtained good performance (accuracy: 0.938; areas under the curve: 0.985). The performance on both datasets was better than traditional machine learning models. Second, in or- der to boost public trust in model prediction, we employed SHAP to improve model explainability, which was further evaluated using a between-subjects ex- periment with three conditions, i.e., text (T), text+SHAP explanation (TSE), and text+SHAP explanation+source and evidence (TSESE). The participants were significantly more likely to trust and share information related to COVID- 19 in the TSE and TSESE conditions than in the T condition. Our results provided good implications in detecting misinformation about COVID-19 and improving public trust.
2021-03-01T05:08:35Z
2021-03-01T05:08:35Z
2021-02-28
Article
https://hdl.handle.net/2027.42/166319
https://dx.doi.org/10.7302/242
Information Processing and Management
https://orcid.org/0000-0003-0274-492X
https://orcid.org/0000-0001-6071-0387
https://orcid.org/0000-0001-6123-073X
Ayoub, Jackie; 0000-0003-0274-492X
Yang, X. Jessie; 0000-0001-6071-0387
Zhou, Feng; 0000-0001-6123-073X
en_US
Manuscript Number: IPM-D-20-01187R1
elsevier
oai:deepblue.lib.umich.edu:2027.42/1002762021-08-03T02:20:59Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2015-01-05T13:54:44Z
urn:hdl:2027.42/100276
Effects of Driver and Secondary Task Characteristics on Lane Change Test Performance
Rodrick, David
Bhise, Vivek
Jothi, Vaithianathan
University of Michigan‐Dearborn, Dearborn, Michigan, USA
The main objective of this study was to examine the sensitivity of the Lane Change Test (LCT) as proposed by International Organization of Standardization by evaluating LCT performance between primary and dual‐task conditions in simulated driving conditions. The study involved four different secondary tasks that involved tracking, visual search, memory, and data entry, each under two different difficulty levels. The primary task involved a series of lane changes on a three‐lane straight roadway where the actual lane change trajectory was compared with a normative model of the trajectory. Thus, the lane change performance was measured by the mean deviation of the actual driving trajectory from the normative trajectory. Twenty‐four participants within three age groups (25–34, 35–45, and >55 years) and equally distributed between male and female took part in the study. Thus, the study also investigated the effect of age and gender on driving performance. The results showed that secondary tasks that require visual attention and psychomotor coordination deteriorated driving performance the most, whereas tasks that required memory scanning and utilization of the auditory modality least affected driving performance. The study also found differences in LCT performances with respect to three different age categories and gender. © 2012 Wiley Periodicals, Inc.
2013-11-01T19:00:53Z
2015-01-05T13:54:44Z
2013-11
Article
Rodrick, David; Bhise, Vivek; Jothi, Vaithianathan (2013). "Effects of Driver and Secondary Task Characteristics on Lane Change Test Performance." Human Factors and Ergonomics in Manufacturing & Service Industries 23(6): 560-572.
1090-8471
1520-6564
https://hdl.handle.net/2027.42/100276
10.1002/hfm.20342
Human Factors and Ergonomics in Manufacturing & Service Industries
Young, K., & Regan, M. ( 2007 ). Driver distraction: A review of the literature. In I. J. Faulks, M. Regan, M. Stevenson, J. Brown, A. Porter, & J. D. Irwin (Eds.). Distracted driving. Sydney, NSW: Australasian College of Road Safety,pp. 379 – 405.
Wynn, T., & Richardson, J. ( 2008 ).Comparison of subjective workload ratings and performance measures of a reference IVIS task. In Proceedings of the European Conference on Human Centred Design for Intelligent Transport Systems, April 3‐4, 2008, Lyon, France.
Strayer, D. L., & Drews, F. A. ( 2007 ). Cell‐phone‐induced driver distraction. Current Directions in Psychological Science, 16 ( 3 ), 128 – 131.
Stutts, J., Feaganes, J., Reinfurt, D., Rodgman, E., Hamlett, C., Gish, K., & Staplin, L. ( 2005 ). Driver's exposure to distractions in their natural driving environment. Accident Analysis & Prevention, 37, 1093 – 1101.
Stutts, J. C., Reinfurt, D. W., Staplin, L., & Rodgman, E. A. ( 2001 ). The role of driver distraction in traffic crashes. Washington, DC: AAA Foundation for Traffic Safety.
Tijerina, L., Parmer, E., & Goodman, M. J. ( 1998 ). Driver workload assessment of route guidance system destination entry while driving: A test track study. Proceedings of the 5th ITS World Congress, Seoul, Korea, CD‐ROM. October 12–16, 1990.
Wickens, C. D. ( 2002 ). Multiple resources and performance prediction. Theoretical Issues in Ergonomics Science, 3, 159 – 177.
Trezise, I., Stoney, E. G., Bishop, B., Eren, J., Harkness, A., Langdon, C., & Mulder, T. ( 2006 ). Report of the road safety committee on the inquiry into driver distraction. Rep. No. 209. Melbourne, Victoria, Australia: Road Safety Committee, Parliament of Victoria.
Burns, P. C., Trbovich, P. L., McCurdie, T., & Harbluk, J. L. ( 2005 ). Measuring distraction: Task duration and the Lane‐Change Test (LCT). Proceedings of the Annual Meeting of the Human Factors and Ergonomics Society, Orlando, FL. September 26‐30, 2005. pp. 1980 – 1983.
Engström, J., & Markkula, G. ( 2007 ). Effects of visual and cognitive task demand on Lane Change Test performance. In Proceedings of the Fourth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. July 9–12, 2007. Stevenson, Washington.
Harbluk, J. L., Burns, P. C., Lochner, M., & Trbovich, P. L. ( 2007 ). Using the Lane Change Test (LCT) to assess distraction: Tests of visual‐manual and speech‐based operation of navigation system interfaces. In Proceedings of the Fourth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Stevenson, Washington, July 9–12, 2007. pp. 16 – 22.
Horberry, T., Anderson, J., Regan, M. A., Triggs, T. J., & Brown, J. ( 2006 ). Driver distraction: The effects of concurrent in‐vehicle tasks, road environment complexity and age on driving performance. Accident Analysis & Prevention, 38 ( 1 ), 185 – 191.
Horrey, W. J., & Wickens, C. D. ( 2006 ). Examining the impact of cell phone conversations on driving using meta‐analytic techniques. Human Factors, 48, 196 – 205.
Hurwitz, J. B., & Wheatley, D. J. ( 2002 ).Using driver performance measures to estimate workload. In Proceedings of the 46th Annual Meeting of the Human Factors and Ergonomics Society. Baltimore: September 30‐October 4, 2002.
Mattes, S., & Hallen, A. ( 2008 ). Surrogate distraction measurement techniques: The Lane Change Test. In M. A. Regan, J. D. Lee, & K. L. Young (Eds.), Driver distraction: Theory, effects and mitigation Boca Raton, FL: CRC Press.
Matthews, R., Legg, S., & Charlton, S. ( 2003 ). The effect of cell phone type on drivers subjective workload during concurrent driving and conversing. Accident Analysis & Prevention, 35, 451 – 457.
National Highway Traffic Safety Administration (NHTSA).( 2009 ). Traffic safety facts (DOT HS 811 216). Washington, DC: NHTSA.
National Highway Traffic Safety Administration (NHTSA).( 2010 ). Driver distraction program (DOT HS 811 299). Washington, DC: NHTSA.
Neyens, D. M., & Boyle, L. N. ( 2008 ). The influence of driver distraction on the severity of injuries sustained by teenage drivers and their passengers. Accident Analysis & Prevention, 40 ( 1 ), 254 – 259.
Pettitt, M., Burnett, G., & Stevens, A. ( 2005 ).Defining driver distraction. Paper presented at World Congress on Intelligent Transport Systems, San Francisco, CA. November 6–10, 2005.
Ramney, T. A. ( 2008 ). Driver distraction: A review of the current state‐of‐knowledge (DOT HS 810 787). Washington, DC: NHTSA.
Strayer, D. L., & Drews, F. A. ( 2004 ). Profiles of driver distraction: Effects of cell phone conversations on younger and older drivers. Human Factors, 46, 640 – 649.
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Wiley Subscription Services, Inc., A Wiley Company
oai:deepblue.lib.umich.edu:2027.42/1633192021-08-03T02:27:43Zcom_2027.42_13913col_2027.42_146790
2020-10-06T21:40:43Z
urn:hdl:2027.42/163319
Emotional Design: An Overview
Zhou, Feng
Ji, Yangjian
Jiao, Roger
University of Michigan, Dearborn
Zhejiang University
Georgia Institute of Technology
Dearborn
Emotional Design, Emotion, Affect, Affective computing, Kansei Engineering
Emotional design has been well recognized in the domain of human factors and ergonomics. In this chapter, we reviewed related models and methods of emotional design. We are motivated to encourage emotional designers to take multiple perspectives when examining these models and methods. Then we proposed a systematic process for emotional design, including affective-cognitive needs elicitation, affective-cognitive needs analysis, and affective-cognitive needs fulfillment to support emotional design. Within each step, we provided an updated review of the representative methods to support and offer further guidance on emotional design. We hope researchers and industrial practitioners can take a systematic approach to consider each step in the framework with care. Finally, the speculations on the challenges and future directions can potentially help researchers across different fields to further advance emotional design.
2020-10-06T21:40:43Z
2020-10-06T21:40:43Z
2020-10-02
Book chapter
https://hdl.handle.net/2027.42/163319
5th edition of the Handbook of Human Factors and Ergonomics
en_US
http://creativecommons.org/licenses/by-nd/4.0/
Attribution-NoDerivatives 4.0 International
oai:deepblue.lib.umich.edu:2027.42/1562492021-08-03T22:45:42Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2020-08-11T21:04:38Z
urn:hdl:2027.42/156249
Investigating drivers’ trust in autonomous vehicles’ decisions of lane changing events
Ayoub, Jackie
Zhou, Feng
PhD Student
Assistant professor
Dearborn
Trust, Autonomous Vehicles, Visual Display,
It is potential to improve the interaction between autonomous vehicles (AVs) and drivers by calibrating drivers’ trust in AVs. In this study, we investigated drivers’ trust in AVs’ decisions of changing lanes on a six-lane highway. We derived the AV lane changing scenarios using a machine learning model. The scenarios were rated by 250 participants recruited from Amazon Mechanical Turks (AMTs) in a survey study. The study was designed as a mixed-subject design where the between-subject variable was the amount of information presented (i.e., 3, 4, 5, 6, 7 pieces of information) and the within-subject variable was the information display format (i.e., tabular or visual forms). The results showed that 1) mental demand was always lower in the visual display compared to the tabular one, 2) trust and risk seemed to be inversely proportional across conditions, and 3) 4, 5, or 6 pieces of information tended to be preferred better than others. These results provide design implications on calibrating trust in AV systems by involving the driver in the decision-making process.
2020-08-11T21:04:38Z
2020-08-11T21:04:38Z
2020-10-05
Conference Paper
https://hdl.handle.net/2027.42/156249
Human Factors and Ergonomics Society
https://orcid.org/0000-0001-6123-073X
https://orcid.org/0000-0003-0274-492X
Zhou, Feng; 0000-0001-6123-073X
Ayoub, Jackie; 0000-0003-0274-492X
en_US
oai:deepblue.lib.umich.edu:2027.42/281752021-08-03T23:10:48Zcom_2027.42_13913col_2027.42_146790col_2027.42_21621
2006-04-07T20:58:11Z
urn:hdl:2027.42/28175
Quality Function Deployment (QFD) processes in an integrated quality information system
Chang, Chia-Hao
Department of Industrial & Systems Engineering University of Michigan-Dearborn, USA
A general design of an integrated total quality information system involving the Quality Function Deployment process is proposed in this paper. Data flow diagram is used to illustrate the structure of the information system. Within it, the Quality Function Deployment process is especially discussed in detail.
2006-04-07T20:58:11Z
2006-04-07T20:58:11Z
1989
Article
Chang, Chia-hao (1989)."Quality Function Deployment (QFD) processes in an integrated quality information system." Computers & Industrial Engineering 17(1-4): 311-316. <http://hdl.handle.net/2027.42/28175>
http://www.sciencedirect.com/science/article/B6V27-481575R-34/2/f344ddc7fc87bd9506de6a7a51dcd26b
https://hdl.handle.net/2027.42/28175
http://dx.doi.org/10.1016/0360-8352(89)90080-6
Computers & Industrial Engineering
en_US
IndexNoFollow
Elsevier