Topic · Elevator Systems and Control
Most cited papers in Elevator Systems and Control
What are the most cited papers in Elevator Systems and Control?
The most cited paper in Elevator Systems and Control is "Vibration Control Using Semi-Active Force Generators" (DEAN C. KARNOPP, M. J. Crosby, R. A. Harwood, 1974), with 1,348 citations. Next come "Modeling and control of magnetorheological dampers for seismic response reduction" (1,343) and "A design framework for teleoperators with kinesthetic feedback" (924).
Showing 50 of 99 papers. Every paper with more than 161 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Vibration Control Using Semi-Active Force Generators DEAN C. KARNOPP, M. J. Crosby, R. A. Harwood · Journal of Engineering for Industry | May 1974 | 1,348 |
| 2 | Modeling and control of magnetorheological dampers for seismic response reduction Shirley J. Dyke, Billie F. Spencer, Mahaveer Kumar Sain et al. · Smart Materials and Structures | Oct 1996 | 1,343 |
| 3 | A design framework for teleoperators with kinesthetic feedback Blake Hannaford · IEEE Transactions on Robotics and Automation | 1989 | 924 |
| 4 | Automatic rule-based checking of building designs Charles M. Eastman, Jae-min Lee, Yeon-suk Jeong et al. · Automation in Construction | 7 Sep 2009 | 720 |
| 5 | Traffic signal timing via deep reinforcement learning Li Li, Yisheng Lv, Fei–Yue Wang · IEEE/CAA Journal of Automatica Sinica | 10 Jul 2016 | 618 |
| 6 | Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning Shu Luo · Applied Soft Computing | 5 Mar 2020 | 580 |
| 7 | Reinforcement learning for building controls: The opportunities and challenges Zhe Wang, Tianzhen Hong · Applied Energy | 12 May 2020 | 576 |
| 8 | Nonlinear adaptive control of active suspensions Andrew G. Alleyne, J. Karl Hedrick · IEEE Transactions on Control Systems Technology | Mar 1995 | 552 |
| 9 | Improving Elevator Performance Using Reinforcement Learning Robert H. Crites, Andrew G. Barto | 27 Nov 1995 | 495 |
| 10 | A High Performance Pneumatic Force Actuator System: Part I—Nonlinear Mathematical Model Edmond Richer, Yildirim Hürmüzlü · Journal of Dynamic Systems Measurement and Control | 23 Jun 1999 | 488 |
| 11 | Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system Jochen Teizer, Ben S. Allread, Clare E. Fullerton et al. · Automation in Construction | 26 Mar 2010 | 458 |
| 12 | Design and application of magneto-rheological fluid Abdul Ghani Olabi, A. Grunwald · Materials & Design (1980-2015) | 29 Nov 2006 | 447 |
| 13 | CommonKADS: a comprehensive methodology for KBS development Guus Schreiber, B. Wielinga, Robert de Hoog et al. · IEEE Expert | Dec 1994 | 442 |
| 14 | Application of the analytic hierarchy process (AHP) in multi-criteria analysis of the selection of intelligent building systems Johnny Kwok Wai Wong, Heng Li · Building and Environment | 23 Jan 2007 | 435 |
| 15 | Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators Thomas George Thuruthel, Egidio Falotico, Federico Renda et al. · IEEE Transactions on Robotics | 12 Nov 2018 | 410 |
| 16 | Development of flexible microactuator and its applications to robotic mechanisms Koichi Suzumori, S. Iikura, Hirohisa Tanaka | 10 Dec 2002 | 405 |
| 17 | Magnetorheological fluid dampers: A review on structure design and analysis Xiaocong Zhu, Xingjian Jing, Cheng Jung Li · Journal of Intelligent Material Systems and Structures | 20 Mar 2012 | 399 |
| 18 | Automatic Train Control System Development and Simulation for High-Speed Railways Hairong Dong, Bin Ning, Baigen Cai et al. · IEEE Circuits and Systems Magazine | 2010 | 371 |
| 19 | A Cloud-Based Smart-Parking System Based on Internet-of-Things Technologies Thanh Nam Pham, Ming‐Fong Tsai, Duc Binh Nguyen et al. · IEEE Access | 2015 | 360 |
| 20 | A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem Kun Lei, Peng Guo, Wenchao Zhao et al. · Expert Systems with Applications | 8 Jun 2022 | 351 |
| 21 | From the Reconstructive Ladder to the Reconstructive Elevator Lawrene J. Gottlieb, Lloyd M. Krieger · Plastic & Reconstructive Surgery | Jun 1994 | 350 |
| 22 | A vision-based motion capture and recognition framework for behavior-based safety management SangUk Han, Sang Hyun Lee · Automation in Construction | 5 Jun 2013 | 347 |
| 23 | Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning Junyoung Park, Junyoung Park, Jaehyeong Chun et al. · International Journal of Production Research | 28 Jan 2021 | 334 |
| 24 | Storage-Retrieval Interleaving in Automatic Warehousing Systems Stephen C. Graves, Warren H. Hausman, Leroy B. Schwarz · Management Science | May 1977 | 308 |
| 25 | Composing crosscutting concerns using composition filters Lodewijk Bergmans, Mehmet Akşit · Communications of the ACM | Oct 2001 | 308 |
| 26 | Deep reinforcement learning for dynamic scheduling of a flexible job shop Renke Liu, Rajesh Piplani, Carlos Toro · International Journal of Production Research | 11 Apr 2022 | 306 |
| 27 | Applying a flexible microactuator to robotic mechanisms Koichi Suzumori, S. Iikura, Hirohisa Tanaka · IEEE Control Systems | Feb 1992 | 303 |
| 28 | Human factors in order picking: a content analysis of the literature Eric H. Grosse, Christoph H. Glock, Patrick Neumann · International Journal of Production Research | 23 May 2016 | 299 |
| 29 | Genetic algorithm solution to the economic dispatch problem Anastasios G. Bakirtzis · IEE Proceedings - Generation Transmission and Distribution | 1994 | 295 |
| 30 | A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling Ling Wang, Zixiao Pan, Jingjing Wang · Complex System Modeling and Simulation | Dec 2021 | 293 |
| 31 | Customer characterization options for improving the tariff offer Gianfranco Chicco, Roberto Napoli, P. Postolache et al. · IEEE Transactions on Power Systems | Feb 2003 | 291 |
| 32 | An expert system for transformer fault diagnosis using dissolved gas analysis Chin E. Lin, Jeeng-Min Ling, Chih‐Ling Huang · IEEE Transactions on Power Delivery | 1993 | 285 |
| 33 | The MAXQ Method for Hierarchical Reinforcement Learning Thomas G. Dietterich | 24 Jul 1998 | 285 |
| 34 | Key challenges in automation of earth-moving machines Siddharth Dadhich, Ulf Bodin, Ulf R. Andersson · Automation in Construction | 25 May 2016 | 277 |
| 35 | Dynamic job-shop scheduling using reinforcement learning agents Mehmet Emin Aydın, Ercan Öztemel · Robotics and Autonomous Systems | Nov 2000 | 277 |
| 36 | A supercapacitor-based energy-storage system for elevators with soft commutated interface Alfred C. Rufer, P. Barrade · IEEE Transactions on Industry Applications | Sep 2002 | 276 |
| 37 | Modeling Crowd and Trained Leader Behavior during Building Evacuation Nuria Pelechano, Norman I. Badler · IEEE Computer Graphics and Applications | Nov 2006 | 274 |
| 38 | Elevator Group Control Using Multiple Reinforcement Learning Agents Robert H. Crites, Andrew G. Barto · Machine Learning | Nov 1998 | 273 |
| 39 | Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning Yan Du, Helia Zandi, Olivera Kotevska et al. · Applied Energy | 5 Nov 2020 | 267 |
| 40 | Development of a novel electrochemically active membrane and 'smart' material based vibration sensor/damper K. Sadeghipour, Robert E. Salomon, Swati Neogi · Smart Materials and Structures | Jun 1992 | 254 |
| 41 | Evolvable graph neural network for system-level incremental fault diagnosis of train transmission systems Ao Ding, Yong Qin, Biao Wang et al. · Mechanical Systems and Signal Processing | 29 Jan 2024 | 250 |
| 42 | Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN Bao An Han, Jianjun Yang · IEEE Access | 2020 | 242 |
| 43 | Benchmarking Model-Based Reinforcement Learning Tingwu Wang, Xuchan Bao, Ignasi Clavera et al. · arXiv (Cornell University) | 3 Jul 2019 | 238 |
| 44 | Application of reinforcement learning for agent-based production scheduling Yi-Chi Wang, John M. Usher · Engineering Applications of Artificial Intelligence | 28 Sep 2004 | 236 |
| 45 | Real-time scheduling for a smart factory using a reinforcement learning approach Yeou-Ren Shiue, Ken-Chuan Lee, Chao‐Ton Su · Computers & Industrial Engineering | 25 Mar 2018 | 230 |
| 46 | Distributed Adaptive Integrated-Sliding-Mode Controller Synthesis for String Stability of Vehicle Platoons Xiang‐Gui Guo, Jianliang Wang, Fang Yang Liao et al. · IEEE Transactions on Intelligent Transportation Systems | 4 Apr 2016 | 230 |
| 47 | A proactive system for real-time safety management in construction sites Alessandro Carbonari, Alberto Giretti, Berardo Naticchia · Automation in Construction | 22 Jun 2011 | 229 |
| 48 | Driver fatigue detection based on eye tracking and dynamic template matching Wen-Bing Horng, Chih‐Yuan Chen, Yi Chang et al. | 2004 | 228 |
| 49 | Intelligent parking systems Dušan Teodorović, Panta Lučić · European Journal of Operational Research | 10 Apr 2005 | 225 |
| 50 | Automated Methods for Activity Recognition of Construction Workers and Equipment: State-of-the-Art Review Behnam Sherafat, Changbum R. Ahn, Reza Akhavian et al. · Journal of Construction Engineering and Management | 8 Apr 2020 | 220 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.