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IEEE International Conference on Systems, Man and Cybernetics

作者： Liwen Situ Zhenyu Lu Weiyong Si Chenguang Yang College of Automation Science and Engineering South China University of Technology Guangzhou China Bristol Robotics Laboratory University of the West of England Bristol UK Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Neuroscience research has demonstrated the sig-nificance of modulating stiffness during human task performance. Similarly, endowing robots with such capability is expected. However, existing methods for robot teleoperation require operators to simultaneously control position and stiffness, resulting in high workload and task inefficiency. On the other hand, learning from demonstration (LfD) offers a feasible approach for autonomously generating stiffness. Therefore, this paper proposes a robot teleoperation system that combines the advantages of teleoperation and LfD. Teleoperation enables precise positioning guided by human operators, while LfD can transfer human stiffness skills to robots. A teleoperation-oriented stiffness-adaptive Gaussian Mixture Model/Gaussian Mixture Regression method is proposed to learn human multi-dimensional stiffness and reproduce robot stiffness on a Riemannian manifold. To enhance generalization and cooperate with teleoperation, reference points and position-driven output are introduced. Furthermore, a teleoperation strategy for both the single-leader-single-follower configuration and the single-leader-dual-follower configuration are designed, which allows operators to control either one or two robot arms with a single leader device. Finally, the effectiveness of our method is verified through a plugging-in task and a continuous flipping task, demonstrating that the proposed system is capable of performing tasks that demand high positioning accuracy and stiffness adjustment. A supplementary video for this paper is available in GitHub**https://***/setowenGit/TOSA-GMM-GMR-Video.

关键词： Manifolds Performance evaluation Hands Accuracy Neuroscience Sockets Manipulators Robots Cybernetics

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Design of a Framework Based on MCDM and Data Analytics for Improving the Inventory Management of Supplies for Clinical Studies: A Case Study in a Research Center of a High Complexity Clinic 26th

Design of a Framework Based on MCDM and Data Analytics for I...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Jiménez-Delgado, Genett Isabel Aslan, Imran Hernández-Palma, Hugo Orozco Bohorquez, Mario Acosta Ortega, Felipe Plazas-Alvarado, Jonny Jiménez-Coronado, Angélica Roncallo-Pichon, Alberto Morales-Espinosa, Roberto Department of Industrial Engineering Institución Universitaria de Barranquilla IUB Barranquilla Colombia Faculty of Health Sciences Bingöl University Bingöl12000 Turkey Faculty of Engineering Industrial Engineering Program Corporación Universitaria Iberoamericana IBERO Bogotá Colombia Department of Computer Science and Electronics Universidad de la Costa CUC Barranquilla Colombia Faculty of Economic Accounting and Administrative Sciences Business Administration Program Fundación Universitaria de Popayan Popayan Colombia Bogotá Colombia Department of Business Transformation - Business Administration Program Corporación Unificada Nacional de Educación Superior – CUN Bogotá Colombia Faculty of Economic Administrative and Accounting Sciences. Accounting Program Corporación Universitaria Americana Barranquilla Colombia International Business Program Fundación Universitaria CEIPA Barranquilla Colombia

ISBN: (纸本)9783031768088

Inventory management in the health sector, specifically in the pharmaceutical service, represents a high percentage of logistics costs, putting pressure on health institutions to optimize inventory management to guarantee the availability of supplies. On the other hand, different regulations worldwide establish the adoption of mechanisms and policies for adequate inventory management, a situation that in low- and middle-income countries is restricted by resource limitations and the low implementation of robust techniques and methodologies. Different applications of models for inventory management have been identified in the literature, such as optimization models, Lean tools, and multicriteria decision-making methods. However, integrated approaches to optimize demand management, ordering, and controlling pharmaceutical services supplies are still under development. Therefore, the present study proposes a three-phase hybrid approach based on MCDM techniques and data analytics to improve inventory management of the pharmaceutical service in a health research center. The first stage consisted of characterizing the process to identify aspects for improvement. In the second stage, a multicriteria ABC classification model based on F-AHP and TOPSIS was applied to classify laboratory supplies for three selected clinical studies. Furthermore, the appropriate forecasting method was chosen for each clinical study, and a combined model (P model and FEFO model) was applied to establish the reorder point. Then, strategies, mechanisms, and policies were proposed to improve inventory management and redesign the process flow. As the main results, a multicriteria ABC method was obtained to classify laboratory supplies, taking into account Rotation (GW = 0.512), Criticality (GW = 0.286), and Availability (GW = 0.203). On the other hand, through data analysis and regression models, the exponential smoothing model with α = 0.10 was identified as the most convenient forecasting model, as

关键词： Decision making

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ODD: Omni Differential Drive for Simultaneous Reconfiguration and Omnidirectional Mobility of Wheeled Robots

arXiv

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arXiv 2024年

作者： Zhao, Ziqi Xie, Peijia Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada

Wheeled robots are highly efficient in human living environments. However, conventional wheeled designs, limited by degrees of freedom, struggle to meet varying footprint needs and achieve omnidirectional mobility. This paper proposes a novel robot drive model inspired by human movements, termed as the Omni Differential Drive (ODD). The ODD model innovatively utilizes a lateral differential drive to adjust wheel spacing without adding additional actuators to the existing omnidirectional drive. This approach enables wheeled robots to achieve both simultaneous reconfiguration and omnidirectional mobility. Additionally, a prototype was developed to validate the ODD, followed by kinematic analysis. Control systems for self-balancing and motion were designed and implemented. Experimental validations confirmed the feasibility of the ODD mechanism and the effectiveness of the control strategies. The results underline the potential of this innovative drive system to enhance the mobility and adaptability of robotic platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Wheels

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Auto Digit Selection for Most Significant Digit First Multiplication

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IEEE Embedded Systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

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Autonomous Number Plate Detection System for Vehicle Identification and Tracking

Autonomous Number Plate Detection System for Vehicle Identif...

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： Neha Sri Nareddy Abhinaya Polneni Rekha Baithi Pratyusha Kunchamwar Sunil Kumar Suvarna Durgabhavani Battu Naveena Budda Vikas Agarwal Premchander Munimanda AI IoT and Robotics Lab (AIR Lab) AGVs Group Autonomous Robotics Systems Limited Hyderabad India Department of Information Technology Malla Reddy Engineering College for Women Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta USA School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. Technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy.

关键词： Visualization Accuracy Law enforcement System performance Transportation Artificial neural networks Real-time systems Security License plate recognition Videos

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Informative and Long-Term Response Generation using Multiple Suggestions and User Persona Retrieval in a Dialogue System

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APSIPA Transactions on Signal and Information Processing 2024年第2期13卷

作者： Hsu, Jia-Hao Chen, Tsai-Yi Wu, Chung-Hsien Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Artificial Intelligence National Cheng Kung University Tainan Taiwan

Enhancing user satisfaction in dialogue systems relies on their ability to understand users and generate responses that meet their expectations. This study proposes a dialogue system that incorporates the Multi-Suggestions Transformer (MST) to generate informative and long-term responses. The MST combines empathy suggestions, system persona suggestions, and knowledge suggestions to produce comprehensive and informative responses. Additionally, the system employs a persona detection model and a persona extraction model to extract the user persona from current sentences and retrieve the most suitable user persona from the dialogue history. This facilitates long-term conversations by enabling the system to remember and respond to sentences relevant to the user persona. The proposed MST-based dialogue system outperforms the baseline in terms of informativeness, as evidenced by higher scores in BLEU, BERT-score, Distinct-n, and Perplexity on the Blended Skill Talk and Multi Session Chat datasets. Furthermore, two novel evaluation metrics, PerP and PerB, introduced in this study demonstrate the system’s effective utilization of the user persona for achieving long-term dialogue. Human subjective evaluation indicates that our model consistently outperforms the baseline, achieving superior scores of 68%, 56%, 52%, and 64% in the four subjective metrics. © 2024 J.-H. Hsu, T.-Y. Chen and C.-H. Wu.

关键词： Speech processing

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Microengineered platforms for characterizing the contractile function of in vitro cardiac models

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Microsystems & Nanoengineering 2022年第1期8卷 29-50页

作者： Wenkun Dou Manpreet Malhi Qili Zhao Li Wang Zongjie Huang Junhui Law Na Liu Craig A.Simmons Jason T.Maynes Yu Sun Department of Mechanical and Industrial Engineering University of TorontoTorontoON M5S 3G8Canada Program in Molecular Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Biochemistry University of TorontoTorontoON M5S 1A8Canada Institute of Robotics and Automatic Information System and the Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China School of Mechanical&Automotive Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai 200444China Institute of Biomedical Engineering University of TorontoTorontoON M5S 3G9Canada Translational Biology&Engineering Program Ted Rogers Centre for Heart ResearchTorontoON M5G 1M1Canada Department of Anesthesiology and Pain Medicine University of TorontoTorontoON M5S 1A8Canada Department of Anesthesia and Pain Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada Department of Computer Science University of TorontoTorontoON M5T 3A1Canada

Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology,disease modeling and drug cardiotoxicity as well as for therapeutic *** still exist in obtaining the complete capability of in situ sensing to fully evaluate the complex functional properties of cardiac cell/tissue *** to contractile strength(contractility)and beating regularity(rhythm)are particularly important to generate accurate,predictive *** new platforms and technologies to assess the contractile functions of in vitro cardiac models is essential to provide information on cell/tissue physiologies,drug-induced inotropic responses,and the mechanisms of cardiac *** this review,we discuss recent advances in biosensing platforms for the measurement of contractile functions of in vitro cardiac models,including single cardiomyocytes,2D monolayers of cardiomyocytes,and 3D cardiac *** characteristics and performance of current platforms are reviewed in terms of sensing principles,measured parameters,performance,cell sources,cell/tissue model configurations,advantages,and *** addition,we highlight applications of these platforms and relevant discoveries in fundamental investigations,drug testing,and disease ***,challenges and future outlooks of heart-on-a-chip platforms for in vitro measurement of cardiac functional properties are discussed.

关键词： cardiac contractile regularity

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Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification 2nd

Combining Weakly Supervised Segmentation with Multitask Lea...

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2nd Workshop on Medical Image Learning with Noisy and Limited Data, MILLanD 2023

作者： Rajapaksa, Sajith Namdar, Khashayar Khalvati, Farzad Neurosciences and Mental Health Research Program The Hospital for Sick Children Toronto Canada Institute of Medical Science University of Toronto Toronto Canada Department of Medical Imaging University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Department of Mechanical and Industrial Engineering University of Toronto Toronto Canada Vector Institute Toronto Canada

ISBN: (纸本)9783031471964

Segmentation plays a crucial role in computer-aided medical image diagnosis, as it enables the models to focus on the region of interest (ROI) and improve classification performance. However, medical image datasets often lack segmentation masks due to cost. In this work, we have introduced a novel end-to-end pipeline that enhances classification by incorporating ROIs generated by a weakly supervised method as an auxiliary task in a deep multitask learning framework. We have demonstrated that our approach outperforms conventional methods that only rely on classification. By using weakly supervised segmentation, we are able to leverage pixel-wise information, which ultimately leads to improved classification performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image enhancement

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Applying CNN Models with Stereo Camera for Object Detection and Distance Estimation in Automatic Micro Electric Vehicle (MEVi)

Applying CNN Models with Stereo Camera for Object Detection ...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Farrell Putra Harimanto Nurul Hasanah Muhammad Khristamto Aditya Pradana Muhammad Nurul Puji Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Research Center for Smart Mechatronics National Research and Innovation Agency KST Samaun Samadikun Bandung Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

Technological advancements in the automotive industry are currently focused on autonomous driving systems or driver assistance systems. Depth estimation is also an important feature of the autonomous driving system as it can accurately estimate the distance to the detected objects. In current developments technologies such as stereo cameras are more popular for a more accurate depth estimation. The process for object detection consists of a combination of classification, localization, and segmentation. Deep convolutional neural networks are the basis of object detection. The methods for object detection are divided into two categories namely the Region Proposal-based method such as RCNN, and the Classification-based method which provides for methods such as YOLO and MobileNet. As a part of the research conducted by the National Research and Innovation Agency (BRIN) on Automatic Micro Electric Vehicles or MEVi, this paper analyzed and compared the performance of several CNN models for object detection as well as the performance of depth sensing using ZED stereo camera which was used for the autonomous system in MEVi. Among several models that had been compared, YOLO v8 model demonstrates high accuracy and moderate speed as indicated by its high mAP (0.50) and FPS (38.46). Based on depth sensing results, the accuracy of distance measurement also depended on brightness and environment with the average of accuracy achieved indoors was 98.2% and outdoors was 96.3%.

关键词： YOLO Accuracy Depth measurement Brightness Cameras Electric vehicles Distance measurement Sensors Convolutional neural networks Autonomous vehicles

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MOSFET Based Active Rectifier Design in Wireless Capacitive Power Transfer for Electric Vehicles

MOSFET Based Active Rectifier Design in Wireless Capacitive ...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Klemens Raphael Dragon Zener Sukra Lie Aam Muharam Juan Luciano Ika Muhamad Arjuna Perdana Amin Abdul Hapid Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Research Center for Transportation Technology Badan Riset dan Inovasi Nasional Bandung Jawa Barat Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

While the rise of electric vehicles reflects a push for clean energy as envisioned by international agreements like the Paris Agreement, wireless charging's convenience can overcome limitations and hasten their adoption, making it a strategic step alongside regulations such Permenhub No. 15/2022 and SDG 9 “Industry, Innovation, and Infrastructure” for cleaner air and sustainable transportation. The aim is to convert the high frequency AC voltage from the coupling capacitor into a stable DC voltage. Further increase the system sensitivity to the load variations, by designing a MOSFET based active rectifier. By changing load parameters, the accuracy of PFC adaptation to the battery State of Charge (SOC) obtained is at 98% with 2% tolerance for errors. It is concluded that the research is said to be successful because it has met the parameters of general research hypothesis where the system is able to convert high frequency AC voltage into a stable DC voltage.

关键词： Wireless communication MOSFET Wireless sensor networks Rectifiers High-voltage techniques Electric vehicles Batteries State of charge High frequency Voltage control

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