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arXiv 2022年

作者： Seidman, Jacob H. Kissas, Georgios Perdikaris, Paris Pappas, George J. Graduate Program in Applied Mathematics and Computational Science University of Pennsylvania United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania United States Department of Electrical and Systems Engineering University of Pennsylvania United States

Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By directly learning maps (operators) between infinite dimensional function spaces, these models are able to learn discretization invariant representations of target functions. A common approach is to represent such target functions as linear combinations of basis elements learned from data. However, there are simple scenarios where, even though the target functions form a low dimensional submanifold, a very large number of basis elements is needed for an accurate linear representation. Here we present NOMAD, a novel operator learning framework with a nonlinear decoder map capable of learning finite dimensional representations of nonlinear submanifolds in function spaces. We show this method is able to accurately learn low dimensional representations of solution manifolds to partial differential equations while outperforming linear models of larger size. Additionally, we compare to state-of-the-art operator learning methods on a complex fluid dynamics benchmark and achieve competitive performance with a significantly smaller model size and training cost. © 2022, CC BY-NC-SA.

关键词： Benchmarking

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The Banking Industry is Undergoing a Digital Transformation as Part of industrial 4.0

The Banking Industry is Undergoing a Digital Transformation ...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Wahyu Sardjono Dewi Sagita Pranata Aditya Agung Dwi Ramdhana Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Department of Informatics Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences King's College London Strands West Minster England Master of Management Faculty of Economics and Business University of Airlangga Surabaya Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

In the current day, digital transformation is imperative; both customer demand and market competitiveness are mostly driving that trend. The fourth industrial revolution has also affected the financial sector. One difficulty that has to be addressed is the desire from customers for quick and useful services. Traditional procedures need to be replaced with digital ones. Furthermore, banking organizations will have a competitive edge in the current market if they are successful in this digital transformation process. Successful transformation, however, does not happen by accident; in fact, the likelihood of completing this change is seen to be very low, necessitating rigorous planning for a company that is ready for the future. There are several components to the preparation in question, both internal and external. In this situation, the company's ability to combine and rearrange its resources quickly is essential for adapting to the ever-changing business environment.

关键词： Industries Leadership Companies Banking Market research Fourth industrial Revolution Planning Faces Accidents

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Ranking Routes In Semiconductor Wafer Fabs

Research Square

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Research Square 2021年

作者： Gupta, Shreya Hasenbein, John J. Kim, Byeongdong Graduate Program in Operations Research and Industrial Engineering Department of Mechanical Engineering University of Texas at Austin AustinTX78712 United States

We develop a method to estimate the quality of processing routes in a wafer fabrication process. Ranking such routes can be useful for identifying the "best" and "worst" routes when making adjustments to recipes. Route categorization is also useful in developing efficient scheduling algorithms. In particular, we propose a method for ranking routes based on count-based metrics such as the number of defects on a wafer. We start with a statistical model to produce a "local" ranking of a tool and then build a "global" ranking via a heuristic procedure. Creating a fully statistical procedure for ranking routes in semiconductor fabrication plants is virtually impossible, given the number of possible routes and the limited data available. Nonetheless, our discussions with working engineers indicate that even approximate rankings are useful for making better operational decisions. © 2021, CC BY.

关键词： Scheduling algorithms

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Passivity-Based Cooperative Dynamic Control for Human-Rigid Body Networks

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2021年第11期141卷 1162-1174页

作者： Murao, Toshiyuki Koshio, Yusuke Kawai, Hiroyuki Yamauchi, Junya Hatanaka, Takeshi Fujita, Masayuki College of Engineering Kanazawa Institute of Technology 7-1 Ohgigaoka Nonoichi-shi Ishikawa921-8501 Japan Graduate Program in Mechanical Engineering Kanazawa Institute of Technology 7-1 Ohgigaoka Nonoichi-shi Ishikawa921-8501 Japan Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan School of Engineering Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan

This paper presents passivity-based cooperative dynamic control for human-rigid body networks. In the human-rigid body network, a human operator sends a command signal to and receives visual information from only a part of the robotic group. In this setting, the proposed dynamic control scheme achieves not only pose synchronization of all mobile robots but also convergence to the reference pose that the human operator specifies. First, we derive robot dynamics of an omni-directional mobile robot through the Newton-Euler equation. Second, we design a control input to the robots with dynamics and show the passivity property of the robot system. Next, we discuss convergence analysis of the closed-loop system through the Lyapunov stability theorem and the LaSalle's invariance principle. The main advantage of the proposed method is to guarantee convergence of not only the position but also the orientation to the reference ones in the human-rigid body network. Finally, we provide simulation results and experimental results using four ground vehicles and a tablet PC to illustrate the performance of the proposed control method. © 2021 The Institute of Electrical Engineers of Japan.

关键词： Mobile robots

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Detecting viruses in contact networks with unreliable detectors

arXiv

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arXiv 2021年

作者： Agrawal, Sudesh K. Hasenbein, John J. Graduate Program in Operations Research & Industrial Engineering Department of Mechanical Engineering University of Texas at Austin AustinTX78712 United States

This paper develops and analyzes optimization models for rapid detection of viruses in large contact networks. In the model, a virus spreads in a stochastic manner over an undirected connected graph, under various assumptions on the spread dynamics. A decision maker must place a limited number of detectors on a subset of the nodes in the graph in order to rapidly detect infection of the nodes by the virus. The objective is to determine the placement of these detectors so as to either maximize the probability of detection within a given time period or minimize the expected time to detection. Previous work in this area assumed that the detectors are perfectly reliable. In this work, it is assumed that the detectors may produce false-negative results. In computational studies, the sample average approximation method is applied to solving the problem using a mixed-integer program and a greedy heuristic. The heuristic is shown to be highly efficient and to produce high-quality solutions. In addition, it is shown that the false-negative effect can sometimes be ignored, without significant loss of solution quality, in the original optimization formulation. © 2021, CC BY.

关键词： Stochastic systems

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An Adaptive Oscillators-Based Approach to Achieve Transparent Control of a Six DoF Lower-Limb Exoskeleton

An Adaptive Oscillators-Based Approach to Achieve Transparen...

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IEEE/ASME International Conference on Mechtronic and Embedded systems and Applications, MESA

作者： Rafhael Milanezi de Andrade Benito Lorenzo Pugliese Abolfazl Mohebbi Paolo Bonato Graduate Program of Mechanical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil Department of Electronics and Telecommunication Politecnico di Torino Turin Italy Department of Mechanical Engineering Polytechnique Montreal Montreal Canada Department of Physical Medicine & Rehabilitation Harvard Medical School Spaulding Rehabilitation Hospital Boston MA USA

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Lower-limb exoskeletons have been effectively used in rehabilitation to reduce gait impairments. However, making the exoskeleton transparent to provide suitable net interaction torques and assist patient’s movements is yet an open challenge. In this study, we designed a torque control method based on adaptive oscillators (AOs) to reduce interaction forces with a six degree-of-freedom exoskeleton, named the ExoRoboWalker. A synchronization layer was designed with a pool of AOs to estimate user’s gait phase, and a baseline torque controller was introduced to generate a torque profile based on user-robot interaction during the previous gait cycles. A zero-torque controller was used in parallel to the main torque controller with the objective of improving users’ control of the system and their balance. The proposed controller was experimentally evaluated in six healthy subjects who walked with the exoskeleton for 200 gait strides at different walking speeds. A transparent controller we previously implemented based on a zero-impedance model was also evaluated and results were compared with the new controller. The controller based on AOs was able to reduce the average interaction torques by 40% at the highest gait speed of 0.8 m/s relative to the zero-impedance controller we previously implemented.

关键词： Knee Ankle Torque Exoskeletons Torque control Control systems Stability analysis Synchronization Hip Standards

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GNN-Based Point Cloud Maps Feature Extraction and Residual Feature Fusion for 3D Object Detection

GNN-Based Point Cloud Maps Feature Extraction and Residual F...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wei-Hsiang Liao Chieh-Chih Wang Wen-Chieh Lin Graduate Degree Program of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these challenges, taking the environment information (HD maps) into account could be beneficial to predetermine where targets are more or less likely to appear. Compared with semantic maps, HD maps formed by point clouds provide much richer information from surrounding static objects and scenes. In this work, we construct a GNN-based feature extraction of point cloud maps to increase the receptive fields of learning map features. Our work is based on PVRCNN, the state-of-the-art LiDAR object detection method. With point-wise and voxel-wise features obtained from PVRCNN, residual feature fusion is proposed to fuse the features from PVRCNN and the map features from GNN. Our approach is evaluated on NuScenes dataset. It achieves a 24.78% average precision improvement for long-range objects at 40–50 meters, the farthest areas with ground truth annotation. Our approach also has a 4.22% reduction of false positives in the entire sensing areas.

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Grinding wheel specification cybernetic recommendation with multi-task multi-imbalanced learning in smart manufacturing system

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Advanced engineering Informatics 2024年 61卷

作者： Yao, Kuo-Ching Chen, Tzu-Li Chen, James C. Li, Chia-Ruei International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Kinik Company New Taipei23942 Taiwan Graduate Institute of Intelligent Manufacturing Technology National Taiwan University of Science and Technology Taipei106335 Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu300044 Taiwan

Over the years, the grinding wheels industry has played a crucial role in mechanical engineering. Grinding wheel specification is composed of various factors such as abrasive type, wheel grade, bond type, and more, which are derived from customer workpiece operation information. Traditionally, the determination process of grinding wheel specifications is complicated. Experienced engineers need to check out the limitation and consider the interaction of each factor. When the new specification order comes, engineers have to spend a lot of time on historical data search and discussion. Recently, the application of artificial intelligence attracts more and more attention in the manufacturing industry. Through collecting historical data, the model has the ability to extract the domain knowledge, predict the specification that does not exist, and reduce the decision time. However, when training the model, this grinding wheel specification faced multiple imbalanced targets and multi-task issues. In the past, research efforts primarily focused on studying single-task learning (STL) algorithms. Seldom of them has a systematic way to discuss the model determination of multiple targets and correlation influence, especially on the tabular-type dataset. In this study, we proposed a multiple resampling chain multi-task learning framework to address the issue of data science. To tackle the multi-task imbalance resampling problem, we also proposed a resampling strategy called Multiple Resampling Chain. Moreover, we conducted a sensitivity analysis to examine the correlation effect on model improvement. Finally, we proposed a cyber-recommendation system that integrates the training model and interface to provide a more intuitive way of specification prediction. This system not only can help engineers to improve the time spent on making decisions, but also make the whole manufacturing process to be more intelligent. © 2024 Elsevier Ltd

关键词： Grinding (machining)

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A Study on Giant Magnetostrictive Actuator for Interior Sound Control System of the Ultra-Compact Mobility: Fundamental Consideration on Formation of the Giant Magnetostrictive Phases Depending on Heat Treatment Condition 27

A Study on Giant Magnetostrictive Actuator for Interior Soun...

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27th International Conference on Electrical Machines and systems, ICEMS 2024

作者： Kato, Taro Naganuma, Ryusei Bando, Koki Sawada, Kentaro Wenbao, Wu Kobayashi, Ikkei Kuroda, Jumpei Uchino, Daigo Ogawa, Kazuki Ikeda, Keigo Endo, Ayato Kato, Hideaki Narita, Takayoshi Furui, Mitusaki Tokyo University of Technology Department of Mechanical Engineering 1404-1 Katakura Hachioji-shi Tokyo192-0982 Japan Sustainable Engineering Program Graduate School of Engineering Tokyo University of Technology 1404-1 Katakura Hachioji-shi Tokyo192-0982 Japan Tokai University Kitakaname 4-1-1 Kanagawa Hiratsuka-shi259-1292 Japan Tokai University Kitakaname 4-1-1 Kanagawa Hiratsuka-shi259-1292 Japan Numazu College Department of Mechanical Engineering Ohoka 3600 Shizuoka Numazu-shi410-0022 Japan Aichi University of Technology Department of Electronic Robotics 50-2 Manori Nishisako-machi Aichi Gamagori-shi443-0047 Japan Hokkaido University of Science Department of Mechanical Engineering 7-Jo 15-4-1 Maeda Teine Hokkaido Sapporo006-8585 Japan Fukuoka Institute of Technology Department of Electrical Engineering Wajiro Higashi 3-30-1 Higashi-ku Fukuoka Fukuoka-shi811-0295 Japan Tokai University Department of Mechanical System Engineering Kitakaname 4-1-1 Kanagawa Hiratsuka-shi259-1292 Japan

ISBN: (纸本)9784886864406

An active noise control system using giant magnetostrictive actuator has been proposed as a countermeasure for interior noise of the ultra-compact mobilities, which is a new transportation tools. In this paper, as basic research toward the development of the proposed giant magnetostrictive actuator for active noise control, we conduct experiments on the material properties when terbium, dysprosium, and iron powder, which make up the giant magnetostrictive material, are mixed and the heat treatment conditions are changed. In this study, the heat treatment temperature was varied from 800°C to 1100°C in 100°C incrementsbased on the equilibrium state temperature of each material, and the heat treatment time was set in 30-minute increments from 1 hour to 3 hours. As a result, TbFe2 was confirmed after heat treatment at an electric furnace temperature of 1000°C for 3 hours, although its strength was insufficient. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Active noise control

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Long short-term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control

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Procedia Computer Science 2023年 216卷 213-220页

作者： Santo Wijaya Yaya Heryadi Yulyani Arifin Wayan Suparta Lukas Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta 55281 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance.

关键词： Model-based Reinforcement Learning Random-Shooting Policy LSTM MPC Nonlinear System

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