检索结果-内蒙古大学图书馆

33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Hazard Analysis of Collaborative automation Systems: A Two-layer Approach based on Supervisory Control and Simulation

Hazard Analysis of Collaborative Automation Systems: A Two-l...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Tom P. Huck Yuvaraj Selvaraj Constantin Cronrath Christoph Ledermann Martin Fabian Bengt Lennartson Torsten Kröger Intelligent Process Automation and Robotics Lab Institute of Anthropomatics and Robotics (IAR-IPR) Karlsruhe Institute of Technology India Department of Electrical Engineering Division of Systems and Control Chalmers University of Technology Gothenburg Sweden

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory Control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

引用

arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Efficient Constrained Dynamics Algorithms based on an Equivalent LQR Formulation using Gauss’ Principle of Least Constraint

arXiv

引用

arXiv 2023年

作者： Sathya, Ajay Suresha Bruyninckx, Herman Decré, Wilm Pipeleers, Goele The Division of Robotics Automation and Mechatronics The Department of Mechanical Engineering KU Leuven Leuven Belgium DMMS-M Lab Flanders Make Leuven Belgium TU Eindhoven Netherlands Materialise NV Leuven Belgium

We derive a family of efficient constrained dynamics algorithms by formulating an equivalent linear quadratic regulator (LQR) problem using Gauss’ principle of least constraint and solving it using dynamic programming. Our approach builds upon the pioneering (but largely unknown) O(n + m2d + m3) solver by Popov and Vereshchagin (PV), where n, m and d are the number of joints, number of constraints and the kinematic tree depth respectively. We provide an expository derivation for the original PV solver and extend it to floating-base kinematic trees with constraints allowed on any link. We make new connections between the LQR’s dual Hessian and the inverse operational space inertia matrix (OSIM), permitting efficient OSIM computation, which we further accelerate using matrix inversion lemma. By generalizing the elimination ordering and accounting for MUJOCO-type soft constraints, we derive two original O(n + m) complexity solvers. Our numerical results indicate that significant simulation speed-up can be achieved for high dimensional robots like quadrupeds and humanoids using our algorithms as they scale better than the widely used O(nd2 + m2d + d2m) LTL algorithm of Featherstone. The derivation through the LQR-constrained dynamics connection can make our algorithm accessible to a wider audience and enable cross-fertilization of software and research results between the fields. © 2023, CC BY-SA.

关键词： Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

PV-OSIMr: A Lowest Order Complexity Algorithm for Computing the Delassus Matrix

arXiv

引用

arXiv 2023年

作者： Sathya, Ajay Suresha Decré, Wilm Swevers, Jan Inria Département d’Informatique École Normale Supérieure PSL Research University France Division of Robotics Automation and Mechatronics The Department of Mechanical Engineering KU Leuven FlandersMake@KULeuven Leuven Belgium

We present PV-OSIMr, an efficient algorithm for computing the Delassus matrix (also known as the inverse operational space inertia matrix) for a kinematic tree, with the lowest order computational complexity known in literature. PV-OSIMr is derived by optimizing the recently proposed PV-OSIM algorithm using the compositionality of the force and motion propagators. It has a computational complexity of O(n + m2) compared to O(n + m2d) of the PV-OSIM algorithm and O(n + md + m2) of the extended force propagator algorithm (EFPA), where n is the number of joints, m is the number of constraints and d is the depth of the kinematic tree. Since the Delassus matrix is an m×m sized matrix and its computation must consider all the n joints, PV-OSIMr’s asymptotic computational complexity is optimal. We further benchmark our algorithm and find it to be often more efficient than the PV-OSIM and EFPA in practice. © 2023, CC BY-SA.

关键词： Trees (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

引用

IEEE International Conference on automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

引用

Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

来源：评论

学校读者我要写书评

暂无评论

Rethinking development and major research plans of Industrial Internet in China

引用

Fundamental Research 2024年第1期4卷 3-7页

作者： Chunxiao Jiang Yang Cong Jiming Chen Chenghong Wang Guozheng Wu Ruizhen Zhao Zhiheng Wang Bin Xiao Ting Chen Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Tsinghua Space Center Tsinghua UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Division III Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China University of Electronic Science and Technology of China Chengdu 611731China

This paper gives a definition of the Industrial Internet and expounds on the academic connotation of the future Industrial *** this foundation,we outline the development and current status of the Industrial Internet in China and ***,we detail the avant-garde paradigms encompassed within the National Natural Science Foundation of China(NSFC)’s“Future Industrial Internet Fundamental Theory and Key Technologies”research plan and its corresponding management *** research initiative endeavors to enhance interdisciplinary collaborations,aiming for a synergistic alignment of industry,academia,research,and practical *** primary focus of the research plan is on the pivotal scientific challenges inherent to the future industrial *** is poised to traverse the“first mile”,encompassing foundational research and pioneering innovations specific to the industrial internet,and seamlessly bridges to the“last mile”,ensuring the effective commercialization of scientific and technological breakthroughs into tangible industrial market *** anticipated contributions from this initiative are projected to solidify both the theoretical and practical scaffolding essential for the cultivation of a globally competitive industrial internet infrastructure in China.

关键词： Industrial Internet Academic connotation Major research plan Scientific problem Policy advice

来源：评论

学校读者我要写书评

暂无评论

Safe-Planner: A Single-Outcome Replanner for Computing Strong Cyclic Policies in Fully Observable Non-Deterministic Domains

Safe-Planner: A Single-Outcome Replanner for Computing Stron...

引用

International Conference on Advanced robotics (ICAR)

作者： Vahid Mokhtari Ajay Suresha Sathya Nikolaos Tsiogkas Wilm Decré Division Robotics Automation and Mechatronics (RAM) and Flanders MakeDepartment of Mechanical EngineeringKU LeuvenBelgium

ISBN: (纸本)9781665436854

Replanners are efficient methods for solving nondeterministic planning problems. Despite showing good scalability, existing replanners often fail to solve problems involving a large number of misleading plans, i.e., plans that do not lead to strong solutions, however, due to their minimal lengths, are likely to be found at every replanning iteration. The poor performance of replanners in such problems is due to their all-outcome determinization. That is, when compiling from non-deterministic to classical, they include all compiled classical operators in a single deterministic domain which leads replanners to continually generate misleading plans. We introduce an offline replanner, called Safe-Planner (SP), that relies on a single-outcome determinization to compile a nondeterministic domain into a set of classical domains, and ordering heuristics for ranking the obtained classical domains. The proposed single-outcome determinization and the heuristics allow for alternating between different classical domains. We show experimentally that SP avoids generating misleading plans, but rather generates weak plans that directly lead to strong solutions. The experiments show that SP outperforms state-of-the-art non-deterministic solvers by solving a broader range of problems. We also validate the practical utility of SP in real-world non-deterministic robotic tasks.

关键词： Scalability Lead Planning Task analysis Robots

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable Smart Grid Management for Electric Vehicle Charging and the Integration of Renewable Energy Sources using Pied Kingfisher Optimizer

Reconfigurable Smart Grid Management for Electric Vehicle Ch...

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： M. Sivaramkrishnan M. Siva Ramkumar Kaparthi Uday Mohammad Kanan C. Mohan Raj Jayant Giri Dept of EEE Karpagam College of Engnieering India Dept of ECE SNS College of Technology EIE Department CVR College of Engineering Ibrahimpatanam Hyderabad Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan Department or EEE Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Coimbatore Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Electric Vehicle (EV) charging management under the Smart Grid (SG) framework with incorporated Renewable Energy Sources (RESs) functions to advance Energy Management (EM). The operational cost of EV charging varies because renewable energy availability can change so this affects the financial performance of charging operations. Integration of RESs with the SG becomes challenging for operational system efficiency particularly when renewable energy generation levels are low. To overcome these drawbacks, this manuscript proposes an efficient approach for SG management for EV charging with the integration of RESs. The suggested method is Pied Kingfisher Optimizer (PKO). The main aim of the suggested technique is to minimize the operational expenses of EV charging as well as maximize the efficiency of the SG system. The PKO is used to optimize the scheduling and allocation of energy resources within the SG system. By then, the suggested approach is implemented in MATLAB and contrasted with several other methods that are currently in use. The suggested technique outperforms all existing approaches such as Bald Eagle Search (BES), Red Kite Optimization (RKO), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), and Coati Optimization Algorithm-Constitutive Artificial Neural Network (COA-CANN). The suggested approach delivers superior performance than conventional methods by reducing operational costs to 638,322$ under coordinated charging and 629,745$ under smart charging without impacting system performance metrics.

关键词： Renewable energy sources Costs System performance Artificial neural networks Power system stability Stability analysis Smart grids Wind turbines Resource management Smart charging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：