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Impedance Adjustments on a Fast 20kV Solid State Switch

Impedance Adjustments on a Fast 20kV Solid State Switch

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Pulsed Power conference, International conference on High-Power Particle Beams and International Symposium on Electromagnetic Launch Technology (EAPPC/BEAMS/EML), Euro-Asian

作者： N. Ayala Cintas D. Baack B. Bromberger N. Kularia W. Nidens M. Osemann C. Piel RI Research Instruments GmbH Dortmund Germany

ISBN: (数字)9789038661353

ISBN: (纸本)9798350352733

A simple way of rapid current pulse generation is to switch a pulse forming line (PFL) onto a resistive load with the same impedance as the PFL. Since switch assemblies become larger with higher voltage requirements, the switch’s geometry can have a significant impact on the pulse propagation properties of the whole circuit. the research in this paper investigates the switch’s propagation characteristics. To test our 20kV solid state switch, we’ve set up a fast PFL circuit for two system impedance situations. In repetitive tests, the switch’s geometrical parameters and numbers of thyristors were changed, to investigate influences on the switching characteristic. the measured results were later used to derive a SPICE model that emulates the switch’s behavior very accurately and takes geometry changes into account. It was found that the rise time is mostly affected by the switch’s distance to its ground plane. the later simulations gave indications that this effect could be caused by the switch’s intrinsic impedance.

关键词： Geometry Pulse generation thyristors Particle beams Switches SPICE Impedance Integrated circuit modeling Electromagnetics Switching circuits

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modeling and Control of Soft Robotic Tail Based Aerial Maneuvering (STAM) system: Towards Agile Self-Righting with a Soft Tail 20

Modeling and Control of Soft Robotic Tail Based Aerial Maneu...

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20th International conference on Advanced Robotics (ICAR)

作者： Butt, Jawad Mehmood Chu, Xiangyu Zheng, Hao Wang, Xiaomei Kwok, Ka-Wai Au, K. W. Samuel Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China

ISBN: (纸本)9781665436847

Recent studies have demonstrated feasible aerial maneuvering with rigid tails. However, soft robotic tails were never investigated for aerial maneuvering applications, and no modeling strategy was found that exploits the soft robotic tail based aerial maneuvering (STAM) system kinematics for flight phase control (aerial self-righting). In this work, we provide the feasible solution to flight phase control of STAM systems (1-DOF and 2-DOF) by proposing their forward kinematics, differential kinematics, and flight phase models. We integrate Piecewise constant curvature (PCC) and Augmented rigid robot (ARR) modeling formulations to model the kinematics of 1-DOF STAM (body-tail pitch or body-tail yaw) system and propose its flight phase model & control. then, we introduce the soft-tail "arbitrary-plane" bending which aids the extension of integrated modeling approaches to model the forward kinematics of 2-DOF (pitch and yaw) soft tail. the 2-DOF STAM system (body-tail pitch and yaw together) is composed of rigid body, cable-driven soft tail, and actuation units, so we develop their differential kinematics which maps the tail shape velocities with the body orientation angular rates and the tail cable velocities respectively. Together with the forward and differential kinematics, we present flight phase model and control of 2-DOF STAM system which ensures the conservation of total angular momentum. the simulations, which demonstrates the self-righting maneuvers with STAM systems are provided and the effective simulation results validates our proposed models.

关键词： Antennas

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Self-Adaptive optimization Techniques for Matrix Production systems

Self-Adaptive Optimization Techniques for Matrix Production ...

引用

IEEE International conference on Automation Science and Engineering (CASE)

作者： Alexandre Sawczuk Da Silva Tim Knissel Gereon Weiss Fraunhofer Institute for Cognitive Systems IKS – Munich Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

the Industry 4.0 era has enabled the concept of Matrix Production systems, where products can be manufactured on demand by dynamically reconfiguring independent modules on the shop floor. this reconfiguration process, however, is not trivial, since certain possibilities incur much higher reconfiguration costs than others. To tackle this challenge, researchers have employed optimization techniques for identifying the best possible reconfiguration solutions. However, these approaches do not consider the self-adaptive nature of a manufacturing system, i.e., the need to re-optimize configurations when the order queue changes. To address this limitation, this paper focuses on the self-adaptive reconfiguration of Matrix Production system modules. Specifically, it proposes a genetic algorithm-based approach for self-adaptive reconfiguration, introducing the use of a solution archive mechanism. this approach is compared to an integer linear programming model, using a dataset that features dynamic updates to the queue of orders. Results show that the genetic algorithm-based approach generally has lower execution times than integer linear programming, though it does not converge to the global optimum.

关键词： Costs Computer aided software engineering Integer linear programming Fourth Industrial Revolution Dynamic programming Resource management optimization Standards Genetic algorithms Manufacturing systems

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CAD-Informed Uncertainty-Aware Sequence and Motion Planning for Robotic Assembly

CAD-Informed Uncertainty-Aware Sequence and Motion Planning ...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Takuya Kiyokawa Ismael Rodriguez Korbinian Nottensteiner Peter Lehner thomas Eiband Maximo A. Roa Kensuke Harada Department of Systems Innovation Graduate School of Engineering Science Osaka University Osaka Japan Institute of Robotics and Mechatronics German Aerospace Center (DLR) Oberpfaffenhofen Weßling Germany Industrial Cyber-Physical Systems Research Center The National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

this study addresses a multi-objective optimization problem in the planning of an uncertainty-aware sequence and motion for mechanical products with intricate structures and numerous contact areas. To generate an optimized sequence and motion that satisfies multiple conditions under mandatory requirements, we use a multi-objective optimization algorithm inspired by Non-Dominated Sorting Genetic Algorithm III, along with contact-rich robotic assembly-oriented constraints and objective functions. the proposed pipeline takes as input the CAD models of robot hardware, workspace, and assembled parts, conducts 3D geometrical and physical simulations of assembly motions, and then optimizes the assembly plan, including parts order, object placement pose, state transition, grasp, and trajectory for the real robot to execute. To obtain the uncertainty-aware sequence and motion, we incorporated a Contingent Contact-Exploring Rapidly-exploring Random Trees (ConCERRT)-based state transition planner and an objective function to evaluate the uncertainty in the multi-objective optimization algorithm. Our experiments on assembly planning for a chainsaw product demonstrated that the proposed method can generate constraint-satisfied assembly plans with a success rate of 99.2% while lowering the uncertainty in the simulations.

关键词： Robotic assembly Solid modeling Uncertainty Linear programming Planning Trajectory Mechanical products Robots optimization Assembly

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Loop Unrolling Based on SLP and Register Pressure Awareness

Loop Unrolling Based on SLP and Register Pressure Awareness

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Zilong Ye Jiye Jiao School of Computing Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Loop unrolling is a common compiler optimization technique that reduces the execution count of loop branch instructions and improves program performance by merging multiple iterations of a loop body into a single iteration. In LLVM, the native loop unrolling model considers only the impact of the number of instructions within the loop body when computing the unrolling factor, without accounting for subsequent optimizations and register resources. To address this issue, a new loop unrolling model is proposed that takes into account subsequent superword level parallelism (SLP) vectorization and register pressure. When computing the unrolling factor, the model uses the benefit for SLP (BSLP) model to identify loops that can be vectorized by SLP after loop unrolling and calculates the unrolling factor to support subsequent SLP vectorization. the register pressure-aware loop unrolling (RPLU) model adjusts the unrolling factor to mitigate excessive register pressure, thereby reducing the impact of register spills. Experimental results show that, compared to the loop unrolling model in LLVM, this model can compute more effective loop unrolling factors and improve program performance. the average speedup on the SPEC CPU 2006 benchmark suite is 2.0%, with a maximum speedup of 5.1%, and the average speedup on the TSVC benchmark suite is 5.1%, with a maximum speedup of 34.4%.

关键词： Program processors Computational modeling Merging Benchmark testing Parallel processing Knowledge discovery Registers optimization Fuzzy systems

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Lightweight and Efficient Attention-based CNN Models for In-field Strawberry Instance Segmentation

Lightweight and Efficient Attention-based CNN Models for In-...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Ziang Zhao Yulia Hicks Xianfang Sun Benjamin J. McGuinness Hin S. Lim School of Engineering Cardiff University United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom School of Engineering University of Waikato New Zealand

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Accurately segmenting strawberries within real-world production settings not only helps the development of automated harvesting robots but also allows for precise calculation of the number and size of strawberries, providing accurate yield information for agricultural planning and resource optimization. this paper proposes lightweight and efficient CNN models specifically designed for strawberry instance segmentation, consisting of an efficient self-attention-based backbone, a feature pyramid network (FPN), and a decoder with an instance branch and a mask branch. the proposed models surpass the original and simplified Mask R-CNN with significant 21.46 and 22.97 AP gains respectively, with the Base backbone achieving the highest AP of 70.22. Additionally, our models demonstrate efficiency by requiring much fewer parameters (17.42M) and floating-point operations (78.3G) compared to Mask R-CNN (35.08M / 877.4G), making them suitable for deployment on devices with limited computational resources.

关键词： Instance segmentation Computer aided software engineering Automation Computational modeling Production Decoding Planning Computational efficiency Robots optimization

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Transition Maneuver optimization of a Hybrid UAV Using Deep Reinforcement Learning

Transition Maneuver Optimization of a Hybrid UAV Using Deep ...

引用

International Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Mishma Akhtar Adnan Maqsood School of Interdisciplinary Engineering and Sciences National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

In the past few years, the increasing need for dynamic mission profiles of UAVs has led to the development of autonomous aerial vehicles with sophisticated path planning, tracking, and control algorithms. Owing to the size and aerodynamic structure of small UAVs, they can achieve maneuvers like VTOL (Vertical Take off and Landing) and transition maneuvers from hover to cruise mode. Trajectory optimization of such maneuvers helps to minimize flight time and path while considering spatial constraints. the emergence of Machine Learning has gained attention for developing control systems based on such algorithms. this study mainly explores the algorithm Deep Deterministic Policy Gradient (DDPG) for path planning and optimization. To enhance the capabilities of small UAVs, a hybrid tiltrotor aircraft model is considered, which aims to hover and transition to an efficient forward flight during the cruise.

关键词： Machine learning algorithms Heuristic algorithms Atmospheric modeling Deep reinforcement learning Autonomous aerial vehicles Aerodynamics Path planning Stability analysis Vehicle dynamics Trajectory optimization

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Life cycle economic policy for multi-level imperfect maintenance and repairman assignment

Life cycle economic policy for multi-level imperfect mainten...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Shunkang Zhao Youjun An Xiaohui Chen Yuanfa Dong Ziye Zhao Bin Zhou College of Mechanical and Vehicle Engineering Chongqing University Chongqing China College of Mechanical and Power Engineering China Three Gorges University Yichang China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In general, a multi-level maintenance strategy is usually more effective than a single-level maintenance strategy for equipment. At the same time, in the previous multi-level maintenance strategies, many majority of preventive optimization models only considered the maintenance cost, while breakdown cost, operation cost and repairman assignment are usually ignored. Under this context, a novel multi-level imperfect maintenance model with minimal repair (MR), preventive maintenance (PM), overhaul maintenance (OM) and replacement activities is established (there are un-equal reliability thresholds and PM cycles in different OM cycles), and the repairman is assigned according to different maintenance activity and its levels. the primary goal is to provide an optimal maintenance plan and repairman assignment that aims to maximize average profit rate. After that, a sequential decision-making method is employed to deal with the constructed maintenance model. Finally, the superiority of proposed maintenance model is demonstrated by comparing with other four maintenance models, and the average improvements are 26.6346%, 2.4427%, 2.9963% and 1.0023%, respectively.

关键词： Costs Computer aided software engineering Automation Electric breakdown optimization models Decision making Maintenance Preventive maintenance Context modeling

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Application Prospects of Large-Scale Model Technology in the Power Industry

Application Prospects of Large-Scale Model Technology in the...

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Feng Zhao Ke Xie Sining Wang Linlin Zhao Guaiqiang Wang Xiaofeng Wu Yingbao Cui Beijing SGITG Accenture Information Technology Center CO. LTD. Beijing China State Grid Information & Telecommunication Group Co. LTD. Beijing China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Generative lager-scale model technology represented by GPT has become a historic turning point in the development of artificial intelligence, marking the transition of artificial intelligence technology from discriminative to generative. Under the new situation, the human-computer interaction mode has changed from single mode to multi-modal, the application form of artificial intelligence has changed from single to systematic, and the application mode has changed from perceptual intelligence to cognitive intelligence and decision-making intelligence collaboration. the next generation of artificial intelligence led by generative big model technology will have a profound impact on personal life, corporate production and even the entire social operation. Facing the vertical professional field of power grids, using big model technology to achieve a major change in the relationship between human and machine interaction, transform traditional business models, and promote architecture optimization and reshaping can be applied on a large scale in the power industry, becoming a new wave of digital transformation of power grids.

关键词： systematics Computational modeling Transforms Production Turning Power industry Power grids Artificial intelligence optimization Next generation networking

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A DDPG-Based Method of Autonomous Catheter Navigation in Virtual Environment 20

A DDPG-Based Method of Autonomous Catheter Navigation in Vir...

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20th IEEE International conference on Mechatronics and Automation, ICMA 2023

作者： Tian, Wei Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science No.5Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

Vascular interventional surgery is the main method for treating cardiovascular diseases. But navigating endovascular catheters through the vascular tree is a highly challenging task even for highly trained specialists. Automation of this task can reduce the burden on surgeons and is expected to improve the surgical outcomes. Although there have been relevant studies utilizing reinforcement learning algorithms to realize autonomous navigation of catheter in the virtual environment Cathsim. However, the kinematics model of the catheter in Cathsim does not conform to the operating mode of the catheter in real vascular interventional surgery. Besides, there are problems such as low success rates of catheter autonomous navigation tasks. To address these issues, this paper modifies the kinematics model of the catheter in Cathsim and designs a catheter autonomous navigation model based on reinforcement learning DDPG (Deep Deterministic Policy Gradient) algorithm. the experimental results show that the agents trained through DDPG in this paper performs better than the agents trained through PPO (Proximal Policy optimization) in other studies in terms of navigation task success rate, completion time, and contact force between the catheter and vascular wall during the navigation process. © 2023 IEEE.

关键词： Reinforcement learning

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