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arXiv 2024年

作者： Acharya, Pritam Bhore, Sujoy Gupta, Aaryan Khan, Arindam Mondal, Bratin Wiese, Andreas Department of Mathematics Indian Institute of Science Education and Research Pune India Department of Computer Science and Engineering Indian Institute of Technology Bombay India Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Mathematics Technical University of Munich Germany

We study the geometric knapsack problem in which we are given a set of d-dimensional objects (each with associated profits) and the goal is to find the maximum profit subset that can be packed non-overlappingly into a given d-dimensional (unit hypercube) knapsack. Even if d = 2 and all input objects are disks, this problem is known to be NP-hard [Demaine, Fekete, Lang, 2010]. In this paper, we give polynomial time (1 + Ε)-approximation algorithms for the following types of input objects in any constant dimension d: disks and hyperspheres, a class of fat convex polygons that generalizes regular k-gons for k ≥ 5 (formally, polygons with a constant number of edges, whose lengths are in a bounded range, and in which each angle is strictly larger than π/2), arbitrary fat convex objects that are sufficiently small compared to the knapsack. We remark that in our PTAS for disks and hyperspheres, we output the computed set of objects, but for a OΕ(1) of them, we determine their coordinates only up to an exponentially small error. However, it is unclear whether there always exists a (1 + Ε)-approximate solution that uses only rational coordinates for the disks’ centers. We leave this as an open problem that is related to well-studied geometric questions in the realm of circle packing. © 2024, CC BY.

关键词： Approximation algorithms

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A Fast-Digital Current Regulator Based on Matched Pole-Zero Discretization for PMSM

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IEEE Transactions on Transportation Electrification 2025年

作者： Mon-Nzongo, Daniel Legrand Lin, Shengyu Ipoum-Ngome, Paul Gistain Lai, Chunyan Jin, Tao Rodriguez, J. Concordia University Department of Electrical Engineering and Computer Engineering MontrealQCH3G1M8 Canada Fuzhou University School of Electrical Engineering and Automation Fuzhou350116 China Universidad San Sebastian Santiago Faculty of Engineering Santiago Chile

Continuous-time methods design the synchronous-frame current regulator in the S-domain and then discretize it into the Z-domain using the Tustin or Euler method for digital implementation. This process causes discretization errors that significantly impair the performance of permanent magnet synchronous motor (PMSM) drives, especially at a low sampling-to-fundamental frequency ratio (SFR). A direct discrete-time method, design the current regulator directly in the Z-domain with improved dynamic performances. However, it results in online calculations of exponential terms that increase the execution time of the control loop, particularly for Interior PMSM (IPMSM). Therefore, this paper proposes a novel design approach of a digital current regulator with a high discretization accuracy and fast execution time. The proposed method is a combination of the multiple-input-multiple-output (MIMO) decoupling and matching pole-zero (MPZ) discretization concepts. MIMO decoupling is used to cancel accurately the poles of the plant that migrate with the motor's speed/frequency change in the S-domain. The MPZ method is used to discretize accurately the current regulator from S-domain to Z-domain. A new approximation concept for the calculation of the exponential matrix is adopted to reduce the complexity of the proposed approach. Thus, with the synthesized current regulator, the execution time is reduced significantly compared to the direct discrete-time design methods while keeping the same decoupling and dynamic performances even under low SFRs. Simulations and experiments performed on IPMSM drives have successfully verified the proposed design method, and results show improved decoupling performance and robustness against the variation of parameters with better current harmonic distortion. © 2015 IEEE.

关键词： Permanent magnets

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Adversarial Attacks on Skeleton-Based Sign Language Recognition 16th

Adversarial Attacks on Skeleton-Based Sign Language Recognit...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Li, Yufeng Han, Meng Yu, Jiahui Lin, Changting Ju, Zhaojie School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou310058 China College of Computer Science and Technology Zhejiang University Hangzhou310058 China School of Computing University of Portsmouth PortsmouthPO13HE United Kingdom

ISBN: (纸本)9789819964826

Despite the impressive performance achieved by sign language recognition systems based on skeleton information, our research has uncovered their vulnerability to malicious attacks. In response to this challenge, we present an adversarial attack specifically designed to sign language recognition models that rely on extracted human skeleton data as features. Our attack aims to assess the robustness and sensitivity of these models, and we propose adversarial training techniques to enhance their resilience. Moreover, we conduct transfer experiments using the generated adversarial samples to demonstrate the transferability of these adversarial examples across different models. Additionally, by conducting experiments on the sensitivity of sign language recognition models, we identify the optimal experimental parameter settings for achieving the most effective attacks. This research significantly contributes to future investigations into the security of sign language recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Musculoskeletal system

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MULTI-SCALE FUSION FOR OBJECT REPRESENTATION

arXiv

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arXiv 2024年

作者： Zhao, Rongzhen Wang, Vivienne Kannala, Juho Pajarinen, Joni Department of Electrical Engineering and Automation Aalto University Finland Department of Computer Science Aalto University Finland Center for Machine Vision and Signal Analysis University of Oulu Finland

Representing images or videos as object-level feature vectors, rather than pixel-level feature maps, facilitates advanced visual tasks. Object-Centric Learning (OCL) primarily achieves this by reconstructing the input under the guidance of Variational Autoencoder (VAE) intermediate representation to drive so-called slots to aggregate as much object information as possible. However, existing VAE guidance does not explicitly address that objects can vary in pixel sizes while models typically excel at specific pattern scales. We propose Multi-Scale Fusion (MSF) to enhance VAE guidance for OCL training. To ensure objects of all sizes fall within VAE’s comfort zone, we adopt the image pyramid, which produces intermediate representations at multiple scales;To foster scale-invariance/variance in object super-pixels, we devise inter/intra-scale fusion, which augments low-quality object super-pixels of one scale with corresponding high-quality super-pixels from another scale. On standard OCL benchmarks, our technique improves mainstream methods, including state-of-the-art diffusion-based ones. The source code is available on https://***/Genera1Z/MultiScaleFusion. © 2024, CC BY.

关键词： Pixels

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A car-following model based on the optimized velocity and its security analysis

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Transportation Safety and Environment 2023年第4期5卷 127-134页

作者： Rong Fei Lu Yang Xinhong Hei Bo Hu Aimin Li School of Computer Science and Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an 710048ShaanxiP.R.China Software Design Department Hangzhou Holly Sys Automation Co.Ltd.No.29Jinye 1st RoadXi’an 710065ShaanxiP.R.China

An enhanced optimal velocity model(EOVM)that considers driving safety is established to alleviate traffic congestion and ensure driving *** headway is introduced as a criterion for determining whether the car is *** the time headway is less discussed to ensure the model's safety and maintain the following state.A stability analysis of the model was carried out to determine than the minimum time headway(TH_(min))or more than the most comfortable time headway(TH_(com)),the acceleration constraints are the stability conditions of the *** EOVM is compared with the optimal velocity model(OVM)and fuzzy car-following model using the real *** show that the EOVM model has the smallest error in average,maximum and median with the real *** confirm the model's safety,design fleet simulation experiments were conducted for three actual scenarios of starting,stopping and uniform process.

关键词： optimized velocity constraint optimization security analysis

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Standardized Post-Quantum Cryptography and Recent Developments in Quantum computers 1

Standardized Post-Quantum Cryptography and Recent Developmen...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Chib, Shiney Devarajan, Harshitha Raghavan Chundru, Swathi Pulivarthy, Padmaja Isaac, R. Augustian Oku, KrishnaMurthy Meghe Institute of Management Studies Department of Management Datta Maharashtra Nagpur India New York University Department of Computer Science United States Motivity labs INC United States Samsung Korea Republic of Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India AT&T Services Inc Tech Lead Infrastructure/Cloud Architecture Department of Automation platform Development India

ISBN: (纸本)9798331518578

Traditional cryptographic frameworks are seriously threatened by the quick development of quantum computing, especially those that depend on public-key cryptosystems like RSA and ECC. Post-quantum cryptography (PQC) seeks to create cryptographic methods that are impervious to quantum computer assaults in order to overcome this difficulty. The National Institute of Standards and Technology (NIST), which has selected and standardised quantum-resistant techniques like CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures, has sparked recent advancements in PQC. At the same time, the need for reliable PQC solutions has increased due to developments in quantum computing, such as IBM's improvements in qubit stability and Google's 'quantum supremacy' assertion. In order to protect data security in the post-quantum age, PQC regulations must be adopted globally, as highlighted by the intersection of these fields. Current developments in PQC standardisation and the changing state of quantum computing technology are highlighted in this paper. © 2025 IEEE.

关键词： Quantum cryptography

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Application of Augmented Reality and 3D Reconstruction Technologies in Power Scenarios 17

Application of Augmented Reality and 3D Reconstruction Techn...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Xu, Mingkai Li, Cong Liu, Chunming Li, Xin Yang, Guodong Yu, Dong Zhang, Chunjie Jinan Power Supply Company State Grid Shandong Electric Power Company Operation and Maintenance Department Jinan China Institute of Automation Chinese Academy of Sciences Cas Engineering Laboratory for Intelligent Industrial Vision Beijing China Beijing Jiaotong University School of Computer and Information Technology Beijing China

ISBN: (纸本)9798350387384

As the power system continues to develop, the application of augmented reality (AR) and 3D reconstruction technologies in power scenarios is becoming increasingly widespread. This paper proposes the integration of these two technologies for the detection, maintenance, and fault diagnosis of power equipment. Through real-time video segmentation and detection operations, AR technology can overlay key information onto the field view, helping engineers quickly identify equipment status and potential issues. Simultaneously, 3D reconstruction technology can generate precise models of power equipment and environments, providing comprehensive visual references and analytical tools that enhance maintenance efficiency and safety. The combined application of these technologies can significantly reduce the risks and costs associated with manual operations. © 2024 IEEE.

关键词： 3D Reconstruction Augmented reality Image Segment Objection detection

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Segmented Prediction Model of Lithium-ion Battery Remaining Useful Life Based on LSTM and SVR 6

Segmented Prediction Model of Lithium-ion Battery Remaining ...

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6th International Conference on Intelligent Robotics and Control engineering, IRCE 2023

作者： Wang, Yingzhou Liu, Chengyuan Zhang, Xiuyu Deng, Yifan Chen, Lei Liu, Hui Liu, Bingzheng Zhang, Yanhui College of Automation Engineering Northeast Electric Power University Jilin China Chongqing Aerospace Polytechnic Department of Computer Engineering Chongqing China China Nuclear Industry Maintenance Co. Ltd Shenzhen Branch Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9798350326338

As an important part of battery health management, accurate prediction on remaining useful life of lithium-ion battery can maintain and replace the batteries in advance to guarantee the safety and stability of the energy storage system. Most extant models use a single model throughout the entire stage for prediction, which may lead to the decrease of the accuracy for subsequent prediction due to insufficient learning, as well as disability to predict battery remaining useful life with data from other batteries. To solve these issues, a segmented prediction model of lithium-ion battery remaining useful life is proposed. In this method, the knee point of the capacity decline curve is detected and then long short-Term memory and support vector regression models are used to predict the remaining useful life before and after the knee point, respectively. The experimental prediction results represent that the proposed method achieves higher prediction accuracy compared with long short-Term memory method. © 2023 IEEE.

关键词： Forecasting

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Design and Implementation of a Novel Integrated Private ISM Radio Link Network in Khorasan Razavi Electrical Power Distribution Company

Design and Implementation of a Novel Integrated Private ISM ...

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Conference on Electrical Power Distribution Networks (EPDC)

作者： Mahdi Khoshbaten Shahram Keikhaei Expert in the field of Automation and smartening department Khorasan Razavi Electrical Power Distribution Company PhD in Electrical Engineering Manager of Automation and smartening department Khorasan Razavi Electrical Power Distribution Company MSc in Computer Engineering

It is essential to achieve stable and high bandwidth communication in order to develop and evolve a variety of platforms, including ERP, video conferencing, VoIP, digital mobile radio, and automation of electric power distribution networks. There are several challenges involved in the implementation of these networks, including the large area of the Khorasan Razavi province (118000 km 2 ) and 31 offices spread across a large geographical area. In this paper, the novel experience of designing and implementing a radio link network that integrates fiber optics is presented in detail. In both IT and OT networks, this infrastructure network provides excellent technical capabilities. By using the proposed plan, 31 offices and 12 sites will be connected to a high-bandwidth, low-jitter, and low-delay network of about 65 Mbps, 30 ms, and 45 ms respectively, which is 2.5 times better than the company's 2020 network. Moreover, the details of the design, simulation, and implementation of the sites, including the equipment list, are being presented for the first time. Other companies could benefit from this. Software packages PathLoss 5.0 and OPNET 14.5 were used for the design and simulation.

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A New Technique of Soluble NPK Nutrient Detection System Using Deep Learning-Based Recurrent Neural Networks (RNN)

A New Technique of Soluble NPK Nutrient Detection System Usi...

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International Electrical engineering Congress (iEECON)

作者： Wallop Tipwong Adisorn Sirikham Jessada Konpang Putthiphong Kirdpipat Yutana Chongjarearn Oradee Preutisrunyanont Department of Electrical and Telecommunication Engineering Rajamangala University of Technology Krungthep Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Thammasat School of Engineering Thammasat University Pathum Thani Thailand Department of Robotic Engineering and Automation System Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

Modern agriculture relies heavily on effective process monitoring, control, and remote data collection, making accurate and cost-effective sensors crucial for innovation. This research focuses on the development of a spectroscopy-based device for real-time and remote monitoring of nutrient levels in hydroponic systems. The device utilizes IoT architecture, incorporating principles of light absorbance to quantify concentrations of essential nutrients during crop growth. Three deep learning techniques, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) were adopted for data analysis. Results demonstrate that tailored implementation of deep learning can significantly enhance the performance of sensor network system (SNS). Specifically, the RNN format achieved an impressive 98% model accuracy during both training and testing phases. The technological advancements in addressing contract variation and photometric levels contribute to increased accuracy as more sensors become available. The findings serve as a comprehensive guide for collecting extensive agricultural data, encompassing environmental factors throughout the entire cultivation period, using state-of-the-art remote control sensor equipment. This work provides a practical, efficient, and cost-effective solution for precise nutrient control and continuous data monitoring in agricultural production.

关键词： Deep learning Training Recurrent neural networks Sensor phenomena and characterization Sensor systems Environmental factors Convolutional neural networks

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