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Digital Analysis and Processing, intelligent Computation (DAPIC), international conference on

作者： Xiaolan Liu Yan Wang Kunpeng Zhao Rui Hao Liang Zhang Harbin Huade University Harbin Heilongjiang China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This study focuses on the hybrid logic dynamic (MLD) modeling method of robot aluminum alloy pulse TIG (Tungsten Inert Gas) welding process based on vision sensing. Welding is a key technology in modern manufacturing industry, especially in the application of aluminum alloy materials, and its welding quality control is facing complex challenges. This study aims to improve the automation and intelligence level of welding process by combining robot technology and visual sensing technology. A data acquisition system based on high-precision vision sensor and advanced image processing algorithm is designed, which realizes real-time and accurate acquisition of weld and molten pool information. The weld contour is identified by machine learning algorithm, and the geometric parameters and dynamic change characteristics of weld pool are extracted, and the statistical relationship model between welding parameters and geometric characteristics of weld pool is established. On this basis, the MLD model is constructed, which can describe the continuous and discrete dynamics in the welding process. The experimental results show that MLD model can accurately predict the change trend of weld pool geometric parameters, effectively identify the welding state, and realize accurate control of welding process. This study not only improves the stability of welding quality, but also promotes the further development of aluminum alloy welding technology, which has important practical application value and broad application prospects.

关键词： Visualization Accuracy Machine learning algorithms Welding Heuristic algorithms Process control Vision sensors Robot sensing systems Aluminum alloys Real-time systems

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Smart Grid Management with AI Driven Analytics in Solar Energy

Smart Grid Management with AI Driven Analytics in Solar Ener...

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Machine Learning and Autonomous systems (ICMLAS), international conference on

作者： Devanshi Timande Nishant Agade Amit Gudadhe Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha India Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This paper presents an AI-driven framework for real-time smart grid optimization tailored specifically for solar energy integration. The proposed system features a multi-layered architecture that integrates data acquisition from solar irradiance sensors, smart meters, and meteorological sources with advanced pre-processing techniques to ensure data quality and fusion. Using advanced deep learning architectures, such as Long Short-Term Memory networks for forecasting solar irradiance and regression methodologies for predicting load, our proposed framework dynamically adjusts energy dispatch and demand response measures to address the inherent variability associated with solar power. Additionally, a combined strategy that incorporates reinforcement learning is implemented to improve real-time grid stability and operational efficiency, and explainable artificial intelligence methods are used to provide clarity in decision-making processes. Simulation studies conducted on an enhanced ieee 24-bus test system have shown to reduce forecast error by up to 30% and prevent the occurrence of grid instability events by about 40% in contrast with traditional methods. These results are promising for AI-based analytics since it promises to enhance smart grid functionality and enable more sustainable use of renewable resources. Further research will develop this model with additional renewable energy sources and increase cybersecurity.

关键词： Solar irradiance Renewable energy sources Real-time systems Demand response Stability analysis Smart meters Sensor systems Smart grids Forecasting Optimization

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Digital Twin Empowered Adaptive-Mpc for IoV Privacy-Preserving Communication in Smart Cloud

Digital Twin Empowered Adaptive-Mpc for IoV Privacy-Preservi...

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ieee international conference on Smart Cloud (SmartCloud)

作者： Yang Hong Guozhi Hao Jun Wu Graduate School of Information Production and Systems Waseda University Japan

ISBN: (数字)9798331596651

ISBN: (纸本)9798331596668

The Internet of Vehicles (IoV) is a core component of intelligent transportation systems in smart cloud, significantly enhancing traffic efficiency and reliability through real-time data sharing and collaborative decision-making. However, the high mobility of vehicles and frequent network topology changes complicate achieving secure and efficient data sharing in IoV. This work proposes an adaptive multi-party secure information-sharing framework based on digital twins (DT). Specifically, we introduce virtual representations of vehicles operating within an edge-centric DT architecture, thereby overcoming the limitations imposed by unstable physical links. Furthermore, we present a DT secure multiparty computation strategy based on a broadcast-request policy, which enables secure information sharing between vehicles in highly mobile and dynamic IoV scenarios. In addition, the proposed scheme incorporates blockchain to ensure the auditing of information sharing between virtual twin nodes. Simulation results using a consensus-based speed advisory system scenario validate the effectiveness of the proposed approach, demonstrating superior communication efficiency compared to conventional methods.

关键词： Cloud computing Network topology Simulation Smart contracts Information sharing Computer architecture Digital twins Blockchains Vehicle dynamics Internet of Vehicles

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Health Prognostic for Lithium-Ion Battery via Time-Series Transfer Learning 8th

Health Prognostic for Lithium-Ion Battery via Time-Series ...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhu, Yehui Cheng, Hanjing Wang, Xiaoliang Li, Zhongpeng Cao, Ke-Cai Wu, Hongjie Gu, Juping School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou China Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center Nanjing China

ISBN: (纸本)9789819602315

Lithium-ion batteries have wide applications in the energy sector, and effective health prediction is crucial for ensuring their safety and performance. Most machine learning-based State of Health (SOH) estimation methods operate under the assumption that battery data follows the same distribution. However, the statistical characteristics of time-series data may change over time, affecting the accuracy of predictions. To address this issue, an adaptive time-series transfer learning method based on knee point is proposed in this paper. For different battery datasets, a knee method is employed to determine segmentation points of the time series, dividing the battery degradation curve into two stages. Then, healthy features are extracted from battery charge-discharge data for input into the transfer learning model, and the effectiveness of these features is validated. Finally, time-series transfer learning is used to train the health features, exploiting the shared information learned from different domains for SOH prediction. Experimental results demonstrate that the proposed method could achieve good performance across multiple battery data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Stealthy False data Injection Attacks Design and Scalable Detection in DC Microgrids 8th

Stealthy False Data Injection Attacks Design and Scalable D...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wu, Zhihua Peng, Chen Tian, Engang Shanghai University Shanghai200444 China University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9789819602247

The main purpose of this article is to design a stealthy false data injection (FDI) attack and corresponding scalable detection mechanism for DC Microgrids. Firstly, a DC microgrid model is established that includes disturbances, measurement noise, and FDI attacks. Then, a novel FDI attack is designed to disrupt voltage balance and evade detectors based on unknown input observers (UIOs). Subsequently, a scalable detection mechanism combining distributed observers and UIOs is proposed to detect the novel attack, which does not rely on global system information (such as fixed topology) and therefore does not require global redesign when DGUs are plugged and unplugged. Finally, the theoretical results are verified by the SimPowersystems Toolbox. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Logistics Demand Forecasting and Scheduling Optimization Using K-Means Clustering and Deep Q-Network

Logistics Demand Forecasting and Scheduling Optimization Usi...

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Electronics, Energy systems and Power Engineering (EESPE), ieee international conference on

作者： Kun Liang Hang Zhang School of Management Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

With the rapid development of e-commerce and supply chain management, logistics demand forecasting and scheduling optimization have become key links in improving logistics efficiency and service quality. To meet this challenge, this paper proposes a logistics demand forecasting and scheduling optimization method that combines K-Means clustering with deep Q network (DQN). Cluster analysis is used to reduce data complexity, and deep reinforcement learning is used to achieve intelligent scheduling. First, the background, challenges and research motivation of logistics demand forecasting and scheduling optimization are introduced. Secondly, the research results in related fields are reviewed, and the shortcomings of current research are pointed out. Then, the K-Means clustering algorithm, deep $\mathbf{Q}$ network theory and specific models of logistics demand prediction and scheduling optimization are elaborated. Then, the performance of the proposed method is designed and tested. The experimental results show that the proposed method can significantly improve the accuracy of logistics demand prediction, up to 0.93, optimize scheduling strategies, reduce logistics costs, and improve customer satisfaction.

关键词： Training Accuracy Costs Computational modeling Customer satisfaction Demand forecasting Clustering algorithms Predictive models Scheduling Logistics

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Attacking Voice Anonymization systems with Augmented Feature and Speaker Identity Difference

Attacking Voice Anonymization Systems with Augmented Feature...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yanzhe Zhang Zhonghao Bi Feiyang Xiao Xuefeng Yang Qiaoxi Zhu Jian Guan Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University China Faculty of Computing Harbin Institute of Technology China Acoustics Lab University of Technology Sydney Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This study focuses on the First VoicePrivacy Attacker Challenge within the ICASSP 2025 Signal Processing Grand Challenge, which aims to develop speaker verification systems capable of determining whether two anonymized speech signals are from the same speaker. However, differences between feature distributions of original and anonymized speech complicate this task. To address this challenge, we propose an attacker system that combines data Augmentation enhanced feature representation and Speaker Identity Difference enhanced classifier to improve verification performance, termed DA-SID. Specifically, data augmentation strategies (i.e., data fusion and SpecAugment) are utilized to mitigate feature distribution gaps, while probabilistic linear discriminant analysis (PLDA) is employed to further enhance speaker identity difference. Our system significantly outperforms the baseline, demonstrating exceptional effectiveness and robustness against various voice anonymization systems, ultimately securing a top-5 ranking in the challenge.

关键词： data privacy data integration Signal processing data augmentation Probabilistic logic Robustness Linear discriminant analysis Information filtering Speech processing Information integrity

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Perceptual Learning Technology Based on Computer Vision and Machine Learning Algorithms

Perceptual Learning Technology Based on Computer Vision and ...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models Computer vision Image segmentation Machine learning algorithms Computational modeling Transfer learning Feature extraction data models Robustness Real-time systems

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UNeLF: Unconstrained Neural Light Field for Self-Supervised Angular Super-Resolution

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ieee Transactions on Circuits and systems for Video Technology 2025年

作者： Zhao, Mingyuan Sheng, Hao Chen, Rongshan Cong, Ruixuan Cui, Zhenglong Yang, Da Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Data Science and Intelligent Computing Laboratory Hangzhou International Innovation Institute Hangzhou311115 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

Compared to supervised learning methods, self-supervised learning methods address the domain gap problem between light field (LF) datasets collected under varying acquisition conditions, which typically leads to decreased performance when differences exist in the distribution between the training and test sets. However, current self-supervised light field angular super-resolution (LFASR) techniques primarily focus on exploiting discrete spatial-angular features while neglecting continuous LF information. In contrast to previous work, we propose a self-supervised unconstrained neural light field (UNeLF) to continuously represent LF for LFASR. Specifically, any LF can be described as the camera pose for each sub-aperture image (SAI) and the two-plane that captures these SAIs. To describe the former, we introduce a SAIs-dependent pose optimization method to solve the issue that arises from the narrow baseline of most LF data, which hinders robust camera pose estimation. This mechanism reduces the number of trainable camera parameters from a quadratic to a constant scale, thereby alleviating the complexity of joint optimization. For the latter, we propose a novel adaptive two-plane parameterization strategy to determine the two-plane that captures these SAIs, facilitating refocusing. Finally, we jointly optimize the camera parameters, near-far planes and neural light field, efficiently mapping each adaptive two-plane parameterized ray to its correspondence color in a continuous manner. Comprehensive experiments demonstrate that UNeLF achieves faster training and inference with fewer computational resources while exhibiting superior performance on both synthetic and real-world datasets. © 1991-2012 ieee.

关键词： Self-supervised learning

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation

Efficient Facial Expression Recognition Network Based on Spo...

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ieee international conference on Industrial Technology (ICIT)

作者： Xin Wen Zhen-Tao Liu Dan Chen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation.

关键词： Knowledge engineering Training Human computer interaction Accuracy Face recognition Computational modeling Noise Neural networks Interference data models

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