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Joint Prediction of SOH and RUL for Lithium Batteries Considering Capacity Self Recovery and Model Drift

IEEE Internet of Things Journal 2025年

作者： Jia, Zhen Li, Zhifei Wang, Zhengdong Liu, Zhenbao Vong, Chi-Man Xi'an University of Architecture and Technology School of Mechanical and Electrical Engineering Xi'an710055 China Northwestern Polytechnical University School of Civil Aviation Xi'an710072 China University of Macau Department of Computer and Informa tion Science Macau999078 China

Accurate monitoring of the state of health (SOH) and remaining useful life (RUL) of lithium batteries is a key technology to realize their stable and economic operation. The current data-driven methods have problems such as local prediction errors caused by capacity self-recovery effect, model drift phenomenon, and inaccurate trend prediction caused by complex degradation process. In this paper, we propose a joint SOH and RUL forecasting based on the fusion of Kolmogorov-Arnold Network (KAN), Deep Bidirectional Long Short-Term Memory Network (DBLSTM) and adaptive mechanism method (KAN-HDBLSTM-AM). The method innovatively introduces an adaptive mechanism that considers the capacity degradation trend for different prediction steps, suppresses the accumulation of errors in the model prediction process, and further improves the prediction accuracy by combining the nonlinear approximation capability of KAN and the mastery of data temporal characteristics of DBLSTM. Experimental validation shows that the proposed method can effectively overcome the prediction errors caused by capacity self-recovery and model drift, and can retain sufficient accuracy under long prediction steps, with the prediction trend being more in line with the actual change trend and the prediction error being smaller compared with other methods. © 2014 IEEE.

关键词： Prediction models

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IALF-YOLO: Insulator defect detection method combining improved attention mechanism and lightweight feature fusion network

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Mei, Zhiyu Xu, Hongzhen Yan, Liyue Wang, Kafeng School of Software East China University of Technology Nanchang330013 China School of Architecture and Urban Planning Shenzhen University Shenzhen518061 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Effect and efficient insulator defect detection is critical for advancing smart grid technologies. Current deep learning-based methods face limitations in small-object recognition accuracy, insufficient key feature extraction, and high computational complexity, which restrict their application in grid inspections. We propose IALF-YOLO, an improved YOLOv5s-based model, to address these problems. Firstly, by fusing the shallow feature map of Backbone and the deep feature map of Neck, a detection layer dedicated to small objects is created in Head, significantly improving small objects’ detection accuracy. Secondly, a S-CBAM attention mechanism is proposed, which addresses the issue of feature information loss in conventional CBAM by synchronizing the extraction channel with spatial attention. Finally, the lightweight GSConv module replaces the convolutional layer in the Neck network to construct a lightweight feature fusion network, which improves detection accuracy while reducing model complexity and the number of parameters. Our method improves mAP by 2.8% and 2.5% on both datasets, respectively. The detection speed is 2× faster than other methods. © 2025 Elsevier Ltd

关键词： Leak detection

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A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

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International Electrical Engineering Congress (iEECON)

作者： Prayoot Akkaraekthalin Tanee Somwong Nonchanutt Chudpooti Nuttawut Hirun Suwaluck. Meesomklin Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation.

关键词： Temperature sensors Temperature measurement Smart agriculture System performance Green products Sensor systems Real-time systems Sensors Internet of Things Monitoring

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Communication-Efficient Stochastic Distributed Learning

arXiv

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arXiv 2025年

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Department of Electrical and Electronic Engineering Imperial College London London United Kingdom School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direction Method of Multipliers (ADMM) to address the challenges of high communication costs, and large datasets. Our design tackles these challenges i) by enabling the agents to perform multiple local training steps between each round of communications;and ii) by allowing the agents to employ stochastic gradients while carrying out local computations. We show that the proposed algorithm converges to a neighborhood of a stationary point, for nonconvex problems, and of an optimal point, for convex problems. We also propose a variant of the algorithm to incorporate variance reduction thus achieving exact convergence. We show that the resulting algorithm indeed converges to a stationary (or optimal) point, and moreover that local training accelerates convergence. We thoroughly compare the proposed algorithms with the state of the art, both theoretically and through numerical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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An improved end-to-end micro-expression recognition system for real-world videos via dual-input CNN architecture

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Expert Systems with Applications 2025年 286卷

作者： Gan, Y.S. Liu, Kun-Hong Wu, Min-Huan Liong, Gen-Bing Liong, Sze-Teng School of Architecture Feng Chia University Taichung40724 Taiwan Department of Digital Media Technology School of Film Xiamen University Xiamen China Bachelor of Science in Senior Wellness and Sport Science Tunghai University Taiwan Faculty of Computer Science & Information Technology University of Malaya Kuala Lumpur50603 Malaysia Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan

Micro-expression (ME) recognition reveals nonverbal emotions through subtle, involuntary facial muscle movements. However, the development and commercialization of ME recognition systems have been hindered by the lack of databases that accurately reflect real-world conditions. This study addresses this challenge by proposing a robust end-to-end system designed to operate effectively in unconstrained environments. Existing methods typically rely on a single apex frame, which may be unreliable due to noise, occlusions, or lighting variations. To address these issues, a 3D facial reconstruction technique is applied as a pre-processing step to normalize pose and lighting. A novel dual-peak frame detection strategy is then introduced to extract two expressive optical flow frames, reducing the impact of noise from any single frame. Finally, a Shallow and Small-size Dual-input (SSD) CNN architecture is designed to jointly process the two frames for improved emotion classification. The proposed system achieves strong performance on the challenging in-the-wild MEVIEW dataset, with accuracy and F1-score of 75 % and 77.68 %, respectively. Comprehensive evaluations further validate the effectiveness of the pipeline, highlighting its potential for real-world ME recognition applications. © 2025 Elsevier Ltd

关键词： Optical character recognition

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The Importance of Being Earnest: Multiple Heterogeneous Container Loading with a Simple Genetic Algorithm 28th

The Importance of Being Earnest: Multiple Heterogeneous Con...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Rusin, Francesco Fiala, Jan Sanker, Julian Bhattarai, Suyesh Ekárt, Anikó Department of Engineering and Architecture University of Trieste Trieste Italy Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic Yale College Yale University New Haven United States Mechatherm International Limited Cradley Heath United Kingdom Aston Centre for Artificial Intelligence Research and Application Aston University Birmingham United Kingdom

ISBN: (纸本)9783031900617

In this study we address the complex practical problem of multiple heterogeneous container loading with a simple genetic algorithm. We demonstrate that with a well-chosen representation including a heuristic and a suitable fitness function the other aspects of the genetic algorithm do not need extensive work for good results. Following systematic study of our method on synthetically generated data, we visually showcase the solution for a company-based problem instance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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A novel high-fidelity reversible data hiding scheme based on multi-classification pixel value ordering

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Journal of Visual Communication and Image Representation 2025年 110卷

作者： Cui, Chen Li, Li Lu, Jianfeng Zhang, Shanqing Chang, Chin-Chen College of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Zhejiang Police College Hangzhou310018 China Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan

Pixel value ordering (PVO) is a highly effective technique that employs a pixel block partitioning and sorting for reversible data hiding (RDH). However, its embedding performance is significantly impacted by block size. To address this, an improved pixel-based PVO (IPPVO) was developed adopting a per-pixel approach and adaptive context size. Nevertheless, IPPVO only considers pixels below and to the right for prediction, neglecting other closer neighboring regions, leading to inaccurate predictions. This study presents a RDH strategy using multi-classification embedding to enhance performance. First, pixels are categorized into four classes based on parity coordinates, obtaining higher correlation prediction values using an adaptive nearest neighbor content size. Second, a new complexity calculation method is introduced, the complexity frequency of pixel regions to better differentiate between complex and flat regions. Finally, an effective embedding ratio and index value constraint are introduced to mitigate the challenge of excessive distortion when embedding large capacities. Experimental results indicate that the proposed scheme offers superior embedding capacity with low distortion compared to state-of-the-art PVO-based RDH methods. © 2025 Elsevier Inc.

关键词： Adaptive modification High-fidelity Multi-classification RDH

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Methods for pitch analysis in contemporary popular music: highlighting pitch uncertainty in Primaal’s commercial works

arXiv

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arXiv 2025年

作者： Deruty, Emmanuel Leroy, Luc Macé, Yann Meredith, David Sony Computer Science Laboratories 6 rue Amyot Paris75005 France Neodrome Entertainment 5 rue Vernet Paris75008 France Department of Architecture Design and Media Technology Aalborg University Rendsburggade 14 Aalborg9000 Denmark

We identify characteristic features of how pitch is manipulated for expressive purposes by Hyper Music, a mainstream commercial music company specialising in advertisement music for global corporations. The study shows that the use and organisation of pitch in the company’s ‘Primaal’ brand differs from Western classical music. Through interviews with producers and in-depth analysis of their work, we reveal that their methods centre on a conscious aim to construct a musical discourse based on pitch uncertainty, contrasting with the clear transmission of well-defined pitches in Western classical traditions. According to the Primaal producers, who acknowledge the influence of artists such as Kanye West and Daft Punk and use widely available technology, pitch uncertainty captures the listener’s attention. We provide analyses of musical excerpts demonstrating their approach, alongside descriptions of the tools and methods employed to achieve their expressive goals. These goals and methods are placed in a broader historical context, contrasting with fundamental principles of pitch organisation in Western music. Techniques used by Hyper Music to introduce and control pitch uncertainty include boosting upper partials, expressive use of inharmonicity, continuous pitch distributions around ‘poles’ tied to specific ‘modes’, and continuously evolving pitch. We examine these techniques from a psychoacoustic perspective, and conduct listening tests corroborating some of the observations. The ultimate goal of the study is to introduce a set of methods suited to the analysis of pitch in contemporary popular *** Codes 00A65 © 2025, CC BY.

关键词： Music

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A NETWORK ANALYSIS OF THE 2010 FIFA WORLD CUP CHAMPION TEAM PLAY

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Journal of Systems Science & Complexity 2013年第1期26卷 21-42页

作者： COTTA Carlos MORA Antonio M MERELO Juan Julin MERELO-MOLINA Cecilia Department of Computer Science University of Mlaga Department of Architecture and Computer Technology University of Granada

This paper analyzes the network of passes among the players of the Spanish team during the last FIFA World Cup 2010,where they emerged as the champion,with the objective of explaining the results obtained from the behavior at the complex network *** team is considered a network with players as nodes and passes as(directed) edges.A temporal analysis of the resulting passes network is also done,looking at the number of passes,length of the chain of passes,and to network measures such as player centrality and clustering *** of the last three matches(the decisive ones) indicate that the clustering coefficient of the pass network remains high,indicating the elaborate style of the Spanish *** effectiveness of the opposing team in negating the Spanish game is reflected in the change of several network measures over time,most importantly in drops of the clustering coefficient and passing length/speed,as well as in their being able in removing the most talented players from the central positions of the ***'s ability to restore their combinative game and move the focus of the game to offensive positions and talented players is shown to tilt the balance in favor of the Spanish team.

关键词： Complex network complex systems FIFA World Cup football La Roja network analysis soccer Spanish team sports.

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From Detection to Traversal: a Probabilistic Framework for UAS-Assisted Landmine Mapping and Circumvention

From Detection to Traversal: a Probabilistic Framework for U...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： J. Josiah Steckenrider Dongbin Kim Pratheek Manjunath Jack Simpson Charles Rosbrook Micah Weiss Department of Civil and Mechanical Engineering United States Military Academy West Point NY USA College of Engineering Technology and Architecture University of Hartford West Hartford CT USA Department of Electrical Engineering and Computer Science United States Military Academy West Point NY USA

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This research presents a robust probabilistic framework for minefield localization, mapping, and avoidance, addressing a technological gap in the field of aerial countermine intelligence, while bypassing the well-established techniques of landmine detection. Our approach propagates the pose uncertainty matrix delivered by a drone's flight controller's Kalman filter to probabilistically estimate the location of detected mines. This probability map then seeds an artificial potential field path generator which creates a safe path for ground traversal by producing waypoints through the minefield. The system's performance is evaluated in simulations and validated through flight trials, demonstrating its potential to improve the efficiency and safety of UAV-assisted minefield navigation and threat avoidance.

关键词： Location awareness Landmine detection Uncertainty System performance Atmospheric modeling Probabilistic logic Aircraft navigation Generators Safety Kalman filters

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