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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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Hybrid deep learning model for automated colorectal cancer detection using local and global feature extraction

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Knowledge-Based Systems 2025年 319卷

作者： Pacal, Ishak Attallah, Omneya Department of Computer Engineering Faculty of Engineering Igdir University Igdir76000 Turkey Department of Electronics and Information Technologies Faculty of Architecture and Engineering Nakhchivan State University NakhchivanAZ 7012 Azerbaijan Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Alexandria21937 Egypt Wearables Biosensing and Biosignal Processing Laboratory Arab Academy for Science Technology and Maritime Transport Alexandria21937 Egypt

Colorectal cancer (CRC) ranks among the most lethal malignancies globally, underscoring the importance of timely and precise diagnosis. Although histopathological examination remains the clinical gold standard, the intricate morphology of tissue samples and inter-observer variability drive the need for robust automated methods. To address these challenges, this paper presents a hybrid deep learning model that integrates InceptionNeXt blocks, enhanced Swin Transformer blocks, and a Residual Multi-Layer Perceptron (ResMLP). In the initial stages, InceptionNeXt blocks employ multi-branch convolutions to capture nuclear morphology, glandular structures, and stromal textures, particularly benefiting limited training data scenarios. Subsequent layers utilize enhanced Swin Transformer blocks with window-based self-attention and shifted windows, effectively modeling long-range dependencies. The ResMLP component further refines feature representation via residual learning. Comprehensive evaluations on two benchmark CRC datasets—NCT-CRC[sbnd]HE-100K and Kather-5K—demonstrated accuracies of 99.96 % and 99.06 %, respectively, outperforming 10 state-of-the-art CNN and 10 ViT-based models. Additionally, Grad-CAM visualizations highlight the critical regions influencing classification decisions, enhancing model interpretability. These results establish the proposed method as a reliable, generalizable, and clinically viable solution for automated CRC detection. © 2025 Elsevier B.V.

关键词： Deep learning

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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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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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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Design of Miniaturized Metamaterial Reflector with Doubly Interdigital Structure for 5G Use Cases

Design of Miniaturized Metamaterial Reflector with Doubly In...

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International Electrical Engineering Congress (iEECON)

作者： Tanee Somwong Pongsathorn Chomtong Jirayut Khamsuthat Nonchanutt Chudpooti Prayoot Akkaraekthalin Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents the design of a miniaturized metamaterial reflector composing of an open-loop transmission line unit cell structure. The transmission line ends are connected by using a double interdigital technique, resulting in a large capacitive value compared to the conventional methods. When combined with the open-loop line employing the trisection step-impedance structure with adjusting of the capacitive value, its second and third resonant frequencies will be controllable. As a result, this approach can reduce the unit cell size from a typical value of λ/2 to an approximate value of λ/16, therefore overall size of the reflector is significantly decreased. The designed unit cell is measured to be 1.05 cm × 1.0 cm. We implemented the proposed reflector on an FR-4 PCB with a relative permittivity of about 4.4, a substrate height (h) of 1.6 mm, and a loss tangent of about 0.02. The operating frequency range targets of 5G use cases were set to be at 1.8 GHz, 2.6 GHz and 3.5 GHz, respectively. When used with a dipole antenna, the bandwidths of 390 MHz, 604 MHz, and 1.1 GHz and the antenna gains of about 8.23 dB, 8.14 dB, and 8.19 dB were obtained at the operating frequencies, respectively.

关键词： Wireless communication 5G mobile communication Dipole antennas Simulation Resonant frequency Reflector antennas Transmission line measurements Metamaterials Gain Substrates

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Criminal emotion detection framework using convolutional neural network for public safety

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Scientific Reports 2025年第1期15卷 1-18页

作者： Raval, Jay Jadav, Nilesh Kumar Tanwar, Sudeep Pau, Giovanni Alqahtani, Fayez Tolba, Amr Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad 382481 India Department of Computer Engineering - AI ML & DS Marwadi University Rajkot 360003 India Faculty of Engineering and Architecture Kore University of Enna Enna 94100 Italy Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia Computer Science Department Community College King Saud University Riyadh 11437 Saudi Arabia

In the era of rapid societal modernization, the issue of crime stands as an intrinsic facet, demanding our attention and consideration. As our communities evolve and adopt technological advancements, the dynamic landscape of criminal activities becomes an essential aspect that requires careful examination and proactive approaches for public safety application. In this paper, we proposed a collaborative approach to detect crime patterns and criminal emotions with the aim of enhancing judiciary decision-making. For the same, we utilized two standard datasets - a crime dataset comprised of different features of crime. Further, the emotion dataset has 135 classes of emotion that help the AI model to efficiently find criminal emotions. We adopted a convolutional neural network (CNN) to get first trained on crime datasets to bifurcate crime and non-crime images. Once the crime is detected, criminal faces are extracted using the region of interest and stored in a directory. Different CNN architectures, such as LeNet-5, VGGNet, RestNet-50, and basic CNN, are used to detect different emotions of the face. The trained CNN models are used to detect criminal emotion and enhance judiciary decision-making. The proposed framework is evaluated with different evaluation metrics, such as training accuracy, loss, optimizer performance, precision-recall curve, model complexity, training time, and inference time. In crime detection, the CNN model achieves a remarkable accuracy of 92.45% and in criminal emotion detection, LeNet-5 outperforms other CNN architectures by offering an accuracy of 98.6%. © The Author(s) 2025.

关键词： Artificial intelligence Convolutional neural networks Deep learning Face emotion detection Public safety

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Balancing accuracy and interpretability: AI-driven predictive modeling of construction schedule performance in India

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Asian Journal of Civil Engineering 2025年 1-16页

作者： Maurya, Sudhanshu Lakkimsetty, Nageswara Rao Manjunath, T. C. Shukla, Anoop Kumar Sethy, Barada Prasad Behera, Rashmi Rekha Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Nagpur India Department of Chemical and Petroleum Engineering School of Engineering American University of Ras Al Khaimah (AURAK) Ras al Khaimah United Arab Emirates Ras al Khaimah UAE Department of Computer Science & Engineering Cyber Security & Blockchain Technology Rajarajeswari College of Engineering Bengaluru India Manipal School of Architecture and Planning Manipal Academy of Higher Education Manipal India NIST University Berhampur India Centurion University of Technology and Management Gajapati India

Construction schedule delays are a persistent challenge in India’s infrastructure sector, leading to significant cost overruns, stakeholder dissatisfaction, and inefficient resource utilization. In this context, the integration of Artificial Intelligence (AI) into project forecasting offers a promising solution. This study presents an AI-driven predictive modeling framework aimed at estimating construction schedule adherence, with a dual focus on predictive accuracy and model interpretability. Three machine learning models—multiple linear regression (MLR), artificial neural networks (ANN), and gradient boosting machine (GBM)—were developed and evaluated using real-world data from 150 construction projects across India. The models were assessed based on standard performance metrics (MAE, RMSE, R²), computational efficiency, and transparency. While MLR offered high interpretability, it lacked accuracy in handling nonlinear interactions. ANN showed high accuracy but limited explainability. GBM outperformed both, achieving the best predictive performance (R² = 0.94) and a favorable trade-off between accuracy and interpretability. Feature importance and sensitivity analyses identified equipment utilization, material availability, and labor productivity as key influencers. Case study validation further reinforced the practical value of GBM in diverse project settings. The findings advocate for the strategic adoption of interpretable AI tools like GBM in construction management to enhance scheduling precision and decision-making efficiency.

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