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A novel internal crack detection method for photovoltaic (PV) panels based on the air-coupled ultrasonic

Sensors and Actuators A: Physical 2025年 392卷

作者： Sun, Tianyi Bai, Tianyang Cao, Shengxian Fan, Siyuan Zhang, Yanhui School of Automation Engineering Northeast Electric Power University Jilin132012 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

Accurately assessing the potential risk of cracks in photovoltaic (PV) panels is crucial for improving the system's energy conversion efficiency and safety. This paper develops a novel internal crack detection device for PV panels based on air-coupled ultrasonics and establishes a dedicated model for PV panel crack detection. Considering the anisotropy of silicon, the Rayleigh–Lamb equation is first used to obtain the dispersion curve of Lamb waves. The continuous wavelet transform (CWT) is then applied to analyze the A0 mode Lamb wave echo signals within a single cycle to distinguish between cracked and non-cracked regions. Further, the original signal is enhanced using singular value decomposition (SVD) to strengthen the signal features, and a direct relationship model between the signal echo and the crack length is proposed. Finally, the model's validity is verified by measuring actual crack data from PV panels. The results show that the optimal frequency of Lamb waves for crack detection in PV panels is 200 kHz, with an attenuation coefficient of 6.4 dB/mm and incidence and exit angles of 11°. Compared to electroluminescence (EL) and inductive thermal imaging methods, the proposed approach achieves higher accuracy, reaching 84.26%. © 2025

关键词： Image enhancement

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Text-to-Decision Agent: Learning Generalist Policies from Natural Language Supervision

arXiv

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arXiv 2025年

作者： Zhang, Shilin Hu, Zican Wu, Wenhao Xie, Xinyi Tang, Jianxiang Chen, Chunlin Dong, Daoyi Cheng, Yu Sun, Zhenhong Wang, Zhi Department of Control Science and Intelligent Engineering Nanjing University China School of Engineering Australian National University Australia Australian Artificial Intelligence Institute University of Technology Sydney Australia Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

RL systems usually tackle generalization by inferring task beliefs from high-quality samples or warmup explorations. The restricted form limits their generality and usability since these supervision signals are expensive and even infeasible to acquire in advance for unseen tasks. Learning directly from the raw text about decision tasks is a promising alternative to leverage a much broader source of supervision. In the paper, we propose Text-to-Decision Agent (T2DA), a simple and scalable framework that supervises generalist policy learning with natural language. We first introduce a generalized world model to encode multi-task decision data into a dynamics-aware embedding space. Then, inspired by CLIP, we predict which textual description goes with which decision embedding, effectively bridging their semantic gap via contrastive language-decision pre-training and aligning the text embeddings to comprehend the environment dynamics. After training the text-conditioned generalist policy, the agent can directly realize zero-shot text-to-decision generation in response to language instructions. Comprehensive experiments on MuJoCo and Meta-World benchmarks show that T2DA facilitates high-capacity zero-shot generalization and outperforms various types of baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Supervised learning

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Hyperbolic Binary Neural Network

arXiv

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arXiv 2025年

作者： Chen, Jun Xiang, Jingyang Huang, Tianxin Zhao, Xiangrui Liu, Yong National Special Education Resource Center for Children with Autism Zhejiang Normal University Hangzhou311231 China Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Binary Neural Network (BNN) converts full-precision weights and activations into their extreme 1-bit counterparts, making it particularly suitable for deployment on lightweight mobile devices. While binary neural networks are typically formulated as a constrained optimization problem and optimized in the binarized space, general neural networks are formulated as an unconstrained optimization problem and optimized in the continuous space. This paper introduces the Hyperbolic Binary Neural Network (HBNN) by leveraging the framework of hyperbolic geometry to optimize the constrained problem. Specifically, we transform the constrained problem in hyperbolic space into an unconstrained one in Euclidean space using the Riemannian exponential map. On the other hand, we also propose the Exponential Parametrization Cluster (EPC) method, which, compared to the Riemannian exponential map, shrinks the segment domain based on a diffeomorphism. This approach increases the probability of weight flips, thereby maximizing the information gain in BNNs. Experimental results on CIFAR10, CIFAR100, and ImageNet classification datasets with VGGsmall, ResNet18, and ResNet34 models illustrate the superior performance of our HBNN over state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

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Design and testing of Hybrid Slime Mold Algorithm for Real-Time Adaptive Mobile Robot Path Planning in Dynamic Environments

Design and testing of Hybrid Slime Mold Algorithm for Real-T...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Sachin Gupta Shivali Gupta Shaik Mohammed Raith Anand Kumar Salil Bharany Vishnu Kant Ch.Sandeep Department of Robotics and Control Engineering School of Electronics and Control Engineering Lovely Professional University Phagwara India Department of Chemistry School of Chemical Engineering and Physical Sciences Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Warangal Telangana

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Dynamic path planning and collision prevention is one of the key tasks for autonomous vehicles in relation to dynamic movement of obstacles. In this paper, a novel hybrid approach based on integration of Slime Mold Algorithm (SMA), Triangle Inequality Principle (TIP), and Partition Method Strategy (PMS) for solution of this problem statement. The SMA algorithm is very flexible and adaptive by nature due to its biological working mechanism which is associated with TIP and PMS strategies in relation to their optimization and efficient path planning capabilities and robust solutions. This integrated module is tested in virtual environment and the results are compared with other updated algorithms such as A*, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the same static and dynamic environment. It has been observed from the results that this hybrid strategy significantly improves the path planning task several times than other algorithms in terms of multiple aspects. Under testing conditions, the path length was reduced to 25% than other algorithms in a dynamic environment. During this calculation, the computational efficiency of the system has been improved to 60% than conventional methods. The chances of collision have been improved to 34.23% especially in environment where the obstacles are moving. The capability of SMA has been improved due to the synergy between TIP and PMS. The dynamic and adaptive nature of this algorithm is suitable for various applications such as search-and-rescue operations, manufacturing units’ automation and other hazardous environment exploration.

关键词： Navigation Heuristic algorithms Virtual environments Path planning Partitioning algorithms Safety Mobile robots Vehicle dynamics Testing Genetic algorithms

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Self-Powered Broadband Photodetectors Based on WS2-Anchored MoS2 with Enhanced Responsivity and Detectivity

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Small 2025年第20期21卷 e2502900页

作者： Kumar, Ashok Chakkar, Atul G. Das, Chayan Kumar, Pradeep Sahu, Satyajit Saliba, Michael Kumar, Mahesh Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland School of Physical Sciences Indian Institute of Technology Mandi Himachal Pradesh Mandi175005 India Department of Physics Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India University of Stuttgart Stuttgart70569 Germany Helmholtz Young Investigator Group FRONTRUNNERIEK5-Photovoltaik Forschungszentrum Jülich Jülich52425 Germany Department of Electrical Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India

Self-powered broadband photodetectors utilizing 2D transition metal dichalcogenides (TMDs) are highly promising due to their remarkable light absorption capabilities and high sensitivity, making them suitable for applications such as military surveillance and wireless light detection systems. However, their performance is constrained by inadequate absorption, suboptimal charge carrier separation, and slow response times. In response to these limitations, the study fabricates a self-powered photodetector employing a heterostructure composed of WS2 nanoparticles anchored to CVD-synthesized MoS2, operating within the visible to near-infrared spectrum. The device demonstrates a responsivity of 283 mA W−1 and a detectivity 6.44 × 1012 Jones, alongside an external quantum efficiency of 61% under exposure of 580 nm. In comparison to pristine MoS2, the MoS2-WS2 photodetector exhibited approximately 12-fold and 11-fold enhancements in responsivity and detectivity, respectively, in addition to fast response time of ≈375 µs and 6 ms. Additionally, density functional theory (DFT) calculations are used to analyze the increase in dark current that is observed following WS₂ nanoparticle anchored on MoS₂. This investigation highlights the potential of 2D heterostructures in the development of high-performance broadband photodetectors, which offer improved responsivity, stability, and self-powered operation for advanced optoelectronic applications. © 2025 Wiley-VCH GmbH.

关键词： Layered semiconductors

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Acoustic feature processing strategy for leak degree identification in non-metallic pipelines

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Applied Acoustics 2025年 238卷

作者： Yu, Yuebo Cui, Xiwang Gao, Yan Han, Xiaojuan Song, Lijiang Lu, Fangcheng School of Instrument Science and Opto-Electronics Engineering Beijing Information Science and Technology University Beijing100192 China State Key Laboratory of Acoustics Institute of Acoustics Chinese Academy of Sciences Beijing100190 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

In response to the challenge of accurately identifying the degree of leaks in non-metallic pipelines, this study proposes acoustic feature processing strategy based on the Principal Component Analysis (PCA) and Bidirectional Long Short-Term Memory Networks (Bi-LSTM). The hydrophone sensors are deployed to collect acoustic pressure signals generated by different degrees of pipeline leak. Acoustic signal features of varying leak degrees are extracted from the perspectives of the time domain, the spectral domain, and waveform shape. Subsequently, PCA is employed to streamline and reduce the dimensionality of the extracted features. The optimal number of principal components is ascertained based on the Cumulative Variance Contribution Rate (CVCR), which eliminates redundant features and preserves principal components. Furthermore, the PCA-Bi-LSTM model is designed to identify the degree of pipeline leak. The principal components are organized chronologically into cells for input into the Bi-LSTM. Comparative analysis is performed using different statistical features and neural network architectures, with additional experiments conducted under varying pressures and leak locations to validate the generalizability of the proposed method. The results indicate that the PCA-Bi-LSTM approach achieves a leak identification accuracy of 98.6 %, which is significantly higher than the 91. 5 % accuracy of the unidirectional LSTM network and the 93.2 % accuracy of the traditional Bi-LSTM network. The results demonstrate that the designed strategy effectively improves the accuracy of identifying the degree of non-metallic pipeline leaks and demonstrates a certain level of generalizability. © 2025 Elsevier Ltd

关键词： Leak detection

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Domain adaptive FL for edge-enabled privacy-preserving MRI analysis

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Alexandria Engineering Journal 2025年 128卷 324-339页

作者： Saeed Iqbal Imran Arshad Choudhry Inam Ullah Kuljeet Kaur Bong Jun Choi Mohammad Mehedi Hassan College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering Gachon University Seongnam 13120 Republic of Korea Electrical Engineering Department École de technologie supérieure Montréal QC H3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India School of Computer Science and Engineering Soongsil University Seoul Republic of Korea Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates.

关键词： Multi-modal data Domain adaptive learning Federated learning Privacy preservation Domain-specific Data privacy

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Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak–Lojasiewicz Condition

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IEEE Transactions on Automatic control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Human-centric assembly in smart factories

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CIRP Annals 2025年

作者： Wang, Lihui Gao, Robert X. Krüger, Jörg Váncza, József Department of Production Engineering KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Aerospace Engineering Case Western Reserve University Cleveland United States Institute for Machine Tools and Factory Management Technical University Berlin Germany Laboratory of Engineering and Management Intelligence Institute for Computer Science and Control Hungary

Assembly in future smart factories needs to address three challenges, including human centricity, sustainability, and resilience. Conventional approaches for automation in assembly have reached a bottleneck in terms of operation automomy, leaving various tasks to continued manual labour by human operators. To ease the burden on humans both physically and intellectually, human-centric assembly enhanced by augmented robots, cognitive systems, mixed reality and collaborative intelligence, assisted by thought-driven brain robotic controls, provides a promising solution. Within the context, this keynote provides an in-depth analysis of the state of human-centric assembly and identifies potentially fruitful research directions in future smart factories. © 2025 The Author(s)

关键词： Assembly Human-centricity Robot

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