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Annual Allerton Conference on Communication, control, and Computing

作者： Vikram Krishnamurthy Cristian R. Rojas School of Electrical and Computer Engineering Cornell University Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

We consider word-of-mouth social learning involving $m$ Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of the previous Kalman filter. The prior is updated by the m-th Kalman filter. When $m=2$ , and the observations are noisy measurements of a Gaussian random variable, the covariance goes to zero as $k^{-1/3}$ for $k$ observations, instead of $O(k^{-1})$ in the standard Kalman filter. In this paper we prove that for $m$ agents, the covariance decreases to zero as $k^{-(2^{m}-1)}$ , i.e, the learning slows down exponentially with the number of agents. We also show that by artificially weighing the prior at each time, the learning rate can be made optimal as $k^{-1}$ . The implication is that in word-of-mouth social learning, artificially re-weighing the prior can yield the optimal learning rate.

关键词： Random variables Kalman filters Noise measurement Standards Convergence

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Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis 4th

Domain Adversarial Interaction Network for Cross-Domain Fau...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Lu, Weikai Chen, Jian Zheng, Hao Fan, Haoyi Wei, Eng Yee Cao, Xinrong Zhang, Deyang School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Henan Provincial Institute of Scientific and Technical Information Zhengzhou450003 China

ISBN: (纸本)9783031200984

Intelligent fault diagnosis has been widely used in the industry and plays a crucial role in the health management of machinery. In recent years, unsupervised domain adaptation (UDA) has been applied to fault diagnosis, showing excellent performance under variable working conditions. However, most existing UDA-based methods do not consider the temporal relations in the fault signal, resulting in sub-optimal performance. In this paper, we proposed a domain adversarial interaction network (DAIN) to solve this problem. By downsampling sub-sequences of fault signals and interacting with their features, DAIN can obtain feature representations containing the temporal relations. In addition, domain adversarial learning and maximum mean discrepancy (MMD) are applied to DAIN to align the domain discrepancy and distribution discrepancy of source and target domains. We conducted extensive experiments on the public Paderborn University (PU) dataset, and the results demonstrate that the proposed method can achieve higher cross-domain fault diagnosis accuracy than the existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Failure analysis

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Detection and Identification of Power Quality Disturbance Signals in New Power System Based on Quantum Classic Hybrid Convolutional Neural Networks 2nd

Detection and Identification of Power Quality Disturbance ...

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Li, Yue Li, Xinhao Jia, Haopeng Liu, Anjiang Wang, Qingle Hao, Shuqing Liu, Hao Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang550002 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819785452

With the rapid development of modern power systems, power quality disturbances are a growing concern, impacting system reliability. Effective detection and identification of PQDs in new distribution systems with high renewable energy integration are crucial. In this paper, we propose a quantum-classical hybrid convolutional neural network for detecting and identifying power quality disturbances using a quantum-classical hybrid convolutional neural network (PQDs-QC-CNN). The PQDs-QC-CNN model includes quantum convolutional layers, fully connected layers, and softmax regression. The proposed PQDs-QC-CNN achieves a certain speed with the time complexity of O(poly(N)) and the space complexity of O(N), where N is the number of qubits. Compared to classical convolutional neural networks, PQDs-QC-CNN leverages quantum algorithms, offering the advantage of lower computational complexity. According to the IEEE Std 1159-2019 standard, the experimental results of our PQDs-QC-CNN achieves 100% detection accuracy. Following the seven types of single disturbance signals, the identification is 99.25%. PQDs-QC-CNN represents a significant exploration of the application of quantum algorithms in the power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Robust control of Boiler Drum Water Level Via H-Infinity

SSRN

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SSRN 2024年

作者： Sinishaw, Getaye Yeshaneh Tekle, Mekdes Mebratu Mitaw, Abibual Abate Kassa, Abrham School of Electrical and Computer Engineering Industrial Control Woldia Institute of Technology Woldia University Woldia Ethiopia School of Electrical and Computer Engineering Electrical Power Woldia Institute of Technology Woldia University Woldia Ethiopia Faculity of Electrical Engineering Industrial Control Bahir Dar Institute of Technology Bahir Dar Ethiopia

The water level in the boiler drum is crucial for factory operations. controlling it is challenging due to swell and shrink phenomena. If the level is below the set point, overheating of the water wall tubes may cause tube falling-out and serious accidents resulting. Damage can also be a result of several or frequently low drum level conditions. If the level goes above the limits of boiler water carryover toward the super heater or turbine may result in damage resulting in expansive maintenance costs or damage of either the turbine or boiler Various linear and nonlinear controllers have been proposed, such as fractional-order PID, LQR, fuzzy adaptive, and sliding mode controllers, but these cannot handle disturbances in steam and water flow *** paper presents a robust H-infinity-based optimal controller designed for high disturbance rejection and stability under all operating conditions. H-infinity controller can be done by selecting proper weight functions. The weights are tuned to obtain satisfactory performance margins such as rise time, percentage overshoot, settling time and steady state error. Proper weight functions are selected to shape the signal for desired performance and robustness. Weight function W1 is a low-pass filter for good disturbance rejection at low frequencies, while W3 is a high-pass filter. The coefficients of these weights are optimized using Particle Swarm Optimization (PSO).From the simulation result H infinity-based optimal controller is compared with LQR. H infinity controller shows that it has good reference tracking and performance than optimal control LQR The H-infinity controller demonstrates superior reference tracking and performance compared to the LQR. © 2024, The Authors. All rights reserved.

关键词： Disturbance rejection

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MAD-BA: 3D LiDAR Bundle Adjustment – From Uncertainty Modelling to Structure Optimization

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IEEE Robotics and Automation Letters 2025年第7期10卷 7254-7261页

作者： Cwian, Krzysztof Giammarino, Luca Di Ferrari, Simone Ciarfuglia, Thomas Grisetti, Giorgio Skrzypczynski, Piotr Poznan University of Technology Institute of Robotics and Machine Intelligence Poznan Poland Sapienza University of Rome Department of Computer Control and Management Engineering "Antonio Ruberti" Italy

The joint optimization of sensor poses and 3D structure is fundamental for state estimation in robotics and related fields. Current LiDAR systems often prioritize pose optimization, with structure refinement either omitted or treated separately using implicit representations. This paper introduces a framework for simultaneous optimization of sensor poses and 3D map, represented as surfels. A generalized LiDAR uncertainty model is proposed to address less reliable measurements in varying scenarios. Experimental results on public datasets demonstrate improved performance over most comparable state-of-the-art methods. The system is provided as open-source software to support further research. © 2016 IEEE.

关键词： Open source software

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DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories 62

DevEval: A Manually-Annotated Code Generation Benchmark Alig...

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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Li, Jia Li, Ge Zhao, Yunfei Li, Yongmin Liu, Huanyu Zhu, Hao Wang, Lecheng Liu, Kaibo Fang, Zheng Wang, Lanshen Ding, Jiazheng Zhang, Xuanming Zhu, Yuqi Dong, Yihong Jin, Zhi Li, Binhua Huang, Fei Li, Yongbin Gu, Bin Yang, Mengfei School of Computer Science Peking University China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education China Alibaba Group China Beijing Institute of Control Engineering China China Academy of Space Technology China

ISBN: (纸本)9798891760998

How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs. To address the knowledge gap, we propose a new benchmark named DevEval, which has three advances. 1 DevEval aligns with real-world repositories in multiple dimensions, e.g., code and dependency distributions. 2 DevEval is annotated by 13 developers and contains comprehensive annotations (e.g., requirements, original repositories, reference code, and reference dependencies). 3 DevEval comprises 1,825 testing samples from 115 repositories, covering 10 popular domains (e.g., Internet, Database). Based on DevEval, we propose repository-level code generation and evaluate 8 popular LLMs on DevEval (e.g., gpt-4, gpt-3.5, StarCoder 2, DeepSeek Coder, CodeLLaMa). Our experiments reveal these LLMs' coding abilities in real-world code repositories. For example, the highest Pass@1 of gpt-4 only is 53.04% in our experiments. We also analyze LLMs' failed cases and summarize their shortcomings. We hope DevEval can facilitate the development of LLMs in real code repositories. DevEval, prompts, and LLMs' predictions have been released. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, engineering, Science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Cloud-Edge Intelligent Collaborative Computing Model Based on Transfer Learning in IoT

Cloud-Edge Intelligent Collaborative Computing Model Based o...

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作者： Yang Long Zhixin Li School of Information and Control Engineering Jilin Institute of Chemical Technology School of Computer Technology and Engineering Changchun Institute of Technology

The deep neural network is a reliable technical support for cloud computing and edge computing. It has excellent nonlinear approximation and generalization capabilities, making it suitable for classifying and predicting Internet of Things data in cloud computing and edge computing fields. However, the increasing size of neural networks poses a challenge for their deployment on devices with limited computing and storage resources. Traditional cloud computing services also suffer from high latency, which hinders real-time tasks. To address these challenges, this paper proposes a cloud-side cooperation model for deep learning based on migration learning technology. This model used migration learning technology to reduce the size of deep neural networks. Specifically, it deployed the deep neural network model(CDLM) in the cloud and the shallow neural network model(EDLM) at the edge. CDLM is used to help train EDLM and improve its performance, enabling it to run independently on edge devices with high accuracy and respond to real-time tasks. This approach reduced the amount of user data transmitted to the cloud, alleviated bandwidth pressure, and protected user privacy. Experimental results show that the proposed model improved the accuracy of EDLM by 19.58% compared with traditional neural network *** findings provide a theoretical and experimental foundation for the study of cloud-edge collaborative models.

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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