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arXiv 2022年

作者： Wang, Wenyao Wu, Chenhao He, Jie Faculty of Innovation Engineering Macau University of Science and Technology China College of Computer Science and Electronic Engineering Hunan University Hunan Changsha China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Guangxi Wuzhou China

Automated bug priority inference can reduce the time overhead of bug triagers for priority assignments, improving the efficiency of software maintenance. Currently, there are two orthogonal lines for this task, i.e., traditional machine learning based (TML-based) and neural network based (NN-based) approaches. Although these approaches achieve competitive performance, our observation finds that existing approaches face the following two issues: 1) TML-based approaches require much manual feature engineering and cannot learn the semantic information of bug reports;2) Both TML-based and NN-based approaches cannot effectively address the label imbalance problem because they are difficult to distinguish the semantic difference between bug reports with different priorities. In this paper, we propose CLeBPI (Contrastive Learning for Bug Priority Inference), which leverages pre-trained language model and contrastive learning to tackle the above-mentioned two issues. Specifically, CLeBPI is first pre-trained on a large-scale bug report corpus in a self-supervised way, thus it can automatically learn contextual representations of bug reports without manual feature engineering. Afterward, it is further pre-trained by a contrastive learning objective, which enables it to distinguish semantic differences between bug reports, learning more precise contextual representations for each bug report. When finishing pre-training, we can connect a classification layer to CLeBPI and fine-tune it for bug priority inference in a supervised way. To verify the effectiveness of CLeBPI, we choose four baseline approaches and conduct comparison experiments on a public dataset. The experimental results show that CLeBPI outperforms all baseline approaches by 23.86%-77.80% in terms of weighted average F1-score, showing its effectiveness. Copyright © 2022, The Authors. All rights reserved.

关键词： computer software maintenance

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Density peaks clustering based on multiple distance measures for manufacturing process

Density peaks clustering based on multiple distance measures...

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International Conference on Information and Automation (ICIA)

作者： Shuai Li Shiying Gao Xiaofeng Zhou Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Key Laboratory of Network Control System Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781509041039

Data analysis and processing of manufacturing process is significant to ensure the stable production safety, maintain quality stabilization, and optimize production profit. Practical manufacturing process often has complex characteristics, such as multimode, nonlinearity, etc. Mode division can divide manufacturing process into multiple modes and is useful for subsequent process monitoring and scheduling optimizing. In this paper, density peaks clustering (DPC) based on multiple distance measures is used for mode division in manufacturing process. Multiple distance measures for computing the local density and minimum distance between the point and any other point with higher density in DPC are compared and analyzed. To illustrate the effectiveness of the clustering method for mode division in manufacturing process, experiments are developed based on penicillin fermentation process and practical foods industrial production process. Experimental results verify the feasibility and efficiency of the clustering method for mode division in manufacturing process.

关键词： Manufacturing processes Density measurement Level measurement Clustering methods Automation Monitoring

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A Novel Incipient Fault Detection Method for Nonlinear Dynamic industrial Process Based on CVD-SVDD

A Novel Incipient Fault Detection Method for Nonlinear Dynam...

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Chinese control and Decision Conference, CCDC

作者： Ranran Mao Hongjun Zhang Kaixiang Peng Jie Dong School of Automation and Electrical Engineering Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing P. R. China Key Laboratory of Metallurgical Industry Safety & Risk Prevention and Control Ministry of Emergency Management Beijing P. R. China

To address the problem that it is difficult to detect incipient faults in nonlinear dynamic industrial processes due to their low magnitude and inconspicuous fault characteristics, this paper proposes an incipient fault detection method based on canonical variate dissimilarity (CVD)-support vector data description (SVDD). Firstly, the correlation between past and future data is maximized by canonical variate analysis (CVA) to establish a state space model based on the training data under normal conditions, and the degree of prediction of past canonical variates (CVs) to future CVs, i.e., CVD, is used to discover small data deviations. Then, a minimal hypersphere is built for CVD using SVDD to complete the distinction between faulty and normal data. Finally, experimental simulations are conducted in the continuous stirred tank reactor system, and the experimental results show that CVD-SVDD has a better effect of incipient fault detection compared with the traditional method.

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Cctseg: A Cascade Composite Transformer Semantic Segmentation Network for Uav Visual Perception

SSRN

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SSRN 2022年

作者： Yi, Shi Jiang, Gang Liu, Xi Li, Junjie Chen, Ling College of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu610059 China College of Mathematics and Physics Chengdu University of Technology Chengdu610059 China Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province Chengdu610039 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University Of Posts And Telecommunications Chongqing400065 China Geomathematics Key Laboratory of Sichuan Province Chengdu University of Technology Chengdu610059 China

Semantic segmentation could obtain the pixel level classification of surrounding environments which is an essential task for autonomous vehicles and mobile robots visual perception. Most existing semantic segmentation networks were focused on the visual perception of autonomous vehicles. Little attention is paid to the semantic segmentation for UAV (Unmanned Aerial Vehicle) visual perception, which is crucial to UAV autonomous flight and landing spot searching. Compared with views from autonomous vehicles, the UAV-based views were more challenging for the semantic segmentation task due to images captured by UAV containing large-scale variation of objects size caused by different altitude and angle. The existing semantic segmentation networks for the visual perception of autonomous vehicles are generally inadequate to effectively extract the representative features of UAV images which required contain context information and local information simultaneously. A cascade composite transformer-based semantic segmentation network is proposed in this study for UAV visual perception. A cascade composite encoder is designed which consists of three transformer-based feature extraction backbones and cascade fused low-level features, middle-level features and high-level features to achieve better feature representation capacity. The spatial enhanced transformer block is implemented as the basic feature extraction block of each backbone to make the extracted features contain context information of environments and local information of objects. A symmetric rhombus decoder is proposed to integrate multi-stage features and make fully utilise of middle stage features which contained abundance of useful information, thus accurately pixel level prediction could be obtained in this way. © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression

arXiv

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arXiv 2022年

作者： Leng, Xinlin Li, Chenxu Xu, Weifeng Sun, Yuyan Wang, Hongtao School of Control and Computer Engineering North China Electric Power University Baoding071051 China School of Computer Science and Technology Shandong University of Finance and Economics Jinan250014 China Beijing Key Laboratory of IoT Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding071051 China

Federated linear regressions have been developed and applied in various domains, where multiparties collaboratively and securely perform optimization algorithms, e.g., Gradient Descent, to learn a set of optimal model weights. However, traditional Gradient-Descent based methods fail to solve problems which contain L1regularization in objective functions, such as lasso regression. In this paper, we present Federated Coordinate Descent, a new distributed scheme called FCD, to address this issue securely under multiparty scenarios. Specifically, through secure aggregation and noise perturbations, our scheme guarantees that: (1) no local information is leaked to other parties, and (2) global model weights are not exposed to cloud servers. The proposed FCD scheme fills the gap between multiparty secure learning and coordinate descent methods, and is applicable for general linear regressions, including linear, ridge, and lasso regressions. Theoretical analyses prove the security guarantees of our scheme against semi-honest attackers. Experimental results on three UCI datasets demonstrate that regressions under our FCD scheme are effective as centralized models. The performance evaluation on synthetic dataset also reveals that our scheme achieves efficient properties on computation cost and communication *** Codes 68Q32 Copyright © 2022, The Authors. All rights reserved.

关键词： Gradient methods

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Experimental verification of group non-membership in optical circuits

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Photonics Research 2021年第9期9卷 1745-1751页

作者： KAI SUN ZI-JIAN ZHANG FEI MENG BIN CHENG ZHU CAO JIN-SHI XU MAN-HONG YUNG CHUAN-FENG LI GUANG-CAN GUO CAS Key Laboratory of Quantum Information University of Science and Technology of ChinaHefei 230026China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China Department of Physics Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science The University of Hong KongPokfulamHong Kong SARChina Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyNSW 2007Australia Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China

The class quantum Merlin–Arthur(QMA),as the quantum analog of nondeterministic polynomial time,contains the decision problems whose YES instance can be verified efficiently with a quantum *** problem of deciding the group non-membership(GNM)of a group element is conjectured to be a member of *** works on the verification of GNM,which still lacks experimental demonstration,required a quantum circuit with O(n~5)group oracle ***,we provide an efficient way to verify GNM problems,in which each quantum circuit only contains O(1)group of oracle calls,and the number of qubits in each circuit is reduced by *** on this protocol,we then experimentally demonstrate the new verification process with a four-element group in an all-optical *** new protocol is validated experimentally by observing a significant completeness-soundness gap between the probabilities of accepting elements in and outside the *** work efficiently simplifies the verification of GNM and is helpful in constructing more quantum protocols based on the near-term quantum devices.

关键词： quantum element polynomial

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

arXiv

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arXiv 2024年

作者： Xie, Yuting Guo, Xianda Wang, Cong Liu, Kunhua Chen, Long School of Computer Science and Engineering Sun Yat-Sen University China School of Computer Science Wuhan University China State Key Laboratory for Management and Control of Complex Systems The Institute of Automation Chinese Academy of Sciences China School of Mechanical and Automotive Engineering Qingdao University of Technology China

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance. © 2024, CC BY.

关键词： Automatic guided vehicles

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Hohmann transfer via constrained optimization

arXiv

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arXiv 2017年

作者： Xie, Li Zhang, Yiqun Xu, Junyan State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China Beijing Institute of Electronic Systems Engineering Beijing100854 China School of Control and Computer Engineering

In the first part of this paper, inspired by the geometric method of Jean-Pierre Marec, we consider the two-impulse Hohmann transfer problem between two coplanar circular orbits as a constrained nonlinear programming problem. By using the Kuhn-Tucker theorem, we analytically prove the global optimality of the Hohmann transfer. Two sets of feasible solutions are found, one of which corresponding to the Hohmann transfer is the global minimum, and the other is a local minimum. In the second part, we formulate the Hohmann transfer problem as two-point and multi-point boundary-value problems by using the calculus of variations. With the help of the Matlab solver bvp4c, two numerical examples are solved successfully, which verifies that the Hohmann transfer is indeed the solution of these boundary-value problems. Via static and dynamic constrained optimization, the solution to the orbit transfer problem proposed by W. Hohmann ninety-two years ago and its global optimality are *** Codes 49J15 Copyright © 2017, The Authors. All rights reserved.

关键词： Constrained optimization

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FTEKFNet: Hybridizing Physical and Data-driven Estimation Algorithms for Vehicle State

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Wang, Yan Hu, Fengqing Tian, Cheng Li, Panshuo Huang, Hailong Yin, Guodong Huang, Chao Department of Industrial and System Engineering The Hong Kong Polytechnical University Hong Kong Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnical University Hong Kong School of Automation and the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China School of Mechanical Engineering Southeast University Nanjing China

Sideslip angle and vehicle velocity are crucial for both traditional and autonomous vehicles. They play essential roles in chassis stability control, as well as in tasks such as path planning and tracking control. However, these states cannot be directly measured by onboard sensors, therefore various vehicle state estimation algorithms have been developed. Most of these algorithms assume that the noise characteristics are known, ignoring the impact of missing measurement data, and cannot simultaneously handle the effects of colored noise and white noise. To address these issues, we propose a fault-tolerant extended Kalman filter network (FTEKFNet), which integrates both physics-based and data-driven methods for vehicle state estimation. Based on the Fault Tolerant Extended Kalman Filter (FTEKF) iterative framework, a pre-trained artificial neural network is utilized to directly predict the Kalman gain, and it is combined with FTEKF to form FTEKFNet. Experimental results under different conditions demonstrate that FTEKFNet can simultaneously deal with unknown noise and data loss problems and has good adaptability to color noise. The estimation performance of the proposed algorithm is better than the traditional FTEKF and EKF methods. IEEE

关键词： Extended Kalman filters

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Intelligence Uv Absorption Spectroscopy Analysis of So2, H2s, Cs2 Mixed Gases Based on Symmetrized D

SSRN

引用

SSRN 2024年

作者： Chen, Peiying Cao, Yupeng Yu, Miao Wang, Qiao Luo, Lin School of Information and Control Engineering Liaoning Petrochemical University Liaoning Fushun113001 China State Key Laboratory of Industrial Control Technology Institute of Cyber-systems and Control Zhejiang University Zhejiang Hangzhou310027 China Beijing Electric Transmission and Transformation State Grid Jibei Electric Power Company Beijing Beijing100054 China

The accurate quantification of SO2, CS2, and H2S, as significant breakdown components of insulating gas sulfur hexafluoride, is critical for determining the type and severity of internal flaws in insulating equipment. This paper proposes a technique for quantitative detection of multi-component gas mixes on the UV absorption spectroscopy platform that combines symmetry dot pattern with deep learning. The approach uses symmetrized dot pattern analysis to fuse local UV spectral information, which may be used to identify trace gas mixes with overlapping absorption peaks. To detect the symmetry dot pattern diagrams, a deep network based on DenseNet is developed, achieving feature representation from multiple scales. The outstanding performance of this method is illustrated and compared to the existing models using the receiver operating characteristic curve. The platform's experimental results reveal that the suggested technique can quantitatively detect the amounts of SO2, H2S, and CS2 ternary gas mixtures online. © 2024, The Authors. All rights reserved.

关键词： Sulfur hexafluoride

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