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Adversarially Robust Object Detection via Deviation Calibration and Content Preservation

IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5900-5911页

作者： Zhang, Xu Peng, Bo Lei, Jianjun Xue, Chao Yao, Yuxuan Huang, Qingming Tianjin University School of Electrical and Information Engineering Tianjin300072 China Tiandy Technologies Tianjin300384 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China

Object detection has achieved a promising development in recent years and played an important role in various applications. However, the performance of object detection networks generally drops significantly when subjected to adversarial attacks. As an effective technique for defending against adversarial attacks, adversarially robust object detection has attracted increasing interest. In this paper, a novel deviation-calibrated and content-preserved network (DCCP-Net) is proposed for adversarially robust object detection by effectively exploring and mitigating the essential negative impact of noise disturbance in the feature space. Specifically, a deviation-calibrated robust feature enhancement module is designed to enhance the feature robustness of adversarial images by removing noise disturbance and supplementing rectified information. Besides, by enabling adversarial image features to imitate corresponding clean image features, a content-preserved consistency information imitation mechanism is proposed to obtain more accurate content information of adversarial images. Extensive experiment results have verified the superiority of the proposed DCCP-Net. © 1991-2012 IEEE.

关键词： Feature extraction Noise Training Object detection Detectors Robustness Perturbation methods Accuracy Calibration Convolution

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Distributed field mapping for mobile sensor teams using a derivative‐free optimisation algorithm

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IET Cyber-Systems and Robotics 2024年第2期6卷 20-34页

作者： Tony X.Lin Jia Guo Said Al-Abri Fumin Zhang School of Electrical&Computer Engineering Georgia Institute of TechnologyAtlantaGeorgiaUSA College of Engineering Sultan Qaboos UniversityMuscatOman Department of Electronic&Computer Engineering Hong Kong University of Science and TechnologyHong KongChina

The authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process(GP).The authors’strategy arises by balancing exploration objectives between areas of high error and high *** computing high error regions is impossible since the scalar field is unknown,a bio-inspired approach known as Speeding-Up and Slowing-Down is leveraged to track the gradient of the GP *** approach achieves global field-learning convergence and is shown to be resistant to poor hyperparameter tuning of the *** approach is validated in simulations and experiments using 2D wheeled robots and 2D flying mini-ature autonomous blimps.

关键词： environment sensing multi-robot systems

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Optimized Consensus for Blockchain in Internet of Things Networks via Reinforcement Learning

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Tsinghua science and Technology 2023年第6期28卷 1009-1022页

作者： Yifei Zou Zongjing Jin Yanwei Zheng Dongxiao Yu Tian Lan Institute of Intelligent Computing School of Computer Science and TechnologyShandong UniversityQingdao 266237China Department of Electrical and Computer Engineering George Washington UniversityWashingtonDC 20052USA

Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumption of computing/communication resources and usually require reliable communications with bounded ***,these protocols may be unsuitable for Internet of Things(IoT)networks because the IoT devices are usually lightweight,battery-operated,and deployed in an unreliable wireless ***,this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement ***,the consensus protocol in this work is designed on the basis of the Proof-of-Communication(PoC)scheme directly in a single-hop wireless network with unreliable communications.A distributed MultiAgent Reinforcement Learning(MARL)algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain *** this algorithm,each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective *** results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed,and the efficiency nearly reaches a centralized optimal solution.

关键词： consensus in blockchain Proof-of-Communication(PoC) MultiAgent Reinforcement Learning(MARL) Internet of Things(IoT)networks

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Scheduling a Single-Arm Multi-Cluster Tool With a Condition-Based Cleaning Operation

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1965-1983页

作者： Qinghua Zhu Hongpeng Li Cong Wang Yan Hou School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102-1982 USA

As wafer circuit widths shrink less than 10 nm,stringent quality control is imposed on the wafer fabrication processes. Therefore, wafer residency time constraints and chamber cleaning operations are widely required in chemical vapor deposition, coating processes, etc. They increase scheduling complexity in cluster tools. In this paper, we focus on scheduling single-arm multi-cluster tools with chamber cleaning operations subject to wafer residency time constraints. When a chamber is being cleaned, it can be viewed as processing a virtual wafer. In this way, chamber cleaning operations can be performed while wafer residency time constraints for real wafers are not violated. Based on such a method, we present the necessary and sufficient conditions to analytically check whether a single-arm multi-cluster tool can be scheduled with a chamber cleaning operation and wafer residency time constraints. An algorithm is proposed to adjust the cycle time for a cleaning operation that lasts a long cleaning ***, algorithms for a feasible schedule are also *** an algorithm is presented for operating a multi-cluster tool back to a steady state after the cleaning. Illustrative examples are given to show the application and effectiveness of the proposed method.

关键词： Chamber cleaning multi-cluster tools scheduling semiconductor manufacturing

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Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

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computer Modeling in engineering & sciences 2023年第9期136卷 2405-2424页

作者： Feisha Hu Qi Wang Haijian Shao Shang Gao Hualong Yu School of Computer Jiangsu University of Science and TechnologyZhenjiang212100China Department of Electrical and Computer Engineering University of NevadaLas VegasNV89154USA

Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian *** the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being *** address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone *** deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone *** default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of *** improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV *** on the above improvements,we create a novel anomaly detection strategy *** method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)*** experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.

关键词： UAV safety kernel extreme learning machine triangular global alignment kernel fast independent component analysis

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Schottky barrier modulation of bottom contact SnO_(2) thin-film transistors via chloride-based combustion synthesis

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Journal of Materials science & Technology 2023年第17期148卷 199-208页

作者： Bongho Jang Junhee Lee Hongki Kang Jaewon Jang Hyuk-Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaegu 42988South Korea School of Electronic and Electrical Engineering Kyungpook National UniversityDaegu 41566South Korea School of Electronics Engineering Kyungpook National UniversityDaegu 41566South Korea Convergence Research Advanced Centre for Olfaction DGISTDaegu 42988South Korea

The enhanced carrier flow at the interface between Au and SnO_(2)semiconductors,which initially form Schottky contacts,is realized using chloride-based combustion ***-based combustion sys-tems can achieve chlorine(Cl)doping effects as well as conversion to crystalline SnO_(2)films at clearly lower temperatures(∼250℃)than conventional *** to the Cl doping effect,the high carrier concentration can induce thin potential barriers at the metal/semiconductor(MS)junctions,resulting in carrier injection by *** a result,compared to conventional SnO_(2)thin-film transistors,the de-vices fabricated by combustion synthesis exhibit significantly improved electrical performance with field-effect mobility of 6.52 cm 2/Vs(∼13 times),subthreshold swing of 0.74 V/dec,and on/offratio of∼10^(7)below 300℃.Furthermore,because of the enhanced tunneling carriers induced by the narrowed barrier width,the Schottky barriers are significantly reduced from 0.83 to 0.29 eV(65%decrease)at 250℃and from 0.42 to 0.17 eV(60%decrease)at 400℃.Therefore,chloride-based combustion synthesis can con-tribute to developing SnO_(2)-based electronics and flexible devices by achieving both high-quality oxide films and improved current flow at the MS interface with low-temperature annealing.

关键词： SnO_(2) Combustion Sol-gel Schottky contact Thin-film transistors

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An algebraic method for moving source localization using TDOA and FDOA measurements with receiver location errors 2

An algebraic method for moving source localization using TDO...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Zhang, Weiwei Li, Ding Wang, Jingyu University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China University of Shanghai for Science and Technology School of Health Science and Engineering Shanghai China

ISBN: (纸本)9798331515669

This paper proposes a solution for locating a moving source using time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements while considering random errors in receiver locations. The proposed solution accounts for these errors to reduce estimation error, and it is analytically shown to achieve Cramér-Rao lower bound (CRLB) accuracy for far-field sources under mild approximations and small Gaussian noise. Numerical simulations validate and support the theoretical analysis. © 2024 IEEE.

关键词： Cramer-Rao bounds

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FPMT: Enhanced Semi-supervised Model for Traffic Incident Detection 27th

FPMT: Enhanced Semi-supervised Model for Traffic Incident De...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Lu, Xinying Xiao, Jianli School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9783031781063

For traffic incident detection, the acquisition of data and labels is notably resource-intensive, rendering semi-supervised traffic incident detection both a formidable and consequential challenge. Thus, this paper focuses on traffic incident detection with a semi-supervised learning way. It proposes a semi-supervised learning model named FPMT within the framework of MixText. The data augmentation module introduces Generative Adversarial Networks to balance and expand the dataset. During the mix-up process in the hidden space, it employs a probabilistic pseudo-mixing mechanism to enhance regularization and elevate model precision. In terms of training strategy, it initiates with unsupervised training on all data, followed by supervised fine-tuning on a subset of labeled data, and ultimately completing the goal of semi-supervised training. Through empirical validation on four authentic datasets, our FPMT model exhibits outstanding performance across various metrics. Particularly noteworthy is its robust performance even in scenarios with low label rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Latent Variable and Classification Performance Analysis of Bird-Drone Spectrograms with Elementary Autoencoder

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2024年 3卷 115-123页

作者： White, Daniel Jahangir, Mohammed Mishra, Amit Kumar Baker, Chris J. Antoniou, Michail University Of Birmingham School Of Electrical Electronic And Systems Engineering United Kingdom Aberystwyth University Department Of Computer Science United Kingdom

Deep learning with convolutional neural networks has been widely utilised in radar research concerning automatic target recognition. Maximising numerical metrics to gauge the performance of such algorithms does not necessarily correspond to model robustness against untested targets, nor does it lead to improved model interpretability. Approaches designed to explain the mechanisms behind the operation of a classifier on radar data are proliferating, but bring with them a significant computational and analysis overhead. This work uses an elementary unsupervised convolutional autoencoder (CAE) to learn a compressed representation of a challenging dataset of urban bird and drone targets and subsequently if apparent quality of the representation via preservation of class labels leads to better classification performance after a separate supervised training stage. It is shown that a CAE that reduces the features output after each layer of the encoder gives rise to the best drone vs bird classifier. A clear connection between unsupervised evaluation via label preservation in the latent space and subsequent classification accuracy after supervised fine-tuning is shown, supporting further efforts to optimise radar data latent representations to enable optimal performance and model interpretability. © 2023 IEEE.

关键词： Drones

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