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International Conference on Computing, Networking and Communications (ICNC)

作者： Rui Xu Gaolei Li Yuchen Liu Yue Zhao Jiawen Kang Jianhua Li School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science North Carolina State University Raleigh USA State Key Laboratory of Integrated Services Networks Xidian University Xi'an China School of Automation Guangdong University of Technology China

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Covert communication enforces the transmitted signal to be indistinguishable from other signals under wireless environment, making it difficult for an eavesdropper to detect and intercept the message. The most common approach within covert systems is the augmentation of artificial noise (AN), ensuring reliable transmission. However, this strategy becomes problematic when the eavesdropper's position is uncertain, as continuous amplification of interference power can precipitate a substantial decline in covert communication capacity. In this paper, we investigate the covert communication between legitimate transmitter (i.e., Alice) and full duplex receiver (i.e., Bob) in the presence of the location uncertainties of the eavesdropper (i.e., Willie). We first derive the sufficient condition for achieving covert communication through robustness analysis. Then, we derive the effective throughput to measure the covert communication capacity. Afterward, by using joint optimization technology, the optimal AN power of Bob and the number of antennas for Alice are jointly designed to maximize the effective throughput subject to a covertness constraint. Theoretical analysis indicates that reducing the AN power while increasing the number of antennas helps to improve the system capacity, the effective throughput between Alice and Bob is inversely proportional to the distance between them. Finally, the validity of our analytical approach is corroborated by simulation results.

关键词： Wireless communication Sufficient conditions Uncertainty Simulation Transmitting antennas Receivers Full-duplex system

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Ore Image Segmentation Method Based on U-Net and Watershed

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computers, Materials & Continua 2020年第10期65卷 563-578页

作者： Hui Li Chengwei Pan Ziyi Chen Aziguli Wulamu Alan Yang School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Amphenol AssembleTech Houston77070USA

Ore image segmentation is a key step in an ore grain size analysis based on image *** traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and *** this article,in order to solve the problem,an ore image segmentation method based on U-Net is *** adjust the structure of U-Net to speed up the processing,and we modify the loss function to enhance the generalization of the *** the collection of the ore image,we design the annotation standard and train the network with the annotated ***,the marked watershed algorithm is used to segment the adhesion *** experimental results show that the proposed method has the characteristics of fast speed,strong robustness and high *** has great practical value to the actual ore grain statistical task.

关键词： Image segmentation ore grain size U-Net watershed algorithm

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AI-based Gas Turbine Multi-Component Health Prognosis via Recurrent Expansion of Gas Path Parameters

AI-based Gas Turbine Multi-Component Health Prognosis via Re...

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International Conference on Control, automation and Diagnosis (ICCAD)

作者： Tarek Berghout Yassine Amirat Ahcene Bounceur Mohamed Benbouzid Laboratory of Automation and Manufacturing Engineering University of Batna 2 Batna Algeria L@bISEN ISEN Yncréa Ouest Brest France Department of Information and Computer Science King Fahd University of Petroleum & Minerals Dahran Saudi Arabia UMR CNRS 6027 IRDL University of Brest Brest France

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Investigating Gas Turbine (GT) degradation based on Gas Path Parameters (GPPs) is essential for its maintenance and operation. Monitoring GPPs is crucial for early detection of degradation, enabling timely maintenance interventions and preventing potential failures. Integrating physics-based thermodynamic models with representation learning significantly improves predictive maintenance studies and provides a solution to challenges posed by expensive and impractical accelerated aging experiments. However, this integration also presents challenges in managing both data complexity and data drift. In this context, this article aims to address these gaps by extending and improving previous research through (i) exclusive use of GPPs; (ii) the implementation of advanced data preprocessing techniques; and (iii) the use of innovative representation learning strategies. Specifically, it introduces ProgMachina, a tool for data quality analysis in prognosis studies that addresses issues related to data complexity in GPPs. Furthermore, to diversify the feature space and improve the adaptability of representation learning to degradation patterns, this paper proposes using Multiverse Recurrent Expansion with Multiple Repeats (MV-REMR) approach, which is based on a series of Recurrent Neural Networks (RNNs). For evaluation, this study incorporates cross-validation and multiple metrics while comparing against multiple RNNs and state-of-the-art works, demonstrating stable and promising performance, making it a suitable choice for GT prognosis tasks.

关键词： Representation learning Degradation Measurement Thermodynamics Recurrent neural networks Predictive models Data models

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CytoGAN: Unpaired staining transfer by structure preservation for cytopathology image analysis

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computers in Biology and Medicine 2024年 180卷 108942页

作者： Wang, Ruijie Yang, Sicheng Li, Qiling Zhong, Dexing School of Automation Science and Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an710049 China Department of Obstetrics and Gynecology The First Affiliated Hospital of Xi'an Jiaotong University Shaanxi Xi'an710049 China Pazhou Laboratory Guangzhou510335 China Research Institute of Xi'an Jiaotong University Zhejiang 311215 China

With the development of digital pathology, deep learning is increasingly being applied to endometrial cell morphology analysis for cancer screening. And cytology images with different staining may degrade the performance of these analysis algorithms. To address the impact of staining patterns, many strategies have been proposed and hematoxylin and eosin (H&E) images have been transferred to other staining styles. However, none of the existing methods are able to generate realistic cytological images with preserved cellular layout, and many important clinical structural information is lost. To address the above issues, we propose a different staining transformation model, CytoGAN, which can quickly and realistically generate images with different staining styles. It includes a novel structure preservation module that preserves the cell structure well, even if the resolution or cell size between the source and target domains do not match. Meanwhile, a stain adaptive module is designed to help the model generate realistic and high-quality endometrial cytology images. We compared our model with ten state-of-the-art stain transformation models and evaluated by two pathologists. Furthermore, in the downstream endometrial cancer classification task, our algorithm improves the robustness of the classification model on multimodal datasets, with more than 20 % improvement in accuracy. We found that generating specified specific stains from existing H&E images improves the diagnosis of endometrial cancer. Our code will be available on github. © 2024 Elsevier Ltd

关键词： Generative adversarial networks

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HiCRISP: An LLM-based Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner

arXiv

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arXiv 2023年

作者： Ming, Chenlin Lin, Jiacheng Fong, Pangkit Wang, Han Duan, Xiaoming He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China The Department of Computer Science University of Illinois Urbana-Champaign IL United States The Department of Engineering Science University of Oxford Oxford United Kingdom

The integration of Large Language Models (LLMs) into robotics has revolutionized human-robot interactions and autonomous task planning. However, these systems are often unable to self-correct during the task execution, which hinders their adaptability in dynamic real-world environments. To address this issue, we present an LLM-based Hierarchical Closed-loop Robotic Intelligent Self-correction Planner (HiCRISP), an innovative framework that enables robots to correct errors within individual steps during the task execution. HiCRISP actively monitors and adapts the task execution process, addressing both high-level planning and low-level action errors. Extensive benchmark experiments, encompassing virtual and real-world scenarios, showcase HiCRISP’s exceptional performance, positioning it as a promising solution for robotic task planning with LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Adaptive Loose Optimization for Robust Question Answering

arXiv

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arXiv 2023年

作者： Ma, Jie Wang, Pinghui Wang, Zewei Kong, Dechen Hu, Min Han, Ting Liu, Jun The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The China Mobile Research Institute China The Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalization ability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and the current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in https://***/reml-group/ALO. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Enhancing Traceability Link Recovery with Unlabeled Data

Enhancing Traceability Link Recovery with Unlabeled Data

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International Symposium on Software Reliability engineering (ISSRE)

作者： Jianfei Zhu Guanping Xiao Zheng Zheng Yulei Sui College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Laboratory of Novel Software Technology Nanjing University China School of Automation Science and Electrical Engineering Beihang University China School of Computer Science University of Technology Sydney Australia

ISBN: (数字)9781665451321

ISBN: (纸本)9781665451338

Traceability link recovery (TLR) is an important software engineering task for developing trustworthy and reliable software systems. Recently proposed deep learning (DL) models have shown their effectiveness compared to traditional information retrieval-based methods. DL often heavily relies on sufficient labeled data to train the model. However, manually labeling traceability links is time-consuming, labor-intensive, and requires specific knowledge from domain experts. As a result, typically only a small portion of labeled data is accompanied by a large amount of unlabeled data in real-world projects. Our hypothesis is that artifacts are semantically similar if they have the same linked artifact(s). This paper presents TRACEFUN, a new approach to enhance traceability link recovery with unlabeled data. TRACEFUN first measures the similarities between unlabeled and labeled artifacts using two similarity prediction methods (i.e., vector space model and contrastive learning). Then, based on the similarities, newly labeled links are generated between the unlabeled artifacts and the linked objects of the labeled artifacts. Generated links are further used for TLR model training. We have evaluated TRACEFUN on three GitHub projects with two state-of-the-art DL models (i.e., Trace BERT and TraceNN). The results show that TRACEFUN is effective in terms of a maximum improvement of F1-score up to 21% and 1,088%, respectively for Trace BERT and TraceNN.

关键词： Training Source coding Bit error rate Software systems Reliability engineering Data models Software reliability

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Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation

arXiv

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arXiv 2023年

作者： Li, Shilei Shi, Dawei Lou, Yunjiang Zou, Wulin Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China Xeno Dynamics Control Department Xeno Dynamics Co. Ltd Shenzhen518055 China

Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance simultaneously, and is optimal for a linear system with Gaussian noises. Unfortunately, The noise in the disturbance channel typically exhibits a heavy-tailed distribution because the nominal disturbance dynamics usually do not align with the practical ones. To handle this issue, we propose a generalized multi-kernel maximum correntropy Kalman filter for disturbance estimation, which is less conservative by adopting different kernel bandwidths for different channels and exhibits excellent performance both with and without external disturbance. The convergence of the fixed point iteration and the complexity of the proposed algorithm are given. Simulations on a robotic manipulator reveal that the proposed algorithm is very efficient in disturbance estimation with moderate algorithm complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Kalman filters

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

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Chinese Control Conference (CCC)

作者： Mengting Ao Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds.

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A Test Suite and An Optimizer for Dietary Nutrition Optimization Problem: From Constrained Many-Objective Perspective

A Test Suite and An Optimizer for Dietary Nutrition Optimiza...

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Congress on Evolutionary Computation

作者： Yaqi Ti Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary nutrition optimizers usually fail to give satisfactory solutions. Therefore, we construct a constrained many-objective nutrition model with more precise nutrient assessments and a scalable constrained many-objective benchmark set. This test suite has great flexibility in evaluating algorithms' performance on high dimensional search and objective spaces with some feasible region fragments. We also propose a kd-tree based dynamic constrained many-objective evolutionary algorithm to search for customized food combinations according to personal daily consumption and intake preference. Experiments show that our algorithm has better diversity maintenance ability in high dimension space.

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