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arXiv 2023年

作者： Xu, Lei Yi, Xinlei Wen, Guanghui Shi, Yang Johansson, Karl H. Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Systems Science School of Mathematics Southeast University Nanjing210096 China The Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

In this paper, we study the distributed nonconvex optimization problem, aiming to minimize the average value of the local nonconvex cost functions using local information exchange. To reduce the communication overhead, we introduce three general classes of compressors, i.e., compressors with bounded relative compression error, compressors with globally bounded absolute compression error, and compressors with locally bounded absolute compression error. By integrating them, respectively, with the distributed gradient tracking algorithm, we then propose three corresponding compressed distributed nonconvex optimization algorithms. Motivated by the state-of-the-art BEER algorithm proposed in [1], which is an efficient compressed algorithm integrating gradient tracking with biased and contractive compressors, our first proposed algorithm extends this algorithm to accommodate both biased and non-contractive compressors. For each algorithm, we design a novel Lyapunov function to demonstrate its sublinear convergence to a stationary point if the local cost functions are smooth. Furthermore, when the global cost function satisfies the Polyak–Lojasiewicz (P–L) condition, we show that our proposed algorithms linearly converge to a global optimal point. It is worth noting that, for compressors with bounded relative compression error and globally bounded absolute compression error, our proposed algorithms’ parameters do not require prior knowledge of the P–L constant. The theoretical results are illustrated by numerical examples, demonstrating the effectiveness of the proposed algorithms in significantly reducing the communication burden while maintaining the convergence performance. Moreover, simulation results show that the proposed algorithms outperform the state-of-the-art compressed distributed nonconvex optimization algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Improved Classification of Benign and Malignant Breast Lesions Based on Contrast Manipulation and Gradient Map

Improved Classification of Benign and Malignant Breast Lesio...

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E-Health and Bioengineering Conference (EHB)

作者： Iulia–Nela Anghelache Nastase Simona Moldovanu Luminita Moraru The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

This study proposes an approach to enhance the contrast breast ultrasound (BUS) images with the preservation of the finer details. The main idea is to use the relevant and most important features in the classification of BUS images as benign or malignant. Among the contrast manipulation techniques, both linear under- and overexposure methods, contrast limiting adaptive histogram equalization (CLAHE) method and homomorphic filtering are used. The gradient maps of original images are generated for further analysis. Also, the gradient maps of original images are improved by the local mean subtraction and divisive normalization. Eight structural features are extracted from each gradient map image type. The extracted features feed a Random Forest (RF) classifier to determine their relevance for BUS images classification. An accuracy of 97.12% has been obtained for improved gradient map images and 93.03% for gradient map images.

关键词： Histograms Ultrasonic imaging Limiting Breast Feature extraction Lesions Standards

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Melanoma Detection using a Random Forest Algorithm

Melanoma Detection using a Random Forest Algorithm

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E-Health and Bioengineering Conference (EHB)

作者： Felicia-Anisoara Damian Simona Moldovanu Luminita Moraru The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

In recent years, statistical evidence has identified melanoma as a major cause of skin cancer deaths. In this study, we propose a skin analysis lesions based on colour features (C) and Hu (H) moments to differentiate benign (nevi) and malignant (melanoma) lesions and, a classification based on Random Forest algorithm. Colour features are found as follows. Pre-processing for hair removing using the Dull-razor method; image segmentation based on the optimal colour threshold set on each RGB colour channel (red, green, blue); 23 colour features extraction using the minimum and maximum RGB intensity and Hu moments; Random Forest (RF) algorithm to accurately differentiate benign and malignant skin lesions. This study used three datasets: 7-Point, Med-Node, PH2. RF classifier performance in terms of accuracy was: 94% for the 7-point dataset with H5, H7, H6, H2, C08 selected as relevant features; 98.1% for the Med-Node dataset and H7, H5, H6, H2, H1 ACC the relevant features; and, 96.5% for the PH2 dataset and H5, H6, H7, C03, H1 selected as relevant features, respectively. Shape features based on Hu moments are found to improve the classification performance of the skin lesions compared to the colour features.

关键词： Visualization Image segmentation Image color analysis Shape Melanoma Skin Classification algorithms

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Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors

arXiv

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arXiv 2022年

作者： Li, Zishuo Niazi, Muhammad Umar B. Liu, Changxin Mo, Yilin Johansson, Karl H. Department of Automation Tsinghua University Beijing100084 China Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States

This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. In other words, at each time, the attacker has the freedom to choose an arbitrary set of no more that p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observer for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. © 2022, CC BY-NC-ND.

关键词： Fault detection

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Swarm intelligent algorithm for re-entrant hybrid flow shop scheduling problems

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International Journal of Simulation and Process Modelling 2019年第1期14卷 17-27页

作者： Han, Zhonghua Tian, Xutian Dong, Xiaoting Xie, Fanyi Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China Department of Digital Factory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Liaoning China Faculty of Electrical Engineering Sichuan College of Architectural Technology Deyang Sichuan China

In order to solve re-entrant hybrid flowshop (RHFS) scheduling problems and establish simulations and processing models, this paper uses wolf pack algorithm (WPA) as global optimisation. For local assignment, it takes minimum remaining time rule. Scouting behaviours of wolf are changed in former optimisation by means of Levy flight, extending searching ranges and increasing rapidity of convergence. When it comes to local extremum of WPA, dynamic regenerating individuals with high similarity adds diversity. Hamming distance is used to judge individual similarity for increased quality of individuals, enhanced search performance of the algorithm in solution space and promoted evolutionary vitality. A painting workshop in a bus manufacture enterprise owns typical features of re-entrant hybrid flowshop. Regarding it as the algorithm applied target, this paper focuses on resolving this problem with dynamic wolf pack algorithm based on levy flight (LDWPA). Results show that LDWPA can solve re-entrant hybrid flowshop scheduling problems effectively. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Hamming distance

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A Combined Alignment Method for Strapdown Inertial Navigation System on Stationary Base

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Journal of Harbin Institute of technology(New Series) 2020年第6期27卷 57-71页

作者： Shuai Chen Xinzhi Liu Qian Sun Ya Zhang School of Information and Control Engineering Liaoning Shihua UniversityFushun 113001LiaoningChina Department of Applied Mathematics University of WaterlooWaterloo N2L3G1Canada College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Electrical Engineering&Automation Harbin Institute of TechnologyHarbin 150001China

Owing to the weak observability of the azimuth misalignment angle,alignment accuracy and time are always the contradictory issues in the initial alignment process of Strapdown Inertial Navigation System(SINS),which requires a compromise between *** this paper,a combined alignment mechanism is proposed to construct an observable and controllable system model,which can effectively achieve higher azimuth alignment accuracy during the fixed time ***,the Reduced Order Kalman Filter(ROKF)alignment algorithm was utilized to calculate the misalignment angles in parallel with the classical gyrocompass alignment ***,the misalignment angles calculated by the gyrocompass alignment method were used to formulate the augmented measurement model with zero velocity ***,the zero velocity model of the ROKF method was switched into the augmented measurement model when the azimuth misalignment angle of the gyrocompass alignment method was close to steady *** combined alignment method was analyzed reasonably by the observability and the mathematical *** comparison results of the numerical simulation and the experimental data test showed that the combined method had good performance in terms of estimation accuracy and consistency of the alignment results.

关键词： inertial navigation instrumentation and measurement gyroscopes accelerometers Kalman filter measurement errors

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Integrating backstepping control of outdoor quadrotor UAVs

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica electrical engineering and computer science 2019年第2期63卷 122-132页

作者： Bodó, Zsófia Lantos, Béla Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Magyar Tudósok krt. 2 BudapestH-1117 Hungary

In this paper an improved approach is presented for integrating backstepping control of outdoor quadrotor UAVs. The controller uses the approximated nonlinear dynamic model, while for simulation or test purposes the quadrotor can be modeled either with the precise or the simplified model. A hierarchical integrating backstepping control algorithm was constructed that has the capability of handling every effect in the dynamic model and in the meantime successfully ignores the realistic measurement noises. The hierarchical control structure consists of position, attitude and rotor control, extended with path design with continuous acceleration and/or continuous jerk. The state estimation is based on sensor fusion. control parameters can be easily tuned. Adaptive laws are elaborated for mass and vertical disturbance force estimation. The tracking algorithm is able to follow the prescribed path with small error. The sensory system and the state estimation are prepared for outdoor applications. The embedded control system contains a HIL extension to test the control algorithms before the first flight under real time conditions. © 2019 Budapest University of technology and Economics. All rights reserved.

关键词： State estimation

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A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

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Computer Modeling in engineering & Sciences 2020年第11期125卷 579-596页

作者： Xing Deng Haijian Shao Liang Shi Xia Wang Tongling Xie School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212003China School of Information Science and Technology Nantong UniversityNantong226019China Department of Electrical and Computer Engineering National University of Singapore117583Singapore The People’s Hospital of RUGAO Nantong226500China

The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff *** of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation *** this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of *** benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.

关键词： COVID-19 detection deep learning transfer learning pre-trained models

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Recent advances in neural network-based inverse modeling techniques for microwave applications

Recent advances in neural network-based inverse modeling tec...

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作者： Jin, Jing Feng, Feng Na, Weicong Yan, Shuxia Liu, Wenyuan Zhu, Lin Zhang, Qi-Jun School of Microelectronics Tianjin University Tianjin China Department of Electronics Carleton University Ottawa Canada Faculty of Information Technology Beijing University of Technology Beijing China School of Electronics and Information Engineering Tiangong University Tianjin China College of Electrical and Information Engineering Shaanxi University of Science and Technology Xi'an China School of Control and Mechanical Engineering Tianjin Chengjian University Tianjin China

Inverse modeling of microwave components plays an important role in microwave design and diagnosis or tuning. Since the analytical function or formula of the inverse input-output relationship does not exist and is difficult to obtain, artificial neural network (ANN) becomes an efficient tool to develop inverse models for microwave components. This paper provides an overview of recent advances in neural network-based inverse modeling techniques for microwave applications. We review two different shallow neural network-based inverse modeling techniques, including the comprehensive neural network inverse modeling methodology and the multivalued neural network inverse modeling technique. Both techniques address the problem of nonuniqueness in inverse modeling. We also provide an overview of recently developed hybrid deep neural network modeling technique and the application to inverse modeling. For the inverse modeling problem with high-dimensional inputs, the relationship between the inputs and the outputs of the inverse model will become more complicated and the inverse modeling problem will become harder. The deep neural network becomes a practical choice. The hybrid deep neural network structure is presented. The recently proposed activation function, specifically for microwave application, and a three-stage deep learning algorithm for training the hybrid deep neural network are reviewed. © 2020 John Wiley & Sons, Ltd

关键词： Deep neural networks

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