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A novel Ba 4 (La 1/5 Pr 1/5 Nd 1/5 Sm 1/5 Eu 1/5 ) 28/3 Ti 18 O 54 high-entropy microwave dielectric ceramic with tungsten bronze-type and high-permittivity

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MATERIALS TODAY COMMUNICATIONS 2024年 40卷

作者： Wang, Geng Li, Zeping Xiong, Gang Wang, Huifeng Fu, Qiuyun Hubei Univ Sci & Technol Sch Elect & Informat Engn Xianning 437100 Peoples R China Huazhong Univ Sci & Technol Engn Res Ctr Funct Ceram Sch Integrated Circuits Minist Educ Wuhan 430074 Peoples R China Hubei Univ Sci & Technol Key Lab Photoelect Sensing & Intelligent Control Xianning 437100 Peoples R China

Ba 4 (La 1/5 Pr 1/5 Nd 1/5 Sm 1/5 Eu 1/5 ) 28/3 Ti 18 O 54 (BLPNSET) high-entropy microwave dielectric ceramic was designed and synthesized utilizing solid-state reaction route. Single-phase tungsten bronze-type BLPNSET ceramic with Pbnm space group achieved over the sintering temperature of 1350-1500 degrees C verified by X-ray diffraction, Raman scattering spectroscopy, and Rietveld refinement. The dielectric characteristics were highly correlated with density, polarizability, packing fraction, and tolerance factor. The exploration of infrared reflectance (IR) spectrum for BLPNSET ceramic indicated that infrared vibrational modes associated with the A-site cation translation at low frequencies provided the majority dielectric contribution. The optimum microwave dielectric characteristics with a high-permittivity (epsilon r epsilon r = 85.34), a high-quality factor (Qxf Q x f =10,456 GHz), and a TCF = +26.8 ppm/degrees C degrees C were obtained in BLPNSET ceramic sintered at 1400 degrees C. This study provided a paradigm for tuning of dielectric characteristics of tungsten bronze-type ceramic via high-entropy strategy.

关键词： High-entropy ceramic Tungsten bronze-type Microwave dielectric characteristics Infrared reflectance spectrum

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Event-triggered asynchronous distributed optimization algorithm with heterogeneous time-varying step-sizes

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NEURAL COMPUTING & APPLICATIONS 2020年第10期32卷 6175-6184页

作者： Xie, Tangtang Chen, Guo Liao, Xiaofeng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

This paper concerns distributed convex optimization problems over time-varying undirected graphs, in which the global objective function is expressed as the sum of individual objective functions of the agents. Each agent only knows its local objective functions. To figure out such problems, an event-triggered asynchronous distributed optimization algorithm (termed as EV-ADOA) with time-varying heterogeneous step-sizes is proposed, which is suitable for undirected graphs changing over time. Under two standard assumptions on strongly convex and smoothness of local objective functions, the EV-ADOA can achieve linear convergence with a proper upper bound of the heterogeneous time-varying step-sizes. EV-ADOA with event-triggered scheme can decrease network communication, and the Zeno-like behavior strictly is excluded. The efficiency of EV-ADOA is demonstrated by experiments.

关键词： Distributed optimization Asynchronous algorithm Event-triggered scheme Heterogeneous time-varying step-sizes Linear convergence

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Decentralized Dual Proximal Gradient Algorithms for Non-Smooth Constrained Composite Optimization Problems

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第10期32卷 2594-2605页

作者： Li, Huaqing Hu, Jinhui Ran, Liang Wang, Zheng Lu, Qingguo Du, Zhenyuan Huang, Tingwen Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Chongqing Univ Coll Comp Chongqing 400044 Peoples R China Texas A&M Univ Qatar Sci Program Doha 23874 Qatar

Decentralized dual methods play significant roles in large-scale optimization, which effectively resolve many constrained optimization problems in machine learning and power systems. In this article, we focus on studying a class of totally non-smooth constrained composite optimization problems over multi-agent systems, where the mutual goal of agents in the system is to optimize a sum of two separable non-smooth functions consisting of a strongly-convex function and another convex (not necessarily strongly-convex) function. Agents in the system conduct parallel local computation and communication in the overall process without leaking their private information. In order to resolve the totally non-smooth constrained composite optimization problem in a fully decentralized manner, we devise a synchronous decentralized dual proximal (SynDe-DuPro) gradient algorithm and its asynchronous version (AsynDe-DuPro) based on the randomized block-coordinate method. Both SynDe-DuPro and AsynDe-DuPro algorithms are theoretically proved to achieve the globally optimal solution to the totally non-smooth constrained composite optimization problem relied on the quasi-Fejer monotone theorem. As a main result, AsynDe-DuPro algorithm attains the globally optimal solution without requiring all agents to be activated at each iteration and thus is more robust than most existing synchronous algorithms. The practicability of the proposed algorithms and correctness of the theoretical findings are demonstrated by the experiments on a constrained Decentralized Sparse Logistic Regression (DSLR) problem in machine learning and a Decentralized Energy Resources Coordination (DERC) problem in power systems.

关键词： Optimization Signal processing algorithms Power systems Machine learning Machine learning algorithms Linear programming Multi-agent systems Convex optimization synchronous and asynchronous decentralized algorithms multi-agent systems non-smooth constrained composite optimization problems decentralized machine learning DSLR problems DERC problems

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Distributed Robust Algorithm for Economic Dispatch in Smart Grids Over General Unbalanced Directed Networks

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IEEE TRANSACTIONS ON INDUSTRIAL informatICS 2020年第7期16卷 4322-4332页

作者： Li, Huaqing Wang, Zheng Chen, Guo Dong, Zhao Yang Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

The increased complexity of modern energy network raises the necessity of flexible and reliable methods for smart grid operation. To this end, this article is centered on the economic dispatch problem (EDP) in smart grids, which aims at scheduling generators to meet the total demand at the minimized cost. This article proposes a fully distributed algorithm to address the EDP over directed networks and takes into account communication delays and noisy gradient observations. In particular, the rescaling gradient technique is introduced in the algorithm design and the implementation of the distributed algorithm only resorts to row-stochastic weight matrices, which allows each generator to locally allocate the weights on the messages received from its in-neighbors. It is proved that the optimal dispatch can be achieved under the assumptions that the nonidentical constant communication delays inflicting on each link are uniformly bounded and the noises embroiled in gradient observation of every generator are bounded variance zero mean. Simulations are provided to validate and testify the effectiveness of the presented algorithm.

关键词： Generators Distributed algorithms Smart grids Delays Cost function Economics Communication delay distributed algorithm directed networks economic dispatch noisy gradient

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A Machine Learning Methodology for Diagnosing Chronic Kidney Disease

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IEEE ACCESS 2020年 8卷 20991-21002页

作者： Qin, Jiongming Chen, Lin Liu, Yuhua Liu, Chuanjun Feng, Changhao Chen, Bin Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing 400715 Peoples R China Kyushu Univ Grad Sch Informat Sci & Elect Engn Dept Elect Fukuoka 8190395 Japan

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. Since there are no obvious symptoms during the early stages of CKD, patients often fail to notice the disease. Early detection of CKD enables patients to receive timely treatment to ameliorate the progression of this disease. Machine learning models can effectively aid clinicians achieve this goal due to their fast and accurate recognition performance. In this study, we propose a machine learning methodology for diagnosing CKD. The CKD data set was obtained from the University of California Irvine (UCI) machine learning repository, which has a large number of missing values. KNN imputation was used to fill in the missing values, which selects several complete samples with the most similar measurements to process the missing data for each incomplete sample. Missing values are usually seen in real-life medical situations because patients may miss some measurements for various reasons. After effectively filling out the incomplete data set, six machine learning algorithms (logistic regression, random forest, support vector machine, k-nearest neighbor, naive Bayes classifier and feed forward neural network) were used to establish models. Among these machine learning models, random forest achieved the best performance with 99.75% diagnosis accuracy. By analyzing the misjudgments generated by the established models, we proposed an integrated model that combines logistic regression and random forest by using perceptron, which could achieve an average accuracy of 99.83% after ten times of simulation. Hence, we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis.

关键词： Chronic kidney disease machine learning KNN imputation integrated model

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Respiratory signal and human stress: non-contact detection of stress with a low-cost depth sensing camera

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第8期11卷 1825-1837页

作者： Shan, Yuhao Li, Shigang Chen, Tong Southwest Univ Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing 400715 Peoples R China Hiroshima City Univ Sch Informat Sci Hiroshima 7313194 Japan Chongqing Key Lab Artificial Intelligence & Serv Chongqing 400715 Peoples R China

Psychological stress may cause various health problems. To prevent the potential chronic illness that long-term psychological stress could cause, it is important to detect and monitor the psychological stress at its initial stage. In this paper, we present a framework for remotely detecting and classifying human stress by using a KINECT sensor that is portable and affordable enough for ordinary users in everyday life. Unlike most of emotion recognition tasks in which respiratory signals (RSPS) are usually used only as an aiding analysis, the whole task proposed is based on RSPS. Thus, the main contribution of this paper is that not only the non-contact devices is used to identify human stress, but also the relationship between RSPS and stress recognition is analyzed in detail. Experimental results on 84 volunteers show that the recognition accuracy for recognizing psychological stress, physical stress, and relaxing state are 93.90%, 93.40%, and 89.05% respectively. These results suggest that the proposed framework is effective for monitoring human stress, and RSPS could be used for stress recognition.

关键词： Stress detection Stress classification Remote sensing respiration signal Physiological features of stress

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Triboelectric nanogenerator based on Teflon/vitamin B1 powder for self-powered humidity sensing

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BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2020年 11卷 1394-1401页

作者： Zhang, Liangyi Li, Huan Xie, Yiyuan Guo, Jing Zhu, Zhiyuan Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China Southwest Univ Coll Elect & Informat Engn Key Lab Networks & Cloud Comp Secur Univ Chongqin Chongqing Peoples R China Zhejiang Univ Ocean Coll Hangzhou Zhejiang Peoples R China

Recently, there has been growing interest in triboelectric nanogenerators (TENGs) that can effectively convert various forms of mechanical energy input into electrical energy. In the present study, a novel Teflon/vitamin B1 powder based triboelectric nanogenerator (TVB-TENG) is proposed. Paper is utilized as a supporting platform for triboelectrification between a commercial Teflon tape and vitamin B1 powder. The measured open-circuit voltage was approximately 340 V. The TVB-TENG can be applied as a humidity sensor and exhibits a linear and reversible response to the relative humidity of the environment. Moreover, the change in relative humidity is also indicated by the change in luminosity of a set of light-emitting diodes (LEDs) integrated in the TVB-TENG system. The TVB-TENG proposed in this study illustrates a cost-effective method for portable power supply and sensing devices.

关键词： humidity sensor self-powered system triboelectric nanogenerators (TENGs) triboelectrification vitamin B1

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A two-layer algorithm based on PSO for solving unit commitment problem

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SOFT COMPUTING 2020年第12期24卷 9161-9178页

作者： Zhai, Yu Liao, Xiaofeng Mu, Nankun Le, Junqing Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China

It is well known that electric generators consume huge amounts of energy every year. Nowadays, research for the unit commitment problem (UCP) has become a very important task in a power plant. However, the existing optimal methods for solving UCP are very easy to fall into local optimum, resulting in poor performance. Moreover, as no separate layering of economic load distribution, the existing algorithms are very inefficient. Toward this end, a new algorithm named improved simulated annealing particle swarm optimization (ISAPSO) is proposed in this paper. The proposed algorithm consists of a two-layer structure which is designed to simplify the complex problem of UCP. Specifically, in the upper layer, the algorithm based on elitist strategy PSO and SA is much easier to jump out of the local optimum when solving UCP and thus gets a better solution. In the lower layer, convex optimization approach is used to improve the search efficiency of ISAPSO. Furthermore, several methods are also designed to solve the problem-related constraints, which can save a lot of computing resources. Finally, the experimental results show that the cost performance of ISAPSO is better than that of the existing algorithms.

关键词： Unit commitment problem Economic load distribution Particle swarm optimization Simulated annealing algorithm

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Offset or Onset Frame: A Multi-Stream Convolutional Neural Network with CapsuleNet Module for Micro-expression Recognition 5

Offset or Onset Frame: A Multi-Stream Convolutional Neural N...

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5th International Conference on intelligent informatics and Biomedical Sciences (ICIIBMS)

作者： Liu, Nian Liu, Xinyu Zhang, Zhihao Xu, Xueming Chen, Tong Southwest Univ Chongqing Key Lab Nonlinear Circuit & Intelligent Chool Elect & Informat Engn Chongqing 400715 Peoples R China

ISBN: (纸本)9781728180229

Micro-expression is a spontaneous facial expression, which may reveal people's real emotions. The micro-expression recognition has recently attracted much attention in psychology and computer vision community. In this paper, we designed a multi-stream Convolutional Neural Network (CNN) combined with the Capsule Network(CapsNet) module,named CNNCapsNet, to improve the performance of micro-expression recognition. Firstly, both vertical and horizontal optical flow are computed from the onset to the apex, and from the apex to the offset frame respectively, which is the first time that the offset frame information has been taken into account in the field of micro-expression recognition. Secondly, these four optical flow images and the grayscale image of apex frame are input into the five-stream CNN model to extract features. Finally, CapsNet completes micro-expression recognition by learning the features extracted by CNN. The method proposed in this paper are evaluated using the Leave-One-Subject-Out (LOSO) cross-validation protocol on CASME II. The results show that the offset information, which is often neglected, is more important than onset information for the recognition task. Our CNNCapsNet framework can achieve the accuracy of 64.63% for the five-class micro-expression classification.

关键词： micro-expression recognition CNN CapsNet deep learning optical flow

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Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape

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DRONES 2024年第11期8卷 642-642页

作者： Zhu, Hongyan Liang, Shikai Lin, Chengzhi He, Yong Xu, Jun-Li Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin 541004 Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Integrated Circuits & Microsyst Guilin 541004 Peoples R China Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Peoples R China Univ Coll Dublin UCD Sch Biosyst & Food Engn Dublin Ireland

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm-partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: Rpre = 0.8143, RMSEP = 171.9 kg/hm2) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: Rpre = 0.7655, RMSEP = 188.3 kg/hm2) from a multispectral camera. The best models for yield prediction were found by applying BPNN (Rpre = 0.8114, RMSEP = 172.6 kg/hm2) built from optimal EWs and ELM (Rpre = 0.8118, RMSEP = 170.9 kg/hm2) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture.

关键词： data fusion machine learning algorithms oilseed rape remote sensing unmanned aerial vehicle (UAV) yield prediction

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