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One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning

Frontiers of Information technology & Electronic Engineering 2022年第2期23卷 278-290页

作者： Minggang DONG Ming LIU Chao JING School of Information Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi key laboratory of embedded technology and intelligent system Guilin 541004China Guangxi key laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Since traditional machine learning methods are sensitive to skewed distribution and do not consider the characteristics in multiclass imbalance problems,the skewed distribution of multiclass data poses a major challenge to machine learning algorithms.To tackle such issues,we propose a new splitting criterion of the decision tree based on the one-against-all-based Hellinger distance(OAHD).Two crucial elements are included in OAHD.First,the one-against-all scheme is integrated into the process of computing the Hellinger distance in OAHD,thereby extending the Hellinger distance decision tree to cope with the multiclass imbalance problem.Second,for the multiclass imbalance problem,the distribution and the number of distinct classes are taken into account,and a modified Gini index is designed.Moreover,we give theoretical proofs for the properties of OAHD,including skew insensitivity and the ability to seek a purer node in the decision tree.Finally,we collect 20 public real-world imbalanced data sets from the Knowledge Extraction based on Evolutionary Learning(KEEL)repository and the University of California,Irvine(UCI)repository.Experimental and statistical results show that OAHD significantly improves the performance compared with the five other well-known decision trees in terms of Precision,F-measure,and multiclass area under the receiver operating characteristic curve(MAUC).Moreover,through statistical analysis,the Friedman and Nemenyi tests are used to prove the advantage of OAHD over the five other decision trees.

关键词： Decision trees Multiclass imbalanced learning Node splitting criterion Hellinger distance One-against-all scheme

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Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

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Computer systems Science & Engineering 2023年第11期47卷 2707-2724页

作者： Xinfei Li Xiaolan Xie Yigang Tang Qiang Guo Guangxi key laboratory of embedded technology and intelligent system Guilin541006China School of Information Science and Engineering Guilin University of TechnologyGuilin541006China

Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data.The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion problems.The permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling.Finally,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization ability.The Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.

关键词： Cloud resource prediction variational modal decomposition permutation entropy long and short-term neural network stacking integration

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Development of High-Speed On–Off Valves and Their Applications

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Chinese Journal of Mechanical Engineering 2022年第4期35卷 37-57页

作者： He Wang Zhen Chen Jiahai Huang Long Quan Bin Zhao key laboratory of Advanced Transducers and intelligent Control system Ministry of Education Taiyuan University of Technology

With the widespread application of the computer and microelectronic technology in the industry, digitization becomes the inevitable developing trend of the hydraulic technology. Digitization of the hydraulic components is critical in the digital hydraulic technology. High-speed on-off valves(HSVs) which convert a train of input pulses into the fast and accurate switching between the on and off states belong to widely used basic digital hydraulic elements.In some ways, the characteristics of the HSVs determine the performance of the digital hydraulic systems. This paper discusses the development of HSVs and their applications. First, the HSVs with innovative structures which is classified into direct drive valves and pilot operated valves are discussed, with the emphasis on their performance. Then, an overview of HSVs with intelligent materials is presented with considering of the switching frequency and flow capacity. Finally, the applications of the HSVs are reviewed, including digital hydraulic components with the integration of the HSVs and digital hydraulic systems controlled by the HSVs.

关键词： High-speed on-off valve Digital hydraulic component Switching frequency Flow rate Innovative structure intelligent material

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Entropy regulation in LaNbO_(4)-based fergusonite to implement high-temperature phase transition and promising dielectric properties

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Journal of Advanced Ceramics 2023年第5期12卷 1067-1080页

作者： Deqin Chen Na Yan Xuefeng Cao Fengrong Li Laijun Liu Qinghua Shen Huanfu Zhou Chunchun Li Guangxi University key laboratory of Non-ferrous Metal Oxide Electronic Functional Materials and Devices College of Material Science and EngineeringGuilin University of TechnologyGuilin 541004China Guangxi key laboratory of embedded technology and intelligent system Guilin University of TechnologyGuilin 541004China

High-entropy effect is a novel design strategy to optimize properties and explore novel materials.In this work,(La_(1/5)Nd_(1/5)Sm_(1/5)Ho_(1/5)Y_(1/5))NbO_(4)(5RNO)high-entropy microwave dielectric ceramics were successfully prepared in the sintering temperature(S.T.)range of 1210–1290℃via a solid-phase reaction route,and medium-entropy(La_(1/3)Nd_(1/3)Sm_(1/3))NbO_(4) and(La_(1/4)Nd_(1/4)Sm_(1/4)Ho_(1/4))NbO_(4)(3RNO and 4RNO)ceramics were compared.The effects of the entropy(S)on crystal structure,phase transition,and dielectric performance were evaluated.The entropy increase yields a significant increase in a phase transition temperature(from monoclinic fergusonite to tetragonal scheelite structure).Optimal microwave dielectric properties were achieved in the high-entropy ceramics(5RNO)at the sintering temperature of 1270℃for 4 h with a relative density of 98.2%and microwave dielectric properties of dielectric permittirity(ε_(r))=19.48,quality factor(Q×f)=47,770 GHz,and resonant frequency temperature coefficient(τ_(f))=–13.50 ppm/℃.This work opens an avenue for the exploration of novel microwave dielectric material and property optimization via entropy engineering.

关键词： high-entropy ceramics microwave dielectric property ion disorder far-infrared

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A Heterogeneous Information Fusion Deep Reinforcement Learning for intelligent Frequency Selection of HF Communication

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China Communications 2018年第9期15卷 73-84页

作者： Xin Liu Yuhua Xu Yunpeng Cheng Yangyang Li Lei Zhao Xiaobo Zhang Guangxi key laboratory Fund of embedded technology and intelligent system Guilin University of Technology College of Communication Engineering Army Engineering University of PLA

The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming. The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems. In this article, we adopt deep reinforcement learning to solve this intractable challenge. First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved. Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network(DQN) framework is designed, which contains multiple sub-networks to process different kinds of information. Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning(HIF-DRL) algorithm is designed for the specific frequency selection. Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection.

关键词：频率选择通讯方法学习速度信息异构熔化环境状态 HF

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Smartphone-based bridge frequency identification using vehicle contact-point response

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Earthquake Engineering and Engineering Vibration 2023年第4期22卷 1031-1043页

作者： Liu Chengyin Zhu Yipeng Zeng Qing Wu Xiaodong Shenzhen key laboratory of intelligent Structure system in Civil Engineering Harbin Institute of TechnologyShenzhen 518055China

Bridge frequency(BF)identification using the vehicle scanning method has attracted considerable attention during the last two decades.However,most previous studies have adopted unrealistic vehicle models,thus finding limited practical applications.This study proposes a smartphone-based BF identification method that uses the contact-point acceleration response of a four degree-of-freedom vehicle model.The said response can be inferred from the vehicle body response measured by a smartphone.For realizing practical applications,this method is incorporated into a self-developed smartphone app to obtain data smoothly and identify BFs in a timely manner.Numerical and experimental investigations are performed to verify the effectiveness of the proposed method.In particular,the robustness of this method is investigated numerically against various factors,including the vehicle speed,bridge span,road roughness,and bridge type.Furthermore,laboratory calibration tests are performed to investigate the accuracy of the smartphone gyroscope in measuring the angular velocity,where anomalous data are detected and eliminated.laboratory experiment results for a simply supported bridge indicate that the proposed method can be used to identify the first two BFs with acceptable accuracy.

关键词： bridge frequency(BF)identification vehicle–bridge interaction four degree-of-freedom(DOF)vehicle model contact-point response smartphone

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Energy-Efficient Multi-UAV Coverage Deployment in UAV Networks:A Game-Theoretic Framework

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China Communications 2018年第10期15卷 194-209页

作者： Lang Ruan Jinlong Wang Jin Chen Yitao Xu Yang Yang Han Jiang Yuli Zhang Yuhua Xu College of Communications Engineering Army Engineering University of PLA Key Embedded Technology and Intelligent System Laboratory College of Information Science and Engineering Guilin University of Technology

UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.

关键词：通讯网络 UAV 精力框架比赛通讯技术纳什平衡最大化

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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Computer systems Science & Engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi key laboratory of embedded technology and intelligent system Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem.Meanwhile, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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A Fine-Grained Object Detection Model for Aerial Images Based on YOLOv5 Deep Neural Network

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Chinese Journal of Electronics 2023年第1期32卷 51-63页

作者： ZHANG Rui XIE Cong DENG Liwei Heilongjiang Provincial key laboratory of Complex intelligent system and Integration School of AutomationHarbin University of Science and Technology

Many advanced object detection algorithms are mainly based on natural scenes object and rarely dedicated to fine-grained objects. This seriously limits the application of these advanced detection algorithms in remote sensing object detection. How to apply horizontal detection in remote sensing images has important research significance. The mainstream remote sensing object detection algorithms achieve this task by angle regression, but the periodicity of angle leads to very large losses in this regression method, which increases the difficulty of model learning. Circular smooth label(CSL)solved this problem well by transforming the regression of angle into a classification form. YOLOv5 combines many excellent modules and methods in recent years, which greatly improves the detection accuracy of small objects.We use YOLOv5 as a baseline and combine the CSL method to learn the angle of arbitrarily oriented targets,and distinguish the fine-grained between instance classes by adding an attention mechanism module to accomplish the fine-grained target detection task for remote sensing images. Our improved model achieves an average category accuracy of 39.2% on the FAIR1M dataset. Although our method does not achieve satisfactory results,this approach is very efficient and simple, reducing the hardware requirements of the model.

关键词： Fine-grain object detection High-resolution aerial images Oriented object detection YOLOv5

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A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization

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Frontiers of Information technology & Electronic Engineering 2020年第8期21卷 1171-1190页

作者： Ming-gang DONG Bao LIU Chao JING College of Information Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi key laboratory of embedded technology and intelligent system Guilin 541004China Guangxi key laboratory of Trusted Software Guilin University of Electronic&TechnologyGuilin 541004China

The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design,overlapping community detection,power dispatch,and unmanned aerial vehicle formation.To address such issues,current approaches focus mainly on problems with regular Pareto front rather than solving the irregular Pareto front.Considering this situation,we propose a many-objective evolutionary algorithm based on decomposition with dynamic resource allocation(Ma OEA/D-DRA)for irregular optimization.The proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto front.An evolutionary population and an external archive are used in the search process,and information extracted from the external archive is used to guide the evolutionary population to different search regions.The evolutionary population evolves with the Tchebycheff approach to decompose a problem into several subproblems,and all the subproblems are optimized in a collaborative manner.The external archive is updated with the method of rithms using a variety of test problems with irregular Pareto front.Experimental results show that the proposed algorithèm out-p￡performs these five algorithms with respect to convergence speed and diversity of population members.By comparison with the weighted-sum approach and penalty-based boundary intersection approach,there is an improvement in performance after integration of the Tchebycheff approach into the proposed algorithm.

关键词： Many-objective optimization problems Irregular Pareto front External archive Dynamic resource allocation Shift-based density estimation Tchebycheff approach

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