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A Novel Method for Identification of Glutarylation Sites Combining Borderline-SMOTE With Tomek Links Technique in Imbalanced Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第5期19卷 2632-2641页

作者： Ning, Qiao Zhao, Xiaowei Ma, Zhiqiang Dalian Maritime Univ Dept Informat Sci & Technol Dalian 116026 Liaoning Peoples R China Northeast Normal Univ Sch Informat Sci & Technol Changchun 130117 Jilin Peoples R China

Glutarylation is a type of post-translational modification that occurs on lysine residues. It plays an irreplaceable role in various cellular functions. Therefore, identification of glutarylation sites is significant for understanding the molecular mechanism of glutarylation. In this study, we proposed a method named DEXGB_Glu to identify lysine glutarylation sites using XGBoost as classifier which was optimized by differential evolution algorithm. Aiming at the imbalance between positive samples and negative samples, Borderline-SMOTE method was employed to synthesize positive samples, increasing their amount equal to negative samples. Then, Tomek links technique was applied to filter out noise data. Analysis of this method and its results showed that differential evolution algorithm obviously improved the performance and the combination of Borderline-SMOTE and Tomek links effectively solved the imbalance between positive samples and negative samples. Finally, the performance of this method was much better than other methods in prediction of glutarylation sites. The data and code are available on https://***/ningq669/DEXGB_Glu.

关键词： Proteins Amino acids Sociology Encoding Predictive models Prediction algorithms Peptides Glutarylation borderline-SMOTE tomek links XGBoost differential evolution algorithm

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Optimal PV system capacity ratio and power limit value selection based on a novel fast calculation method of IGBT junction temperature and IGBT annual damage analysis

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ENERGY REPORTS 2022年 8卷 348-355页

作者： Zhang, Bo Gao, Yuan Li, Tiecheng Hu, Xuekai Yang, Enze North China Elect Power Univ Key Lab Distributed Energy Storage & Microgrid He Baoding 071003 Peoples R China Hebei Elect Power Res Inst Shijiazhuang 050021 Hebei Peoples R China State Grid China Xiong An Power Supply Co Hebei Prov Xiongan 071000 Hebei Peoples R China

In order to solve the problem of long calculation time of insulated gate bipolar transistor (IGBT) junction temperature, the XGBoost machine learning algorithm is used to calculate IGBT junction temperature in the annual damage assessment process. The XGBoost machine learning algorithm can greatly reduce the calculation time of IGBT junction temperature while ensuring the accuracy, which provides conditions for finding the optimal PV system capacity ratio and power limit value by heuristic algorithm such as differential evolution algorithm later. For PV system capacity ratio and power limit, it is necessary to consider the annual damage of the PV inverter, the increase of power generation due to capacity ratio and the power generation loss due to power limit. This paper proposes an optimization goal that considers the above factors, and uses the differential evolution algorithm to obtain the optimal PV system capacity ratio and power limit value. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： XGBoost machine learning algorithm Capacity ratio Power limit value differential evolution algorithm Annual damage

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MODECP: A Multi-Objective Based Approach for Solving Distributed Controller Placement Problem in Software Defined Network

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SENSORS 2022年第15期22卷 5475-5475页

作者： Liao, Chenxi Chen, Jia Guo, Kuo Liu, Shang Chen, Jing Gao, Deyun Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

Software-Defined Network is an emerging networking paradigm that enables intelligent and flexible network management. Specifically, the design of the control plane is crucial. Therefore, in order to avoid a single point of failure, multiple controllers are deployed constantly in a distributed manner on the control plane. In this paper, we propose a controller placement approach based on multiple objectives (MODECP), including network delay, network security, load-balancing rate, and link occupancy. In the controller placement stage, an improved multi-objective differential evolution algorithm is proposed to search for controllers' positions and assign switches to controllers reasonably. Furthermore, an improved affinity propagation algorithm is proposed to obtain the number of controllers placed in the network partition stage, comprehensively considering the delay, node security, and load. Simulations are performed based on several topologies from Internet Topology Zoo. Extensive results show that the proposed algorithm can realize trade-offs among multiple objectives and improve network performance in delay, security, controller load, and link occupancy compared to the single-objective based approach. Moreover, compared with the genetic algorithm and random placement algorithm, the proposed algorithm performs better with low latency, high security, low load rate, and low link overhead.

关键词： software-defined network distributed controller placement multi-objective differential evolution algorithm

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A unified and simple control strategy for a class of n-link vertical underactuated manipulator

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ISA TRANSACTIONS 2022年第PartA期128卷 198-207页

作者： Wang, Lejun Lai, Xuzhi Zhang, Pan Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Hubei Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Hubei Peoples R China

In this paper, we propose a unified and simple trajectory planning-based control strategy for the n-link (n >= 2 boolean AND n is an element of Z) vertical underactuated manipulator (VUM) with an underactuated joint. The proposed method does not need to partition the entire motion space like the traditional methods, and can quickly realize the control objective of swinging the endpoint of the system up from the vertical downward starting position and stabilizing it at the vertical upward ending position. The trajectory planned for each active link includes two stages. The first stage is to make the first active link and passive link reach the intermediate states, and other active links reach the ending states. The second stage is to move the first active link and the passive link to the ending states, while other active links keep at the ending states. Considering the above targets of two stages, the trajectory of each active link is designed and the trajectory parameters are optimized via utilizing the differential evolution algorithm. Then, the tracking controllers and stabilization controllers are designed to track the two-stage trajectories and to stay the endpoint at the ending position. Finally, the numerical simulations are carried out to show the feasibility and superiority of the proposed method.

关键词： n-link VUM with an underactuated joint Trajectory planning differential evolution algorithm Controller design

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Trajectory optimization of multistage launch vehicle based on an improved differential evolution-pseudo spectral method

Trajectory optimization of multistage launch vehicle based o...

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第35届中国控制与决策会议

作者： Yunqing Tang Zhuo Xiang Xiangyu Du Bo Wang Lei Liu Huijin Fan National Key Laboratory of Science and Technology on Multispectral Information Processing.School of Artificial Intelligence and Automation. Huazhong University of Science and Technology

This paper studies the orbital flight phase trajectory optimization of multistage launch vehicle based on an improved differential evolution(DE)-pseudo spectral method(PSM).PSM is a classic method which solves the trajectory optimization problem with a high precision and a fast convergence under the condition of good initial ***,the free motion of launch vehicle in the sliding phase usually causes bad initial state which will greatly affect the convergence and accuracy of *** solve the problem,DE algorithm is adopted by employing its high search ***,the cost function of the optimization problem is adjusted for the orbital flight phase which make it more suitable for the requirements of orbital *** adaptive operator of the mutation process in DE is adjusted to avoid falling into local optimum,and the fitness function is improved with the orbital *** PSM shows the advantages of high accuracy and good convergence with good initial guess searched by *** comparing the simulation results,it is verified that the proposed algorithm can improve the accuracy of the launch vehicle in the orbital flight phase.

关键词： Trajectory optimization differential evolution algorithm Pseudo spectral method Multistage launch vehicle

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Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第11期13卷 346-356页

作者： Bai Ya-meng Wang Yang Wu Shen-shen Jiaozuo Univ Sch Informat Engn Jiaozuo 412000 Henan Peoples R China

The analysis of human movement has attracted the attention of many scholars of various disciplines today. The purpose of such systems is to perceive human behavior from a sequence of video images. They monitor the population to find common properties among pedestrians on the scene. In video surveillance, the main purpose of detecting specific or malicious events is to help security personnel. Different methods have been used to detect human behavior from images. This paper has used an efficient computational algorithm for detecting anomalies in video images based on the combined approach of the differential evolution algorithm and cellular neural network. In this method, the input image's gray-level image is first generated. Because it may be possible to identify several large areas in the image after the threshold, the largest white area is selected as the target area. The images are then used to remove noise, smooth the image, and fade the morphology. The results showed that the proposed method has higher speed and accuracy than other methods. The advantage of the algorithm is that it has a runtime of three seconds on a home computer, and the average sensitivity criterion is 98.6% (97.2%).

关键词： Cellular neural network detection of abnormalities differential evolution algorithm video images

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Artificial immune algorithm-sparrow search algorithm and its application in network intrusion detection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 5001-5011页

作者： Liu, Jianhua Wang, Zhiheng Xian Univ Posts & Telecommun Sch Comp Sci Xian 710000 Shaanxi Peoples R China

In order to solve the problem that the population diversity of sparrow search algorithm (SSA) decreases and easily falls into the local optimal solution when it approaches the global optimal, an artificial immune algorithm-sparrow search algorithm (AIA-SSA) is proposed in this paper by combining artificial immune algorithm and sparrow search algorithm. This paper uses 10 benchmark functions for experimental simulation of AIA-SSA algorithm, and compares it with five widely used intelligent algorithms and SSA. Experimental results show that AIA-SSA overcomes the deficiency of SSA and improves the search accuracy, convergence speed and stability of the algorithm. Meanwhile, this paper applies AIA-SSA to network intrusion detection and constructs a network intrusion detection model based on support vector machine (SVM). After testing, the accuracy of AIA-SSA-SVM prediction for various network attacks has been greatly improved. It not only shows that AIA-SSA-SVM has a broad application prospect in the field of network security, but also verifies the feasibility and advanced nature of AIA-SSA in solving practical engineering problems.

关键词： Intrusion detection SSA intelligence algorithm adaptive search differential evolution algorithm

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A Resource Allocation Scheme for Joint Optimizing Energy Consumption and Delay in Collaborative Edge Computing-Based Industrial IoT

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第9期18卷 6236-6243页

作者： Jin, Zilong Zhang, Chengbo Jin, Yuanfeng Zhang, Lejun Su, Jian Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Engn Res Ctr Digital Forens Minist Educ Nanjing 210044 Peoples R China Yanbian Univ Dept Math Jilin 133002 Jilin Peoples R China Yangzhou Univ Coll Informat Engn Yangzhou 225127 Jiangsu Peoples R China

Attributable to the emergence of mobile edge computing (MEC), the hardware-constrained industrial devices have further computational and service capability in industrial Internet of Things (IIoT) systems. Nevertheless, unreliable network environments and unpredictable processing delays are intolerable factors for any service application. Therefore, this article studies the associated constraint problem of how to optimize the offloading decision and resource allocation in collaborative edge computing networks with multiple IIoT devices and MEC servers. In order to attain this purpose, the optimization problem is mathematically derived as a mixed-integer nonlinear programming problem which is a large-scale NP-hard problem. Then, an improved differential evolution algorithm (IDE) is proposed to obtain the optimal solutions in an accessible time complexity. Finally, the performance of the IDE-based resource allocation scheme has been compared with other baseline schemes. Simulation results demonstrate that the IDE-based optimization scheme could significantly reduce the system delay and energy consumption.

关键词： Task analysis Delays Industrial Internet of Things Servers Energy consumption Resource management Computational modeling Collaborative edge computing differential evolution algorithm Internet of Things (IoT) offloading decision resource allocation

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evolution Performance of Symbolic Radial Basis Function Neural Network by Using evolutionary algorithms

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Computer Systems Science & Engineering 2023年第10期47卷 1163-1184页

作者： Shehab Abdulhabib Alzaeemi Kim Gaik Tay Audrey Huong Saratha Sathasivam Majid Khan bin Majahar Ali Faculty of Electrical and Electronics Engineering Universiti Tun Hussein Onn MalaysiaParit Raja86400JohorMalaysia School of Mathematical Sciences Universiti Sains Malaysia USM11800PenangMalaysia

Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally *** paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability *** by evolutionary algorithms,which can iteratively find the nearoptimal solution,different evolutionary algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic algorithm(GA),evolution Strategy algorithm(ES),differential evolution algorithm(DE),and evolutionary Programming algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming *** the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the *** has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.

关键词： Satisfiability logic programming symbolic radial basis function neural network evolutionary programming algorithm genetic algorithm evolution strategy algorithm differential evolution algorithm

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A Consecutive Control Strategy Based on Quadratic Differentiable Trajectory for Plane Multi-DoF Underactuated Manipulator with Last Passive Joint 6

A Consecutive Control Strategy Based on Quadratic Differenti...

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IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Huang Zixin Hou Mengyu Wei Ziang Wang Wei Hua Yong Wang Lejun Wuhan Text Univ Sch Elect & Informat Engn Wuhan Inst Technol Hubei Key Lab Digital Text Equipment Wuhan Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Complex Syst & Bion Control Chongqing Peoples R China

ISBN: (纸本)9798350311259

This paper puts forward a consecutive control approach depending on quadratic differentiable trajectory, aims at dealing with the control problem of plane Multi-DoF underactuated manipulator system. This control approach can control both active links and passive link move from initial status to their target status. Firstly, the Multi-DoF underactuated manipulator's dynamic model is constructed, and depending on it, the special system characteristics can be obtained. Next, the quadratic differentiable trajectories are devised for each active link, which are used to compel active links arrive at target states. By adopting the differential evolution algorithm (DEA), the optimal trajectory parameters can be obtained, thus, the underactuated linkage can also be controled to its target status. Then, the controllers are devised to compel all active linkages track the motion trajectory accurately. In the end, the corresponding simulations are carried out to demonstrate the feasibility and superiority of the presented approach.

关键词： Plane multi-DoF underactuated manipulator Trajectory designing Tracking control differential evolution algorithm

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