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Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

Nano-Micro Letters 2018年第1期10卷 12-19页

作者： Xi Yin Jing Yang Feng Xiao Yang Yang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Computer Science Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.

关键词： Transmembrane a-helices Structure prediction Machine learning Contact map prediction Relative accessible surface area

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Brain Storming Optimization Algorithm for Heating Dispatch Scheduling of Thermal Power Plant 29

Brain Storming Optimization Algorithm for Heating Dispatch S...

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第29届中国控制与决策会议

作者： Lu Kang Yali Wu Xinrui Wang Xiaohua Feng School of Automation and Information Engineering Xi'an University of Technology Shannxi Province Key Laboratory of Complex System Control and Intelligent Information Processing School of Marine Science and Technology Northwestern Polytechnical University

ISBN: (纸本)9781509046584

In the traditional scheduling mode of the current thermal power plants,only the power load distribution of the unit was *** there was no enough guidance to the heat source *** can not meet the current energy-saving and emission-reduction *** this paper,an optimal heat load dispatching model is set up under the premise of power load distribution of thermal power *** kinds of heat loads including to high-pressure,medium-pressure and low-pressure are taken into account in the scheduling *** to the characteristics of the problem,Brain Storming Optimization(BSO) algorithm is used to get the optimal distribution *** comparison with the original scheme shows the correctness of the model and the optimization ***,the optimization ability and effectiveness of brain storming optimization algorithm in solving complex optimization problems are verified by comparing the proposed algorithm with the other different optimization algorithms,such as Particle Swarm Optimization(PSO) and Differential Evolution(DE) algorithm.

关键词： Heating Dispatching Brain Storming Optimization Algorithm Load Distribution

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Optimization design of superconducting cusp magnetic field structure 13

Optimization design of superconducting cusp magnetic field s...

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13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

作者： Shangbin, Jiao Dongbin, Hou Ding, Liu Weichao, Huang Natl. and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian710048 China

ISBN: (纸本)9781538637579

Based on the analysis of the Cusp magnetic field suppression convection principle, the simulation model of superconducting Cusp magnetic field is built by the finite element method. In the case of satisfying the magnetic field strength of the crucible wall and giving the excitation current, the number of layers of the superconducting coil is calculated by using the established model. The influence of coil spacing, thickness of shielding thickness, horizontal distance between coil and shielding thickness and longitudinal distance on magnetic induction strength, horizontal distance and longitudinal distance between coil and the shielding are analyzed under the condition that the number of superconducting coils is constant. The influence of the force on the coil, the influence of the thickness of the shielding body on the magnetic leakage strength of the magnetic field. The simulation results show that the optimized Cusp magnetic field structure parameters can be used in the actual design. © 2018 IEEE.

关键词： Finite element method

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Cognitive Navigation for intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1933-1943页

作者： Qiming Liu Xinru Cui Zhe Liu Hesheng Wang Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Laboratory of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong UniversityShanghai 200240China

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory *** introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity *** tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual ***,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy *** from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory *** validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

关键词： Graph neural networks(GNNs) spatial memory topological map visual navigation

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Early Forest Fire Recognition Method Based on C-GhostNet Network 5

Early Forest Fire Recognition Method Based on C-GhostNet Net...

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5th International Symposium on Autonomous systems, ISAS 2022

作者： Li, Mengna Zhang, Youmin Mu, Lingxia Xin, Jing Yu, Ziquan Jiao, Shangbin Liu, Han Xie, Guo Yi, Yingmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Shaanxi Xi'an710048 China Concordia University Industrial and Aerospace Engineering Department of Mechanical MontrealQCH3G 1M8 Canada Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing211106 China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an Shaanxi 710048 China

ISBN: (纸本)9781665487085

Early forest fire image recognition plays an important role in timely fire fighting. This paper proposes an early forest fire recognition method based on C-GhostNet network. The C-GhostNet network is an improved version of GhostNet, which mainly consists of a series of c-ghost bottlenecks, each c-ghost bottleneck mainly consists of two c-ghost modules. Through the concat operation and channel shuffle, the c-ghost module establishes the relationship between the input data and the output features generated by the original ghost module, so that GhostNet network has the ability to capture contextual features. The experimental results show that the C-GhostNet network achieves better recognition performance than GhostNet without introducing additional computational load, and the running speed reaches about 20FPS, which basically realizes real-time recognition. In addition, like GhostN et, the C-GhostNet network is also a lightweight network. © 2022 IEEE.

关键词： Image recognition

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Distributed Estimation for Radio Environment Map in Cognitive Radio Networks 33

Distributed Estimation for Radio Environment Map in Cognitiv...

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第三十三届中国控制会议

作者： LIU Jianzhi HAN Hui CHEN Cailian LI Li'an GUAN Xinping The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE) Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

ISBN: (纸本)9789881563842

Radio Environment Map(REM)is a map that indicates the radio signal strength(RSS)over a finite geographical *** enables cognitive radio(CR)users to opportunistically access to the spectrum unoccupied by the primary users(PUs)who are licensed to use specific *** this paper,a basis expansion based scheme is presented to estimate *** take the sparsity of preselected basis functions and the inherent time-varying spectrum usage of primary users into account such that the RSS estimation is formulated as a recursive least squares(RLS)problem with an l1norm constraint.A distributed algorithm is presented to solve the RLS *** SU only need to exchange information with its one-hop neighbours to sketch the REM with radio signal ***,simulation studies are presented to validate the performance of the proposed method.

关键词： Cognitive radio,Basis expansion,Cooperative spectrum sensing,Distributed estimation

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High-adaption locomotion with stable robot body for planetary exploration robot carrying potential instruments on unstructured terrain

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Chinese Journal of Aeronautics 2021年第5期34卷 652-665页

作者： Kang XU Shoukun WANG Junzheng WANG Xiuwen WANG Zhihua CHEN Jinge SI School of Automation Beijing Institute of Technology.Beijing 100081China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of TechnologyBeijing 100081China Key Laboratory Ministry of Industry and Information Technology Beijing Institute of TechnologyBeijing 100081China

There is a strong demand for Planetary Exploration Mobile robots(PEMRs)that have the capability of the traversability,stability,efficiency and high load while tackling the specialized tasks on planet *** this paper,an electric parallel wheel-legged hexapod robot which has high-adaption locomotion on the unstructured terrain is ***,the hybrid control framework,which enables robot to stably carry the heavy loads as well as to traverse the uneven terrain by utilizing both legged and wheeled locomotion,is also *** on this framework,robot controls the multiple DOF leg for performing high-adaption locomotion to negotiate obstacles via Gait Generator(GG).Additionally,by using Whole-Body control(WBC)of framework,robot has the capability of flexibly accommodating the uneven terrain by Attitude control(AC)kinematically adjusting the length of legs like an active suspension system,and by Force/torque Balance control(FBC)equally distributing the Ground Reaction Force(GRF)to maintain a stable *** simulation and experiment are employed to validate the proposed framework with the physical system in the planetary analog ***,to smoothly demonstrate the performance of robot transporting heavy loads,the experiment of carrying 3-person load of about 240 kg is deployed.

关键词： Attitude control Gait generation High-adaption locomotion Obstacle avoidance Planetary exploration robot

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Research on weak signal detection based on adaptive multi-stable stochastic resonance under α stable noise

Research on weak signal detection based on adaptive multi-st...

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Annual Conference of Industrial Electronics Society

作者： Shang-bin Jiao Yi Wang Qing Zhang Le Fan Di Sun Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

In this paper, the influences of system parameters on stochastic resonance output effect is analyzed, which takes the multi-stable stochastic resonance system as the model. That is, the vibration condition of multi-stable stochastic resonance system, and weak signal detection method based on the multi-stable stochastic resonance under α stable noise is investigated. Then considering the real-time detection of weak signals in practical engineering, the multi-stable stochastic resonance system parameters a, b, c are optimized by particle swarm optimization(PSO), which takes the output signal-to-noise ratio(SNR out ) as the fitness function. Finally, multi-frequency weak signals detection with α stable noise is achieved, and the above method is applied to the vibration fault diagnosis of turbine. Both simulation and experiment results show that this method can quickly and effectively detect multi-frequency weak signals submerged in strong noise background, which lays a foundation for its application in engineering practice.

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EPM-Net: A better way for optical and infrared scene matching via edge point 12

EPM-Net: A better way for optical and infrared scene matchin...

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SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Huang, Shixiang Chi, Longyun Fu, Wenxing Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

As a crucial auxiliary technique in inertial navigation, scene matching has been widely applied in aircraft navigation and guidance. To enhance the adaptability and efficiency of scene matching algorithms in complex environments, existing approaches have gradually shifted towards heterogeneous scene matching, such as optical/ infrared scene matching based on deep learning features, with a focus on point features. However, due to the complex and diverse nonlinear distortions between optical and infrared images, the matching accuracy often fails to meet the practical requirements of navigation and positioning. In view of this problem, an EPM-Net algorithm based on edge features is proposed to improve matching performance by extracting more stable and discriminative edge point descriptors. Experimental results on infrared datasets demonstrate that this method achieves an average matching accuracy improvement of more than four times compared to traditional and existing deep learning methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Assessment and prediction of leakage degree of expansion joints in underground network heating pipe network based on LS-SVM and ARIMA model 13

Assessment and prediction of leakage degree of expansion joi...

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13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

作者： Shang-Bin, Jiao Le, Fan Peng-Yue, Wu Lei, Qiao Yong-Ze, Jin Qian-Kun, Shen Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China Xi'an Thermal Power Research Institute Co. Ltd Xi'an710054 China

ISBN: (纸本)9781538637579

The study shows that the leakage of the expansion joints in the underground heating primary pipe network will lead to the fluctuation of soil temperature and conductivity around the leakage point. Based on the online monitoring of changes of physical quantities around the expansion joint, this paper established the assessment model of temperature and conductivity as the input parameters and the degree of expansion joints leakage as the output parameters, which took the radial basis function as kernel function and made use of PSO algorithm to achieve the optimization of the kernel function parameters. Then, the temperature and conductivity were predicted based on the ARIMA model, and the LS-SVM assessment of expansion joint leakage was realized according to the prediction results. Both simulation and field experiments show that the prediction model based on ARIMA has higher accuracy, which has identified the foundation for on-line prediction and accurate assessment of the expansion joint leakage in the underground heating primary pipe network. © 2018 IEEE.

关键词： Particle swarm optimization (PSO)

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