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Classification of Images Based on a System of Hierarchical Features

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Computers, Materials & Continua 2022年第7期72卷 1785-1797页

作者： Yousef Ibrahim Daradkeh Volodymyr Gorokhovatskyi Iryna Tvoroshenko Mujahed Al-Dhaifallah Department of Computer Engineering and Networks College of Engineering at Wadi AddawasirPrince Sattam Bin Abdulaziz UniversityAl-Kharj11991Saudi Arabia Department of Informatics Kharkiv National University of Radio ElectronicsKharkiv61166Ukraine Control and Instrumentation Engineering Department and Interdisciplinary Research Center(IRC)for Renewable Energy and Power SystemsKing Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia

The results of the development of the new fast-speed method of classification images using a structural approach are presented.The method is based on the system of hierarchical features,based on the bitwise data distribution for the set of descriptors of image description.The article also proposes the use of the spatial data processing apparatus,which simplifies and accelerates the classification process.Experiments have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure,for which the sets of descriptors are compared.The introduction of the system of hierarchical features allows to further reduce the calculation time by 2–3 times while ensuring high efficiency of classification.The noise immunity of the method to additive noise has been experimentally studied.According to the results of the research,the marginal degree of the hierarchy of features for reliable classification with the standard deviation of noise less than 30 is the 8-bit distribution.Computing costs increase proportionally with decreasing bit distribution.The method can be used for application tasks where object identification time is critical.

关键词： Bitwise distribution computer vision descriptor hierarchical representation image classification keypoint noise immunity processing speed

Bearing Fault Diagnosis Method of Wind Turbine Based on Improved Anti-noise Residual Shrinkage Network

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Energy Engineering 2022年第2期119卷 665-680页

作者： Xiaolei Li Beijing Goldwind Science&Goldwind Huineng Technology Co. Ltd.Beijing100176China

Aiming at the difficulty of rolling bearing fault diagnosis of wind turbine under noise environment,a new bearing fault identification method based on the Improved Anti-noise Residual Shrinkage Network(IADRSN)is proposed.Firstly,the vibration signals of wind turbine rolling bearings were preprocessed to obtain data samples divided into training and test sets.Then,a bearing fault diagnosis model based on the improved anti-noise residual shrinkage network was established.To improve the ability of fault feature extraction of the model,the convolution layer in the deep residual shrinkage network was replaced with a Dense-Net layer.To further improve the anti-noise ability of the model,the first layer of the model was set as the Drop-block layer.Finally,the labeled data samples were used for training model and the trained model was applied to the test set to output the fault diagnosis results.The results showed that the proposed method could achieve the fault diagnosis of wind turbine bearing more accurately in the high noise environment through comparison and verification.

关键词： Bearing fault diagnosis improved residual shrinkage network noise immunity

Estimation of plasma equilibrium parameters via a neural network approach

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Chinese Physics B 2019年第12期28卷 248-254页

作者：朱子健郭勇杨飞肖炳甲李建刚 Department of Engineering and Applied Physics University of Science and Technology of ChinaHefei 230026China Institute of Plasma Physics Chinese Academy of SciencesHefei 230031China Department of Medical Information Engineering Anhui Medical UniversityHefei 230026China

Plasma equilibrium parameters such as position, X-point, internal inductance, and poloidal beta are essential information for efficient and safe operation of tokamak. In this work, the artificial neural network is used to establish a non-linear relationship between the measured diagnostic signals and selected equilibrium parameters. The estimation process is split into a preliminary classification of the kind of equilibrium(limiter or divertor) and subsequent inference of the equilibrium parameters. The training and testing datasets are generated by the tokamak simulation code(TSC), which has been benchmarked with the EAST experimental data. The noise immunity of the inference model is tested. Adding noise to model inputs during training process is proved to have a certain ability for maintaining performance.

关键词： neural network fusion plasma equilibrium noise immunity

Evolutionary Pseudo-Relaxation Learning Algorithm for Bidirectional Associative Memory

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Journal of Computer Science & Technology 2005年第4期20卷 559-566页

作者：杜升之陈增强袁祉 DepartmentofAutomation NankaiUniversityTianjin300071P.R.China

This paper analyzes the sensitivity to noise in BAM (Bidirectional Associative Memory), and then proves the noise immunity of BAM relates not only to the minimum absolute value of net inputs (MAV) but also to the variance of weights associated with synapse connections. In fact, it is a positive monotonically increasing function of the quotient of MAV divided by the variance of weights. Besides, the performance of pseudo-relaxation method depends on learning parameters (λ and ζ), but the relation of them is not linear. So it is hard to find a best combination of λ and ζ which leads to the best BAM performance. And it is obvious that pseudo-relaxation is a kind of local optimization method, so it cannot guarantee to get the global optimal solution. In this paper, a novel learning algorithm EPRBAM (evolutionary psendo-relaxation learning algorithm for bidirectional association memory) employing genetic algorithm and pseudo-relaxation method is proposed to get feasible solution of BAM weight matrix. This algorithm uses the quotient as the fitness of each individual and employs pseudo-relaxation method to adjust individual solution when it does not satisfy constraining condition any more after genetic operation. Experimental results show this algorithm improves noise immunity of BAM greatly. At the same time, EPRBAM does not depend on learning parameters and can get global optimal solution.

关键词： genetic algorithm pseudo-relaxation method associative memory neural network noise immunity digital pattern