Dendritic Learning-Incorporated Vision Transformer for Image Recognition

Dear Editor,This letter proposes to integrate dendritic learnable network architecture with Vision Transformer to improve the accuracy of image recognition.In this study,based on the theory of dendritic neurons in neuroscience,we design a network that is more practical for engineering to classify visual features.Based on this,we propose a dendritic learning-incorporated vision Transformer(DVT),which out-performs other state-of-the-art methods on three image recognition benchmarks.

An Information-Based Elite-Guided Evolutionary Algorithm for Multi-Objective Feature Selection

Dear Editor, This letter is concerned with the evolution strategy for addressing multi-objective feature selection problems in classification. Previous methods suffer from limitations such as being trapped in local optima and lacking stability. To overcome them, we propose a novel eliteguided mechanism based on information theory. Firstly, an elite solution is generated through a dimension reduction strategy and incorporated to the initialization population.

Dendritic Deep Learning for Medical Segmentation

Dear Editor,This letter presents a novel segmentation approach that leverages dendritic neurons to tackle the challenges of medical imaging segmentation.In this study,we enhance the segmentation accuracy based on a SegNet variant including an encoder-decoder structure,an upsampling index,and a deep supervision method.Furthermore,we introduce a dendritic neuron-based convolutional block to enable nonlinear feature mapping,thereby further improving the effectiveness of our approach.

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution

Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.

Impact Analysis of the Electromagnetic Radiation from Substation

Damage and interference of the electromagnetic radiation on human body and electronic communication equipment generated by substa- tion were introduced firstly. By using scientific research method, strengths of the electromagnetic field in substations of 35, 110 and 220 kV was measured. According to detection result, distribution range and influence characteristics of the electromagnetic radiation generated by substation were analyzed. Finally, we put forward some countermeasures and suggestions for prevention and control of the electromagnetic radiation.

Impact Analysis of Electromagnetic Radiation from Communication Stations

[Objective] The study aims to analyze the effects of electromagnetic radiation from the communication stations on on surrounding environment. [ Method] The electromagnetic field intensity of the environment around communication stations was detected firstly, and then the impact of electromagnetic radiation from the communication stations on surrounding environment was analyzed, finally a series of countermeasures and suggestions were proposed to prevent electromagnetic radiation pollution. [ Result] The comprehensive electromagnetic field intensity of the environ- ment around mobile stations has positive correlation with the number of transmitting and receiving antennas. In addition, the comprehensive electro- magnetic field intensity is higher at high points compared with that at low points, showing that the comprehensive electromagnetic field intensity is positively correlated with point height. [ Conclusion] The research is of important significance to environmental protection and public health.

Dendritic Learning and Miss Region Detection-Based Deep Network for Multi-scale Medical Segmentation

Automatic identification and segmentation of lesions in medical images has become a focus area for researchers.Segmentation for medical image provides professionals with a clearer and more detailed view by accurately identifying and isolating specific tissues,organs,or lesions from complex medical images,which is crucial for early diagnosis of diseases,treatment planning,and efficacy tracking.This paper introduces a deep network based on dendritic learning and missing region detection(DMNet),a new approach to medical image segmentation.DMNet combines a dendritic neuron model(DNM)with an improved SegNet framework to improve segmentation accuracy,especially in challenging tasks such as breast lesion and COVID-19 CT scan analysis.This work provides a new approach to medical image segmentation and confirms its effectiveness.Experiments have demonstrated that DMNet outperforms classic and latest methods in various performance metrics,proving its effectiveness and stability in medical image segmentation tasks.

An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

Energy issues have always been one of the most significant concerns for scientists worldwide.With the ongoing over exploitation and continued outbreaks of wars,traditional energy sources face the threat of depletion.Wind energy is a readily available and sustainable energy source.Wind farm layout optimization problem,through scientifically arranging wind turbines,significantly enhances the efficiency of harnessing wind energy.Meta-heuristic algorithms have been widely employed in wind farm layout optimization.This paper introduces an Adaptive strategy-incorporated Integer Genetic Algorithm,referred to as AIGA,for optimizing wind farm layout problems.The adaptive strategy dynamically adjusts the placement of wind turbines,leading to a substantial improvement in energy utilization efficiency within the wind farm.In this study,AIGA is tested in four different wind conditions,alongside four other classical algorithms,to assess their energy conversion efficiency within the wind farm.Experimental results demonstrate a notable advantage of AIGA.

Cause Analysis of Rail Corrugation in Metro Lines Based on System Vibration Characteristics

The mechanism of rail corrugation remains unclear,and the research methods require improvement.The vibration characteristics represent the system's external manifestations,but a comprehensive analysis is still lacking.Taking the vibration characteristics of the wheel-rail system as a starting point,this study investigates the formation mechanisms of rail corrugation on measured metro lines.The line sections included both steel spring floating slab tracks and long sleeper embedded tracks.First,the wavelength and frequency attributes of rail corrugation were obtained through field measurements.Then,referencing line conditions,three-dimensional finite element numerical models were established,frequency response calculations were performed,and the relationship between the vibration responses of the wheel-rail system and rail corrugation was analyzed.Finally,a parameter sensitivity analysis of the wheel-rail system was conducted to control the further development of rail corrugation.The results show distinct corrugation phenomena on both inner and outer rails in the measured sections.The characteristic wavelengths of inner and outer rail corrugation on the steel spring floating slab track are 34 mm and 59 mm,respectively,and the characteristic wavelengths of inner and outer rail corrugation on the long sleeper embedded track are 46 mm and 47 mm,respectively.The frequency response analysis indicates that the numerical results exhibit eigenfrequencies close to the passing frequencies of the measured corrugations.The formation mechanism of inner rail corrugation on the steel spring floating slab track is attributed to the third-order bending vibration of the wheelset,which leads to the generation of inner rail corrugation.In contrast,the formation mechanism of outer rail corrugation is attributed to the lateral bending vibration of the outer rail.For the long sleeper embedded track,inner rail corrugation is generated by the lateral bending vibration of the inner rail,while outer rail corrugation results from the lateral bending vibration of the outer rail.Appropriate adjustments to the fastener's vertical and lateral stiffness,as well as the steel spring's vertical and lateral stiffness,can shift the rail corrugation eigenfrequencies,thereby inhibiting the development of corrugation with the original wavelength.Changes in other parameters have no effect on the rail corrugation eigenfrequencies and only influence the development speed of corrugation with the original wavelength.This research effectively elucidates the cause of rail corrugation from the system vibration perspective and provides a valuable complement to the corrugation analysis method.