Reviewerly/exHarmony huggingface dataset:
An Improved Multi-Objective Particle Swarm Optimization Algorithm Based on Competitive Mechanism and Fitness Ranking

Most MOPSO algorithms only use a single search strategy and update method to guide particle iteration, which can lead to difficulties in solving some difficult MOPs. Especially when solving problems with many objective irregular Pareto front, there may also be an imbalance between convergence and diversity. To tackle this problem, this paper proposes a multi strategy particle swarm optimization algorithm (CF-MOPSO), which uses competition mechanism and fitness ranking method. Using a competitive mechanism to select particles, CF-MOPSO introduces an enhanced fitness allocation strategy to keep the solution widely and evenly distributed. And in the selection strategy, the recently proposed θ- dominance are used to sort solutions, which increases the pressure to choose the optimal solution while also improving convergence speed. The proposed CF-MOPSO algorithm was applied to several scalable benchmark multi-objective problems for testing, and compared with five recently proposed MOPSO algorithms. The experimental results plots that the CF-MOPSO algorithm achieves significant improvements in optimization quality and convergence speed while balancing convergence and diversity. This result demonstrates its significant advantage in obtaining Pareto optimal sets with good performance.
Improved BP neural network application model based on PSO

The back propagation(BP) neural network performs relatively well in predicting nonlinear problems. However, it’s easy to fall into the local optimal solution and slow convergence speed, which will always impact the prediction results. In this paper, the optimization of the BP neural network is taken as the research object, the particle swarm optimization(PSO) is used to optimize the queues and weights of the BP neural network, and the mean square error function is selected as the basis of particle fitness adjustment, establishing the PSO improved BP neural network application model. The experimental results showed that compared with the Autoregressive Integrated Moving Average model(ARIMA), Extreme Learning Machine(ELM), and BP neural network, the accuracy of the proposed model rose 23.53%, 15.13%, and 6.4 respectively, presenting better performance in prediction and providing a constructive basis for subsequent personnel to improve the prediction ability of BP neural network.
Research on key technologies for database containerized deployment

In view of the problems of high resource allocation, low utilization and high cost based on physical or virtual machine deployment, a database containerization deployment scheme based on Kubernetes container arrangement technology is proposed. In this paper, the key technologies such as high performance, high availability and easy operation and maintenance are studied and demonstrated. The scheme fully investigates the advantages of container technology and the characteristics of database deployment, optimizes the High Availability Scheme of Local PV and Cluster based on the existing functions of Kubernetes.
Eye Tracking in Human-computer Interaction Recognition

Eye tracking interaction has a wide range of application prospects in the field of human-computer interaction, and it is generally invasive, the calibration process is complex and expensive, and the resolution of ordinary monocular camera sensors is low. In this paper, an eye movement behavior recognition method based on front-facing camera video source using directional gradient histogram (HOG) feature + SVM+LSTM neural network is proposed, and then a simple human-computer interaction application is realized. Firstly, the face is located and tracked, the binocular area is obtained according to the coordinates of the four key points at the corner of the eye after the face alignment operation, the SVM model is used to determine the closed and non-blinking states of the eyes, and then the position of the center of the eye between adjacent frames is analyzed to roughly judge the eye movement, and the suspected intentional eye potential differential video sequence between frames is input into the LSTM network for prediction, and the eye movement behavior recognition results are output, and then the computer command is triggered to complete the interaction. After testing 20 000 samples (about 10% of which were negative samples) in the self-made data sample set, the accuracy of dynamic blink recognition and 99.3% of eye movement behavior prediction was better than 95%.
Research on Scientific Training System of Track and Field Athletes Based on Computer 3D Visualization Technology

Using Kinect motion capture technology combined with Angle and bone motion characteristics, three-dimensional visualization of human muscles was realized. This design takes ARM microcontroller as the control center. The system has the functions of radio frequency identification, image acquisition, alarm prompt and so on. Then a motion association recognition method based on human motion characteristics is proposed. The test results show that an automatic measuring device for distance of athletes in track and field can accurately measure the performance of athletes in standing long jump. The system runs smoothly. The output results are within the theoretical error range.
MCLSS: Multi-level Contrastive Learning for Semantic Segmentation

Semantic segmentation is a base mission of computer vision, with the aim of dividing the input picture into different parts which represent a semantically interpretable category. Besides, semantic interpretability means that the part is meaningful in the real world. Contrastive Learning is a desirable method, so we introduce it to our work to improve performance, which is meaningful. In this paper, we propose Multi-level Contrastive Learning for Semantic Segmentation (MCLSS) method to solve the problem. Specifically, we regard the images with the same label as positive pair and the image with a different label as negative pair, this paper forces on narrowing the gap between identical examples. Experiments on PASCAL VOC2012 dataset demonstrate that our model outperforms the original model, which means our model efficiently narrows the gap between the images with the same label. For the analysis, our model can effectively improve the semantic segmentation performance and achieve an average mIOU of 80.2.
Application of deep learning in traffic sign detection and recognition system

With the rapid development of economy and science and technology, motor vehicles quickly enter thousands of households, causing traffic congestion has become a common phenomenon in many cities, traffic safety problems are becoming more and more serious. With the national strategic layout and the development of 5G technology, driverless driving will play a huge role in solving this problem. In the unmanned driving system, the detection and recognition of traffic signs is an indispensable part. However, the existing traffic sign detection and recognition technology either has low accuracy or poor real-time performance, which is difficult to meet the real demand. Therefore, based on the above research significance and existing problems, this paper, on the basis of mainstream deep learning object detection algorithms, aims to improve the performance of the original algorithm, improve the accuracy and speed of traffic sign detection, and further study the design and implementation of traffic sign detection and recognition system on the embedded platform, aiming at the target characteristics of traffic signs and the shortcomings of current algorithms.
Knowledge Distillation for Intent Detection and Slot Filling

Intent detection and slot filling are two fundamental tasks in the spoken language understanding field (SLU). SLU is used to detect the user's intention and fill slots so that the system can correctly determine user's intention and react in a way that meets their demands. At the same time, the two tasks are highly dependent. To improve the accuracy of intention detection and slot filling, the Stack-Propagation model is adopted in this paper, in which intention information is directly used as input parameter for slot filling layers, so as to utilize semantic information and accuracy. Token-level intent detection is applied to effectively mitigate error propagation. To further improve the framework's efficiency, this paper pretreats the training set. Attain the POS tagging results of the word with slot 0 randomly and removes words with the same tagging in the training set. Based on the Stanford Core NLP package, we carry out POS tagging to achieve knowledge distillation. We conducted experiments on two benchmarks, ATIS and SNIPS. Finally, our optimized framework equipped with knowledge distillation shows a faster and more accurate performance.
A Comprehensive Study on Nonlinear Mapping Functions and Unsupervised Training Algorithms for Vehicle Detection in Challenging Environments

In this study, we address the challenge of vehicle detection in complex environments by proposing novel improvements to nonlinear mapping functions and unsupervised training algorithms. Existing techniques, such as Faster R-CNN, YOLOv4, and RetinaNet, have limitations when applied to complex environments, necessitating advancements in vehicle detection technology. We begin by reviewing the current state of vehicle detection techniques and the role of deep learning in the field. We then present a comprehensive analysis of nonlinear mapping functions and unsupervised training algorithms, identifying their importance in vehicle detection tasks. In our research, we propose improvements to both the nonlinear mapping functions and unsupervised training algorithms, detailing the methodology, experiment design, and results. Our experimental evaluation demonstrates that the proposed improvements lead to enhanced vehicle detection performance in complex environments when compared to baseline methods. We further validate our approach through a series of experiments on a diverse dataset with various evaluation metrics. In conclusion, our study presents a significant contribution to the field of vehicle detection by improving nonlinear mapping functions and unsupervised training algorithms for better performance in challenging environments. We also discuss potential future research directions, including the exploration of other unsupervised learning techniques and the integration of our approach into real-world applications.
Index t-SNE Based on Sobol Sequence Initialized Harris Hawk Optimization Algorithm

It is of no doubt that artificial intelligence (AI) is a cutting-edge technological science, in which manifold learning methods have become a hot research topic in recent years. t-distribution stochastic neighbor embedding (t-SNE) is a representative nonlinear dimensionality reduction method in manifold learning. The 2 main advantages of t-SNE are its ability to maintain structural characteristics and solve congestion problems. However, t-SNE is not parametric, so the 2 initializations of the algorithm will result in 2 different embeddings, thus, a guiding index is added to t-SNE, and thus index t-SNE algorithm has been formed. Nevertheless, like traditional t-SNE, the optimization method of index t-SNE is still prone to fall into the trap of local minimum, resulting in no global optimal solution. And the closer to the global optimal solution, the more oscillatory the parameter optimization path will be, which means that the optimization efficiency of index t-SNE is not high. The root of causing the above problems is that the optimization method of index t-SNE relies on gradient or derivative information. In this paper, a derivative-free swarm intelligence method - Sobol sequence initialized Harris hawk optimization (HHO) has been utilized to overcome the above shortcomings, and the advantages of the proposed method have been proved via numerical experiments.
Research on Visual Object Feature Extraction

The rapid development of the computer industry has promoted the development of the entire field of computer vision. The research on target feature extraction is a prerequisite for the application of computer vision technology. The article focuses on the target feature extraction technology of monocular cameras. It proposes to design a feature extraction algorithm based on the OpenCV open-source computer vision library on the basis of obtaining workpiece images from BASLER industrial cameras, which can improve the accuracy and speed of industrial robot grasping and meet the dynamic and real time requirements in actual production.
Analysis on Computer-Aided Diagnosis of Breast Tumors Based on GA-SVM

In order to improve the accuracy of breast tumor recognition, this paper proposes a breast tumor aided diagnosis analysis model. The method combines support vector machine (SVM) and genetic algorithms (GA). Firstly, GA is used to reduce the dimension of feature data obtained from breast tumor and the grid search method based on k fold cross validation error is used to select the model parameters of SVM. Following, the classification and recognition of breast tumors are implemented by the proposed method. In comparison with the results of LVQ, PSO-SVM and BP neural network breast tumor diagnosis methods, recognition accuracy of GA-SVM based method is more better than the traditional classification methods. As a result, it can provide decision support and assist doctors to minimize and avoid missing and faulty diagnostic cases when fine needle aspiration cytopathology is used, which is the traditional examination method, to diagnose breast tumors.
Memetic Beluga Whale Optimization for Feature Selection

With the increase in the dimensionality of data, feature selection has gained significant attention in recent times. Feature selection is a complex task due to the large search space involved. Swarm intelligence has recently been successfully used to address feature selection problems because of its global search ability. However, Beluga Whale Optimization (BWO), a swarm intelligence algorithm, is seldom used for feature selection due to its limited exploration ability at the end of iterations. Therefore, this paper proposes a new method called Memetic Beluga Whale Optimization (MBWO). By adding local perturbation and modifying search operators, MBWO can result in better performance in feature selection. Experiments comparing MBWO with other algorithms also show that the efficiency of the MBWO algorithm has significantly improved by these modifications.
HJADE: An Efficient Differential Evolution with Complex Nonlinear Population Size

The differential evolution with optional external archive (JADE) algorithm is a competitive improvement based on differential evolution (DE) algorithm, but it tends to converge to local optima. We propose a new enhancement based on JADE, namely HJADE, which incorporates a strategy of regulating the population size using hybrid functions to strike a more favorable balance between exploration and exploitation. We used 30 problems from the IEEE CEC 2017 benchmark set to assess its implementation and compare HJADE with other JADE-based algorithms using statistical tests. The results show that HJADE exhibits superior performance.
Design of a Virtual Reality-Based Mandala Painting Assisted Therapy System

The risk of depression among college students in China is prominent, but there are still many deficiencies in the current mental health education in colleges and universities. The Mandala Painting Assisted Therapy System is designed based on Virtual Reality (VR) technology, in which individuals express themselves by painting in a specific virtual scene to effectively alleviate depression and assess the level of mental health. Combined with the characteristics of mandala painting, the paper introduced the mandala painting-assisted therapy system from four viewpoints, namely, design idea, core module construction, intervention process and effect, and core highlights and development prospects. The system was expected to effectively alleviate individuals' depressive moods and improve college students' mental health. The paper aimed to provide reference experience for improving and developing China's college and university mental health education and facilitating the practical application of VR.
An Improved A* Algorithm for Multi-Environmental Factor Lunar Rover Path Planning

Based on the improved A* algorithm, a lunar rover path planning algorithm based on slope, roughness, Solar illumination conditions and Earth visibility conditions is proposed in this paper. Because the South Pole of the Moon contains many permanently Shadowed areas, where large amounts of water ice and related resources are concentrated, it is considered a potential lunar base landing site. Lunar rovers on missions need path planning to reduce driving costs and risks. This paper uses DEM image to generate slope and roughness information, combined with the lunar South Pole solar illumination and the earth visibility condition, considering the convergence speed and environmental complexity of the algorithm, improves the traditional A* algorithm, and gives the simulation results.
Insulator Defect Detection Based on Deep Learning

Insulators hold great significance in ensuring electrical insulation and power support in transmission lines. However, detecting insulator defects in UAV power images is challenging due to variations in insulator scale, dense image texture, and complex transmission line backgrounds. To address these challenges, we propose an advanced network model based on YOLOv5, incorporating Coordinate Attention (CA) and Transformer encoder blocks at multiple scales. The integration of CA mechanism enhances the accuracy of insulator defect detection, while replacing the C3 network with Transformer encoder blocks improves the identification of various insulator defects in dense scenarios. Additionally, a small target-oriented detection head is added to detect tiny insulator defects. Experiments demonstrate a significant improvement of our optimized model over the original YOLOv5, with an increase of3. 5% in mAP accuracy. Comparative analysis with the previous state-of-the-art detection model further validates the effectiveness and reliability of our improved model for insulator defect detection.
Video Super-Resolution with Pyramid Flow-Guided Deformable Alignment Network

Video super-resolution (VSR) aims to recover high-resolution frames from their multiple low-resolution counterparts. How to fully exploit the spatio-temporal information among video sequences for VSR is significant but challenging. The challenges can be summarized as: 1) How to perform frame alignment when encountering displacement and occlusion. 2) How to effectively utilize spatio-temporal information for performance improvement. To address the above core problems, this paper proposes a pyramid flow-guided deformable alignment network for VSR (PFDVR) to achieve precision frame alignment and efficient spatiotemporal feature exploitation. Specifically, a pyramid flow-guided deformable alignment module (PFGDA) is proposed to perform feature alignment in a coarse-to-fine manner, and we employ bidirectional recurrent feature propagation to excavate temporal information. To capture the long-term spatio-temporal dependency, we propose an omniscient progressive fusion module (OPF) to achieve multi-level feature fusion both in spatial and temporal dimensions. The experimental results have demonstrated that our PFDVR can achieve promising SR performance.
Visible-Light Insulator Defect Detection Based on Improved YOLOv3

Insulators are an important part of transmission lines, accurate and rapid identification of insulator damage is very important for the transmission of electric energy, with the development of deep learning, more and more research is committed to improving model detection accuracy, speed improvement And in modern drone inspection, the insulator pictures obtained will be blurred due to the problem of shooting angle and light, which will have a serious adverse effect on the detection of algorithms based on deep learning. In this paper, YOLOv3 is used for the identification and detection of insulator self-explosion images, and the collected data set is expanded and preprocessed. Then, the backbone features of YOLOv3 are extracted from the network special transposition Darknet19 to reduce the complexity of the network to obtain faster detection speed, and secondly, the loss function of YOLOv3 is replaced with GIOU loss, which solves the situation that the non-intersecting box has a gradient of 0 and cannot optimize the objective function, and improves the accuracy of insulator image recognition. Then, the data is organized in the form of VOC datasets and then the model is trained, so that the original insulator pictures that cannot be recognized or have low recognition rate become pictures with normal clarity. After image preprocessing, feature extraction network and loss function replacement, the mAP of insulator defects was improved by 23.58%, and the detection speed was improved by 8. 375ms.
Design and implementation of transformer state sensing and predictive operation and maintenance system interface based on Ant Design Vue

The transformer state sensing and predictive operation and maintenance system can monitor comprehensive and multi-type indexes of the transformer under the condition of no power outage, which ensures the stable operation of the transformer equipment and thus improves the stability of the whole system. Aiming at the design problem of transformer state sensing and predictive operation and maintenance system, this paper presents the interface design and implementation of transformer state sensing and predictive operation and maintenance system based on Ant Design Vue. This paper first introduces the business requirements of the relevant interface design, then introduces the relevant interface design layout and key component technology, and finally gives the implementation of the relevant interface. By using Ant Design Vue component, Vue component, Echarts component and the combination of various components, three business modules including condition monitoring, trend analysis and fault assessment are realized, which provides some experience and reference for the interface design of related online monitoring system.
Research on the Application of Deep Learning Object Detection in Rust Defect Detection of Power Equipment

In order to improve the intelligence level of power grid operation and enhance the credibility of rust defect detection in power equipment, this paper addresses the significant differences in image quality in actual situations of unmanned aerial vehicle (UAV) inspection. A rust detection method incorporating an attention mechanism into the Faster R-CNN model and considering background brightness and gamma transformation is proposed. This method can adaptively normalize the images and effectively identify rust areas in complex backgrounds, large brightness differences, and noise-contaminated conditions. By introducing three assessment indicators: rust rate, rust confidence, and rust heat map, comprehensive rust defect information is provided to maintenance personnel, ensuring the safe and stable operation of power equipment.
Research on Obstacle Avoidance of a 7-DOF Manipulator

A model was build to resolve obstacle avoidance problem of a 7-DOF redundant manipulator. It proposes that the link of redundant manipulator is enveloped by cylindrical space, and the obstacle is enveloped in a spherical space. The RRT algorithm and the RRT-Connect algorithm were employed to execute avoiding obstacles in the workspace. The simulation results showed that the RRT-Connect algorithm was superior to the RRT algorithm by shorter running time and less path length. A 7-DOF redundant manipulator was tested to verify the obstacle avoidance performance of the RRT-Connect algorithm. It can avoid obstacles and reach the target quickly.