Reviewerly/exHarmony huggingface dataset:
Research on People Counting Methods of Subway Cars Based on Improved Random Hough Transform

In order to count the number of subway cars, guiding passengers to choose the waiting car reasonably and improve the comfort of the car, a real-time number of subway cars based on improved random Hough transform (RHT) is proposed. This method collects the video image of the car after the subway starts. After the background difference and binarization, the canny operator is used to realize the edge detection and contour extraction. According to the characteristics of the human head like circle and its contour on the same curve edge, the improved RHT algorithm is used to detect the circle, and the circles with a radius of 8.0-10.0CM are selected for marking and counting. Experimental results show that this method can solve a large number of invalid accumulations caused by random sampling in traditional RHT algorithm and improve the detection speed.
Research on a network covert channel based on blockchain

Covert channel refers to the channel where malicious communication parties encode and transmit information by modifying the value, characteristics or status of shared resources. With the birth of blockchain technology serving Bitcoin, its characteristics of decentralization, anonymity and non tamper make blockchain as a covert communication carrier superior to traditional networks, databases and operating systems. We summarized the development process of covert channels, and compared traditional network covert channels with blockchain based network covert channels. The blockchain network covert channels are divided into four types: transaction address type, smart contract type, signature algorithm type and P2P broadcast mechanism type, and the modeling definitions are given. Three evaluation indexes are proposed for the concealment, transmission efficiency and communication cost of the hidden channel in the blockchain network.
Construction and Accurate Retrieval Method of Knowledge Graph of Automobile Engine Fault

In order to improve the efficiency and accuracy of automobile engine fault maintenance, an accurate retrieval method of automobile engine fault driven by knowledge graph was proposed. Firstly, the definition and framework of knowledge graph are discussed. The entity extraction of engine fault features was carried out by multi-source neural network, and the disambiguation of fault entities was carried out by integrating entity link technologies; Secondly, fault knowledge reasoning is carried out to eliminate the wrong knowledge in the knowledge base and infer new knowledge to form a complete knowledge graph.. On this basis, the retrieval subgraph of engine fault semantics is designed. Combined with the influence of physical distance and proximity, the retrieval result evaluation model is established, and the subgraph matching was carried out based on the similarity calculation of graph structure and semantic information. Finally, four knowledge graphs including entity equipment graph, ontology graph, maintenance rule graph and history graph were constructed by selecting some automobile engine fault cases from 2017 to 2020. Finally, the process architecture of engine fault search and analysis is constructed and the effectiveness of the proposed method was verified by precision rate and recall rata, which provides a new idea for accurate and efficient engine maintenance.
Method of Clothing Image Retrieval Based on Convolution Neural Networks

Brand clothing image retrieval has important application value in life. In view of the lack of accuracy of ordinary image retrieval methods, this paper proposes to fuse the deep and shallow features of convolutional neural network VGG16 model based on the feature fusion strategy. At the same time, it fuses the color features extracted separately by histogram, and measures the similarity to achieve brand clothing image retrieval. Comparative experiments show that the improved image retrieval method improves the retrieval accuracy to 84.63%, with about 2 percentage points higher than the original VGG16 model on the clothing image data set, and has certain application value.
Distributed Data Multi-Level Storage Encryption Method Based on Full-Flow Big Data Analysis

This paper firstly proposes a multi-level storage encryption method model for distributed data based on security-marked objects by taking advantage of the respective advantages of mandatory access control, attribute-based encryption and object storage. Then this paper designs a detailed multi-level trusted management model for collaborative storage security model, key management and data synchronization schemes. Again, this paper transforms the original ciphertext into coupled ciphertext by means of coupling and extraction method and further decomposes it into extraction ciphertext and encapsulation ciphertext. Finally, a multi-copy generation and damaged data recovery algorithm based on multi-level network coding is proposed. The algorithm improves the coding matrix of erasure codes based on multi-level network coding to form a multi-level coding matrix. Simulation experiment analysis shows that the performance loss caused by data security management is within an acceptable range. At the same time, the system has good scalability and can adapt to the application environment of large-scale distributed storage system.
Research on intelligent storage monitoring of power grid based on YOLOv5 converged attention

With the promulgation of Made in China 2025, as an important support for the realization of industrial informatization, smart warehousing has become the focus of current enterprises and scholars. In this paper, aiming at the real-time monitoring of large cargoes in the grid storage scenario, based on the target detection network YOLOv5, combined with the ECA channel attention mechanism and SIoU loss function, a real-time and efficient cargo detection method based on YOLOv5 fusion attention is proposed for the grid operation site. In order to alleviate the imbalance between positive and negative samples, make full use of the information of difficult samples and improve the detection effect of difficult samples, Focal loss function is introduced. At the same time, on the basis of the original SPP multi-scale fusion, the improved YOLOv7 multi-scale detection method was fused. In view of the lack of power cargoes data sets at home and abroad, we have built a small but high-quality power cargo datasets. The experiment shows that the method in this paper has significantly improved in accuracy, speed, accuracy and generalization ability, and can achieve effective detection in complex operation scenarios, which can provide technical reference for the safety of power grid operators and reduce the intensity of human monitoring.
Research on trajectory tracking control algorithm in 4WS mode

In indoor shooting training, most of targets are fixed targets or tracked target vehicles that move on a fixed track,while in actual combat, most of targets are moving targets with uncertain orientation.This paper designs a four-rudder wheel moving target vehicles chassis and proposes chassis kinematics model in view of the omnidirectional moving targets,and achieves four-rudder wheel chassis trajectory tracking control model by using model predictive control algorithm,and deduces the mobile chassis trajectory tracking control algorithm according to the technical performance indicators and constraints of the vehicle motion,and proves that applying the model predictive control algorithm to the four- rudder wheel chassis to realize trajectory tracking has feasibility in the locomotion mode of 4WS according to the simulation experimental results,and conducts trajectory tracking comparative simulation experiments by selecting different prediction time domain,control time domain and sampling time.The experimental results shows that the model predictive controller with less sampling time and lower control time domain has better effect on target path tracking.
Building a Robot Path Optimization in Prefabricated Buildings

Intelligent robot technology can complete certain work in some construction enterprises. With the arrival of 5G era, its application will be more in-depth and extensive, and it is also a key factor for the sustainable development of the construction industry. In this paper, the hybrid algorithm of genetic ant colony is proposed, which improves the traditional two-dimensional path planning algorithm and three-dimensional path planning algorithm. The experimental test is carried out by using the robot embedding correlation algorithm. The experiment shows that the improved two-dimensional and three-dimensional path planning algorithm has advantages over the traditional algorithm. Moreover, the hybrid ant colony algorithm is obviously superior to the ant colony algorithm, genetic algorithm and particle swarm optimization algorithm, which verifies the effectiveness of the hybrid algorithm and has certain feasibility in the application of prefabricated building environment.
Construction of domain knowledge graph based on open source intelligence

In order to deeply tap the potential information of open source intelligence, facilitate people to understand domain knowledge and build a domain knowledge graph. First, we use web crawler technology to crawl open source data, and then we use word segmentation and other methods to preprocess the data. Then we use deep learning methods to extract entities, attributes, and relationships from the data to establish a domain knowledge base. Finally, we build a domain knowledge graph to facilitate people to search and query related domain knowledge, and improve the efficiency and quality of network search.
Intrusion detection of intelligent substation video surveillance based on average background interframe difference method

According to the characteristics of intelligent substation integrated monitoring system, such as many kinds of equipment, large amount of information and wide range of involved fields, this paper studies the intrusion detection technology of intelligent substation video surveillance on the basis of intelligent substation. In this paper, moving target intrusion detection algorithm in video surveillance subsystem is deeply studied. Combining the advantages and disadvantages of existing detection methods, an improved interframe difference method based on average background is proposed. The performance of the algorithm before and after improvement is analyzed by simulation. The results show that the performance of the improved algorithm is significantly improved, the precision rate is increased by 30% and the recall rate is increased by 41.33% compared with that before the improvement, which has a certain promotion effect on the linkage of the monitoring system.
Study of binocular parallax estimation algorithms with different focal lengths

Existing binocular parallax estimation algorithms all use standard stereo cameras, which are unable to achieve high definition for monitoring both near and far objects due to the camera's own parameters. In order to solve this problem, a parallax estimation method is proposed for images taken by cameras with different focal lengths, which can directly calculate parallax maps for near and far views. A calibrated stereo correction method is proposed to parameterise the binocular image scaling for direct image correction at different focal lengths; the monocular portion of the results is calibrated by comparing the parallax results in the binocular range and introducing monocular parallax correction parameters. Experimental results on the Middlebury Stereo public dataset, which was used to simulate images of different focal lengths, show that the proposed parallax estimation algorithm was able to generate both far-field and near-field parallax maps with an avgerr of 8.33%, which is significantly better than the monocular parallax estimation method.
Gesture Recognition Technology Based on Depth Convolutional Neural Network

In recent years, with the development of computer hardware capabilities and machine learning and other related disciplines, gesture recognition based on computer vision has become a hot topic and is widely used in virtual reality, remote communication, driving safety, medical surgery and other fields. In order to meet the needs of real-time and accuracy of gesture recognition, a gesture recognition method based on deep convolutional neural network is designed in this paper, which automatically carries out feature extraction, classification and iterative training on gesture samples. The experimental results show that the recognition accuracy of the trained model on the test set can reach 98.60% and has strong robustness.
Game Image Detection and Application Based on Improved YOLOv5

With the rapid technological innovation in the field of deep learning and computer vision, convolutional neural networks have gained good results in target detection and image recognition, but there are fewer research applications for game images. In this paper, a deep learning based AIMBOT is proposed. The YOLOv5 model with fast detection speed and high accuracy is used to construct the model. In order to solve the problem of uneven training samples in the data, the Focal-EIoU loss function is introduced, so that the high IoU positive samples have greater loss and the network may learn the parameters of the excellent samples more easily. The experiments show that the model with Focal-EIoU loss has 5 percentage points higher mean average precision and 3 percentage points higher recall. Additionally, the CPU-based OpenVINO acceleration scheme and the GPU-based TensorRT acceleration scheme are used. The comparison experiments show that the inference speedup is 2 times higher after the OpenVINO framework acceleration and 10 times higher after the TensorRT acceleration. Finally, based on the above model scheme, the AI vision-based self-targeting system is designed and optimized using FOV principle. Aimbot game platform is used for testing, results show that tracking function of the model reached the first place in the test with 1,672 people, with a lost target time of 0 milliseconds. It also reached the first place in single-target shooting test with 30,017 people, with a hit rate of 100%, far exceeding the second place. The optimized system based on FOV principle reached the first place in multi-target shooting with 19,811 people with 97.4% hit rate.
Research on the application of service choreography in the intelligent customer service system

The background of this paper is to study the situation that the intelligent customer service system needs different response processes in different scenarios, including the high cost of developing and managing these processes. This paper applies the process engine framework Zeebe, which configures the sequence of various question-and-answer processes and other automatically triggered external processes through flexible scheduling. This application enables the system to respond flexibly to business process changes and achieve rapid and agile responses to new requirements. This system can be customized for different scenarios. The advantages of this system are that it reduces development costs and improves system scalability.
Research on Multi-robot Material Picking and Autonomous Path Planning System in Industrial Environment

Industrial 4.0 has been proposed in 2014 and has received many achievements so far under the development of robotics and artificial intelligence. Among all of the research projects on smart factory, transfer robots are one of the most common applications for robotics in industrial field. To improve the navigation accuracy when locating products as well as complete more tasks such as picking materials, multi-robot path planning algorithms are necessary to combine with manipulators like mechanical arms. This work is focused on designing a transfer robot and a mechanical arm for industrial environment. Meanwhile, a modified A* algorithm named HCA algorithm is implemented into the multi-robot transfer system with PID controlled mechanical arm. The system can be used to finish transfer missions in large-scale warehouses or narrow industries. To further develop this system, continuous actions should be used instead of discrete ones. Moreover, computer vision methods can be added to the mechanical arm to increase the grasping accuracy and flexibility.
Algorithmic Study of Gibbs Sampling Selection Based on Information Criterion

Feature selection is a key research direction in the current big data era, which can effectively reduce the dimension of data, simplify the time of model training and improve the prediction effect, attracting the attention of researchers in the field of statistics and informatics. In recent years, with the expansion of the application scope and field, the research on the feature selection method gradually approaches to the comprehensive index of time and efficiency. This paper applies the idea of Gibbs sampling based on the random search strategy, and improves the feature selection method based on the Gibbs sampling algorithm, analyzes the frequency of each feature as the important degree value of the corresponding feature, and selects a suitable threshold value to realize the feature selection according to the size of the important degree value.
Research and Implementation of Information Security System Based on Chaos Algorithm

Since Pecora and Carroll of the US Naval Laboratory discovered chaos synchronization in 1990, chaos and its research and development in the field of information security have become an upsurge. After more than 30 years of research and development, chaotic secure communication and chaotic cryptography are becoming new growth points of high-tech in the field of information security technology. In this paper, the information security system based on chaos algorithm will be briefly studied to provide theoretical reference for relevant staff to further strengthen the construction of information security system.
Recognition of Italian Gesture Language Based on Augmented YoloV5 Algorithm

In practical production applications, the efficiency and success rate of gesture recognition directly affect the user’s experience and work efficiency. To address the difficulties of existing gesture recognition in meeting practicality while ensuring a high accuracy rate, this paper proposes a method based on the YOLO+Keep Augment model for the Italian gesture public data sets. Because the experimental dataset is small, the data is first augmented using Keep Augment, then the processed dataset is trained using the YOLOv5 model and tested on a dataset of runtime video streams. The random enhanced hyperparameters are determined through pre-training in the public data set. The experimental data shows that N=4 and M=8 are the best parameters, and the training accuracy is maintained at 97.56 ± 0.2 under the best parameters. The analysis results show that the YOLO+Keep model has good recognition efficiency and practicability.
Temporal Convolutional Knowledge Tracking Model with Embedded Graph Association Information

In recent years, deep knowledge tracking (DKT) and other deep learning-based knowledge tracking work has achieved relatively good results in terms of performance compared with traditional methods, but there are also some shortcomings: first, the existing methods fail to effectively use the deep information between question and knowledge points for effective mining, and the model interpretability needs to be improved; second, the original DKT model has long-term dependency problems, and it cannot use more previous interaction sequence to model students’ knowledge state more accurately. Based on this, a temporal convolutional knowledge tracking method with embeded graph association information is proposed in this paper, using graph attention network to mine the high-dimensional graph correlation information between knowledge points embedded in the input of the original model to enhance its interpretability, and then using temporal convolutional network (TCN) to extract students’ dynamically changing knowledge states, using expanded convolution and deep neural network to expand the sequence learning range and alleviate the long sequence dependence problem. The results show that the model outperforms traditional deep knowledge tracking methods in terms of AUC evaluation metrics under public dataset conditions, captures students’ knowledge states more accurately, and predicts students’ future performance more efficiently.
Research on power customer service composite intention recognition based on dependency syntax and graph attention network

With the rapid development of computer technology, intelligent task-based dialogue systems emerge in endlessly. Among them, intention recognition is an essential task of natural language understanding in human-computer dialogue. Aiming at the difficulty of composite intention recognition in the power customer service field, this paper combines the advantages of dependency syntax analysis and graph attention neural network. It adopts a graph attention neural network model integrating the dependency matrix. This model uses dependency information and its relationship types for context information modeling, distinguishes the importance of context features through the attention mechanism, and has flexible calculation, high accuracy, and good generalization.
Blind Lane Detection Algorithm Based on Improved YOLOv5s

With the development of technology and society, there is a growing demand for Blindness aid devices. Detection algorithms for blind lanes are very important. In this paper, the network structure of YOLOv5s is used to greatly improve the recognition rate, and the CA is introduced to improve the network performance by considering both inter-channel relationships and long-distance location information without increasing the computational overhead. Changing PAN to BiFPN in the hope of achieving multi-scale feature fusion easily and quickly. In addition, considering that most of the Blindness aid devices are embedded, this paper also make lightweight improvements to the network replacing the traditional convolutional module with Ghost Module, which is intended to generate feature maps with less cost. Based on these improvements this paper improved the recognition rate of the network on the dataset to 98.3%, reduced model size by 26% and reduced FLOPS by 22% The FPS value reached 235Hz.
GAN-based image deblurring: A comparison

In recent years, image deblurring algorithms based on Generative Adversarial Network (GAN) have been proposed. The motion deblurring has been studied for some time. The multi-scale architecture of convolutional neural networks (CNN) can clearly recover blurred images, but the processing time is long. Generative Adversarial Networks (GAN) performs well in style conversion and can clearly recover blurred images under a non-multi-scale architecture. The authors reviewed the popular papers related to DeblurGAN, compared the algorithm structure of different papers, related data sets and the accuracy of experimental results, which helped GAN based image deblur field to summarize and summarize, and enabled researchers to better promote the development of related fields. The authors review six papers, introduce their research motivation, research methods and experimental results, and show the differences of different deblur algorithms. Some excellent algorithms can work flexibly with a wide backbone, balancing performance and efficiency. They run faster than simple DeblurGAN while maintaining near-state of the art results.