Abstract:As one of the most challenging problems in combinatorial optimization, the traveling salesman problem has attracted extensive attention from the academic community since its birth, and a large number of methods have been proposed to solve it. The ant colony optimization (ACO) is a heuristic bionic evolutionary algorithm for solving complex combinatorial optimization problems, which is effective in solving the traveling salesman problem. This study introduces several representative ACOs and makes a literature review of the improvement, fusion, and application progress of ACOs to evaluate the development and research achievements of different versions of ACOs in solving the traveling salesman problem in recent years. Moreover, the improved ACOs are summarized in categories in terms of the framework structure, setting and optimization of algorithm parameters, pheromone optimization, and hybrid algorithms. The research provides an outlook and basis for the ACO application to solve the traveling salesman problem and further develop the research content and focuses of other fields.

Abstract:Negotiation refers to the process in which people communicate with each other on certain topics to reach an agreement. Automated negotiation aims to reduce negotiation costs, improve negotiation efficiency, and optimize negotiation results by using negotiating agents. In recent years, deep reinforcement learning techniques have been applied to the field of automated negotiation with good results. However, there are still problems such as the long training time of agents, dependence on specific negotiation domains, and insufficient utilization of negotiation information. Therefore, this study proposes a negotiation strategy based on the TD3 deep reinforcement learning algorithm, which reduces the exploration cost of the training process through pre-training and improves the robustness of the negotiation strategy by optimizing the state and action definitions, so as to adapt to different negotiation scenarios. In addition, it makes full use of the interaction information of the negotiation by multi-head semantic neural network and opponent preference prediction module. The experimental results show that the strategy can perform the negotiation task well in different negotiation environments.

Abstract:Non-intrusive load monitoring (NILM) is an important part of intelligent power utilization and energy saving techniques and has attracted extensive attention. Due to the superior performance of newly-developed deep learning methods in various tasks in recent years, some representative deep learning methods have been successfully applied to the load decomposition task in NILM. To systematically summarize the research status and progress of deep learning methods applied to NILM, this study focuses on analyzing and summarizing the research literature on deep learning based NILM in recent years. Firstly, the NILM framework is outlined, and then the feature extraction method and the public data set of NILM are introduced. In addition, the load decomposition methods based on deep learning in NILM are analyzed and summarized. Finally, the study points out several challenges in this field and provides an outlook on its opportunities and future research directions.

Abstract:This study is conducted to study the influence of computer virus spread on the security situation of network systems. It analyzes the relationship between the SIR epidemic spread model and computer network security and proposes an SIPM model for network security situation prediction. Specifically, the SIPM model adds the memory function of nodes for different virus propagation, supports the independent propagation of multiple viruses in the network at the same time, and improves the dynamic propagation equation on the basis of the SIR model. It allows the independent setting of the infection ability of viruses to different device nodes and that of the resistance of device nodes to different viruses, which is closer to the real network environment. The experimental analysis uses a typical campus network architecture for simulation, and the results show that the model can analyze and predict the network security situation from many aspects.

Abstract:In the process of image acquisition, the image often contains certain noise information, which will destroy the texture structure of the image and thus interfere with semantic segmentation tasks. Most of the existing semantic segmentation methods based on noisy images adopt models featuring first denoising and then segmentation. However, they often lead to the loss of semantic information in denoising tasks, which thus affects segmentation tasks. To solve this problem, this study proposes a multi-scale and multi-stage feature fusion method for semantic segmentation of noisy images, which uses the high-level semantic information and low-level image information of each stage in the backbone network to enhance the semantic information of target contours. By constructing a staged collaborative segmentation denoising block, collaborative segmentation and denoising tasks are iterated, and then more accurate semantic features are captured. In addition, quantitative evaluation is carried out on PASCAL VOC 2012 and Cityscapes datasets. The experimental results show that the model still achieves positive segmentation results under the noise interference of different variances.

Abstract:In a heterogeneous Hadoop cluster scenario, the hybrid use of erasure codes and replica storage modes, as well as the real-time computing capability difference of server nodes lead to the low efficiency of MapReduce job processing. To deal with this problem, this study implements a scheduling strategy that dynamically adjusts MapReduce job assignment in multi-concurrent scenarios according to data storage situations and the real-time load of nodes. This strategy dynamically controls the concurrent amount of tasks of each node by modifying data storage location strategies in the current Hadoop framework, so as to achieve more balanced resource allocation among jobs when multiple jobs are concurrent. The experimental results show that the scheduling mode proposed in this study can shorten the job completion time by about 17% and effectively avoid the starvation phenomenon faced by some jobs compared with the two default job scheduling strategies of Hadoop.

Abstract:As the core system of provincial meteorological service, the meteorological big data cloud platform (referred to as Tianqing) needs to work stably and efficiently for uninterrupted 7×24 hours. The Tianqing system has many operation modules and complex processing tasks. However, the traditional manual monitoring mode has low monitoring efficiency and cannot find faults or other problems existing in the operation in time. In this study, the Java, Python, and Bash shell languages are used to develop a full-process monitoring and warning system for Tianqing based on Enterprise WeChat. The system collects and formats the comprehensive status information generated during the operation of each module of Tianqing into monitoring and warning information and finally sends it to the operation and maintenance personnel through the Enterprise WeChat, which realizes the quick perception of the operation status of each operation module of Tianqing. The operation effect of the system shows that the system is safe, reliable, and stable, and it can help operation and maintenance personnel to locate system faults in time and improve the efficiency of fault handling promptly. In addition, it has achieved positive application effects in Tianqing data monitoring and operation guarantee.

Abstract:To solve the problems of low prediction accuracy in aeroengine remaining useful life (RUL) prediction due to insufficient representative feature extraction, this study proposes an RUL prediction method based on multi-feature fusion for aeroengines. Exponential smoothing (ES) is performed to reduce the interference noise in the original data and thereby obtain relatively stable feature data. The time series features of the feature data are extracted by the bidirectional long short-term memory (Bi-LSTM) network and then assigned weights through the multi-head attention mechanism (Multi-attention). A convolutional long short-term memory (Conv-LSTM) network is designed to extract the spatio-temporal features of the feature data. Then, the handcrafted features of the feature data are extracted, and weights are calculated from the Softmax functions. A feature fusion framework is designed to fuse the above features, and RUL prediction is finally achieved by fully connected network regression. The commercial modular aero-propulsion system simulation (C-MAPSS) dataset is used to simulate and verify the proposed model. Compared with Bi-LSTM and other models, the proposed model achieves higher prediction accuracy and better adaptability.

Abstract:Accurate recognition of crop pests is essential for timely crop protection and treatment. However, crop pests in natural environment are small in size and have almost the same color as the environment. Moreover, crop pest images are affected by various factors such as noise and complex background. Therefore, it is difficult for existing crop pest recognition models related to deep learning to balance the requirements of recognition accuracy and robustness and be deployed on mobile devices with limited computational resources and low performance. In this study, ShuffleNetV2 0.5×, which has the fewest model parameters in the ShuffleNetV2 network structure, is selected as the benchmark network, and a lightweight crop pest recognition model based on high-order residual and attention mechanism (HOR-Shuffle-CANet) is designed. Specifically, the high-order residual in the early stage can provide rich pest features for the subsequent network layer, which significantly improves the recognition accuracy of the model. The coordinate attention (CA) mechanism can further suppress the background noise and focus on the key information about crop pests, which effectively enhances the robustness of the model. The bi-tempered logistic loss function with label smoothing regularization (LSR) can solve two shortcomings of logistic loss functions in training noisy data sets and make the model more adaptable to noise. The experimental results show that the HOR-Shuffle-CANet model achieves a recognition accuracy of 91.22% on the test dataset of ten types of common crop pest images in natural scenarios, which is 3.54 percentage points higher than the benchmark network. On the basis of maintaining lightweight computing, its recognition accuracy is also higher than that of the existing classical convolutional neural networks such as AlexNet, VGG-16, GoogLeNet, Xception, and ResNet-34, as well as lightweight network models such as MobileNetV3-Small, EfficientNet-B0, etc. Due to its high recognition accuracy, strong robustness, and excellent anti-interference performance, the proposed model can meet the practical application requirements of crop pest recognition.

Abstract:As clean energy, wind power plays an increasingly important role in improving China’s energy structure. Data on wind farm units and equipment may contain relevant privacy-sensitive information. Once the information is divulged, it will bring huge economic and legal risks to the wind farm. Federated learning (FL) is an important privacy-preserving computing technique, through which model training and inference are completed without transmitting raw data, so as to achieve joint computation among all participants without privacy disclosure and effectively deal with challenges in analyzing wind power data. However, significant communication overheads generated during FL computation have become a major performance bottleneck that has limited the application of the FL technique in wind power scenarios. Therefore, this study takes the typical FL algorithm, namely, XGBoost, as an example and deeply analyzes the communication problems in FL computation. In addition, the study proposes a solution that RDMA shall be utilized as the underlying transport protocol and designs a set of high-performance FL platform communication libraries, which effectively improves the performance of the FL system.

Abstract:A virtual sports interaction system based on real-time video perception is proposed in response to the problems that traditional sports are limited by venues and equipment in the context of ongoing COVID-19 response, and the related products in the market are expensive and not scalable. The system is designed with a video data acquisition module and a human joint point extraction module, which can acquire human joint point coordinates in combination with OpenPose and capture human gestures and body movements in real time. The action semantic understanding module includes motion action understanding and drawing action understanding. The former recognizes the motion action semantics depending on the relative position relationship of the limb joints in motion. The latter generates the drawing action trajectories of wrist joints as sketch images, uses AlexNet to recognize and classify them, and resolves them into the corresponding drawing action semantics. The classification accuracy of the model is 98.83% in edge-side devices. A Unity-based sketch game application is used as the visual interaction interface to realize motion interaction in a virtual scene. The system adopts the interaction mode of real-time video perception to achieve home exercise and fitness without other external devices, which is more participatory and interesting.

Abstract:As the core technology to realize the information and digital transformation of the construction industry, BIM (building information modeling) technology has high research value in the whole life cycle of railway construction. In the design of railway communication equipment rooms, station yards, and intrastations, the spatial morphology of railway communication entity, such as spatial position, shape, size, and relationship, is described digitally. According to railway communication design specifications, relevant railway BIM standards, and professional design requirements, the digital engineering design system for railway communication is studied and developed. Supported by spatial morphological data and based on the decomposition standard of railway engineering entity structure, the system realizes the intelligent layout of indoor cabinet of railway communication equipment, the path planning of station communication trench cables, and the accurate layout of intrastation communication information points in a three-dimensional environment. Based on the digital engineering model and the basic principle of graph theory, the system obtains the logical relationship from the digital engineering model and generates the communication logical network diagram. Verified by the actual project, the system has greatly improved the design efficiency and accuracy of railway communication digital engineering, realized the delivery and application of digital achievements of railway communication engineering from the source of the project, and promoted the upgrading of technologies and innovation of digital modes in the whole life cycle of railway communication engineering project.

Abstract:Geriatric cognitive impairment is becoming a major threat to elderly’s life quality, but preventive measures, treatment technologies, and health care models are still immature for the elderly with cognitive impairment. Additionally, there is a lack of data systems for cognitive impairment in the elderly that can store these medical data in a complete and disaggregated manner. These lead to inaccurate diagnosis of cognitive impairment, delayed treatment of cognitive impairment, and unavailable appropriate medical care for cognitive impairment patients. To address these problems, this study designs a B/S-based multidimensional data management system for cognitive impairment in the elderly. The system takes advantage of the FastDFS distributed file storage system to ensure the security and stability of the system data. The recursive tree structure is adopted to extract the tabular data and speed up the screening process. The system is compatible with current major browsers.

Abstract:The adversarial robustness of deep neural networks is of great significance in the field of image recognition. Relevant studies focus on the generation of adversarial samples and the robustness enhancement of defense models but lack comprehensive and objective evaluation. Thus, an effective benchmark to evaluate the adversarial robustness of image classification tasks is developed. The main functions of this system are list display, adversarial algorithm evaluation, and system optimization management. At the same time, computing resource scheduling and container scheduling are applied to ensure the evaluation task. This system can not only provide a dynamic import interface for a variety of attack and defense algorithms but also evaluate the advantages and disadvantages of the existing algorithms from all aspects in the confrontation between attack and defense algorithms.

Abstract:The factory environment is complex and changeable, with many dangerous areas, and illegal entry can bring serious harm to the life and health of workers. Considering the complex operation and poor recognition effect of traditional detection methods, this study proposes an alarm system for workers’ intrusion in dangerous areas on the basis of the improved YOLOv5s model. Firstly, the binocular ranging technology based on the SGBM algorithm is integrated into YOLOv5s object detection, and the trigger condition of spatial distance is added. Hence, the sound and light alarm will be triggered only when workers approach the camera within a certain range. Furthermore, the attention mechanism is introduced into YOLOv5s. Comparative experiments prove that the introduction of the CA module improves the average accuracy of mAP@0.5 by 1.86%. The results show that this method can accurately identify the intrusion of a worker in dangerous areas and gives a sound and light alarm to remind the worker, which provides a new means for factory safety management.

Abstract:In water surface garbage detection, large differences occur in target shape and scale, and it is difficult to distinguish the background and the small target. Thus, this study proposes the SPMYOLOv3 detection algorithm to identify surface garbage. Firstly, massive surface garbage datasets are collected and annotated, and an improved K-means clustering method is applied to generate the priori boxes that better match the datasets. Secondly, the SE-PPM module is added after the backbone network of YOLOv3 for strengthening the feature information of the target, ensuring that the target scale remains unchanged and the global information is preserved. The multidirectional FPN is then applied to fuse the feature maps of different scales so that the feature maps after fusion contain richer context information. Finally, the Focal Loss is adopted to compute the confidence loss of negative samples, which alleviates the imbalance of positive and negative samples in YOLOv3. The modified algorithm is tested on the water surface garbage dataset, and the results show that the accuracy of the modified algorithm is 3.96% higher than that of the original YOLOv3 algorithm.

Abstract:The stock market is an important part of the financial market, and it is of great importance for stock price prediction. Meanwhile, deep learning has powerful data processing capability to solve the problems caused by the complexity of financial time series. In this regard, this study proposes a hybrid neural network model (ATLG) that combines a self-attention mechanism, a long short-term memory (LSTM) network, and a gated recurrent unit (GRU) for stock price prediction. The experimental results show the followings: (1) The ATLG model has higher accuracy than LSTM, GRU, RNN-LSTM, and RNN-GRU models. (2) The introduction of the self-attention mechanism makes the model more focused on the information of stock characteristics at important time points. (3) Comparison reveals that the two-layer neural network plays a more distinct role. (4) The backtesting with the moving average convergence and divergence (MACD) indicator achieves a 53% return, which is higher than the return of CSI 300 in the same period. The results prove the effectiveness and practicality of the model in stock price prediction.

Abstract:Compared with traditional logistics warehouses, many automated warehouses use automatic guided vehicles (AGVs) instead of workers to sort the goods, which changes the working mode from “man to goods” into “goods to man”. This change not only liberates the labor of workers but also combines the mechanization and automation of automated warehouses, greatly improving working efficiency. Path planning is an important part of AGVs in the process of sorting goods in automatic warehouses. For the path planning of AGVs, the traditional A* algorithm is improved because the paths planned by the traditional A* algorithm are too long and not smooth enough and have large turning angles. In view of the above defects, the method of dynamic weighting and changing the search neighborhood is proposed to improve the traditional A* algorithm, which reduces the search nodes and raises the search speed. At the same time, the path planned by the improved A* algorithm is smoothed by higher-order Bessel curves many times, which lowers the number of turning points. Finally, the comparison of three groups of simulation experiments proves that the improvement proposed in this study is of the reference value.

Abstract:Longitudinal tear detection of conveyor belts is one of the important issues in coal mine safety production. In the longitudinal tear detection of mining conveyor belts, insufficient detection accuracy, false detections, and missing detections occur due to insufficient data, diversified damage patterns, and extreme aspect ratios. In this study, an improved YOLOv4 longitudinal tear detection algorithm for conveyor belts is proposed. First, the existing data is expanded by data enhancement to construct a longitudinal tear data set for conveyor belts. Secondly, the variable convolution is added to the backbone network to enhance the feature extraction ability of the model for diverse damage patterns. Finally, in the feature fusion stage, the cross-stage partial network (CSPNet) structure is introduced to improve the longitudinal tear detection performance of the model for extreme aspect ratios, and further reduce missing detection and false detection. The experimental results show that the accuracy of the longitudinal tear detection for the conveyor belt reaches 92.5%, and the F1 score reaches 93.1%, which basically meets the requirements of the longitudinal tear detection for the conveyor belt.

Abstract:To address the problems of missed detection of faces, the insufficient computing power of mobile platforms, and the limited hardware resources of face recognition applications under epidemic prevention and control, this study proposes an improved lightweight detection model for faces with masks based on YOLOv5. In this model, the C3 module in the original network is replaced with a lightweight C3Ghost module to compress the computations of the convolution process and the size of the model. Moreover, an attention mechanism is added to the backbone network to improve the feature extraction capability of the network, and the border regression loss function is improved to improve the speed and accuracy of detection. The experimental results indicate that the amount of calculation and parameters of the improved model are decreased by 29.79% and 33.33%, respectively, with the weight file size of only 2.8 M. The improved model reduces the dependence on the hardware environment, and its detection rate reaches 96.6%. Compared with the existing models, it has outstanding advantages and can be effectively applied to face recognition.

Abstract:Entity alignment aims to find equivalent entities located in different knowledge graphs and is an important step for knowledge fusion. Currently, mainstream entity alignment methods are those based on graph neural networks. However, they often rely too much on the structural information of graphs, as a result of which models trained on specific graph structures cannot be applied to other graph structures. Meanwhile, most methods fail to fully utilize auxiliary information, such as attribute information. In response, this study proposes an entity alignment method based on a graph attention network and attribute embedding. The method uses the graph attention network to encode different knowledge graphs, introduces an attention mechanism from entity application to attribute, and combines structure embedding and attribute embedding in the alignment stage to improve the effect of entity alignment. The proposed model is verified on three real-world datasets, and the experimental results show that the proposed method outperforms the benchmark methods for entity alignment by a large margin.

Abstract:The detection and recognition of ship numbers are of great significance for the intelligent management of ports and can solve the time-consuming and labor-intensive problems caused by the traditional manual supervision of fishing boats. Since the ship number plates feature non-uniform hanging positions, background colors, and numbers of characters, this study proposes a two-stage detection and recognition method with two models. First, the study introduces a DP-DBNet ship number location detection model that combines dual path networks (DPN) with a differentiable binarization network (DBNet). Secondly, the study presents an MHA-CRNN ship number recognition model that combines the multi-head-attention mechanism (MHA) with the improved convolutional recurrent neural network (CRNN). Finally, this study uses the data from the new modern smart fishing port project in Zhifu District of Yantai and carries out an algorithm comparison experiment analysis. The experimental results show that the two-stage recognition method with two models can make the recognition accuracy rate of the ship number reach 76.39%, which fully proves the effectiveness and application value of the model in marine port management.

Abstract:Automatic program repair is an effective technology for ensuring software quality and improving development efficiency. At present, most automatic repair tools use test cases as the final method of patch correctness verification. However, program can barely be fully tested by limited test cases. Consequently, patch sets generated by automatic repair tools contain a large number of incorrect patches. To identify such patches, this study identifies the effectiveness of repair patches by comparing the execution paths of successful tests before and after defect repair and the methods of test case generation to solve the low accuracy problem of automatic repair tools. When the proposed method is applied to evaluate 132 patches generated by six classic repair tools, it successfully excludes 80 incorrect patches, without excluding correct ones. This result shows that the proposed method can effectively exclude incorrect patches and improve the accuracy of automatic repair tools.

Abstract:With the rapid growth of Internet finance and electronic payment business, resulting personal credit problems are also increasing. Personal credit prediction is essentially an imbalanced binary sequence classification issue. Such an issue is faced with a large size and high dimension of data samples and extremely imbalanced data distribution. To effectively distinguish the credit situation of applicants, this study proposes a personal credit prediction method based on feature optimization and ensemble learning (PL-SmoteBoost). This method involves the construction of a personal credit prediction model within the boosting ensemble framework. Specifically, data initialization analysis with the Pearson correlation coefficient is conducted to eliminate redundant data; some features are selected with the least absolute shrinkage and selection operator (Lasso) to reduce data dimension and thereby lower high dimensional risks; linear interpolation among the minority classes in the dimension-reduced data is carried out by SMOTE oversampling to solve the class imbalance problem; finally, to verify the effectiveness of the proposed algorithm, this study takes the algorithms commonly used to deal with binary classification issues as comparison methods and tests the algorithms with the high dimensional imbalance datasets downloaded from the open databases of Kaggle and Microsoft. With the area under the curve (AUC) as the algorithm evaluation index, the test results are analyzed by the statistical test method. The results show that the proposed PL-SmoteBoost algorithm has significant advantages over other algorithms.

Abstract:Generating short and readable regular expressions from finite automata is an important topic in computer theory. In the classical regular expression generation algorithms, the state sequence is the key factor that affects the quality of regular expressions. To search for excellent state sequences quickly and efficiently, this study takes the theory of the carnivorous plant algorithm as the core, combines the ideas of other heuristic algorithms for design and optimization, and proposes a state sequence search method based on the carnivorous plant algorithm. Through experiments, this method is compared with some existing search algorithms using heuristic rules. The experimental results demonstrate that the proposed state sequence search method is superior to other algorithms, and the length of the generated regular expressions is significantly shorter than that of other heuristic algorithms. For example, compared with the results of the DM algorithm, the length can be shortened by more than 20% with the increase in the order of automata, and compared with the results of the random sequence algorithm, the length can be shortened by several orders of magnitude.

Abstract:Taking the point cloud data from unmanned aerial vehicle (UAV) images of expressways as the research object, this study proposes a road green belt segmentation algorithm based on double decision factors. For this purpose, the point cloud data is serially down-sampled to retain as many point cloud feature points as possible in addition to reducing the number of point clouds; then, orthorectification of the down-sampled point cloud data is performed; finally, a point cloud segmentation algorithm featuring double decision with the normal vector angle and random sample consensus (RANSAC) plane segmentation is proposed, and accurate segmentation of the green belts in expressways is thereby achieved. The information on the environment of expressways is ultimately segmented with the green belt boundary extraction algorithm. Taking the point cloud from the UAV images of the Fengxiang section of G85 Expressway as the experimental data, this study verifies the proposed algorithm, the segmentation algorithm based on the normal vector angle, and the one based on RANSAC plane fitting. The experimental results show that the road green belt segmentation algorithm based on double decision factors can better resist the interference from environmental noise and outliers, effectively filter the high curvature points on the road surface, and ultimately obtain better extraction results.

Abstract:In order to correct the nonstandard riding postures of riders, a parametric modeling method for realizing standardized riding is proposed. Firstly, a manikin and a bicycle model are created, and parameters of the bottom layer, middle layer, and high layer are defined, so as to realize model parameterization. Secondly, force analysis during the riding is carried out, and a kinetic model is established, so as to ensure that the virtual riding conforms to the natural motion law. Finally, the constraint relationship between human upper and lower limb parameters and bicycle parameters is established to realize the coordinated movement of human joints, and the motion during the riding is simulated. The simulation results show that this method can provide correct posture guidance for riders.

Abstract:Manufactured sand refers to artificial sand whose particle size is less than 2.36 mm after the repeated crushing of gravels by sand-making machines. In experiments, stone powder and mud contents in the manufactured sand are called fine powder content, which represents the cleanliness of the manufactured sand. In this study, a method for predicting the fine powder content in the manufactured sand based on the XGBoost network is proposed. First, a completely closed image acquisition device is used to collect images of a solution made of fine powders in the manufactured sand, so as to guarantee that the outside light will not affect shooting. Then pre-treatment is carried out, such as picture cropping, RGB value reading, and LCH color space shifting, and an XGBoost network model is built. Through the Bayes principle, loop iteration of parameters is conducted, and the model is optimized, so as to make the r2_score of the model higher and finally predict the fine powder content in the manufactured sand. The results show that the r2_score of the data predicted by this model can reach 0.967 762. In addition, the r2_score predicted by the traditional multiple linear regression models, BP neural network, and traditional XGBoost network is 0.896 144, 0.914 598, and 0.950 670. In contrast, the prediction accuracy of the proposed model is significantly improved. In practical application, this method can shorten the measurement time and simplify the measurement steps of the fine powder content in the manufactured sand. Therefore, it is a new method for predicting the fine powder content in manufactured sand.

Abstract:As small targets in the early stage of a fire are difficult to detect during flames and smoke detection of fires, this study proposes an improved YOLOX-nano (ASe-YOLOX-nano) object detection algorithm based on natural exponential loss (eCIoU). Firstly, a new object detection function, the eIoU loss function, is proposed to replace the traditional IoU loss, which solves the problems of no intersection between the prediction box and the real frame in small target detection and the inability to react to the influence of width and height. Secondly, the attention module is introduced in the network model to vaguely locate the target position in the early stage of the network and improve the accuracy of the detection of targets, especially small targets, in the later stage of the network. In addition, the soft pooled spatial pyramid pooling structure is employed to extract spatial feature information of different sizes, which can improve the robustness of the model for spatial layout and object degeneration. In this way, sufficient features can be extracted when the target is small. Moreover, the Mosaic enhancement technology is used to preprocess the dataset to improve the generalization ability of the model for further improvement in network performance. The comparative verification of the target data set shows that the mAP index reaches 70.07%, which is 3.46% higher than that of the original model, and the model enjoys accuracy of flame and smoke detection of 84.66% and 74.56%, respectively, and a stable FPS of 73, which has better fire detection ability than the traditional YOLOX-nano algorithm.

Abstract:Manual planning of city marathon routes has low efficiency. In view of this, this study adopts a greedy and backtracking algorithm to carry out intelligent planning of a city marathon route. The specific method is described as follows. A road network connected by the topological relationship of longitude and latitude coordinate points is built through the urban road network information, and a traversal search is performed by the greedy and backtracking algorithm on the coordinate points. In addition, according to the special requirements of the city marathon route, strategies are adopted, such as direct approximation, heuristic distance, heuristic approach, and direction estimation, so as to realize the intelligent planning of the route. On this basis, a marathon route evaluation method is proposed, which integrates five dimensions including POI heat value, road width suitability, route smoothness index, comfort for turning, and POI density. Finally, a comparative analysis of artificial and intelligent route planning for Beijing and Hefei marathons is carried out. The results show that the proposed method can realize fast and efficient marathon route planning.

Abstract:In order to realize the automatic optimization of the optimal parameters of grayscale image enhancement, an adaptive image enhancement method based on an improved squirrel search algorithm is proposed. A bilateral search strategy is introduced into the position updating of the squirrels on normal trees to increase the likelihood of obtaining an optimal solution. A cyclone foraging strategy is used to update the position of the squirrels on acorn trees to improve the convergence rate and search accuracy of the algorithm. In addition, the proposed squirrel search algorithm with bilateral search and cyclone foraging (BCSSA) is compared with the bat algorithm (BA), whale optimization algorithm (WOA), basic squirrel search algorithm (SSA), and two improved SSAs on CEC 2017 test suite. The results indicate that BCSSA has higher stability and faster convergence rate. Finally, the proposed BCSSA is applied to grayscale image enhancement, and its performance is compared with that of the classical histogram equalization method and SSA in terms of four evaluation indicators, which thus validates the superiority of BCSSA.

Abstract:Traditional three-dimensional (3D) dense captioning methods have problems such as insufficient consideration of point-cloud context information, loss of feature information, and thin hidden state information. Therefore, a multi-level context voting network is proposed. It uses the self-attention mechanism to capture the context information of point clouds in the voting process and utilizes it at multiple levels to improve the accuracy of object detection. Meanwhile, the temporal fusion of hidden state and attention module is designed to fuse the hidden state of the current moment with the attention result of the previous moment to enrich the information of the hidden state and thus improve the expressiveness of the model. In addition, a “two-stage” training method is adopted in the model, which can effectively filter out the generated low-quality object proposals and enhance the description effect. Extensive experiments on official datasets ScanNet and ScanRefer show that this method achieves more competitive results compared to baseline methods.

Abstract:In recent years, research on the named entity recognition of poetry in digital humanities is emerging, but few studies have been conducted with regard to the feature expressiveness of character features, word segmentation accuracy, and the effectiveness of domain-specific knowledge in poetry texts. According to the characteristics of Chinese pictographs and the particularity of poetry texts, a recognition method of named poetry entities with a feature enhancement unit and a feature extraction unit is proposed, which integrates multiple features such as characters, radicals, sounds, and metrical rules. The method presents the knowledge vectors obtained from the knowledge triples of tune pattern titles through the ANALOGY model as the knowledge vectors of tune pattern titles. Then, the radical vector, character vector, metrical rule vector, sound vector, and knowledge vector of tune pattern titles are deeply fused through the bidirectional long short-term memory network and attention mechanism models. In this way, the recognition method of named poetry entities fusing multi-features is constructed. The results of comparative experiments and ablation experiments on the self-made corpus of Translation of Among Flowers (Hua Jian Ji) (《花间集全译》) show that the proposed method can effectively use multi-features to improve the recognition performance of named entities, and its F1 score reaches 85.63%, which means it completes the recognition task of named poetry entities.

Abstract:Exploring and protecting fish is an important part of maintaining the balance of the marine ecological environment. However, the complex underwater environment affected by light, water quality, and occlusions makes it difficult to identify blurred fish images captured underwater and consequently restricts the speed and accuracy of underwater fish target detection. To solve the above problem, this study proposes a marine fish identification model based on improved fully convolutional one-stage object detection (FCOS). Specifically, the model takes the one-stage FCOS algorithm as the basic structure and uses the lightweight MobileNetv2 as the backbone network, which not only ensures the detection accuracy but also improves the detection; then, an adaptive spatial feature fusion (ASFF) module is introduced to avoid the inconsistency in scale features and improve detection accuracy; finally, the center-ness branch is introduced into the regression branch, and the generalized intersection over union (GIoU) loss is introduced to improve detection performance. Regarding the experimental dataset, the pictures in the public dataset Fish4Knowledge (F4K) and video frame screenshots are utilized, and the model with the optimal training performance is selected for evaluation. The results show that the average detection accuracy of the proposed new model on the above datasets is 99.79% and 99.88%, respectively. Compared with the original model and other detection models, the proposed model provides higher detection accuracy and identification speed. The model in this study can provide a reference for marine fish identification.

Abstract:The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station will become the main target of network attacks if it lacks grid binding. In order to perceive the network attack events on the subscriber station side in time, a method combining real-time detection and active defense of random domain names on the subscriber station side is proposed. A capsule network (CapsNet) combined with a long short-term memory (LSTM) network is used to classify the domain names extracted from the traffic data. When a random domain name is detected, instructions are sent to routers and switches to update their security policies or shut down the service interfaces of routers and switches to block network attacks through the remote terminal protocol (Telnet). The experimental results show that the use of the CapsNet combined with the LSTM classification algorithm can achieve an accuracy of 99.16% and a recall of 98% in random domain name detection. Through the Telnet, routers and switches can be linked to make active defense without interrupting services.

Abstract:The weakly connected components of the time-evolving graph have been widely used in many areas, such as traffic network construction, information push of recommendation systems, etc. However, most methods for the weakly connected components ignore the impact of the non-uniform memory access (NUMA) architecture, that is, the high remote memory access delay leads to low execution efficiency. This study proposes a NUMA-based delayed sending method to find the weakly connected components of the time-evolving graph. It minimises the number of remote accesses and improves computational efficiency through reasonable data memory layout and controlling the number of exchanges between NUMA nodes. The experimental results show that the performance of the NUMA-based delayed sending method is better than the methods provided by the current popular graph processing systems Ligra and Polymer.

Abstract:In the path planning of unmanned aerial vehicles (UAVs), the traditional algorithm has the disadvantages of complex computation and slow convergence, while particle swarm optimization (PSO) features simple principle, strong universality, and comprehensive search, which is mainly used in UAV route planning. As the conventional PSO algorithm is easy to fall into the local optimum, this study integrates the global extreme variation and acceleration terms based on the adaptive parameter optimization to balance the global and local search efficiency and avoid the population falling into “premature”. Through the test of a variety of benchmark functions, the results show that the improved PSO algorithm proposed in this study has faster convergence speed and higher convergence accuracy. In the example verification part, the flight scene features are first extracted, and the environment modeling is carried out based on the UAV performance constraints. Then multiple constraints and the expected minimum flight time are converted into penalty functions. With the minimization of penalty functions as the objective, the route planning model is constructed, and the improved PSO algorithm is adopted to solve the problem. Finally, the effectiveness and practicability of the improved PSO algorithm are verified by comparative simulation.

Abstract:It is essential for banks to accurately predict whether clients will use their credit and analyze key factors influencing credit utilization after these clients have been approved for credit, so as to improve their client service level and profitability. Currently, machine learning algorithms are rarely applied to credit utilization prediction, and there is a lack of research on model interpretability in the financial credit utilization field. Therefore, this study proposes an interpretative TreeSHAP credit utilization prediction model based on CatBoost. Specifically, a credit utilization prediction model is constructed by CatBoost and is compared and optimized by using three hyperparameter optimization algorithms. Then, the model is experimentally compared with baseline models in terms of four main performance metrics. The results show that the model optimized by the TPE algorithm outperforms other models. Finally, the interpretability of the model is enhanced locally and globally by the TreeSHAP method. Furthermore, factors influencing client credit utilization are interpretively analyzed, so as to provide a decision-making basis for banks to make accurate marketing to clients.

Abstract:To identify the running fault of motors quickly and effectively from the temperature data collected by thermal imagers, this study combines dropout, nonlinear wavelet transform coefficient enhancement (NLWTCE), and convolutional neural network (CNN) algorithm to identify the motor image. Firstly, the image dataset of the motor is established according to the data collected by the thermal imager and the data image is enhanced by nonlinear wavelet transform (NLWT). Then an improved CNN (ICNN) model is built to identify the image with the extracted features as the final recognition features. Finally, compared with the normal motor images, the faulty motor images are effectively and accurately identified. The experimental results show that the ICNN model not only has a high recognition accuracy but also further simplifies the complex extraction of image features. The validity and reasonableness of the method are verified, and the method is suitable for engineering application.