bluuebunny/crossref_metadata_2025_split huggingface dataset:
Traffic Sign Detection Algorithm Based On Improved YOLOv4
Authors: Wu Xiru , Cao Haozhe 
Published: April 2022

Abstract
This paper proposes a traffic sign detection algorithm using improved lightweight YOLOv4 in order to solve the problems of large parameters quantity, poor real-time performance, and low accuracy for the unmanned driving system. Firstly, we use the MobileNetV2 network to replace the original feature extraction network in YOLOv4 to improve the detection speed by reducing the number of model parameters. Secondly, we add an attention mechanism to the backbone network to effectively extract feature information. Trim the network layer of the prediction part and add residual structure, so that feature reuse enhancement can avoid the gradient disappearing when the network layer is deeper. The size of the improved network model is reduced by 79.5%. The experimental results show that the average accuracy of the network model is 2.5% lower than that of the YOLOV4 network, and the detection speed is 58.8% higher than that of the YOLOv4 network.
A hierarchy superpixel algorithm dealing with stripe
Authors: Zhou ZhenDong , Chen Yu 
Published: April 2022

Abstract
The hierarchy superpixel algorithm, in computer vision, aims to generate multi-scale superpixels with boundary perception. The commonly used strategy to obtain hierarchical structure is utilize the similarity between adjacent finer superpixels, then iteratively generate coarser superpixels based on finer ones. However, these methods may not achieve desired result when dealing with stripe targets. The alternating parts of the stripes are too different in image features, thus computer believes these parts are unrelated. In human visual perception, however, these parts actually belong to one striped object. We propose a new ISF-based method to solve this problem. Firstly, we present a improved ISF algorithm in finest scale segmentation with the help of sliding window strategy, which runs faster without reducing accuracy. Then, in order to generate hierarchy superpixels of stripe images, we design a new cross-neighbor path-cost function and add a stripe-pattern detector. Experimental results demonstrate that the proposed algorithm is accurate and efficient.
GA-based fuzzy neural network control for ROV
Authors: Lu Ziling , Xin Shaojie 
Published: April 2022

Abstract
Aiming at the problem of uncertainty and static instability of underwater robot system, a motion control algorithm of underwater robot based on fuzzy neural network optimized by genetic algorithm is proposed in this paper. The algorithm makes use of the characteristics of neural network which can predict and approximate the target value well and the complementarity of fuzzy function to neural network, and uses genetic algorithm to optimize the parameters of fuzzy neural network membership function, so as to reduce the computation of neural network. The experimental results show that the controller has good robustness and enhances the stability of the control system.
Overview of Crow Search Algorithm
Authors: Wang Tingting , Zhang Chengming , He Ankun , Dong Wei 
Published: April 2022

Abstract
Crow Search Algorithm (CSA) is a swarm intelligence optimization resolving schemes. Its optimization method simulates the behavior of crows tracking each other. The paper elaborates the basic principle of the algorithm, discusses the main parameters affecting the algorithm performance, and analyzes the search mechanism of the algorithm. The improvement strategies of the algorithm are summarized. Examples are given to illustrate the application of the algorithm in 0-1 knapsack problem, Image processing, Scheduling issues, feature selection and parameter optimization. Finally, according to the research and application status of crow search algorithm, the future research and development direction are prospected.
Application of UAV path planning based on parameter optimization GA-PSO fusion algorithm
Authors: Lin Chaoxiong , Zhang Xuecong 
Published: April 2022

Abstract
Aiming at the complexity of the unmanned aerial vehicle (UAV) path planning problem and the great influence of genetic algorithm parameters on the stability of the results, a fusion algorithm based on parameter optimization is proposed in this paper. In the iterative process, the GA-PSO fusion algorithm uses particle swarm optimization algorithm to search the optimal value of crossover rate and mutation rate in genetic algorithm, which makes the algorithm convergence speed is fast and search ability is strong. In addition, the core part of the algorithm fusion framework is realized by introducing the optimal adaptive value of the population after crossover and mutation as the adaptive value of the parameter particle. Finally, we design two groups of experiments and compare the proposed fusion algorithm with the classical particle swarm optimization algorithm (PSO), ant colony algorithm (ACA), genetic algorithm (GA), artificial fish swarm algorithm (AFSA), Wolf pack algorithm (WPA), artificial bee colony algorithm (ABC) and the improved algorithm through experimental simulation. The experimental results show that: In general, the path planned by the GA-PSO fusion algorithm in this paper is 10% shorter than that planned by other algorithms on average. Simulation results also show that the convergence speed of the fusion algorithm in this paper is faster, and the final search path is smoother.
An effective metaheuristic for the robust distributed flowshop scheduling problem
Authors: Guo Hengwei , Sang Hongyan , Sun Xiaobin , Zhao Yasheng , Tian Mengxi , Li Zhongkai 
Published: April 2022

Abstract
This paper discusses the distributed permutation flowshop scheduling problem under uncertain processing time and considers the sequence-dependent setup times (denoted as DPFSP_UPT_SDST). Uncertain processing times are described by interval scenarios. An expectation-standard deviation robust model is used to describe the DPFSP_UPT_SDST. Based on the characteristics of the DPFSP_UPT_SDST, four heuristics are proposed. In addition, a discrete Fruit Fly optimization algorithm with an age mechanism is proposed, which helps the algorithm to avoid local optimum and increases the diversity of solutions. The parameters of the proposed algorithm are calibrated by a detailed design experiment. Finally, the effectiveness of the proposed heuristics and metaheuristic is demonstrated by a comprehensive comparison with the closely related and state-of-the-art algorithms.
HRT-YOLO: A transformer-based high-resolution representation model for face flaw detection
Authors: Sun Yage , Li Xueming , Zhang Xianlin 
Published: April 2022

Abstract
Nowadays, it is imperative to accurately detect and repair face flaws to obtain high-resolution, detail-preserving face images in social life. However, there exists a significant gap between face flaws and natural scene features. In this paper, we propose a framework of HRT-YOLO to erase the problems of small scale, irregular shape, and regional overlap of face flaws. Based on YOLOv5, we introduce high-resolution representation, where the main network of high-resolution feature maps is gradually added in parallel to the sub-network of low-resolution feature maps, allowing for more accurate localization of flaws. We also implement transformer detection heads, which use a multi-head attention to mine contextual features and improve the defect-intensive regions detection. Finally, we present a high-resolution face flaw dataset to evaluate our approach. The evaluation results show that HRT-YOLO has excellent performance on face flaw detection, with 3.23% improvement in mAP and 5.33% improvement in accuracy compared to the baseline (YOLOv5).
Physiological Signal Emotion Recognition Based on Temporal Convolutional Networks
Authors: Sheng Yong , Hu Qinghong , Cao Jun 
Published: April 2022

Abstract
Human emotion recognition has become an important research field. Because of its objectivity, physiological signals have become one of the most robust cues for emotion recognition. In recent years, deep learning methods have made great progress in the field of emotion recognition, especially the superiority of recurrent neural networks (RNN) in time series models, and more and more tasks are completed based on RNN. However, RNN has problems such as time-consuming and gradient disappearance and explosion, and the feature input and sentiment output of RNN are not aligned. To avoid these problems, this paper is based on the temporal convolutional networks (TCN) model and the connectionist temporal classification (CTC) algorithm to process the emotion recognition task. First, generate a spectrogram representation of the physiological signal in each channel; Second, use the TCN to learn long-term dynamic features, and use CTC to align the dynamic features and their sentiment labels, and then feed the learned deep features into the neural network to predict the sentiment of each channel; Finally, take the best result as the final emotion representation. Experimental results on the AMIGOS dataset show that the proposed method outperforms existing methods.
Multi-view Attention Mechanism Learning for POI Recommendation
Authors: Zhu Fujian , Dai Shaojie 
Published: April 2022

Abstract
POI(point of interest) recommendation is a very necessary research field in both academic and commerce, however, predicting users’ potential points of interest is always faced with the problems of data sparsity and context semantics. Some studies have shown that graph embedding technology alleviates the problem of data sparsity to a certain extent. However, neither graph embedding techniques nor unsupervised learning models can adaptively learn the different effects of multiple relations between users and POIs, respectively. In view of this, we leverage the contextual information of users and POIs to build the multi-view affinity graphs(e.g. User-User, POI-POI and User-POI), and learn the latent representations of users and POIs based on the Graph Embedding technology and Attention mechanism, namely the GEA model. In particular, we first construct multi-view affinity graphs by using user’s social relationship, geographical distance and check-in behaviour, and embed them into a low dimensional shared space to learn the latent representation of users and POIs. Afterwards, in order to take advantage of the different effects of multiple relationships in the final recommendation task, we exploit the attention mechanism to obtain the fused latent representation and make recommendation according users’ potential preferences. Finally, we design a multi-task objective function for joint optimization to obtain more accurate recommendation results. Extensive experiments on Gowalla have verified the effectiveness of our model.
Optimal scheduling model based on unmanned collector truck arrival time variation field bridge under balanced strategy
Authors: Chen JiaHong , Zhou JiaJing , Zheng YuTing , Ma RongZhe 
Published: April 2022

Abstract
The scheduling of container yard bridges has an important impact on the operational efficiency of the terminal. For the future automated terminal, to reasonably schedule the field bridges, under a given batch of tasks, considering the change of the arrival time of unmanned trucks and the balance of the workload of each field bridge, a balanced strategy is established based on the change of the arrival time of unmanned trucks. The optimal scheduling model of field bridges is given, and an example of field bridge scheduling is given through experiments. Provides a scientific solution for the future automated dock, yard, and bridge scheduling
Person identification using a new CNN-based method and radar gait micro-Doppler signatures
Authors: Huang Yu , Jiang Enshuo , Xu Haodong , Zhang Guangbo 
Published: April 2022

Abstract
The use of gait micro-Doppler signatures to identify a person is a hot topic of research. In this paper, we present a new CNN-based method called Multi-Scale CNN (MS-CNN) to obtain features at multiple scales. It extracts shallow features at low-level multi-scale blocks by using multiple kernels at the same time, then extracts deep features and fuses multi-branch embedding features at high-level multi-branch blocks. Experimental results reveal that our method outperforms other commonly used CNN algorithms in terms of accuracy, allowing it to be used for personal identification.
Hybrid Electric Vehicle Energy Online Allocation Strategy Based on Fuzzy Reinforcement Learning
Authors: Xu Dong 
Published: April 2022

Abstract
To lessen fuel consumption of hybrid electric vehicle, energy online distribution strategy based on fuzzy reinforcement learning is proposed. Vehicle dynamic model and key component model are built, so that hybrid power system simulation model is given. Energy distribution problem of hybrid electric vehicle is transferred to constrained optimization problem of stochastic dynamic system through modelling, and solving problem based on fuzzy reinforcement learning is put forwarded. Fuzzy reinforcement learning is used to optimize fuzzy inference system real-time, which makes fuzzy inference system adjust with driving cycle adaptively, so that optimal control under any driving cycle come true. Clarified by simulation, fuel consumption under the control of rule-based is 3.89L/km, and it is 3.45L/km under the control of fuzzy reinforcement learsning, which saves 11.31% fuel consumption compared with rule-based. The data above proves validity of fuzzy reinforcement learning on hybrid electric vehicle energy management.
A Change Detection Method Based on Full-scale Skip Connections and Mixed Pooling Module
Authors: Tian Dashuai 
Published: April 2022

Abstract
Change detection is one of the main driving forces for the continuous development of remote sensing image processing technology. As a pixel-level detection task, change detection attaches great importance to the utilization of original spatial information as well as contextual information. In this paper, we propose a deep learning-based change detection method that combines full-scale skip connections and mixed pooling module (MPM). Firstly, full-scale skip connections make full use of the spatial information of features at different scales to improve change detection performance. In addition, we introduce MPM in this method due to the complexity of scene information in images, which can enhance the understanding of the scene with rich contextual information and boost the detection ability of strip shape targets. Finally, our experimental results on two public datasets demonstrate that the proposed method has good performance.
Finite Time Speed and Position Control for Dynamics of HST with Time-Varying Delays
Authors: Wang Runze , Liang Tiantian 
Published: April 2022

AbstractIn this paper, a finite time robust controller is proposed for tracking the speed and position of high speed trains. Considering the aerodynamic flag, mechanical rolling resistance, additive resistance and wind gust, the dynamics of high speed train with time-varying delay is established in longitude. Aerodynamic flag and mechanical rolling resistance are seen as the coefficient of the state variables, wind gust and additive resistance are seen as the external disturbance, and time-varying delay in speed are considered as the train is often running in bad weather or severe working conditions. Then a delay-dependent finite time robust controller is designed. This controller not only guarantees the closed-loop error dynamics error is finite time boundness, but also is robust to the external disturbance consisting of wind gust and additive resistance in finite time. Sufficient conditions of the controller design are given, and the gain matrices of the controller are formulated as linear matrix inequality. A 5-car simulation example is given to show the effectiveness of the proposed method.
Unsupervised Video Summarization Based on An Encoder-Decoder Architecture
Authors: Li Xin , Li QiLin , Yin Dawei , Zhang Lijun , Peng Dezhong 
Published: April 2022

Abstract
The purpose of video summarization is to facilitate large-scale video browsing. Video summarization is a short and concise synopsis of original video. It usually composed of a set of representative video frames from the original video. This paper solves the problem of unsupervised video summarization by developing a Video Summarization Network (VSN) to summarize videos, which is formulated as selecting a sparse subset of video frames that best represents the input video. VSN predicts a probability for each video frame, which indicates the possibility of a frame being selected, and then takes actions to select frames according to the probability distribution to form a video summary. We designed a novel loss function which takes into account the diversity and representativeness of the generated summarization without labels or user interaction.
CSChecker : A binary taint-based vulnerability detection method based on static taint analysis
Authors: Guo Wei , Wei Qiang , Wu Qianqiong , Guo Zhimin 
Published: April 2022

Abstract
Embedded devices such as routers not only bring convenience to people’s daily life, but also increase the attack surface and security risks of devices. Embedded device applications tend to be closed source and therefore cannot be searched for vulnerabilities through source code audits. Even open source applications can be insecure because they reference third-party libraries. Binary file vulnerability mining is an important means to solve this kind of problem, but it has the problems of path explosion and low efficiency. This article uses the static stain analysis with the method of combining the vulnerability characteristics, in the type of stain into classes and class assignment holes for testing. Based on function call graph, this paper uses atomic combinational optimization to detect the vulnerability of router firmware. The prototype tool -- CSChecker is implemented in D-Link, Tenda, the test was carried out on 267 firmware files of Netgear and other well-known brands, and the experimental results showed that the accuracy of CSChecker in the data set reached 92.51%, indicating that CSChecker could effectively search the injection vulnerabilities and assignment vulnerabilities of binary files.
Fault Diagnosis of Civil Aircraft Electrical Power System Based on Triangular Fuzzy Number and Grey Relational Analysis
Authors: Jiang Xiaoting 
Published: April 2022

Abstract
The stable operation of the aircraft electrical power system is closely related to aircraft’s function implementation and safety. Therefore, research on fault diagnosis of aircraft electrical system is important and necessary. This paper proposes a fault diagnosis method of civil aircraft power system based on triangular fuzzy number and grey relational analysis. For the purpose of obtaining complete data, it takes into account single fault symptom and historical data, and keeps the fuzzy feature from initial estimation of diagnosis scheme to the end. The example analysis shows that this method has feasibility and efficiency in practical application.
Underwater target passive detection method based on Hough transform track-before-detect
Authors: Wang Xuemin , Lu Renwei , Li Wenhai 
Published: April 2022

Abstract
Aiming at the low detectability and concealment detection requirements of underwater targets such as underwater unmanned vehicles (UUV), an underwater target passive detection method based on Hough transform track-before-detect was proposed. In order to deal with the approach reconnaissance of UUV, the passive detection model of UUV was constructed; In order to obtain the target position information required for track-before-detect, the cross-positioning method of multiple passive sonar buoys was adopted to realize the data pre-processing and noise reduction before concealment detection; In order to improve the detection performance of passive sonar in complex underwater acoustic environment, Hough transform method and point trace correlation were used to realize the demand of low detectability of underwater targets. Simulation results show that this method can still maintain high detection probability under the conditions of low signal-to-noise ratio, multi-target and complex noise.
Experimental research on occlusion face detection method based on attention mechanism
Authors: Zhang Kexin , Zhu Qing , Li Weiran 
Published: April 2022

Abstract
Face occlusion is a common problem in real life. Aiming at the problem that the face detection effect is poor when there is occlusion in the natural environment, this paper proposes the RetinaNet occlusion face detection network based on the fusion image enhancement network and the attention mechanism. Traditional RetinaNet, as a typical single-stage detector, does not explicitly give the process of extracting candidate regions and directly obtains the results of object detection. Compared with the two-stage detector, the processing speed is faster, but the accuracy will also be reduced. In this paper, the attention mechanism and image enhancement strategy are introduced to improve the detection accuracy while ensuring the detection speed. The experimental results on the MAFA dataset show that the RetinaNet model based on the attention mechanism proposed in this paper achieves 89.7% accuracy in detecting occluded faces. The feasibility and effectiveness of the model are verified.
Research on electrical automatic control system based on PLC
Authors: Feng Jingxiao , Wang Xijuan 
Published: April 2022

Abstract
At present, automatic control technology is widely used in the field of drying. Although the existing processing technology can basically ensure the quality of drying, there are some problems, such as low production efficiency, low thermal efficiency, uneven hot air cloth, inaccurate temperature control and so on. The application of electromagnetic technology can improve the efficiency of drying and processing. It has remarkable energy-saving effect. In the drying process, aiming at the technical problems such as incomplete volatilization of water and difficult stability of temperature in rapid drying, a drying system based on PLC control is designed in this paper. Using PLC as the main controller of the control system, through the reasonable control of the changes of temperature and humidity in the drying process, a more efficient processing process can be realized. The drying quality can be improved, and the process flow of mass processing and production can be realized. Through the experimental analysis of the drying process, it is proved that the control accuracy of the research content in this paper is more accurate, which not only improves the automation level in the drying process, but also reduces the labor cost in the process, and has high technical application value.
The Apollo ATCA design for the CMS track finder and the pixel readout at the HL-LHC
Authors: Albert A. , Demiragli Z. , Gastler D. , Hahn K. , Hazen E. , Kotamnives P. , Noorudhin S. , Peck A. , Rohlf J. , Strohman C. , Wittich P. , Zou R. 
Published: April 2022

Abstract
The challenging conditions of the High-Luminosity LHC require tailored hardware designs for the trigger and data acquisition systems. The Apollo platform features a “Service Module” with a powerful system-on-module computer that provides standard ATCA communications and application-specific “Command Modules” with large FPGAs and high speed optical fiber links. The CMS version of Apollo will be used for the track finder and the pixel readout. It features up to two large FPGAs and more than 100 optical links with speeds up to 25 Gb/s. We study carefully the design and performance of the board by using customized firmware to test power consumption, heat dissipation, and optical link integrity. This paper presents the results of these performance tests, design updates, and future plans.