bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of explosive charge mass on reflected shock wave of high explosion
Authors: Gao Rui , Wang Bairong , Luo Xia 
Published: September 2021

Abstract
Overpressure is an important metric of the destructive effect of the shock wave produced by high explosion. Investigating the overpressure of shock wave on the surface is an essential way for evaluating the power of explosive device. This study has important implications for quantitative analyses for establishing the relationship between parameters of reflected shock wave by using the numerical simulation software AUTODYN with different TNT explosive charge mass. The research indicates that with the increase of explosive charge mass, the initial value of peak overpressure of high explosive increases and the attenuation rate accelerates, it also provides the condition that the similarity law can be established in the relationship between peak overpressure and scale distance.
Hardware Design and Implementation of Image Processing System Based on FPGA
Authors: Zhao Dongmei , Zhou Bo , Song Yang , Li Ling 
Published: September 2021

Abstract
Discussed the hardware design and implementation of the image processing system with Virtex-XC5VSX95T as the algorithm processing chip. The large-capacity data storage chip XCF128X is selected to store the program flow. The user clock, DDR2 clock, SATA clock and other clock modules are designed. Designed LVDS and SATA as a high-speed data interface for image data transmission. Designed RS-422 as a communication interface for parameter, command and status data transmission. DDR2 memory modules and NAND Flash are as memory for image data caching. Dedicated voltage regulator chip is selected to meet the requirements of each module of the system for DC voltage. The test proves that the hardware design of the image processing system is correct and can realize the expected function.
An Auxiliary Identification System for Destructive Pests
Authors: Mai Weijian , Wu Fengjie , Chen Jiangtao , Tang Yisheng , Liu Qingkun 
Published: September 2021

Abstract
This work takes this Asian Hornet’s invasion of Washington in 2020 as an example to build an auxiliary identification system for Destructive Pests to help the government’s rescue work. To help the government to prioritize the resource to the most possible reports and improve the investigation efficiency, we built a binary classification model to classify the report that contains images and text. For image classification, we design a VGG16-based convolutional neural network pretraining with ImageNet, which achieved superior performance with a high F1 score (95.08%) on our dataset. For text classification, we use the Latent Dirichlet Allocation (LDA) model to cluster words into different themes and adopt cosine similarity to calculate the similarity between the new report and the benchmark positive/negative sets. The results demonstrated that our approach can identify target pests efficiently and help the government to prioritize the resource to the most possible reports, improving the investigation efficiency.
Research on Image Classification Algorithm Based on Convolutional Neural Network-TensorFlow
Authors: Zhao Jiantao , Li Lili 
Published: September 2021

Abstract
Image Classification is one of the important processing methods in the field of image processing. Traditional Classification algorithms, such as KNN (K-NearestNeighbor), have been unable to meet the accuracy standards of image Classification. And convolutional neural networks (CNN) are increasingly used to solve the problem of image classification. This paper briefly ces the convolutional neural network, which is constructed using the TensorFlow deep learning framework, and conducts experiments on 2000 pieces of custom data set of 10 classification obtained by crawler technology. At the same time, a comparative experiment is conducted on the traditional image classification KNN algorithm. Experimental results show that the application of convintroduolutional neural network in image classification has relatively large advantages, and the accuracy has been greatly improved.
Visible and infrared missile-borne image registration based on improved SIFT and joint features
Authors: Xue Song , Zhang Xiaorong , Zhang Hang , Yang Chuandong 
Published: September 2021

Abstract
For the acquired visible and infrared missile-borne images, in order to provide accurate and reliable image data for the subsequent processing of images to achieve complementary information, we carried out visible and infrared missile-borne image registration research. Firstly, the shape context is used to extract the rough edge features of the image. Then we improve the SIFT gradient definition to overcome the difference in image gray value, and combine with the coherent point drift algorithm to increase the number of correct matching points to achieve global image registration. Experimental results show that, compared with the existing registration methods, the proposed method has a good visual effect of registration, greatly improves the correct registration points and registration accuracy, and can better solve the problem of visible infrared missile-borne image registration.
Prediction and Analysis of Short-Term Load Forecasting Model Based on Similar Day Clustering and CatBoost
Authors: Chen Zhonghua , Chen Jiamin , Wang Caiqian 
Published: September 2021

Abstract
Accurate load forecasting provides a reference of vital importance for power generation and power dispatching systems. A short-term load forecasting model combining similar day clustering and CatBoost method is proposed in this paper. Similar day clustering is a method for evaluating the similarity between the days to be forecast and historical days. Similar day clustering is based on K-means clustering which can classify samples with similar features into the same category. Then, CatBoost is used to build load forecasting models. CatBoost enables us to directly handle categorical features without pre-processing. This paper utilized the information collected by a substation in Hangzhou as the data set, and compare the proposed model with the existing forecasting model based on Autoregressive Moving Average, Gradient Boosting Decision Tree, CatBoost, Long Short-Term Memory. The experimental results show that the proposed model provides more accurate results compared with other models in terms of MAPE, RMSE.
Topic evolution analysis of radar research using a dynamic topic model based on latent Dirichlet allocation
Authors: Huang Xiaoguang , Fang Hui 
Published: September 2021

Abstract
This work applies a dynamic topic model based on latent Dirichlet allocation to investigate the evolution of radar research and applications. To address the problem that processing a large number of papers exceeds the capacity of computers, we propose a method to sample documents according to their citation count. Of 104,428 sampled articles, 108 research topics are extracted. The evolution of topics is analysed from two dimensions: content and strength. The results show that radar technology, which arose mainly from physics and engineering science, has been widely applied in studies in the civil engineering, geographical, environmental, meteorological, geological, agricultural, ecological, among others. In the long-term development of radar, new technologies have continuously been produced. At the individual topic level, the research content has changed over time. The development objectives of radar systems are to enhance functionality, extract more information and improve clarity.
Voxel-wise binocular energy models for binocular disparity decoding
Authors: Zheng Hongna , Chen Maoming , Yao Li , Long Zhiying 
Published: September 2021

Abstract
Binocular disparity is a powerful cue for depth perception in three-dimensional (3D) space. Some neurophysiological studies proposed the binocular phase-shift and position-shift energy models to predict the responses of individual disparity-tuned neurons in cats and macaques. By far, it is unclear how to use binocular energy models to characterize the voxels’ responses in human visual cortex. In this study, we introduced the binocular energy models to the functional magnetic resonance imaging study and constructed the position-shift receptive-field model (Position-RFM) and the phase-shift receptive-field model (Phase-RFM) to predict voxel responses to disparity and to identify novel disparity levels from voxel responses. The results revealed that Phase-RFM outperformed Position-RFM in fitting the voxel responses for all the visual regions. Moreover, the novel disparity levels can be better identified from voxel’s responses in visual regions by Phase-RFM than Position-RFM. The findings may suggest that Phase-RFM can better encode the responses of disparity-tuned neuron populations than Position-RFM for human visual regions.
Weakly and semi-supervised learning for sound event detection using image pretrained convolutional recurrent neural network, weighted pooling and mean teacher method
Authors: Cai Xichang , Yu Dongchi , Liu Duxin , Wu Menglong 
Published: September 2021

Abstract
In this paper, we propose a sound event detection (SED) method which uses deep neural network trained on weak labeled and unlabeled data. The proposed method utilizes a convolutional recurrent neural network (CRNN) to extract high level features of audio clips. Inspired by the impressive performance of transfer learning in the field of image recognition, the convolutional neural network (CNN) in the proposed CRNN is an image-pretrained model. Although there is a significant difference between audio and image, the image-pretrained CNN still has competitive performance in SED and can effectively reduce the amount of training data needed. To learn from weak labeled data, the proposed method utilizes a weighted pooling strategy which enables the network to focus on the frames containing events in an audio clip. For unlabeled data, the proposed method utilizes a mean teacher semi-supervised learning method and data augmentation technique. To demonstrate the performance of the proposed method, we conduct the experimental evaluation using the DCASE2021 Task4 dataset. The experimental results demonstrate that the proposed method outperforms the DCASE2021 Task4 baseline method.
MOOC Dropout Prediction Based on Multi-view Learning
Authors: Li Feifan , Wei Zhengang 
Published: September 2021

Abstract
Massive Open Online Courses (MOOCs) provides a promising way to support education for all. Nonetheless, one central challenge is the remarkably high dropout rate, with completion rates for MOOC recently reported to be below 5%, and high dropout rates limiting their effectiveness. Building on the analysis of dropout as closely related to user and course interaction patterns, this paper presents an end-to-end multi-view interactive framework (EMIF) to predict user dropout in MOOC. We extract learning activity information, user enrolment information, and course knowledge information through a set of lD-CNNs, obtain mutual information of these and fuse the above information-view loss through a self-weighing method. Experimental evaluation of our solution on the KDDCUP 2015 and MOOCCube datasets showed better results (AUC & F1 scores). We have a better performance compared to several existing methods, without using different architectures for different datasets.
Adaptive Weight Fusion Algorithm of Infrared and Visible Image Based on High-Frequency Domain CNN
Authors: Wang Chuanyun , Yang Guowei , Sun Dongdong , Zuo Jiankai , Wang Ershen 
Published: September 2021

Abstract
Aiming at covering the shortage of single source sensor imaging and improving the contrast between the target and the background in image, this paper proposes an adaptive weight fusion algorithm of infrared and visible image based on a High-frequency Domain Convolutional Neural Network (HDCNN). Firstly, the high and low frequency components of the original image are obtained by using the Daubechies wavelet transform, and then a high-frequency domain convolutional neural network which can detect the frequency information ratio of infrared and visible light in the high-frequency subband is trained. Secondly, the network is used to perform adaptive weight fusion for the high frequency components and regional energy is used for fusion of the low frequency components. Finally, the fusion image is obtained by inverse wavelet transform. A large number of experiments have proved that the algorithm in this paper has a greater improvement over similar comparison algorithms in objective evaluation metrics such as standard deviation, spatial frequency and average gradient. The algorithm enhances the contrast between the target and the background in the fusion image, and enriches the characteristic information of the target itself.
Design and Implementation of Face Emotion Recognition System Based on CNN Mini_Xception Frameworks
Authors: Sun Lili , Ge Chenhao , Zhong Yuanchang 
Published: September 2021

Abstract
In the new era, the rapid development of information technology makes the face recognition technology in artificial intelligence also develop rapidly, among which, face expression recognition has become a research hotspot. In recent years, due to deep learning, convolutional neural network and multilayer perceptron and other related algorithms have become the research focus of scholars, so their wide application in the field of facial expression recognition is also the direction of face emotion recognition exploration and research. In addition, computer software is widely used in human daily life, so it is also very important to design and implement an intelligent, real-time and universally applicable UI interface for facial emotion recognition system. Therefore, this paper firstly completed the facial expression recognition model based on the Mini_Xception framework of CNN by training the FER2013 expression database. Secondly, the system UI interface is designed and realized through PyQT5, OpenCV, Keras and other libraries. The final results show that the system is based on the algorithm model, and the UI interface designed and realized can not only recognize the saved pictures, but also recognize the real-time face emotions through the camera, and the overall effect of the system is outstanding.
An efficient backbone network for target detection in aerial images
Authors: Zhang Sihan , Liu Yaping , Wang Xile , Zhang Miaohui 
Published: September 2021

Abstract
Target detection in aerial images by high altitude unmanned aerial vehicles is one of the hot research topics. A detection that is efficient and accurate is of a high value in the military and civilian fields. However, due to the irregular distribution of targets and their various scales shown in aerial images, it is difficult for existing backbone networks to effectively extract target features based on deep learning. To address these problems, in the paper, an efficient backbone network called AerNet is proposed, to which a local feature enhancement module (LFEM) is added to fully extract discriminative features of aerial targets via multi-scale convolutional layers. The network AerNet consists of 55 layers, which can keep high spatial resolution in deeper layers and maintain a large receptive field. Experimental results show that our AerNet has achieved a satisfying detection performance on the DOTA benchmark.
Intention prediction of UAVs based on improved DDQN
Authors: Chen Tianpei , Liu Haotian , Wang Yuhui 
Published: September 2021

Abstract
Intention prediction plays an indispensable role in future informationized air combat, which will help the command and control center to make a more correct decision. In this paper, an intelligent intention prediction approach based on an improved double deep Q network (DDQN) is developed to generate real-time intention flight paths for unmanned aerial vehicles (UAVs) in complex air combat environment. Initially, by introducing an actual topographic map, the different threats of UAVs in different terrains on the map are analyzed, based on which, a terrain environment reward function is constructed. Secondly, by splitting a complete maneuver action into six basic maneuver units and determining the probability value of each unit, a maneuver reward function is obtained. Further, to improve the real-time performance and accuracy of the standard DDQN algorithm, an improved DDQN algorithm is proposed by using temporal-difference (TD) method and binary tree data structure. Finally, the simulation results verify show that the proposed algorithm has achieved better results under complex terrain conditions.
Chaos-GA-BP Neural Network Power Load Forecasting Based on Rough Set Theory
Authors: Li Dianwen , Ji Xiu , Tian Xin 
Published: September 2021

Abstract
In order to accurately grasp the trend of power load changes and provide guidance for stable and efficient operation of the power system, this paper proposes a Chaos-GA-BP power load forecasting model based on rough sets. First, screen the main factors that affect power load changes, remove redundant data, and optimize the input of the prediction model; Second, rely on the BP neural network prediction model to adjust its topology, optimize weights and thresholds, and build a rough set-based Chaos-GA-BP power load forecasting model; Finally, considering the impact of severe power load fluctuations on the forecast results, rough set theory is introduced to perform forecast compensation when the slope on both sides of the peak is large. Applying the above method to a one-month power load forecast in a certain area in Northeast China, the change trend of the forecast curve closely follows the actual load curve. Compared with the traditional method, the average absolute error percentage is reduced by 2.67%. The experiment shows that this method has certain practicability.
Research on Prediction of Oxygen Consumption in Converter Steelmaking Based on IGWO-SVM Model
Authors: Wen Daoyuan , Zhu Yingtao 
Published: September 2021

Abstract
The oxygen supply for converter steelmaking is the main factor affecting the quality of molten steel. To improve the accuracy of the oxygen consumption prediction model for converter steelmaking, an improved gray wolf optimization algorithm is proposed to optimize support vector machines to establish an oxygen consumption prediction model (IGWO-SVM), effectively improving the prediction accuracy of oxygen consumption in converter steelmaking. Firstly, aiming at the problem of slow convergence of the standard gray wolf algorithm and easy to fall into local optimality, Bernoulli chaotic initialization is introduced to enhance the uniformity and ergodicity of the initial population; and an adaptive decreasing convergence factor is introduced to balance the global search of the gray wolf algorithm and local search capability, while adopting adaptive inertia weight strategy to update the population position and speed up the convergence speed. Secondly, the benchmark function is used for testing, and the results show that the improved gray wolf optimization algorithm has improved convergence speed and search accuracy. Finally, based on the measured data of a steel plant to predict the oxygen consumption of converter steelmaking, the simulation results show that the oxygen consumption prediction model of converter steelmaking based on IGWO optimized SVM has high accuracy and strong generalization ability.
Face Replacement and Image Animation System in Cultural Experience
Authors: Li Jiayue , Wang Qing , Li Shiji , Zhou Qian , Zhong Qiang 
Published: September 2021

Abstract
As a unique representative of Chinese culture and civilization, Chinese traditional portrait painting has a certain cultural influence all over the world. However, what kind of human-computer interaction system can attract more people’s interest and attention, is still a field worthy of exploration. This paper focuses on the instantiation expression of Chinese traditional portrait, improves the regional feature point matching based on the research of Photo to Chinese Portrait method (P-CP), compares the effects of different color correction approaches on the experimental results, and introduces the form of real-time image driving animation. We hope that we can provide new inspiration for the dissemination of traditional culture and the application of modern technology.
An aerodynamics-centric framework for multidisciplinary coupling analysis and its application
Authors: Jiang Xiong , Wang Ziwei , Lu Fengshun , Liu Duoneng , Zhao Bendong , Wang Wenkai 
Published: September 2021

Abstract
There are many multifaceted problems in the aerospace engineering domain, which demand the multidisciplinary simulations for coupling analysis and the development of coupling frameworks. In order to address the issue of lacking domestic coupling framework, we propose an aerodynamics-centric framework for multidisciplinary coupling analysis, and then demonstrate the usage of it in developing a structured-unstructured grid coupling software, which is validated with an external store separation case study.
Research on Weak Current Detection System Based on Embedded System
Authors: Long Xiaoli , Li Xinxue , Guan Jian 
Published: September 2021

Abstract
In today’s society, the rapid development of science and technology requires the detection of weak current signals in many fields of scientific research. The research of its detection technology plays a very important role in promoting the development of science and technology. Considering that the weak current signal has strong background noise, it is easy to be submerged, the detection is difficult, and the professional requirement is strong. This research proposes a weak current detection system based on an embedded system that can detect nan oampere-level weak currents. The detection system is mainly composed of signal conversion amplifier filter circuit module, signal acquisition circuit, STM32, FPGA, upper computer and so on. Through experimental inspection, the researched detection system has the advantages of high accuracy and good stability.
Research on Motion Intensity Monitoring and Motion Scheme Recommendation Based on Internet of Things
Authors: Chen Yu , Zhu Jiadong , Wang Mengnan 
Published: September 2021

Abstract
With the development of Internet of Things technology, digital sports training will become a hot topic in the field of sports science. Digital exercise training is a training mode of monitoring the quality of the training in real time in the process of using technology to train practitioners and adjusting the physical training process according to the data. By combing the main application of international Internet of technology in sports training, this paper proposes the use of technology to monitor the movement intensity, and realizes the sports scheme intelligently recommended technical scheme, and implements it, and carries out the practical application and achieves commercial benefits according to the monitoring results.
Complementary Filter for Attitude Estimation Based on MARG and Optical Flow Sensors
Authors: Li Xiang , Xu Qing , Shi Qi , Tang Yanmei 
Published: September 2021

Abstract
The combination of tri-axial magnetometer, accelerometer, and gyroscope has been widely used for three-dimensional attitude measurement, and this combination is also called MARG sensor. However, the measurement of accelerometer contains both the gravitational and motional acceleration, and only the former is useful for attitude estimation. As a result, MARG-based attitude estimation is easily disturbed by motion acceleration. In this paper, we introduce a complementary filter that estimates gravity and geomagnetic vectors in parallel, and utilize an optical flow sensor to detect and compensate motion acceleration. Experiment results show that the proposed algorithm has better performance than the existing ones when experiencing linear acceleration.