bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hyperspectral image classification based on composite kernel relevance vector machine
Authors: Sun Cheng , Liu Donghao , Han Jie Guo , Yang Bei , Cheng Zhaoxiang 
Published: July 2021

Abstract
This paper presents a composite kernel Relevance Vector Machine(RVM) algorithm, for enhanced classification accuracy of hyperspectral images. This paper constructs three forms of composite kernels based on properties of kernels. The spatial feature is extracted using multi-scale morphological method from the image after principal components transform. The final classification is achieved by composite kernel RVM classifier. The proposed approach is tested in experiments on AVIRIS data. Compared with spectral kernel RVM, the OA and Kappa coefficient of composite kernel RVM increased obviously. However, the training time dose not increased. Meanwhile, composite kernel RVM has ability to get high accuracy with relative small training set. The proposed method has practical use in hyperspectral imagery classification.
Image recognition and detection based on fast area convolutional neural network
Authors: Zhang Hanwen , Qin Zhen , Xie Hua 
Published: July 2021

Abstract
Nowadays, image recognition and detection technologies based on traditional artificial neural networks and convolutional neural networks are slightly inadequate in terms of training and recognition time and accuracy, and are difficult to deploy on devices with limited hardware resources. Therefore, this article proposes a recognition and detection technology based on fast regional convolutional neural networks. We use RPN (Region Proposal Network) instead of Selective Search method to rebuild the network, and add a new ROI pooling layer before the fully connected layer of CNN. Determine the category. The average detection accuracy on our data set can reach 83.8%, and the training time is only 0.34 hours.
Nodes detection in CDN with enhanced naive bayes tree
Authors: Hou Ruijjie , Guo Changyong , Zhang Zhaoxin , Li Ning 
Published: July 2021

Abstract
In recent years, Content Distribution Network (CDN) plays a critical and central part of Internet infrastructure. There are more and more studies on CDN. Nodes detection in CDN, as a key technology among them, has become a hot research topic. Current methods mainly focus on collecting one or some CDN vendors nodes by manually constructing features set. However, because CDN nodes detection is not restricted by specific CDN vendors, these methods are not applicable. In this paper, we proposed a novel machine learning algorithm, i.e. enhanced Naive Bayes Tree to identify CDN IP addresses. This algorithm makes full use of the advantages of Decision Tree and Naive Bayes algorithm, and further improves the performance through enhanced part. We build this classifier based on analysis of the characteristics of DNS resolutions, HTTP logs and WHOIS lookup. Experimental results show that our approach outperforms in terms of accuracy and amount of packages. Moreover, we separately tested the enhanced part and it performs well. We believe that this method could be applied to CDN IP addresses detection.
Anonymous node location based on clustering
Authors: Luan Jihai , Xia Yong , Xie Yuancheng , Zhao Dong , Zhang Zhaoxin , Li Ning 
Published: July 2021

Abstract
With the rapid development of network technology, the network’s scale is increasing and becoming more complex. It is imperative to understand the structure of the network for network management and network security. Therefore, the construction of the routing topology has become a top priority. Routing topology analysis is already a relatively mature technology, but anonymous nodes’ positioning has not received enough attention. Nowadays, the primary solutions to anonymous nodes are graph analysis and tomographic tree construction. Based on these two methods, this paper proposes a rough location of anonymous nodes based on the method of three-level clustering. They are the K-means clustering of data before anonymous identification, hierarchical clustering of feature values in anonymous identification, and central point calculation based on clustering ideas during anonymous positioning. Experiments have proved that performing the first-level K-means clustering before anonymous node identification can effectively improve anonymous positioning accuracy. And the three-layer clustering idea proposed in this paper can roughly get the location of anonymous nodes based on reducing the complexity of the original method.
Research on improvement of hybrid particle swarm algorithm in underwater active electric field positioning technology
Authors: Xu Tianyang , Cai Ding , Li Beiming 
Published: July 2021

Abstract
Since the underwater active electric field positioning algorithm based on the MP-MUSIC algorithm has a slower positioning search speed, the hybrid particle swarm algorithm is introduced to speed up the positioning search speed and positioning accuracy. This paper improves the particle swarm algorithm, adopts dynamic learning factors, and uses natural selection-hybrid particle swarm algorithm to improve the search speed and positioning accuracy of the positioning algorithm.
UWB/PDR integrated indoor positioning method based on robust adaptive square root cubature Kalman filter
Authors: Li Zhiyu , Zhang Lijie , Tian Guangliang 
Published: July 2021

Abstract
Aiming at the problem that the ultra wide band (UWB) wireless signal is easy to be interfered, a robust adaptive square root cubature Kalman filter (RA-SRCKF) is proposed to be used in UWB/PDR integrated indoor positioning. RA-SRCKF is based on square root cubature Kalman filter (SRCKF) and its statistical characteristic of measurement noise is updated with the method of first estimating and then modifying. The experimental results show that the proposed RA-SRCKF can effectively improve the accuracy and stability of UWB/PDR integrated positioning.
UWB/IMU integrated inspection robot positioning in underground substation
Authors: Zhang Xiaowei , Guo Xiucai 
Published: July 2021

Abstract
The task of the underground inspection robot is to detect the environment of the underground substation and the operating parameters of the electrical equipment in it, and precise positioning is the guarantee for the inspection robot to complete the above tasks. In order to solve the problems of large drift of inertial sensors based on micro-electro-mechanical systems, and susceptibility to NLOS errors in UWB positioning, a fusion positioning algorithm with extended Kalman filtering as the core was designed. The algorithm suppresses the drift of position information by estimating the deviation of the inertial device, and integrates the UWB ranging information to improve the long-term positioning stability of the carrier. Finally, a parallel window smoother is designed to smooth the fusion result. The simulation results show that the fusion algorithm has a strong ability to resist drift and non-line-of-sight errors. Compared with the traditional fusion algorithm, the error is reduced by 38%.
Object detection and recognition system based on computer vision analysis
Authors: Liu Haitao , Li Yuge , Liu Dongchang 
Published: July 2021

Abstract
Artificial intelligence based on deep learning enables the machine to have the ability of understanding and cognition, but the application of artificial intelligence technology in supermarket shopping scene is limited. In the post-epidemic era, the contactless self-checkout of unmanned supermarket is more in line with the development needs of modern society. We build Pytorch environment, first to collect pictures of a large number of commodities and labeling information, and training model is obtained by YOLO neural network algorithm, finally through a call to model to realize the recognition of goods. Neural network algorithm is used to improve the recognition rate of goods step by step and achieve the detection and recognition of objects. We have tested our model on the real supermarket commodity data set and the public data set ImageNet, and the results show that our model can achieve a certain practical effect.
A survey of offensive security research on PLCs
Authors: Ma Rongkuan , Wei Qiang , Wang Qingxian 
Published: July 2021

Abstract
Industrial Control Systems (ICSs) are widely deployed in critical infrastructures, such as electric power, water filtration and distribution. Programmable Logic Controllers (PLCs) work as the core components of an ICS. The offensive security technology of PLCs has attracted the popular interests of both attackers and researchers. In this paper, we firstly abstract a 4-layers model of PLC’s implementation, and then summarize and classify attacks against PLCs into 5 types. Further, we point out the features and trends of the related attack studies, which is helpful to promote the research in the ICS security community and provide vital suggestions for defenders.
A novel quantization method combined with knowledge distillation for deep neural networks
Authors: Hu Zhou , Fan Chunxiao , Song Guangming 
Published: July 2021

Abstract
The massive parameters and intensive computations in neural networks always limit the deployment on embedded devices with poor storage and computing power. To solve this problem, a novel quantization algorithm combined with Knowledge Distillation (KD) is proposed to reduce the model size and speed up the inference of deep models. The proposed method consists of two phases, KD-Training and Quantization-Retraining. KD-Training attempts to train a compact student model with pre-quantized weights by the proposed pre-quantized constraint loss. In Quantization-Retraining, the pre-quantized weights are quantized to 2n and the first and last layers of the network are retained to make up for the accuracy loss caused by quantization. Experiments on the CIFAR-10 dataset show that the proposed method can obtain a low-precision (2-5bit) quantized student model with a compact structure, and the test accuracy even exceeds its full-precision(32bit) reference as the improvement of the generalization ability. It can get higher performance compared with that obtained by other quantization methods. Also, since the quantized weights are constrained in { ± 2n}, it is suitable for the acceleration of the network calculation in hardware.
A patch-based and multi-instance learning strategy for pneumothorax classification on chest X-rays
Authors: Tian Yuchi , Chang Yan , Yang Xiaodong 
Published: July 2021

AbstractPneumothorax is a lung emergency. Automated computer-aid pneumothorax diagnosis based on chest X-ray can help reduce the diagnostic time and save valuable time for the treatment. A total of 21,759 patient’s frontal-view chest X-ray images from one medical center are used in this study. The dataset is divided into two categories: pneumothorax and non-pneumothorax, which are evaluated by two radiologists with over ten years of practical experience. A two-stage training for pneumothorax classification based on multi-instance learning (MIL) are proposed, first training a patch-level classifier, followed by an image-level classifier training, which is initialized with the patch pre-trained weights. The image-level classifier initialized with patch pre-trained weights achieves good classification performance with the F1-score, accuracy and recall of 0.869, 0.915 and 0.843 respectively, which are larger compared to that of the model initialized without patch pre-trained weights (0.785, 0.878 and 0.783). The two-stage training strategy can improve the performance of pneumothorax classification and does not require too high GPU memory and long training time.
A diagnostic method of high myopia based on transfer learning
Authors: Chen Baibing , Wan Cheng 
Published: July 2021

Abstract
In order to promote the development of computer-aided diagnosis, improve the efficiency of high myopia diagnosis, and help healthcare workers assist in diagnosis, this paper uses 6571 fundus color images of high myopia and 6212 normal color images from the Geriatric Hospital of Nanjing Medical University as the dataset. The initialization parameters pre-training on ImageNet are loaded into the VGG-16, VGG-19, ResNet-50, ResNet-101, Inception-V3, and EfficientNet-B0 models, Learning rate changes dynamically according to the number of training epochs, and saving the model with the highest area under curve (AUC) value during the training process. Taking the best classification model from different networks, this paper achieves classification accuracy of 93.86%, sensitivity of 0.9126, specificity of 0.9677 and AUC value of 0.9866 on the test set. Each image takes 0.039 seconds, which is within the acceptable range meeting real-time of medically assisted diagnosis. Results show the fine-tuned ResNet-101 network meets the needs of medical assist diagnosis and can be used for screening high myopia, with high accuracy and real-time performance.
A key distribution protocol based on quantum entanglement swapping for unmanned surface vehicle
Authors: Qin Lingyu , Li Ying , Wang Shibo 
Published: July 2021

Abstract
Aiming at the problem of Unmanned Surface Vehicle’s (USV) secure communication, this paper proposes a key distribution protocol based on quantum entanglement swapping for USV. Compared with the current techniques, this paper uses the principle of quantum unclonability and quantum entanglement in quantum mechanics to realize the secure communication of USV. Meanwhile, in view of the low visibility at sea and the influence of weather such as wind and waves, this study adopts a key distribution architecture of quantum relay to realize the establishment of quantum channel between any two ships and obtain the absolutely secure key eventually. Therefore, the proposed method can provide long-distance, secure and reliable communication for USVs in the civil and military fields.
Analysis of sun-moon gravitational acceleration and engineering realization of space target
Authors: Huang Xiaobin , Zhang Yan , Xiao Rui , Lu Li , Jiang Yuan 
Published: July 2021

Abstract
Space target orbit determination is a core function of space target surveillance radar, which involves very complicated mathematical principles, and it is very difficult for professional radar system designers with no orbital mechanics to develop corresponding software modules. The difficulty in developing orbit determination software is the calculation of perturbation acceleration, which is the basis for realizing high-precision numerical orbit calculations. To this end, this paper takes the solar-moon gravitational perturbation that cannot be ignored in the precision orbit determination of high-orbit satellites as an example, introduces its mathematical principles, gives the program design ideas for acceleration calculation, implements the interface function and integrates it into the author’s early development radar orbit determination library (RadarOrbDet). The simulation results verify the effectiveness of the interface function and show that the orbit determination function library can assist scientific researchers in radar system design. The research results of this paper can be used as a reference for the spacecraft orbit analysis of interplanetary flight.
Research on operating conditions of electric propulsion ships under transformer interturn short circuit
Authors: Ma Bo , Wu Xixiu , Ma Wenyu , Zhang Qichao , Hou Bowen , Wan Pengpeng , Feng Xiaotong , Hou Hui 
Published: July 2021

Abstract
Most ships currently use electric propulsion systems. In this system, the transformer plays a key role in power distribution. The probability of interturn short circuit occurs in the transformer is high, it will cause faults such as three-phase voltage imbalance and endanger the safe operation of the ship. The simulation model of ship electric propulsion system established in this paper includes propellers and transformers, and the influence of the interturn short circuit of a transformer on the sailing conditions is studied. The research results show that the transformer interturn short circuit will generate a huge inrush current and have a huge impact on the torque and speed of the propeller.
Research on the verification method of flyback high-frequency transformer (FHFT) based on field-circuit coupling model
Authors: Zhang Qichao , Wu Xixiu , Ma Wenyu , Ma Bo , Wan Pengpeng , Hou Bowen , Feng Xiaotong , Hou Hui 
Published: July 2021

Abstract
In view of the current status of research on verification of the correctness and effectiveness of FHFT design, a field-circuit coupling based FHFT design verification method is proposed in this paper. This paper proposes a FHFT design verification method based on field-circuit coupling. The steps of FHFT verification using field-circuit coupling method are elaborated in detail. A three-dimensional physical model of FHFT based on AP method is established. The calculation of FHFT flux density and the verifying of transformer output waveform are completed by using the field-circuit coupling method of alternating iteration of non-linear magnetic field solution and circuit. On the basis of completing the flux density calculation, the field circuit coupling method is used to verify the output waveform of the transformer. It is found that the output waveform of FHFT is not an ideal square wave, but has some distortion, which is caused by neglecting the leakage inductance of transformer in the design process. This shows that the influence of transformer leakage inductance is very important and should be paid attention to.
Multi-stage perforation and hydraulic fracture stage selection based on machine learning methods
Authors: Yu Yulong , Xu Jiafang 
Published: July 2021

Abstract
In unconventional oil and gas resources, especially shale oil and gas resources with extremely low permeability and porosity, in order to develop effectively, it is necessary to establish perforation and multi-stage fracturing. The machine learning algorithm k-means clustering was used to cluster the five features selected to describe reservoir properties: shale content, porosity, total organic carbon content and those reflecting rock mechanical properties: Young’s modulus and Poisson’s ratio. The data were classified in a high-dimensional space to determine different perforation fracture stages. The classification algorithm XGboost was then used to predict different perforation stages using four conventional logging curves GR, NPRL, VP and DEN. The K-means clustering algorithm based on Euclidean distance can well classify the selected features in the high-dimensional space. The Hopkins statistics of clustering trend is 0.94, showing a good clustering trend. When the classification algorithm is used for prediction, the average accuracy is 0.92, the average recall rate is 0.90, and the average F1 score is 0.90, which can predict different perforated fracture stages well and optimize the design of the perforated fracture stage.
Hardware in the loop simulation training system based on HLA architecture
Authors: Wu Tao , Zou Qiubai , Wu Jixiang , Wang Hong , Zang Mengyang 
Published: July 2021

Abstract
A simulation training system is designed, which uses HLA architecture to realize the coordinated operation of network simulation, hardware in the loop access and electromagnetic computing. The system uses the standardized virtual real access interface to make the communication equipment simulator share the equipment parameters and interactive data with the network simulation model. VRNET is used for network simulation and interacts with the electromagnetic calculation module to form a complex electromagnetic environment. The system can be used in communication simulation training, electromagnetic interference array analysis and other fields.
A wideband low phase noise VCO design
Authors: Cheng Ze , Zhang Dianwei , Li Weimin 
Published: July 2021

Abstract
A frequency synthesizer with high reliability and low noise was designed and manufactured by 0. 13um SiGe BICMOS process. The frequency synthesizer circuit is capable of generating 7. 5GHz-15GHz RF output signals (the entire VCO output band);The normalized phase noise of the frequency synthesizer reaches-230 dBC / Hz, and the VCO phase noise of the frequency synthesizer reaches-119dBC/Hz@1MHz at the oscillation frequency of 13GHz.
Design of adaptive humidity control system based on ultrasonic humidification in room temperature
Authors: Deng Jun , Chang Limin , Cui Gang , Wang Ping , Lu Yongji 
Published: July 2021

Abstract
Humidity is an important parameter of ambient air. Humid air has a great influence on discharge and fuel combustion. In order to simulate a real humid environment for studying the effect of humidity on different experment, based on ultrasonic humidification mechanism, an adaptive humidity control system is designed. The system can produce uniform wet air without liquid water. By self-adaptive control, humidity can reach the required value and keep stable. The response time and humidity accuracy is investigated. The results show that the system can provide uniform gaseous humid air. Its response time can be up to 2s. Control accuracy is up to ±2%RH. These works are significant and can be widely used for combustion and discharge.
Research on temperature field model of hot air reflow soldering process
Authors: Pang Zhanming , Zheng Yi , Shen Hongqiao , Ji Chengyang , Pan Kailin , Gong Yubing 
Published: July 2021

Abstract
In this paper, two typical temperature models on the hot air reflow soldering process are studied. The critical difference between the two model is the approach used to compute the convective heat trans coefficient during the soldering process. In the model 1, the Martin formula and k-epsilon (2eqn) equation is applied to obtain the average convective heat transfer coefficient. And in model 2, the liquid and solid coupling CFD model is built to compute the convective heat transfer coefficient inherently. The comparing between the simulation and the test is conducted and shows that the simulation result of model 2 are closer to the real value. The comparing between these two models shows that the gas temperature, the gas velocity and the convective heat transfer coefficient around the PCBA is quite different. It is indicated that the average convective heat transfer coefficient calculated by Martin formula was larger. The CFD model is prefer for simulating the temperature distribution of the PCBA during the air reflow soldering when higher result precision is needed.
Research on agricultural machinery operation path optimization based on traveling salesman problem (TSP)
Authors: Liao Huichuan 
Published: July 2021

Abstract
Based on the characteristics and properties of TSP, a new heuristic algorithm is designed. Firstly, the nodes on the graph are divided into regions, and then the minimum spanning tree is found in the region. Then, the nodes larger than 2 are broken and reconnected into trees with 2 degrees except the start and end points. Finally, the start and end points of adjacent regions are connected to get the solution of the problem. Two examples show that this heuristic algorithm can effectively avoid the round trip of several points between different regions. The algorithm is applied to the optimization of agricultural machinery operation path, which provides a reference for the daily operation of agricultural machinery households.