bluuebunny/crossref_metadata_2025 huggingface dataset:
An Audio-Visual Whisper Database in Chinese
Authors: Zhou Jian , Hu Yuting , Lian Hailun , Pang Cong , Wang Huabin , Tao Liang
Published: July 2019

Converting whisper to normal vocalized speech has been a hot research topic in speech signal processing area. A complete and large scale whisper database is a major basis for this task. In this paper, we propose a multimodal whisper database in Chinese mandarin. A total of 103 syllables and 100 sentences were carefully selected. 5 male and 5 female participants pronounced the syllables and sentences in whisper and normal styles respectively, result in 4096 parallel speech utterances and 263, 849 frames of voicing face and lip image sequences. The beginning and ending sample point of each syllable were labeled both for speech signal and voicing face video. The lip region of interest were also extracted and provided in the proposed database. Experiments in various speech conversion tasks in different speech database show the effectiveness of the proposed multimodal whisper speech database.
A tracking method combines with detection algorithm based on deep separable convolution network
Authors: Shi Yanni , Liang Xun
Published: July 2019

With the emergence of a large number of artificial intelligence technologies, deep learning has become the key technology in computer vision area. Object tracking is one of the most important technology in the field of computer vision. Thus we studied about tracking algorithms and proposed a method mainly hopes to solve the occlusion problem in complex tracking scene. Using object detection algorithms based on deep learning to increase the speed of associations and improve tracking effect. It can return the position of the tracking object unsupervised. Then extract features to store in features library, so that the prediction of trajectory whose features can highly be matched is more accurate and the associations are more reliable. Experiment shows our tracking algorithm combines with detection algorithm based on depthwise separable convolution networks not only has a smaller and faster model but also achieved a robustness and real-time tracking in scene where objects are under occlusions.
Research on Small Target Recognition Technology Based on Deep Learning
Authors: Li Yuewu , Hu Jianwang , Ji Bing
Published: July 2019

All small target recognition has always been a difficult problem in the field of target recognition. Traditional target detection methods have met a bottleneck. With the development of deep learning, the field of target recognition has witnessed a boom based on deep learning. According to the characteristics of the small target recognition, this paper briefly introduces the traditional target recognition method, and studies the methods of target detection based on the deep learning. It mainly summarized the development of research on target detection algorithm based on deep learning. Finally, it summarized the influence of the depth study on target recognition and the trend of development in the future.
Analysis of the design of military building skin under the guidance of camouflage concept
Authors: Zhou Ruochong , Zhu Kaer , Liu Yang
Published: July 2019

It is very important to apply the camouflage concept to military building design. With the increasing attention of the building skin, there are more and more researches on the use of the building camouflage skin to achieve the purpose of militarization. Based on this, this paper summarizes and studies the ideas, means and methods of building skin design, with a certain reference value for the use of the military building skins in China.
RETRACTED: Research on Clustering Algorithm Based on Big Data Background
Authors: Wu Chunqiong
Published: July 2019

In recent years, machine learning has developed rapidly and clustering is an important research direction in the field of machine learning. After continuous and in-depth research, the clustering algorithm for small-scale data sets has made great progress and many effective clustering algorithms have been studied. However, these algorithms are difficult to obtain satisfactory results when dealing with large-scale data sets. The reason is mainly due to the high computational complexity and the weak ability to process high-dimensional data. It is more and more important to process these massive data in time. Therefore, the research on clustering algorithm for large-scale data sets has become one of the important tasks in the field of machine learning. This paper summarizes the meaning of big data and clustering algorithm, sorts out the clustering algorithm suitable for big data and gives suggestions on the research of clustering algorithm under big data. It is hoped that some research achievements can be achieved in the following studies.
Research on Application of Missile Blockchain Based on Nation Secret Algorithm
Authors: Fang Yi , Cong Linhu , Deng Jianqiu , He Weixin , Chen Yuliang
Published: July 2019

Missile data has the characteristics of multiple data items, large data volume and high data value, which puts forward higher requirements for data storage security. The application of block chain technology in missile business data storage can realize the safe, reliable and decentralized storage of data. The SM3 algorithm is a Hash function that supports the data structure and detects whether the data is tampered with in the blockchain. The SM3 algorithm is applied to the missile business blockchain to realize the localization of encryption, and the operation of SM3 algorithm is optimized under the condition that the missile business data contains a large number of documents, tables and pictures. Finally, the Go language was used for testing, and the results showed that the operation speed of SM3 algorithm could be improved by about 30% through optimization, and the optimization effect was obvious.
Aeronautical γ Spectrum Noise Reduction Method Based on LS-SVM Segmentation Regression
Authors: Ci Ying , Liu Jun , Chen Lei , Li Guang , Yang Feng
Published: July 2019

In order to reduce noise of the aaeronautical γ spectrum caused by factors such as less counting, short measuring time and poor measuring environment, this article adopts a segmented noise reduction method combined with the machine learning method, LS-SVM(Least Squares Support Vector Machines) and weighted stacking method according to the energy window distribution of the spectrum. The experimental results show that the segmented noise reduction method based on LS-SVM can significantly reduce the statistical fluctuation. In addition, the method presents good adaptability and generalization ability.
Research and Design of Integrated Information Transmission System for Large Warships Formation
Authors: Wang Nan , Liu Yi
Published: July 2019

The Navy is a comprehensive and highly integrated service consisting of submarines, surface ships, aviation forces, coastal defense forces and marines. The Navy is a systematic and technology-intensive military. Compared with other military services, the naval equipment requires higher system performance. The naval combat unit is mainly a fleet composed of several different fleets of warships and aircraft. Information warfare of naval fleet is divided into information level (network) attacks and defenses. In the future maritime information warfare, it is necessary to strengthen the defense capability of formation information warfare to ensure the security of communication and computer infrastructures. The integrated information transmission system of large naval fleet is designed to improve the information defense capability of naval operations. It has strong comprehensive defense capabilities of computer anti-reconnaissance, anti-jamming, anti-virus and anti-destruction. In the final analysis, it is to improve the security of computers.
Research and Application of License Plate Recognition Technology Based on Deep Learning
Authors: Yao Li , Zhao Yingbin , Fan Jinghua , Liu Min , Jiang Jianpeng , Wan Yan
Published: July 2019

There are many types of vehicle license plates in China, including new energy license plates, large truck license plates, government vehicle license plates, and military license plates. The existing commercial license plate recognition system only targets common license plates and does not completely cover the full range of license plates. Therefore, this paper proposes an SSD-based end-to-end license plate recognition system (LPR-SSD). The LPRSSD network architecture consists of upper and lower classification networks: the upper layer network is used for vehicle license detection and classification, and the lower layer network is used for license plate character detection and classification. In order to enhance the generalization performance of the LPR-SSD network, in addition to the real license plate image captured by the camera, this paper synthesizes 50K simulated license plates for each type of license plate according to the legal document [1]. Experiments show that LPR-SSD achieved a faster convergence speed during training. After the test set verification, the accuracy of license plate location detection and classification reaches 98.3%, and the character recognition accuracy rate reaches 99.1%.
Research on Construction Method of Chinese Domain Ontology Based on Relation Extraction
Authors: Qi Shanshan , Zheng Limin , Yang Lu
Published: July 2019

Domain ontology plays an increasingly important role in the information extraction process and has become a useful tool for knowledge acquisition, knowledge sharing, and modeling. However, the existing domain ontology construction methods are mainly based on manual mode, lacking systematic and engineering methods. This paper proposes a construction method of Chinese domain ontology based on relation extraction, which fully considers the automatic extraction of semantic hierarchical relations, and guarantees the semantic richness and accuracy of relational triples. The construction method of Chinese domain ontology based on relation extraction is applied to the field of food safety events. The constructed ontology has the advantages of reusability and scalability, which proves that the method is logical and operability.
Research on Digital Encryption Security Information System Based on Identity Authentication
Authors: Cheng Yunli
Published: July 2019

In order to solve the problems in the current authentication system, a new authentication system scheme is proposed. The user identity is used as a key production factor by using the CPK (Combined Public Key) combination public key system. From the perspective of security design, and combining the theory of CPK combined public key system, the overall design framework of CPK key management system is proposed. According to the functional requirements of the system, the main modules of the system are divided, and its main functions are stipulated. The system uses a secure authentication card as the only identity of the user. The authentication card has a certain computing power and safe storage space. It realizes the secure storage of the user’s key. Finally, from the actual application scenario, the data flow of the user in the process of registration and use in the system is given. The main modules of the system are tested successfully through the security authentication card.
Design of express delivery equipment based on STM32
Authors: Deng Wenzhe , Fang Haoteng , Qian Chengbing , Ding Zhenglong
Published: July 2019

this paper introduces the current situation of campus express service points, points out the problems existing in campus express service points and the working situation of express delivery personnel. Through the design of an automatic courier processor. In the hope of automation as a solution to find ways to improve the efficiency of express delivery and solve the pain points in the development of campus express service points. This scheme not only provides a solution to the low efficiency of express processing in campus express service points, but also improves the automation level of express terminal processing in the express industry
A Design Method of Active Damping for Gyroscopic Systems Using Direct Velocity Feedback
Authors: Yu Nan-Hui , Zhang Ke-Wei , Liu Hai-Min , Liu Xiang , Zhang Jia-Fan
Published: July 2019

For gyroscopic dynamic systems, active damping using direct velocity feedback control with collocated actuator/sensor pairs is discussed in this paper. The proposed method of designing the damping is actually to assign the negative real parts of the poles for specific modes of the uncontrolled system. The targeted values of the negative real parts are determined by desired damping ratios to be gained for the specific modes. The procedure is formulated as solving constrained nonlinear equations or a constrained nonlinear least squares problem with elements of the control gain matrix as variables. Finally, it is illustrated via an example rotor system that the presented method is effective and easier to implement.
Robot Dynamic Parameter Identification based on BBO Algorithm
Authors: Xu Yu-xuan , Ke Shao-yu
Published: July 2019

In this paper, a linearized robot dynamics model is first established. The DH parameters and physical constraints of the robot are given. The joint motion trajectory based on Fourier series is designed then. By setting the conditional number of the observation matrix as the objective function, an Biogeography-based optimization algorithm is applied to identify the dynamic parameters of the robot. The physical constraint calibration and predictive torque results are compared with the traditional genetic algorithm to verify the advanced nature of our method.
Radar Imaging based on Orthogonal Matching Pursuit via Sparse Constraint
Authors: Xia ChaoYu , Yu Jie , Gao YuXiang , Tian Xiang
Published: July 2019

This paper presents constraint generalized orthogonal matching pursuit (C-gOMP) approach to tackle the problem of clutter mitigation for radar imaging in the compressive sensing context. The generalized orthogonal matching pursuit algorithm (gOMP) in radar imaging process is susceptible to environmental noise interference. Therefore, a particular constraint condition is added to the recovery signal, and the noise component is restrained after the greedy iteration. The C-gOMP algorithm uses the cost function to impose more constraints on each coefficient to ensure the convergence of the whole function. To further analyze the performance of C-gOMP algorithm, the effectiveness of this algorithm is demonstrated by simulations. The results show that the proposed approach is very obvious at suppressing unwanted clutter and enhancing the desired targets, and C-gOMP algorithm brings an evident performance improvement and application prospect.
Vegetable Pest Image Recognition Method Based on Improved VGG Convolution Neural Network
Authors: Ye Haijian , Han Hang , Zhu Linna , Duan Qingling
Published: July 2019

Vegetables are one of the main crops in China, and pests are one of the important factors affecting the quality of vegetables. In order to improve the recognition accuracy of vegetable pest images, a vegetable pest image recognition method based on improved VGG convolution neural network is proposed. Based on the VGG16 and VGG19 models, the method optimizes the number of full connection layers, replaces the original SoftMax classifier in VGGNet with the three-label SoftMax classifier, optimizes the structure and parameters of the model, and uses the weight parameters of convolution layer and pooling layer in the pre-training model in transfer learning. Experiments were carried out on the self-expanding data set of vegetable pest images, and the performance of the method was tested. Tensorflow was used to train the network model. The experimental results showed that the pre-trained models (VGG16, VGG19, Inception V3, ResNet50) were trained on the vegetable pest image data set to adapt to the recognition of vegetable pest images. The experimental results also showed that compared with Inception V3 and ResNet50, the recognition accuracy of the pre-trained models using VGG16 and VGG19 were higher, and the test accuracy of the two models were 99.90% and 99.99% respectively. Finally, the methods were compared with the traditional VGG method in self-expanding data sets. The results showed that the accuracy of VGG16 model and VGG19 model were improved from 85.90% and 86.21% to 100% and 100% respectively; the classification accuracy of VGG16 model was improved from 64.02% to 99.90%, and the classification accuracy of VGG19 model was improved from 85.83% to 99.99%, which effectively improved the recognition accuracy.
Video Logging Casing Damage Image Recognition Based on Improved Convolutional Neural Network
Authors: Hu Hongtao , Cheng Yiyao
Published: July 2019

Oil casing damage detection is the key point to ensure the smooth production of oil fields. In recent years, the automatic image recognition technology based on deep learning has become a researchful hot topic. But the common deep learning models have some defects in identifying the target features of casing damage images in the complex environment. This paper proposes an oil casing damage image recognition model based on DS-CNN(deep and shallow convolutional neural network). Based on VGG19, this model integrates the shallow convolution neural network. It combines global features extracted by the shallow network and the local features extracted by the deep network to form the input of the fully connected layer. The joint training of the shallow network and the deep network enables the image to be expressed in multiple scales to improve the recognition accuracy of the entire model. The experimental data is obtained from the downhole casing image dataset of an oil field in Sichuan. Experimental result shows that the macro-average F1 scores of the DS-CNN are 4.41 and 5.74 percentage points higher than those of the VGG19 model and the GoogleNet model, indicating that this model improves the recognition accuracy of oil casing damage images.
Object Detection under Natural Illumination Conditions using Superpixels and Local Binary Pattern Feature
Authors: Zhao Chuanyuan , Li Xiangjuan , Zhao Yue , Shi Xiaomin
Published: July 2019

This paper proposes a novel object detection method under natural illumination conditions in which a set of features, texture features and Local Binary Pattern (LBP) features, are extracted from the images acquired by a colour camera. The goal of this study is to develop a robust and fast algorithm to detect immature green citrus fruit in individual trees from colour images acquired under natural outdoor conditions. Colour and shape features are used to remove the complex background as much as possible. Since leaves share much more similarities with green citrus fruit in colour and to some extent in the shape, texture features are used for citrus detection. Statistical features, Tamura features and LBP features are used to build the KNN classifier. Experimental results show that the proposed approach provides fairly good object detection performance and confirms an efficient way for outdoor green citrus detection.
TDOA Location Accuracy Experiment
Authors: Wen Xiping , Wang Jinxia
Published: July 2019

TDOA-based wireless signal location technology has been extensively used in engineering applications. Due to the lack of quantitative TDOA location accuracy index benchmark, there is the absence of reference basis in the development of TDOA application system indicators and performance evaluation. In this paper, the engineering radio frequency receiver network is used to form the TDOA location system. Under the condition of non-multipath, TOA time is used to simulate TODA to monitor the signal of the location target radio frequency signal source, and the relationship between the location accuracy of TDOA and the signal bandwidth is evaluated. The experimental results can be used as a benchmark for performance evaluation of TDOA location system.
Low Profile Microsrip Antenna with Broadside Radiation Patterns and Low Cross Polarizations
Authors: Lai Ping , Xu Chao , Xu Zhuo , Luo Kang
Published: July 2019

A low profile microstrip antenna with adjustable bandwidth around 3 GHz is proposed. The compact antenna consists of a square patch with a square aperture to tune the resonance center frequency. The input impedance bandwidth is about 2.67% with stable gain around 3.2 dBi. Besides, the 3-dB beam width is about 100°. Both the side-fed and bottom-fed modes are analyzed. According to the simulation, good broadside radiation patterns with low cross polarizations are observed in the two principal planes. The detailed design considerations are described and discussed while the surface current distribution and radiation characteristics are analyzed. This design is suitable for mobile wireless communication.
Trademark Image Retrieval Based on Faster R-CNN
Authors: Wang Wenmei , Xu Xinxin , Zhang Jianglong , Yang LiFang , Song Gege , Huang Xianglin
Published: July 2019

Automatic retrieval of digital trademark images is significant for improving the efficiency of trademark examination and management. In this paper, we proposed a method based on deep learning for trademark retrieval. Faster R-CNN was first applied to trademark retrieval. The global feature descriptor of the image is extracted by Faster R-CNN and the local feature of the image is extracted through the object proposal regions by the Region Proposal Networks (RPN). Retrieval strategies consist of initial ranking and spatial reranking. The experimental results show that our proposed method achieves remarkable performance in trademark image retrieval.
Unsupervised Image Feature Extraction Based on Scattering Transform and Self-supervised Learning with Highly Training Efficiency
Authors: Zheng Guoshu , Zhu Qiuyu
Published: July 2019

Convolutional neural networks (CNN) can effectively extract high-level semantic features of images, however, to learn these features, a large number of labelled data is required. In order to extract image features without labelled data, this paper proposes an unsupervised image feature extraction method based on self-supervised learning. To learn image features, we train the neural network to identify the two-dimensional rotation applied to the image. The first few layers of the convolution network are replaced with a scattering network to speed up training process, and get good image features in the last few layers of the convolutional network. We input the extracted features into convolutional network for supervised learning, and take recognition accuracy as a criterion of feature validity. The experimental result show that the recognition accuracy gets 84% on CIFAR10, reaching the mainstream results of unsupervised method; and 62.17% on CIFAR100, which is very close to the rate of supervised learning. This method can be applied to the applications that do not have massive labelled training data and have limited computing resources.
Research on Online Junction Temperature Measurement of MOSFET Devices
Authors: Jiang Bo-yang , Guo Chun-sheng , Luo Lin
Published: July 2019

In order to detect the MOSFET junction temperature, the paper measures the MOSFET junction temperature by electrical method without affecting the normal operation of the devices and the circuits. In addition, the method of optimizing the temperature calibration curve proposed is helpful for engineers to efficiently monitor the junction temperature and prevent the risk of failure caused by high temperature.
Design of reconnaissance system based on STM32 small multi-rotor real-time image transmission
Authors: Liu Jiawei , Zhang Lu , Shi Daosheng
Published: July 2019

This project analyses the main technical points of aircraft design, makes a preliminary discussion on rotor dynamics and mechanical analysis of four-axis aircraft, analyses the particularity of aircraft structure, control flexibility and control principle, establishes three-dimensional rigid body coordinate axis for aircraft. Describes the relationship between flight attitude and coordinate axis. Chooses a multi-channel attitude fusion algorithm, anti-jamming data transmission mode, adjusts the relationship between frequency and rate of wireless data transmission in detail. At the same time, the principle and arithmetic of the PID controller are analyzed in detail, and it is applied to the speed control of DC motor of aircraft. Through experimental verification and test, a good stable control effect is obtained. The design and implementation of the flight control system and remote control software and hardware are completed, and the test and flight test of the whole aircraft are completed. The flight control stability is good. It has the characteristics of small size, fast response and long communication distance. It can fully meet the reconnaissance requirements of obtaining real-time high-definition images in various complex situations. The upper computer software can quickly decode image data, convert it to video output to ground personnel, and realize FPV reconnaissance.
Encryption Cipher Text Retrieval Scheme Based on Fully Homomorphic Encryption Enterprise Cloud Storage
Authors: Wang Lijuan , Ge Lina , Geng Bo , Wang Qiuyue
Published: July 2019

With the development of computer networks, cloud storage has become the mainstream way for people to store information. However, some essential information is disclosed during the storage process and the security of information has become the most concerned issue at present. In order to use the powerful computing of cloud storage to realize resource sharing between relevant departments without revealing information of other unrelated departments, this paper proposes an encryption cipher text retrieval scheme based on fully homomorphic encryption enterprise cloud storage (ECRS), and designs an Enterprise-side security model. The model uses full homomorphic encryption, decryption and spatial vector cipher text retrieval to achieve the security of information and resource sharing among enterprise departments. ECRS determines the employee who can encrypt or decrypt the files, and who has access to the files by modifying the number of attributes intersected between attribute sets. The solution can provide the security for cloud storage, resources for different departments, and improve the accuracy for cipher text retrieval.
Improved Chaotic Sequence Generation Method Based on Direct Spread Spectrum
Authors: Liu Fang , Jia Shiyao , Xu Xiaodan , Tian Minghao
Published: July 2019

Aimed at the shortcomings of one-dimensional chaotic sequences as spread spectrum address codes in DS/SS(Direct Sequence Spread Spectrum) communication system, such as low complexity, simplicity of generation and easy to be estimated backwards, etc, an improved chaotic sequence is proposed, which is composed of two one-dimensional chaotic maps and the fractal parameter of one map is controlled to control the fractal parameter of another map in a certain mathematical form. Then the performance analyses of the sequence are carried out from the angles of complexity, correlation, randomness and anti-interference ability. The results of simulation show that the improved chaotic sequence not only improves the complexity obviously, but also has good correlation and randomness, and the anti-interference ability in DS/SS communication is more prominent.
Research on Preview-Fuzzy Control Strategy Based on MR Damper in Railway Vehicle
Authors: Xinna Ma , Wenrui Shi , Jingwei Liu
Published: July 2019

With the increasing speed, the vibration control of high-speed railway vehicle suspension system becomes more and more important. In view of the nonlinearity of high-speed vehicle and the time-delay problem of control system, a new intelligent material magnetorheological damper (MRD) is introduced. On the basis of analyzing the dynamic characteristics of MR damper, a vertical dynamic model of high-speed railway vehicle system based on MR damper is established. In order to suppress the vertical vibration of suspension system, a preview-fuzzy control strategy based on MR damper is proposed. In order to verify the availability of the control strategy, a lot of simulations are carried out. The results show that the preview-fuzzy control strategy based on MR damper can effectively reduce the vibration amplitude of the suspension system and solve the problem of time delay to a certain extent.
Uncertainty of Geomagnetically Induced Current in Xinjiang 750kV Planned Power Grid Based on Polynomial Chaos Expansion
Authors: Cui Xikai , Liu Qing , Wang Yinglan
Published: July 2019

Geomagnetically inducted current (GIC) can cause DC bias of the transformer. The derivative effect of DC bias may threaten the safety of power equipment and power grid. To study the GIC uncertainty caused by the grid parameters variation is of great significance for the assessment and defense of GIC during magnetic storm. Based on the polynomial chaos expansion (PCE) method, the chaos polynomial expansion of GIC is constructed assuming the line impedance, transformer impedance and grounding impedance in the power grid as input variables. For planned Xinjiang power grid in 2020, the uncertainty analysis for the GIC of substations flowing into the earth is carried out by using the derived polynomial chaos expansions. The 95% confidence interval, mean and variance statistics of the GIC are obtained. Comparison between PCE method and Monte Carlo (MC) method shows the validity and the efficiency of the PCE method.
Research on the Role of Reader Behavior Development in Reading Promotion Promoted by New Media Communication
Authors: Hu Jianfei , Meng Luyang , Zhang Bin
Published: July 2019

Reading promotion is an important means to create a strong reading atmosphere and cultivate good reading habits. With the continuous popularity of new media, the form of reading promotion also needs to keep pace with the times and adjust strategies according to the reader’s behavior. Based on this phenomenon, this paper analyses the positive and negative effects of new media on reading behavior, summarizes the law of the development of reading behavior, and then puts forward some reading promotion strategies.
LMS Adaptive Filtering of Drilling Tool Vibration Signal
Authors: Simin Kang , Yuelong Wang , Tao Sun , Yi Gao , Yan He
Published: July 2019

As the dynamic measurement of attitude parameter of the steerable drilling tool under vibration may be not accurate, the Least Mean Square (LMS) adaptive filtering algorithm is adopted to filter the influence of drilling tool vibration on the attitude measurement in this paper. The simulation results show that the measurement error of inclination angle after LMS adaptive filtering can be less than 0.1°, and the measurement error of tool face angle is less than 6°, which could effectively improve the attitude measurement accuracy of vertical steerable drilling tools; the results of inverse analysis on actual drilling data show that the actual measurement error of inclination angle after LMS adaptive filtering is about 3°, which is much smaller than that before filtering. It shows that LMS adaptive filtering can effectively filter the vibration signal of drilling tools and greatly improve the dynamic measurement accuracy of the tool attitude.
Energy Balanced Clustering Protocol for Wireless Sensor Networks
Authors: Wang Lihong , Liu Ping
Published: July 2019

Wireless sensor network is composed of a large number of sensor nodes by self-organization in the monitoring area. The energy, computing and communication capabilities of sensor nodes are very limited, and therefore a good routing protocol is very important to prolong the lifetime of the network. An energy-balanced clustering protocol is proposed to solve the inequality problem of consumption in routing protocol. Clustering protocol considers the residual energy of nodes and the distance between sink nodes in the selection of cluster head node, which can be more evenly distributed cluster head. On the basis of estimating the optimal cluster number, the clustering method is improved, the number of clustering is reduced, and the network energy consumption is saved. The simulation experiment shows that the Clustering protocol makes the whole network energy consumption more balanced and increases the life cycle of the wireless sensor network.
Application of LSTM Neural Network in Forecasting Foreign Exchange Price
Authors: Qu Yaxin , Zhao Xue
Published: July 2019

LSTM neural network and RNN neural network models in deep learning are used to forecast the price of foreign exchange financial time series. The existing foreign exchange price and technical analysis indexes are taken as input parameters. By comparing the evaluation indexes of two deep learning models, the optimal neural network model is selected. The experimental results show that the LSTM neural network model has smaller root mean square error (RMSE) and mean absolute error (MAE) than the RNN network model, and the predicted price is more accurate.
Equipment State Assessment Based on Convolutional Neural Network
Authors: Yanglong Chen , Yanheng Ma , Yuanwei Yin
Published: July 2019

Aiming at the problem of the defect of less-information and poor reliability of assessment results when assessing equipment state, this paper proposes a method of equipment status assessment based on convolution neural network by utilizing the ability of equipment under cognitive testability design to easily obtain multi-source information. The assessment system is divided into feature layer and information layer. In the feature layer assessment, the analytic hierarchy process is used to determine the constant weight, then the variable weight theory is used to realize the real-time adjustment of the weight. And the convolutional neural network model is used in the assessment of the information layer. Finally, through the example verification and comparison with BP algorithm, it can be seen that this assessment method has fast recognition speed, high accuracy, more reliable assessment results, and has certain versatility and application value in engineering.
An improved Chang-Lee’s smart card-based authentication scheme
Authors: Chiou Shu-Fen , Fajar Cahyadi Eko , Yang Cheng-Ying , Hwang Min-Shiang
Published: July 2019

It is useful to verify a legal user from a remote terminal through the Internet with the user authentication schemes based on the smart card. Usually, the remote user has to use his/her identification accompanying the password to access the system. This action is an important scheme to protect the user’s privacy and confidentiality. To achieve the robustness and the efficiency, Chang and Lee proposed a scheme that employs a smart card-based user authentication with the characters of implementation practically and easily. Also, Chang and Lee claim that the scheme could resist the replay attacks, the impersonation attacks, the identity disclosure attacks, and the perfect forward secrecy. However, this work shows that the scheme is in the risk with an on-line guessing identity and password attacks and denial of service attack. Hence, in this paper, it proposes an improved user authentication scheme that holds the capacity to withstand the vulnerability as that in Chang-Lee’s smart card-based scheme.
A new waveform design method of Military Anti-jamming Communication
Authors: Tang Zhiqiang , Quan Houde , Cui Peizhang , Sun Huixian
Published: July 2019

The advantages and disadvantages of wireless communication systems are the key factors that determine victory or defeat in the war. The construction of military informatization has accelerated in various countries, and the struggle for electromagnetic space has become more intense. Therefore, the communication anti-jamming techniques received extensive concern. It is of great significance to explore a waveform that can communicate effectively under interference. In this paper, the complex electromagnetic environment, typical means of military communication jamming and anti-jamming are analyzed thoroughly. A double-sequence frequency hopping waveform design method based on pseudo-random chirp is proposed, which has some practical value in engineering.
Study on Preparation and Stability of Coal Water Slurry
Authors: Zhang Yan-Min
Published: July 2019

The preparation of coal water slurry (CWS) as a new substitute for oil and the factors affecting the stability of CWS were studied in this paper. Dry method was used to prepare CWS. Dry coal water slurry refers to the preparation of CWS by grinding coal into powder, then adding water and additives, mixing and stirring. Preparation process is simple and convenient, and has low technical requirements. There are many factors affecting the stability of CWS. In this paper, the coal-water ratio, mixing time and coal particle size are selected to study, and the effects of various variables on the stability of CWS in a certain range are determined. The final experimental results show that the CWS prepared by mixing for 15 minutes with 65% water, 34% coal and 1% additive is the most stable.
Effects of Diagonal Braces on Dynamic Performance of the Van Carriage Frame
Authors: Deng T S , Chen M N
Published: July 2019

In this paper, the modal analysis of the van carriage frame is carried out, the natural frequency and vibration modes of the frame are compared, and the effects of the diagonal braces on the dynamic performance of the van carriage frame is studied by comparing the natural frequency and the vibration modes of the frame, under the condition of no diagonal brace and welded diagonal braces. The research shows that welding diagonal brace is beneficial to improve the overall dynamic stiffness of the van carriage frame. The results of this paper have theoretical reference value for the van carriage frame design.
A New Protection Principle of Hybrid Bipolar HVDC Line Based on Voltage Fault Component Rate of Change
Authors: Hangjian Zhu , Shuping Gao , Zhouxing Fu
Published: July 2019

At present, hybrid HVDC transmission has become a research hotspot. In this paper, the fault-added state characteristics of the hybrid bipolar HVDC transmission system with different faults on the DC transmission line are analyzed by connecting small inductors on both sides of the HVDC transmission line. A single-ended quantity protection method using voltage transient components is obtained. By analyzing the rate of change of voltage fault component, the transient energy component is used to calculate the instantaneous energy magnitude of the transient voltage component to distinguish the fault in the region. The energy function is constructed by the transient voltage of the positive and negative rectifiers to fault selection. The simulation results verify the correctness of the proposed protection principle and can achieve fault pole selection.
Research and Design of Lightweight Workflow Engine Based on SCA
Authors: Yang Huaizhou , Lv Bowen , Shi Wenbo , Sun Jingxin
Published: July 2019

In order to overcome the shortcomings of the traditional workflow engine structure and defects, this study proposes a loosely coupled lightweight workflow engine based on SCA (Service Component Architecture) and BPEL (Business Process Execution Language for Web Service, also known as WS-BPEL or BPEL4WS) engine. The support for traditional workflow engines for heterogeneous is improved by the separation between business logic and transport protocol, so that the engine is no longer limited to a specific transport protocol.
Research on abnormal detection of one-class support vector machine based on ensemble cooperative semi-supervised learning
Authors: Wu Dinghai , Liu Chunhua , Fan Hongbo , Song Bin
Published: July 2019

In order to promote the accuracy of anomaly detection model under the condition of only a small number of labeled samples and large number of unlabeled samples, abnormal detection of One-class Support Vector Machine(SVM) based on ensemble cooperative Semi-supervised Learning is proposed. A kind of One-class SVM model which bring supervision with a small number of abnormal samples can classify samples with max interval. The semi-supervised learning methods easily suffer from the low accuracy because the mistake labeled sample are chosen as training sample set. Refer to the semi-supervision method of Tri-training, the K-Nearest Neighbour(KNN) and Naive Bayes classifier are used to uses to assist the One-class SVM based on ensemble cooperative Semi-supervised learning method which can classify the large number of unlabeled samples as accurate as possible. The weight is also given after ensemble cooperative Semi-supervised Learning. Then the proposed semi-supervised One-class SVM would be trained with the result and used to classify test samples. The experimental results on UCI dataset show that the proposed algorithm achieves higher classification accuracy with less labeled samples and it improves generalization performance and reduces the labelling cost.
Model of Green Evaluation Index of Petrochemical Enterprises by Consistency AHP
Authors: Tao Sun
Published: July 2019

AHP can be easily influenced by human subjective consciousness, which may lead to inconsistencies. So it is necessary to process AHP with mathematics methods to achieve logical compatibility. In this paper, the evaluation index model of petrochemical enterprises is constructed according to the improved consistency AHP method, so as to perform comprehensive evaluation for enterprises and strengthen the environmental pollution control. Based on the specific characteristics of petrochemical enterprises, the relevant green index model is established after the specific theoretical framework of index scoring, hierarchy and synthesis of indexes is perfected, and the indexes of green characteristics are decomposed. In order to realize the scientificity of index transfer, the power exponent with case analysis is used to calculate the specific values of indexes in this paper, and the scientifically evaluated green index which is easy to be compared is calculated, which provides the basis for policies of environmental protection in petrochemical industry.
Working space analysis of one 3-DOF aircraft motion simulator
Authors: Gu Yunlu
Published: July 2019

In this paper, the working space of one 3-DOF aircraft motion simulator is studied. Two types of methods are used to generate searching space, i.e. generate randomly or generate uniformly. Then, the feasible points are selected under the same selection rules, and the performances of these two types of methods are compared. The results show that Monte Carlo simulation based on random method is suitable for understanding the working space of aircraft motion simulator. Whereas the uniform method based on equal step size is more suitable for deep study of the working space because it has high density and uniformly distributed feasible points.
An Interactive Visual System for Generating Striking Pseudo Base Stations Decisions
Authors: Yu Fujia
Published: July 2019

The Pseudo base stations cause a great number of social and economic security problems with spreading spam messages. However, it is a serious challenge for security department to recognize the behavior patterns of pseudo base stations, then make use of limited police resources to prevent their activities and even to arrest related criminals. In this paper, a novel visual analytic approach to reveal the spatial-temporal behavior patterns of pseudo base stations is presented. In this approach, it takes advantage of density clustering over high frequency positions, and curve fitting on each cluster. This system supports decision makers to formulate patrol routes on the basis of above approximate trajectory. This approach has been demonstrated effective by means of case studies of authority data and has been evaluated novel and valuable in the view of domain experts.
Dispersion Model of Volatile Organic Compounds Based on RBF Neural Network
Authors: Anqi Liu , Zhao Dongfeng
Published: July 2019

In this paper, we try to establish the dispersion model of Volatile Organic Compounds (VOCs). Whereas the mechanism dispersion model is too complicated and only suitable for the case where only one release source exists, we establish the black-box model based on data for the case where multiple release sources exist. After the input and output variables are determined, the Radial-Basis Function (RBF) neural network is used to describe the relationship between the inputs and the outputs because it can approximate arbitrary function with arbitrary precision. Considering the difficulty in selecting the parameters of RBF neural network, we use the Swarm Cuckoo Search (SCS) algorithm to obtain the suitable parameters. The simulation experiments demonstrate the effectiveness of the proposed model.
Application and research of modular design for general chassis of garlic machinery
Authors: Gao Xiang , Zhao Dong , Xu Shengjie , Liu Cheng , Huang Weitao
Published: July 2019

The traditional research and development cycle of garlic operation machinery chassis is long and the production cost is high, which hinders the development of this product. We can find out the condition that sub-functions become independent modules by the judgment matrix. Sub-functions can be divided based on correlation calculation and module division. The modular design of garlic operation machinery chassis is not only conducive to the innovative design and upgrading but also reducing the cost and the products.
Analysis and Solution of Frequent Start-up of Incremental Differential Protection
Authors: Hongyue Zhang , Zhiwen Liu , Hailong Chen , yanhua Shen , Zhigang Xiao , ruidong Zhao , Liming Xu
Published: July 2019

In this paper, it is analyzed that the reason of frequent start-up of incremental differential protection is caused by double SC capacitor banks in a converter station. No abnormality was found in the secondary circuit of CT. In the process of inspection, it is found that the accuracy adjustment coefficient of 010A converter protection device 2 does not need to be adjusted, and the accuracy meets the error requirement. The accuracy adjustment coefficient of 010B converter protection device 2 can meet the error requirement only after individual adjustment. However, one year later, when the substation was out of service, it was found that the accuracy adjustment coefficient of the individual channels of 010B converter and rheological protection device 2 was quite different.

Because the accuracy error of the external circuit meets the requirement, it is confirmed that the acquisition circuit of 010B converter protection device 2 is abnormal. Therefore, the acquisition circuit of 010B converter protection device 2 is replaced, including AC input circuit and sampling and holding circuit. After replacement, double SC capacitor banks were put into operation many times, and no start-up phenomenon occurred. In view of this incident, this paper also proposes two optimization schemes. One is to enhance the anti-interference measures of AC input plug-in of converter protection device. Secondly, dual redundancy is used in differential channel of single CPU plug-in.