bluuebunny/crossref_metadata_2025 huggingface dataset:
The effect of Zn2+ on the anion vacancies in ZnO thin-films grown using chemical bath deposition.
Authors: Molefe F V , Mofokeng S J , Khenfouch M , Achehboune M , Dhlamini M S , Mothudi B M , Koao L F
Published: September 2019

Zinc Oxide (ZnO) thin-films were grown on glass substrate using chemical bath deposition method (CBD) from precursors containing varying molar concentrations of zinc acetate. The structure, morphology, optical and luminescence properties of the films were investigated using X-ray diffraction (XRD), Scanning electron microscopy (SEM), Ultraviolet visible spectroscopy (UV/VIS/NIR) and Photoluminescence spectroscopy (PL). The XRD result showed that at low molar concentrations of zinc acetate the structure consists of Zn(OH)2 and ZnO phases. As the molar concentration of zinc acetate increases the well-known hexagonal wurtzite structure of ZnO was developed. The average crystalite size estimated using Scherrer’s formula was about 41.19 nm. It was found that the average crystallite size increased with an increase in the molar concentration of zinc acetate. SEM observations showed the presence of nanoparticles forming aggregated nanoflakes. The surface morphology was found to be dependent on the concentration of zinc acetate. The UV-Vis spectra showed that the absorption band edge shift to the higher wavelength with an increase in molar concentration of zinc acetate. The band gap energy of ZnO thin-films determined from UV reflectance spectra was found to decrease from 3.17 to 2.73 eV with an increase in the zinc acetate concentration. The PL results showed that the luminescence intensities decrease with an increase in the molar concentration of zinc acetate without any noticeable shift in position.
Research and Development of Network Security and Risk Management
Authors: Zhang Qingfeng , Ma Yaping , Bai Shuya
Published: September 2019

With the advent of the 21st century, China is in a period of social transformation and is in the stage of deepening reform. Social contradictions are complex, hot events are frequent, and public opinion is active. Under the conditions of the Internet, especially in the context of the prosperous media, the public opinion pattern has undergone new changes. With the popularity of the Internet and the increasing number of Internet users, the Internet has become a large social place. After a hot event occurs, it often causes heated discussion, and overheated public opinion or embarrassing grievances will also form corresponding events. Improper handling often results in “secondary disasters”. In recent years, the concept of lyricism has been frequently used, and public opinion analysis has been promoted at the same time in both theoretical research and practical application. The related network risk management has also received increasing attention. Various situations have shown that lyric research is heating up. Based on this status quo, this paper sorts out and comments, summarizes the research status at home and abroad, and focuses on the rational situation of the situation of public opinion research.
Research and Application of Artificial Intelligence Technology in the Field of Risk Perception
Authors: Bai Shuya , Fang Danhui , Zhang Qingfeng
Published: September 2019

In recent years, the breakthrough of data, algorithms and computing power has made artificial intelligence ushered in the third wave. The rapid development of artificial intelligence will profoundly change human society and the world, promote human understanding, perception and the ability of decision-making in the world. Applying artificial intelligence to disaster risk perception research can improve risk perception and accuracy and effectively avoid risks. Using big data technology, artificial intelligence algorithm and threat intelligence technology to study the construction of information security situational awareness system, the model, architecture and concrete implementation of information security situational awareness system are proposed. This study combines artificial intelligence and risk perception to sort out the application and development trend of artificial intelligence in disaster risk perception in recent years, and proposes factors that affect public disaster risk perception and coping behavior.
An Efficient Short Blind Proxy Re-signatures Scheme
Authors: Yang Xiaodong , Ma Tingchun , Chen Chunlin , Li Yutong , Wang Meiding
Published: September 2019

The existing proxy re-signature schemes have drawbacks in terms of privacy protection and signature efficiency. Our scheme is based on the proxy re-signature scheme, which combines the main idea of short blind signature and proposes the definition of short blind proxy re-signature scheme. Then, using the signature framework of the random oracle model, we propose a short blind proxy re-signature scheme based on the random oracle model, and prove the security of the scheme under the adaptive chosen-message attacks. The analysis shows that compared with the existing similar schemes, our scheme does not add additional verification information under the premise of shortening the signature length, and the applicability is better in the interactive transmission of devices with limited calculation and transmission performance.
An Intrusion Detection Method for Industrial Control System Based on Gate Recurrent Unit
Authors: Chen Tusheng , Lin Peng , Ling Jie
Published: September 2019

We proposed an industrial control system intrusion detection method based on gate recurrent unit neural network to handle the problem that the intrusion detection method based on traditional machine learning algorithm such as SVM, decision tree, NN etc., cannot effectively deal with massive, high-dimensional, time related network traffic data in industrial control system. We used the update gate and the reset gate of GRU to save the information of the data in the time dimension. Its deep structure can fully learn the data features, and we used Adam algorithm to optimize the gradient training process of the neural network. Comparison experiments were conducted with the intrusion detection method based on machine learning algorithms such as SVM, decision tree, NN, RNN, LSTM etc. The results show that the proposed method has higher classification accuracy than SVM, decision tree, NN and RNN, and the accuracy is basically the same as LSTM but the training time is greatly reduced.
DBN-Based Fingerspelling Recognition Approach using Feature fusion
Authors: Hu Yong
Published: September 2019

Sign language recognition offers effective and precise approach of recognizing gestures or postures. In this work, a vision-based framework is presented for recognizing fingerspelling alphabets and a comparison is conducted to show the efficiency of feature fusion. Fused features and Deep Belief Network are used in the proposed framework. In the experiments stage, a comparison between the fused features and the individual ones is performed by using two public fingerspelling datasets. Experiment results show the improvement of the feature fusion.
Research on the Educational Mechanism for Cooperative Training of Innovative and Entrepreneurial Talents in University
Authors: Chen Xuegang , Duan Sheng , Mo Dongmin , Wang Luda , Wu Qian , Zhang Meiling
Published: September 2019

From the perspective of the cooperative education mechanism between university and enterprise, and studies the theory of education, the mechanism of university education and the relationship of the talent training mode-the mechanism of education, takes the cooperative training cases of three universities of china as the data source, puts forward a talent training mechanism of cooperative innovation and entrepreneurship. This mechanism clarifies the educational goal, improves the curriculum-teaching method, increases the feedback mechanism of enterprise-university, redefines the evaluation method of education, ensures good communication of university-enterprise-student, and then guarantees the normal operation of the cooperative education mechanism. The mechanism is market demand-oriented, to cultivate talents adapted to social development, to solve the problem of disconnection between theory and practice, students lack of practice, and to provide a strong theoretical basis for promoting the training of national innovative entrepreneurs.
Multi-authority Attribute-Based Encryption with User Revocation and Outsourcing Decryption
Authors: Zhang Xinyue , Chen Yue , Yan Xincheng , Jia Hongyong
Published: September 2019

Attribute based encryption scheme is widely used to share sensitive data in cloud storage environment because it can realize fine-grained access control. Aiming at the problem of user revocation and high cost of decryption in cloud storage environment, a multi-authority attribute based encryption scheme with efficient user revocation and outsourcing decryption was proposed. It supports large universe. Each authority manages a specific set of attributes and distributes attribute keys to users with corresponding attributes, avoiding the problem that a single authority may easily become a system bottleneck. The user revocation algorithm of the scheme can realize the efficient revocation of the authorized users, ensuring the forward security of ciphertext. It’s proved to be statically-secure under random oracle and the efficiency analysis shows that it reduces user decryption cost greatly
Semi-Physical Simulation Design of UAV Simulation Training System
Authors: Zheng Xing , Bao Chuanmei , Lin Dongsheng , Liu Changliang
Published: September 2019

The UAV simulation training system simulates the pre-flight ground commissioning and in-flight operation monitoring of UAV. The UAV semi-physical simulation training system is built by integrating mathematical model, physical model, simulation computer, simulation software and other auxiliary equipment. This paper describes the functions and structure of the UAV simulation training system based on semi-physical simulation technology, and specifies the approach to implementation of simulation module algorithm, design and key technologies of the system.
Research on Rating Error and Quality Metrics for Collaborative Filtering Recommendation Methods
Authors: Zhao Kun , Pi Jiaming
Published: September 2019

The collaborative filtering recommendation system has been widely used in E-commerce as a relatively successful recommendation system. At present, the focus of collaborative filtering recommendation research is mainly on how to improve the accuracy of recommendation by improving the recommendation algorithm. However, in real world, the user’s rating behaviour is not perfectly rational. It is no odd that there is deviation of rating to any a given item for a user in real evaluation. In this case, what it means for the improvement of collaborative filtering recommendation methods, and how they performed when we use the commonly used quality metrics to evaluate the collaborative filtering recommendation methods? In view of these problems, this paper presupposes that the user’s rating behaviour is a bounded rational behaviour, and based on the normality hypothesis of rating error, introduces a simulated rating experiment innovatively to analyse the effect that rating prediction can achieve in sense of the commonly used quality metrics. This study is of significance for the research and application of collaborative filtering recommendation technology.
Public Opinion Analysis of Big Data Based on Machine Learning
Authors: Chen Xuegang , Fan Hongbin , Duan Sheng , Wang Luda , Zhang Jialu
Published: September 2019

Network public opinions can be regarded as a collection of network comments, that is, a network comment can be regarded as the basic unit of network public opinion, based on this, network comment can be divided into spam comment, subjective comment and objective comment, we combine feature extraction method with machine learning method, machine learning method includes naive bayes and threshold delineation, and classify different types of network comments. By a large number of experiments, we have proved that the classifying accuracy of network comments is high, and the machine learning method can reduce the noise of text classification.
Air Combat Command and Guidance Situation Assessment Based on Attack Area
Authors: You Hanghang , Yu Minjian , Han Qisong
Published: September 2019

Aiming at the problem that the situation function in traditional air combat command and guidance situation assessment lacks comprehensive consideration of advantages and threats, an air combat command and guidance situation assessment method based on attack area is proposed. In this method, combined with a certain type of air-to-air missile attack area, aiming at the comprehensive situation of advantages and threats, the best angle, distance, speed and height are proposed, a new situation function is constructed, and the air combat command and guidance situation evaluation model is improved. Through case analysis and comparison with traditional methods, the accuracy and effectiveness of the method are verified.
A Hybrid Search Scheduler for Dynamic Auto-driving Team Scheduling with Time Window under Cloud Plan
Authors: Li Ming
Published: September 2019

The car cloud plan means that the traveller in the cloud plan does not need to buy an automatic driving vehicle, only need to join the cloud plan, send the travel instruction to the cloud scheduler through the mobile device in hand, and then the cloud scheduler provides the travel service. With the development of actual travel, more and more problems are moving towards the direction of dynamic travel. In this paper, a hybrid solver based on ant colony optimization system search architecture is designed. In the ant colony algorithm, a new algorithm is constructed by combining local search heuristic algorithm with path problem. This paper combines the cloud planning platform with automatic driving vehicle scheduling to quickly collect customer orders and have rapid large-scale processing capabilities, using the order information sent by the traveller to the cloud, through an ant colony searcher to achieve rapid processing of orders.
Medical Image Fusion Based on Sparse Representation and Guided Filtering
Authors: Huang Sa , Chu Guangyu , Fei Yifan , Zhang Xiaoli , Wang Hailiang
Published: September 2019

In this paper, we proposed a medical image fusion algorithm based on sparse representation and guided filtering. One of attractive features in the algorithm is that it can preserve the structural information of structural image and color information of functional image. We use a sparse representation in low frequency, initialize the dictionary by using DCT transform, and train the dictionary with each input source image as a training example. It not only ensures time complexity, but also ensures that the low-frequency fusion rules are adaptive. At high frequencies, we use the method of guided filtering to extract structural information from high-frequency images, and use the injection method to fuse high-frequency sub-bands to ensure the validity and richness of structural information. Experimental results show that the proposed fusion algorithm is superior to comparative algorithms in terms of subjective and objective evaluation methods.
Application of Intelligent Traceability Management System in Agriculture—Take Aodong Fruit and Vegetable Planting Cooperative as an Example
Authors: Gao Hongmei , Wang Zhida , Liu Yuchuan
Published: September 2019

At present, strengthening the construction of agricultural product quality and safety traceability system and vigorously developing digital and intelligent agriculture have become the focus of future agricultural development. This project concentrates on the development of Golden Pear planting and processing industry chain in Aodong Village, Shigezhuang Town, Wuqing district, Tianjin. It combines Internet of Things, QR code traceability technology with cloud big data to develop an intelligent management system for Yongbei Golden Pear industry chain. The intelligent platform management makes fine traceability management from production to consumption possible, so as to further enhance the characteristic brand of cooperative agricultural products, create cooperative agricultural products sales channels and new image, and improve quality and efficiency of agriculture.
An Improved Tracking Algorithm Based on MDNet
Authors: Wang Biyan , Guo Baolong , Li Qianying , Liu Runzhi
Published: September 2019

The MDNet algorithm works well on tracking problems of the video sequence, but the speed is very slow. We have made some improvements to accelerate the process of feature extraction. It enhances the expressive ability of the feature map by removing the max pooling layer and using the method of expanding convolution to increase the receptive field of each point on the feature map. In addition, MDNet is building on CNN, and there are problems that similar targets have a large interference to the results. To address this problem, We use RNN to capture the long-term dependence of the target before and after the target data in the sequence data, and introduce the RNN to model the structure information of the target object, and then fuse the RNN feature and CNN feature of the tracked target object. In addition, another new loss term is introduced to make the targets in different domains away from each other in the shared feature space, thereby improving the algorithm’s ability to discriminate similar interferers. Compared with MDNet, our improved algorithm is much faster and the accuracy is improved.
A Kind of Static Software Birthmark Based on Control Flow
Authors: Zhao Yalin , Lei Juchao
Published: September 2019

In traditional static k-gram birthmark algorithm, the result of plagiarism detection is inaccurate. It is a new method for plagiarism detection that using the software birthmark based on program control flow in this paper. First, it is used to the result of static that analysis of the Java program as meta information, analyze meta information to get byte stream instruction in method. Then, a byte instruction stream in the method to get the control flow structure of the program has been analyzed, and replaced the external references with external control flow structures, using this control flow structure as a software birthmark. Finally, it is calculated that the similarity between two birthmarks by VF2 algorithm, thus determining whether there is plagiarism between the two programs. It is shows that birthmark which presented in this paper are more credible than traditional SKB birthmarks in experimental results.
On-Line Parameter Identification of a Squirrel Cage Induction Motor
Authors: Li Shanshan , Zhu Ninghui , Wei Biao , Fu Yunji
Published: September 2019

Based on the principle of coordinate transformation, a mathematical model of induction motors in the three-phase stationary coordinate system was transformed to that in the synchronous rotating coordinate system. For motors with low-speed rotors, the multi-variable, high-order, strong-coupling and nonlinear equation of motors was further transformed to a linear equation. However, it was found difficult to solve the deduced linear equation through the method of least squares due to its high singularity. The steady-state stator resistance could be predetermined by temperature revision on the stator resistant in the motor nameplate. Identification of four motor parameters (Ls
,M, Rr
, Lr
) was followed by the recursive least-squares method. Simulations of motor parameter estimation were carried out in Simulink of Matlab. The recursive least-squares method was used to deal with the measured data. The results demonstrate that the real-time electrical parameters (Ls
, σ, Tr
) can be accurately predicted. Given the assumption of Ls
= Lr
in the classical motor theory, the other three electrical parameters (M, Rr
, Lr
) can also be identified. The identification of these parameters is of great importance for the analysis, control and fault diagnosis of induction motors.
A Pilot Study of Chaos Criteria with Hilbert Transform and Mutual Information
Authors: Chen Yahui , Zhou Yan , Li Wenshi
Published: September 2019

Chaos feature criteria tree has many fruits with geometric maps and calculated values. To easily understand how to study chaos identification, we propose one new learning combination solution based on Hilbert transform (HT) and mutual information (MI). The most important coding probe (x-axis) used the HT of locked uniformly distributed random number (LUSRN), so far the y-axis in contrast standard map points to LUDRN. The measure between contrast standard map and unknown map (normalized test data, HT of LUSRN) used MI values. The test cases cover five chaos equations and eighteen quasi-period functions. The results show in statistics that our new maps and corresponding MI values can classify three kinds of signals with periodic, chaotic and random states. And this work may promote rapid growth of an apple embedded in novel chaos criteria.
Asynchronous Advantage Actor-Critic Algorithms Based on Residual Networks
Authors: Tang Lili
Published: September 2019

Deep reinforcement learning is one of the fastest-growing technologies in machine learning. The Asynchronous Advantage Actor-Critic algorithm completely uses the actor-critic framework and utilizes the idea of asynchronous training, which greatly speeds up the training and improves performance. Although A3C algorithm puts actor-critic into multiple threads to train synchronously, effectively utilizes computer resources and improves training effectiveness, it is still difficult to train in deep neural network. Deep networks have proved to be capable of extending to thousands of layers and still have improved performance. However, every one percent increase in accuracy almost doubles the cost of layers, so it is not easy for A3C to train both actor and critic networks. In response to this problem, we innovatively utilize the residual network to apply to the asynchronous advantage actor-critic algorithm and has achieved improvement greatly in the inverted pendulum problem.
Research on the Effects of Machining Parameters on the Surface Location Error for a Milling Process
Authors: Deng Congying , Miao Jianguo , Zhang Bin , Feng Yi , Yang Kai
Published: September 2019

The surface location error is an inevitable phenomenon which is caused by the forced vibration under a stable machining process. The surface location error (SLE) is dependent on the machining parameters. Therefore, this paper proposes a method to study the effect of each machining parameter on the surface location error. An algorithm based on the cutting force dynamic model is proposed to predict the SLE in frequency domain. And then a case study is performed on the milling process of a real vertical machining center. The variation ranges of the spindle speed, axial cutting depth, radial cutting depth and feed rate per tooth are first determined. Eight specific values of each machining parameter is selected, and an orthogonal experiment table is established to arrange the schemes to predict the corresponding SLEs. Then the range analysis is used to analyse the values of the machining parameters and the obtained SLEs. Results show that the spindle speed is the most dominant factor affecting the SLE and an optimal machining parameters combination related to a minimum SLE is determined based on the optimal level of each machining parameter.
Flow Control and Optimization Technologies of Information System
Authors: Li Xianyu , Wang Hua , Chen Zhiyuan , Liu Weina
Published: September 2019

On the basis the description of information flow connotation, the paper describes the information flow system frame, proposes the flow construction method and information flow utilization mode, puts forward flow modelling technology, semantic comprehension technology, data excavating technology and process optimization technology, and presents related test analysis and flow improvement of the information system, so as to further strengthen policy decision function, and enhance the timeliness and accuracy of situation disposition and information processing of information system.
An Anonymous Network Website Fingerprinting Attack Method Based on Wavelet Transformation
Authors: Lai Qi , Ling Jie
Published: September 2019

The anonymous network in protecting user privacy information, but also for the use of criminals, for its illegal acts, and increase the cover monitoring, tracking and obstacle of the government organs forensics, Aiming at the problem of anonymous network, this paper proposes a website fingerprinting attack method for TOR anonymous network. Wavelet transform is introduced to deal with the traffic generated by visiting the website. SVM algorithm is used to learn and generate classifiers, establish fingerprint feature library, and then compare the data generated by the target visiting website with the feature library to confirm its visiting website. In this way, the experimental results show that the average recognition accuracy of target visited websites is 96.7% under all known features of websites, which is superior to literature [17].
Identifying Behavioural Intents in a Complex Search Process Management System
Authors: Zhu Mengliang , Xu Ruikang , Sun Mingzhen , Zhang Yin
Published: September 2019

Understanding search intents is essential for improving search results and query recommendations. Currently, most search intent identification methods work on plain form search logs consisting of queries and clicks. Meanwhile, new search tools are being introduced by the CHI/HCI communities, allowing users to perform more types of behaviors than querying and clicking, and generating rich form search logs with more information than that in plain form search logs. In this paper, we seek to identify search intents by considering various types of behaviors performed in a complex search process management system. We use random forest to identify search intents based on the behaviors performed. Experimental results show that search intents could be identified by analyzing search behaviors, and certain types of behaviors can be good indicators of search intents.
Application Status of Intelligent CAD in Mechanical Manufacturing
Authors: Huang Jiangge , Tao Bo , Li Gongfa
Published: September 2019

Machinery manufacturing takes up a very heavy proportion in the national economy, which is the core industry as well as the cornerstone of a country’s national economy. Mechanical manufacturing has been slowed down because the manufacturing process is complex. With the development of social economy, and the advent of the era of science and technology, the production manufacturing technology level is increased sharply in order to promote the development of China’s industrialization process, gradually in the mechanical manufacturing will be tedious manual production mode transformation to the intelligent technology development, the automation and intelligent technology applications, the application of intelligent CAD is one of them. The application of intelligent CAD technology in machinery manufacturing is conducive to improve the development level of machinery manufacturing industry and it is an important measure for the transformation of industrial production towards digitalization and modernization. This paper analyses the application status of intelligent CAD technology in mechanical manufacturing and discusses its future development trend Introduction.
Research Status and Trend of Fault Diagnosis Based on Deep Belief Network
Authors: Xiong Bitao , Tao Bo , Li Gongfa
Published: September 2019

Modern industrial systems are moving towards large-scale, complex, and high-speed. It is difficult to solve a series of technical problems that rely on traditional fault feature extraction methods. Since the concept of deep learning has been proposed, it has shown obvious advantages in many aspects. It includes feature extraction and pattern recognition. Therefore, many scholars have conducted deep learning to solve the problems of complex industrial system fault diagnosis. The deep belief network is the typical deep learning technologies in the field of control. This paper mainly introduces the deep belief network and describes its main ideas and methods. Finally, the paper summarizes the problems faced by the current deep belief network in the area of fault diagnosis and the future research direction.
A Novel Similarity Search Approach for Streaming Time Series
Authors: Ding Yiming , Luo Wei , Zhao Yufei , Li Zhen , Zhan Peng , Li Xueqing
Published: September 2019

Time series similarity search has been widely used in many applications, such as financial data analysis, meteorological data forecasting, and multimedia data retrieval. The original task of this research was to find time series in a database similar to the query sequence, where both the results and the query sequence are static. However, along with the arrival of Industry 4.0 era, a large number of detecting instruments in various fields have produced a large amount of time series stream data. The new challenges for Similarity-based time series retrieval can be attributed as one-pass search and real time response. In this paper, we propose a novel multi-resolution search scheme to find a set of sequences similar to the query sequence in streaming time series efficiently and exactly. The scheme performs multi-resolution filtering on the raw data, and most dissimilar sequences can be filtered out at a lower level to avoid unnecessary computation and to improve the search efficiency. Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the superiorities of our method.
Use Big Data + Internet Thinking to Solve the Problem of Data Governance
Authors: Zhou Guixian , Chen Kaijian
Published: September 2019

Nowadays, with the development of cloud computing, Internet of things and mobile Internet, big data is exerting a huge influence on the global economy and society. The era of big data has brought new challenges to social governance, requiring us to grasp the characteristics of social governance in the era of big data, change the research methods of social governance, and meet the needs of social governance in the era of big data. Government affairs open service platform to integrate distributed and heterogeneous system of information resources, eliminate the “information island” phenomenon, through the open service platform, service background, e-government service management platform, monitoring platform four subsystems to achieve between different institutions, different application system and database based on the different transmission protocol of data exchange, information sharing and business collaboration, thus to provide good data to support the government and the enterprise information construction environment.
Implementation of High Order QAM Modulation on SDR
Authors: Wu Hao , Lu Min , Li Yan , Liu Mengrong
Published: September 2019

Nowadays, SDR (software-defined radio) has increasingly occupied a place in scientific research and teaching with its powerful signal processing capability and custom ability. XSRP is a development platform which uses an SDR architecture. In this paper, we develop a wireless real-time audio transmission system with high order modulation (64QAM) based on XSRP platform. We use LabVIEW and MATLAB to write the program’s overall framework and functions. We also analyse the performance of different modulation modes by comparing the constellations and waveforms of the transmitter and receiver under the real situation. The results show that high order QAM modulation achieves a good balance between efficiency and quality.
Adaptive RSA Encryption Algorithm for Smart Grid
Authors: Wang Fang , Wang Zhenyu , Zhu Yan
Published: September 2019

Information security has been paid more and more attention as it is a high-weight factor that affects many aspects in our life. Many technologies have been introduced to improve information security. Encryption algorithm is one of the most frequently used method. There are many encryption algorithms. RSA algorithm, which uses asymmetric encryption method, is a secure encryption algorithms. RSA’s encryption effect is pretty good due to difficulty in cracking its private key. Smart grid is the upgrade of power grid, which is based on integrated, high-speed two-way communication network. In smart grid, there are lots of data to be transferred along the network. In order to ensure that the data are not stolen or tampered with, it is necessary to encrypt the data during transferring. However, it is ineffective to use conventional RSA algorithm directly in smart grid. To solve the problem, we propose an adaptive RSA algorithm to generate primes, which modifies the application scope of RSA algorithm, so that the algorithm can be successfully applied in smart grid. According to the time limit of the encryption algorithm, the prime number is generated adaptively, so that the encryption time of the algorithm is reasonably controlled. The data is encrypted by generating the public key and the private key used for encryption through the generated prime number. The experiments verified the effectiveness of the algorithm.
Search Engine and Performance Optimization of Drupal-based Websites
Authors: Song Jie , Du Hui , Huang Qi
Published: September 2019

In view of incomplete and in-depth of research on search engine and performance optimization of Drupal-based websites, using the method of documentation, the common solutions are summarized. Their implementation methods are described and put into practice. Reference and guidance are provided for the search engine and performance optimization of Drupal-based websites.
Development Status of Equipment Testability Verification Technology
Authors: Luo Jian-Hua , Wang Qing-Shu , Li Hua
Published: September 2019

Testability verification of equipment refers to the test and evaluation for testing whether the testability of equipment given by design and manufacture meets the specified testability requirements. Testability verification can lay a theoretical and technical foundation for the testability and maintainability transformation of active equipment, and provide a development platform for the development of equipment maintenance support equipment and system of grass-roots units. Its research work has very important military and economic significance for improving the overall level of information and intelligence of equipment and the security of use. This paper summarizes the development status of testability test and verification technology of equipment at home and abroad, analyses the key technologies of testability verification, and points out the key and difficult problems to be solved urgently at present. Finally, based on the comprehensive analysis of the current testability physical verification and non-physical verification technology schemes, some suggestions are given for further research and development of testability verification technology schemes.
An overall scheme design and application of a wheeled robot for martial arts challenge competition
Authors: Huang Shanshan
Published: September 2019

With the rapid development of artificial intelligence, driven by the government’s strong support and the transformation and upgrading of traditional industries, the research on robot continues to gain popularity, and robot competitions are attracting more and more attention. In this study, an overall scheme design of a wheeled robot for martial arts challenge competition is introduced. It had been successfully applied in the robot competition of colleges and universities in Jilin province of China in 2018 and the robot fighting competition of the world in 2018. The author achieved good ranks. This shows that the scheme has strong operability.
Catenary Fault Identification Based on PSO-ELM
Authors: Yi Lingzhi , Zhao Jian , Yu Wenxin , Liu Yue , Yi Chuyang , Jiang Dan
Published: September 2019

Catenary has been exposed outdoors for a long term, and its failure rate is very high, which has seriously affected the operation and development of traction power supply system. Due to the problems of long detection time, backward detection means and influenced by human factors in traditional catenary fault identification methods, this paper proposed a fault identification method based on PSO-ELM. This method could reduce the hidden layer nodes of traditional ELM and improve the accuracy of identification. In this paper, this method was compared with ELM, GA-ELM, BP, GA-BP and PSO-BP. A sample of catenary detection data of a power supply section in 2018 was selected. The results show that PSO-ELM is an efficient method for the fault identification of catenary.
Optimal Trajectory Planning of Eye-head Gaze Shifts for Bionic Robot Vision System on Orchard Environment Perception
Authors: Ma Jianping , Cheng Yuanhao , Wang Sun’an
Published: September 2019

To make orchard robots obtain environmental information quickly and smoothly, this paper studies the neuro-mechanism of primates’ gaze shifts. Meanwhile, according to the optimal control theory, this paper considers time and energy as factors of formulating cost function and then analyses kinematic relationship between eye and head during gaze shifts to find constraint conditions. Solving the two-point boundary value differential equations of optimal control, this paper can extract the best control signal of coordinated eye and head movements. By building a multi-degree-of-freedom robot vision system corresponding to the biological model, this paper simulates primates’ gaze shifts and verifies the feasibility and validity of the method proposed.
The Application of Artificial Intelligence in Electronic Commerce
Authors: Song Xia , Yang Shiqi , Huang Ziqing , Huang Tao
Published: September 2019

With the rapid development of science and technology and economic society, the application of artificial intelligence(AI) is more and more common, its development has a profound impact on our work and lifestyle. In the field of e-commerce, AI technology has also been well applied and achieved good results. AI has become an important driving force for the development of e-commerce. This paper simply described the e-commerce development situation and prospects of AI technology, analyzes the present situation of the application of AI technology in the field of e-commerce, mainly study and discuss in detail from the aspect of assistant of AI, intelligent logistics, recommendation engine and the optimal pricing application through the research of e-commerce intelligent operation instance - Baidu take-away, probes into the important impact and great significance on the e-commerce development of artificial intelligence.
Research on Small Sample Image Recognition Based on Transfer Learning
Authors: Yang Guhai , Tang Baihui , Cao Sanxing
Published: September 2019

With the improvement of electronic hardware configuration performance, high-speed computing of large and complex data can be realized, and artificial intelligence, which requires a large amount of data calculation has been rapidly developed. Nowadays, artificial intelligence has developed rapidly in image recognition, natural language processing and speech recognition, and also has achieved good results in these areas. Among them, the convolutional neural networks plays an important role in image recognition. Some excellent convolutional neural networks such as VggNet, GoogleNet and ResNet have reached more than 90% in image recognition accuracy. But these convolutional neural network models are based on big data, consume a lot of computing time, which is very costly. Therefore, when data is limited, hardware configuration is low, and time is tight, a research on small sample image recognition based on transfer learning becomes very necessary. In this paper, the inception-V3 model is used to transfer learning. The training nodes of the model are skillfully used to train the model on the data set of ten kinds of fruits. The model is tested by the training model and good accuracy is obtained. The advantage of the experiment is that the training time is short, the required data set is small, and the image recognition accuracy is high
An Improved Algorithm Inspired by Natural Biology for Image Enhancement Used in Space Station
Authors: Wei Bo , Hu Lei , Shu Sihao , Yang Rong , Miao Jianguo
Published: September 2019

In the space station, the uneven illumination of image brings significant challenges for image enhancement in vision system due to high dynamic range of light intensity and the light reflection of metal facilities. In order to solve this problem in monitoring, unattended operation and robot operation, Retinex algorithm was proposed to estimate the luminosity. However, the effectiveness of this method is highly dependent on the selected path and consumes huge computational cost. This paper proposes an algorithm inspired by the Archimedes spiral in natural environment to improve the processing time and enhance the images quality. First, the structure and mechanism of Human Visual System (HVS) are analyzed. Second, the original image is decomposed into the brightness and color layers based on the structural human eyes mechanism. Third, the brightness layer is processed by the improved Retinex algorithm to enhance the original image by merging it with the color layer. The proposed algorithm is validated with the experimental results.
A GRU Model for Aspect Level Sentiment Analysis
Authors: Xing Yongping , Xiao Chuangbai
Published: September 2019

Sentiment analysis is a basic task of natural language processing, while aspect level sentiment analysis is an important topic in sentiment analysis. In the same sentence, different words have different influence on the sentiment polarity of aspect, so the key to solve the problem is how to build a relation model between the aspect and the words in the sentence. In this paper, by using two recurrent networks, we built a model for sentence and introduced attention mechanism to fuse aspect information, so as to achieve a better effect. An experiment on public dataset show that the proposed algorithm obtain a better result without carrying out complex feature engineering.
Research Status and Tendency of Intelligent Industrial Robot
Authors: Wen Bo , Tao Bo , Li Gongfa
Published: September 2019

In order to adapt to the overall industrial upgrading of the global manufacturing industry, adding the artificial intelligence technology to the field of industrial robots has a very realistic and far-reaching significance. This paper will analys the research status of intelligent industrial robots at home and abroad. Then we will focus on the introduction of industrial robots based on deep learning and machine vision. According to the present situation of industrial robot industry at home and abroad, we will put forward the development tendency of intelligent industrial robot in the future.
Double-layer Support Vector Machine for Robust and Efficient Classification
Authors: Wu Yuting , He Yigang , Shi Luqiang , Sui Yongbo , Huang Yuan , Cheng Tongtong
Published: September 2019

In this paper, double-layer support vector machine (DLSVM) for binary classification is proposed that can expeditiously learn a classification model under the premise of ensuring accuracy. In the first layer, least squares support vector machine (LSSVM) is used to evaluate all samples in the data set. Support vector machine (SVM) utilize a sparse data set to train the binary classifier in the second layer. In order to obtain excellent sparse data set for SVM, the normalized norm sequence and the normalized Lagrange multiplier Shannon entropy sequence are introduced. DLSVM provides low computation cost because the two-layer structure reduces lots of iterative operations. Experimental results show that the proposed method not only has similar classification accuracy compared to SVM but also has higher efficiency.
A Review of Online Sequential Extreme Learning Machines
Authors: Deng Chang Hui , Wang Xiao Jun , Gu Jun , Wang Wei
Published: September 2019

One of the challenges for machine leaning on big data is the effective and efficient leaning of large-scale and on-going explosion data which is always with the concept drift problem. To meet the challenge, learning algorithms/techniques performed well on large-scale data and also with the evolvable property are desired. The OS-ELM family has strong potential as viable alternative techniques for the computation of large-scale and on-going explosion data in more fields of applications/tasks. This work reviews the most important and latest works in OS-ELM family. The review consists of two topics, one related to the improved version of OS-ELM which aims at overcoming the disadvantages of OS-ELM, and the other related to the extended version the goals of which is to add some specialties to OS-ELM. It is expected that the review will support a certain research in the future.
Stability-Based Parameter Selection for Data-Driven Model-Free Adaptive Controllers
Authors: Deng Kai , Yang Chunhua
Published: September 2019

The algorithm of model-free adaptive control normally includes a control algorithm and a pseudo-partial derivative estimation algorithm. The selection of each parameter in the algorithm directly affects the control performance and stability of the system. Two artificial additional parameters have been introduced for the algorithm, but the corresponding theoretical basis needs to be improved. Although the range of artificial additional parameters is given in the control algorithm, the stability condition of the system cannot be guaranteed. In this paper, the theoretical basis for introducing two additional parameters is provided by modifying the cost function. Furthermore, the selection conditions of control parameters are given such that the controlled system stable and not entirely dependent on the trial-and-error. The simulation results are given to confirm the theoretical findings.
BiLSTM-CRF Chinese Named Entity Recognition Model with Attention Mechanism
Authors: Wan Zhongbao , Xie Jie , Zhang Wei , Huang Zhaohua
Published: September 2019

In order to make up for the weakness of insufficient considering dependency of the input char sequence in the deep learning method of Chinese named entity recognition task, this paper proposes a method, which integrate Bidirectional Long Short-Term Memory (BiLSTM), attention mechanism and add the information of word vector. Firstly, the proposed model obtains the char vector feature extracted from the text corpus, which is then input to the BiLSTM model; Secondly, the attention mechanism is used to calculate the relevance between the current input char and the other input char of the BiLSTM model; Finally, the global feature is obtained according to relevance, concatenating the word vector feature, which is introduced to the Conditional random field(CRF) layer to perform the mutual constraint between tags. Thus, the classified result can be obtained. Based on the corpus of the Chinese Peoples’ Daily Newspaper in 1998, our experiments show that the proposed method can improved the performance and efficiency of named entity recognition, compared to the existing deep-learning method that combines word vector and char vector.
A new type of fruit and vegetable preservation equipment based on PLC control
Authors: Xie Yonghui , Liu Kai , Zhou Quan
Published: September 2019

In this paper, a new air conditioning and fresh-keeping control system is designed and studied, PLC technology, frequency conversion technology, sensor technology, parameter self-tuning PID technology and configuration monitoring technology affecting the fresh-keeping of environmental factors such as temperature, gas composition, humidity intelligent, energy-saving control and dynamic monitoring, ultimately achieve prolong time for fruits and vegetables, reduce the loss of the fruits and vegetables preservation equipment operation and energy saving purpose.