bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical prediction on resistance reduction of multi-purpose amphibious vehicle (MAV) due to air-cushion effect in a regular wave
Authors: Ain Abd Rahman Nur , Abd. Malik Adi Maimun , Nakisa Mehdi , Abd Rahman Norhanifah , Manap Norhashidah
Published: January 2020

In this paper, the resistance acting on Multi-Purpose Amphibious Vehicle (MAV) hull navigated in regular waves condition was investigated numerically. A simplified MAV model was established for computing the resistance of air cushion effect on regular head waves. Simulations were carried out in finite element analysis ANSYS CFX 15.0 in 0.5m wave height conditions. The resistances of the MAV model with and without the air cushion effect were compared in a graph of total resistance versus MAV speed. According to the results, the maximum resistance reductions occur at forwarding speed 6kn with 0.2 l/s airflow rate injection for wave height 0.5m at 10.89%.
Simulation study on photovoltaic panel temperature under different solar radiation using computational fluid dynamic method
Authors: Leow W Z , Irwan Y M , Safwati I , Irwanto M , Amelia A R , Syafiqah Z , Fahmi M I , Rosle N
Published: January 2020

The electrical production is the primary performance of any solar photovoltaic (PV) system. The PV panel operating temperature is inversely proportional to the electrical production of the PV panel. The operating temperature of PV panel is influenced by solar radiation absorbed and the ambient temperature. In the present work, Computational Fluid Dynamics (CFD) method is used to investigate a three-dimensional (3-D) model of a PV panel. It is also essential to estimate the thermal behaviour of the PV panel under various environmental conditions. The primary purpose of this current work is to analyse temperature distribution from the PV panel under given operating conditions. The model geometry is built by using CATIA design software. ANSYS software was simulated the different intensity of solar radiation that applied to the PV panel in order to observe the temperature distribution on each layers of the PV panel. The ambient temperature of the simulation is fixed 35 °C according to the maximum ambient temperature captured in Malaysia. The simulation results show that an increase in solar radiation intensity along with the PV panel operating temperature increase.
Battery monitoring for stand-alone photovoltaic system
Authors: Syafiqah Syahirah Mohamed Nor , FaezahBintiAbdGhani Noor , Farhana Abd Hamid Nurul
Published: January 2020

This project deals with the design of a system to monitor the performance of Photovoltaic (PV) battery for Stand-alone system. This monitoring system is developed by using a Peripheral Interface Controller (PIC) a microcontroller as a control unit. The data measurement will be sent to a personal computer (PC) by Universal Asynchronous Receiver/Transmitter (UART) device and save in database file that can be visualized in table by Microsoft Excel software. The battery monitoring will measure and displayed on the LCD (Liquid Crystal Display) the several parameters of the PV system such as voltage, current, solar irradiance, ambient and cell temperature of the Stand-alone PV system. The State of Charge (SOC), Depth of Discharge (DOD) and ampere-hour (AH) of the battery have been analysed to prove the battery performance of the Stand-alone PV system.
Design of multiple Pulse Width Modulation (MPWM) Transformerless Photovoltaic Inverter (TPVI) system
Authors: Alam H , Irwanto M , Mashor Y M , Masri M
Published: January 2020

A normal inverter always uses a transformer. By presenting a transformer in the inverter causes its size, losses and efficiency to be relative big, high and low, respectively. An alternative to remove the transformer is very important to solve the problems, but it is still has good performance, especially in the required power quality. This paper presents a design of multiple pulse width modulation (MPWM) transformer less photovoltaic inverter (TPVI) system. It consists of a full bridge inverter circuit, a photovoltaic (PV) voltage storage circuit, a pulse driver circuit, and a circuit of LC filter. An experimental setup is conducted to observe the performance of MPWM TPVI system. The result shows that its performance depend on the solar irradiance and temperature because it operates directly on the sunny day.
Life-cycle assessment of 5MW grid-connected photovoltaic system in east coast of Malaysia
Authors: Sulaiman S I , Iskandar A N , Nordin A H M , Hamidun F H
Published: January 2020

The technical and economic feasibility of photovoltaic (PV) power plants throughout their lifetime must be evaluated prior to their development and operation such that the sustainability of the project can be justified. This paper presents the Life Cycle Assessment (LCA) of energy and the LCA of economics for a 5 MW grid-connected photovoltaic (GCPV) power plant located in the East Coast of Malaysia. Firstly, an LCA of energy was conducted to evaluate the expected energy yield, energy payback period and energy return of investment throughout the project lifetime. Later, an LCA of economics was performed to determine several economic performance indicators such as the net present value, internal rate of return, payback period and return of investment.
Improving the performance of solar panels by the used of dual axis solar tracking system with mirror reflection
Authors: Alif Ismail Mohd , Ramanathan Kreshnaveyashadev A/L , Hafizi Idris Muhd , Ananda-Rao Kumuthawathe , Mazlan Mazwin , Fairuz Nur
Published: January 2020

This work proposes the dual axis solar tracker with mirror reflection for optimum output of solar panel by using arduino unoR3 as the control unit. The objectives of this work are to track and optimize the maximum output power of the solar panel by designing and developing a dual axis solar tracker with mirror reflection. The system includes a 10 watt solar panel, an arduino unoR3 and a customized mechanical body to carry the solar panel. This system will track and detect the angle of the sun to locate the surface of solar panel at the position and the angle where it can get maximum amount of energy. The sensors will detect the position of the sun and servo motors act as free moving neck to make it easier to move freely depending on the angle detected. The Light Depending Resistor (LDR) will be used in tracking system. These LDR will detect the existence of sunlight and therefore the mechanical hardware will move horizontal and vertical axis depending on the value of LDR detected to follow the angular degree of sun in order to get maximum and best result of absorbing energy. The final result obtained from dual axis solar tracker showed that the output power has been maximized compared to stationary panel. Based on the experimental result, it show that the designed system successful improve the performance of the solar panel.
Improved hill climbing algorithm with fast scanning technique under dynamic irradiance conditions in photovoltaic system
Authors: Jawad Khadhim Alrubaie Ali , Faridun Naim Tajuddin Mohammad , Eddine Khalil Zidane Tekai , Azmi Azralmukmin
Published: January 2020

The perturb and observe (P&O) algorithm is an easy and effective method used for tracking maximum power point. However, this technique suffers from deviation when irradiation changes suddenly. Moreover, the impact of this deviation is high when the insolation variation is rapid. This error is due to the incorrect decision taken by the conventional P&O method throughout the first step-change in the duty cycle during the increase in irradiation. The proposed P&O is a modified conventional P&O that focuses on using additional dI parameter with variable step size ΔDn. In this manner, the conventional P&O algorithm is allowed to identify the source of deviation caused by rapid irradiance changes. The efficiency of the proposed P&O is assessed using simulation in MATLAB/Simulink. Results show that the proposed P&O effectively tracks maximum power and prevents deviations in rapidly changing climate conditions within a short time, which is lesser than the conventional P&O method. In addition, the proposed P&O has a rapid dynamic response. A DC–DC boost converter is utilized in this work to validate the proposed P&O algorithm.
Advance synthetic aperture radar images for characterization of oil spills disaster in ocean using Daubechies analysis
Authors: Jagdish Mukta
Published: January 2020

Background: Researchers have developed various algorithms to identify the occurrence of an oil spill in these oceans. However, knowing the type of oil that is spilled in the ocean is important to assess and plan the restoration process. To predict the type of oil that is spilled in the ocean by using machine learning techniques. Fifty satellite images of three types of an oil spill, namely petroleum, crude oil, and diesel were examined to identify the type of oil spill over the affected area. The oil spills were initially identified from the images using K-Nearest Neighbor algorithm. Color-based, Statistical, Textural and Geographical features are extracted after applying various types of wavelets to obtain the features relevant to the physical parameters and type of oil in the ocean. The features were then trained and classified using K-Nearest Neighbor algorithm to identifying the type of oil.

Methods: For wavelet analysis (Daubechies analysis db1, db2, db3, db4, db5, db6, db7, db8, db9, db10) and machine learning (k-nearest neighbor algorithm) applied to optimize the oil spill feature sets. Features included color-based, statistical, texture and geological features. This experiment was conducted on SAR images. The features were classified using a k-nearest neighbor algorithm. Seventy percent of features used for training and thirty percent for testing.

Results: The results show that oil spill classification achieved by wavelet transforms and machine learning algorithms outperformed very well with similar parameter settings, especially with 70% training data and 30% testing data using confusion matrix. It also represents 99% accuracy for petrol oil using Daubechies 5 analysis which indicates better characterization of oil spills. Results denote oil spill detection using Synthetic Aperture Radar (SAR) remote sensing which provides an excellent tool in oil spill characterization various features can be extracted from SAR data set.
In-building commercial mobile telecommunication system design for mass rapid transit station
Authors: Khor B , Pang W L , Wong S K , Chan K Y , Lee I E
Published: January 2020

The mobile telecommunication system provides voice, data and video communications between stations, Operation Control Centers, depots and all necessary areas and facilities. The Preliminary Design is an overall system design that elaborates on the proposed system configuration with an emphasis on how the interface requirements are to be achieved. The contractor verified the preliminary design for the coverage of the wireless access network with respect to the General Specification (GS) and Particular Specification (PS) of the mobile telecommunication system. The mobile service provider (Telco) should ensure that the mobile network coverage in Mass Rapid Transit tunnels and underground stations provide 95% coverage for all the defined areas. The Telco should ensure seamless handover success rate > 98% and proposed the required overlap distance in between the antennas. Based on the design assumption made on the link budget, the indoor system design to be aligned within 15m radius coverage, which will contribute to distance in between antennas in between antennas will be at 30m horizontal distance. This placement can be varied within 10-15% and subject to line of sight, physical obstruction or fixing constraint at side.
Natural Language Explanation Model for Decision Trees
Authors: Silva Jesús , Hernández Palma Hugo , Niebles Núñez William , Ruiz-Lazaro Alex , Varela Noel
Published: January 2020

This study describes a model of explanations in natural language for classification decision trees. The explanations include global aspects of the classifier and local aspects of the classification of a particular instance. The proposal is implemented in the ExpliClas open source Web service [1], which in its current version operates on trees built with Weka and data sets with numerical attributes. The feasibility of the proposal is illustrated with two example cases, where the detailed explanation of the respective classification trees is shown.
Neural Networks for Tea Leaf Classification
Authors: Silva Jesús , Hernández Palma Hugo , Niebles Núñez William , Ruiz-Lazaro Alex , Varela Noel
Published: January 2020

The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers.
Neural Networks for the Web Services Classification
Authors: Silva Jesús , Senior Naveda Alexa , Solórzano Movilla José , Niebles Núñez William , Hernández Palma Hugo
Published: January 2020

This article introduces a n-gram-based approach to automatic classification of Web services using a multilayer perceptron-type artificial neural network. Web services contain information that is useful for achieving a classification based on its functionality. The approach relies on word n-grams extracted from the web service description to determine its membership in a category. The experimentation carried out shows promising results, achieving a classification with a measure F=0.995 using unigrams (2-grams) of words (characteristics composed of a lexical unit) and a TF-IDF weight.
Analyzing Prediction Performance between Wavelet Neural Network and Product-Unit Neural Network
Authors: Suhailayani Suhaimi Nur , Othman Zalinda , Ridzwan Yaakub Mohd
Published: January 2020

Analyzing the performance of a particular approach in a field very dependent on the problem it’s aimed to solve. Artificial Neural Network (ANN) widely used for prediction in many areas including medical, environment, business intelligence and education. The uniqueness of ANN is the dynamic of hidden layer can be improvised mapped with the data problem and the structure of architecture can be enhanced such as Wavelet Artificial Neural Network (WANN) and Product Unit Neural Network (PUNN). This research aimed to analyzed the performance between WANN and PUNN towards water quality data of Chini Lake. Real world data comes with dynamic stream data and dynamic parameters based on its area of data collection method. Handling dynamic data would be misleading if the approach used very dependent towards data classes. The measurement to analyze the data based on performance accuracy, data sensitivity, data precision and specification of both method with regards of the regular ANN. The findings demonstrate the ability to obtain satisfactory prediction accuracy for both WANN and PUNN compared to regular ANN. The model accuracy for this case study by using WANN and PUNN were 75.34 % and 66.86 %, respectively. Therefore, WANN would be a competitive tool for prediction with conventional ANN.
Corneal arcus classification for hyperlipidemia detection using gray level co-occurrence matrix features
Authors: Ramlee R A , Subramaniam S K , Yaakob S B , Rahman A S F , Saad N M
Published: January 2020

The arcus cornea is an eye problem that is often encountered among older people, but the situation is risky to teenagers and young people, in which it gave a sign the presence of lipid abnormalities in their blood and the risk of disease problems such as diabetes and heart disease. This paper presents the classification of the arcus cornea, using the extraction of texture features of the gray level co-occurrence matrix, along with several models of the classifiers, namely as scale conjugate gradient, Bayesian regulation, and Levenberg-Marquardt. Percentage fractions for training, testing and validation for classifier are 70%, 15%, and 15% respectively. The comparison of the classifiers used by the past researchers for classification the eye abnormalities, also were analyzed and studied in this work. In this experiment, a total of 125 image eyes were used, consisting of two classes of the eye image, which is normal and abnormal. The best result demonstrated in this proposed framework using Bayesian regulation classifier is, a sensitivity of 96%, and a specificity of 100%. However, this classifier did not achieve perfectly classification or an accuracy of 100%. Nevertheless, it is able and evident that the system is effective by the output of 98.4% accuracy.
Machine learning in intelligent manufacturing system for optimization of production costs and overall effectiveness of equipment in fabrication models
Authors: Carla Acosta P , Terán Héctor C. , Arteaga Oscar , Terán María B.
Published: January 2020

The study proposes optimize the production costs with the implementation of an intelligent autonomous system applied to adaptive control and supervision to in computer-integrated manufacturing. For the validation, a horizontal band saw was used with 3 axes of displacement implementing 2 cameras with stereoscopic vision and finding an estimation of depth in the cut. With the dimensional deviations (x, y, z) of the cut, the shape and dimension of the cuts in the pipe, are defined to be manipulated and classified in correct and in-correct cuts by means of a separator coupling. For this purpose, algorithms were developed on two computer platforms: LabVIEW, which obtains the images, controls the automatic separator and the material feeder; Matlab, which processes dimensional deviations by recognizing patterns with the “Principal component analysis” (PCA) technique, in turn compares with an ideal pattern and optimizes the cutting parameters: Cut speed, cutting index, through a derivative Integral Proportional PID algorithm with the interaction of machine learning (ML) based on SVM theory. Autonomously corrects errors without human supervision, obtains the lengths and depths with the optimum cut-offs and result of adaptive supervision, increases production, product quality and reduces operating costs for each cutting cycle by com-plying with Overall Equipment Effectiveness parameters (OEE) and integrating into intelligent manufacturing systems.
Real-time classification of national and international students for ICT and mobile technology: an experimental study on Indian and Hungarian University
Authors: Verma Chaman , Illés Zoltán , Sttofová Veronika
Published: January 2020

This paper focused on the classification of student national-level status such as national and international. A primary survey was conducted in the academic year 2017-2018 to analysis the circumstances of trending ICT and Mobile Technology (MT) in Indian and Hungarian higher education. The main objective was to identify the student’s answers provided in the survey based on their national-level status. For the classification tasks, we used Logistic regression (LR), Support Vector Machine, Multilayer perceptron (MLP) and Random Forest (RF)on both balanced and unbalanced datasets with K-fold Cross-Validation (KCV), Leave One Out (LOO), and Hold Out (HO) methods. Also, Xtreme Gradient Boosting (XGB) classifier was also implemented to enhance the classification accuracy of existing classifiers. The findings of the study showed that the XGB classifier outperformed others with the highest accuracy of 95% with 18 significant features. Also, class balancing improved significantly the accuracy of classification. Further, the authors recommended this predictive model to be implemented as a real-time function utility on the website of the university.
Dimensionality Reduction and Regression Analysis Based on the Data of Educational Participation of Student Party Member
Authors: Li Jing , Wang Dengshan , Xu Xiaojuan
Published: January 2020

This study adopts the multi-stage random sampling method of grade, major and class. Data were obtained through questionnaires. Through the analysis of the questionnaire data, we find that the grade and identity distribution of the respondents are relatively uniform among the respondents. Taking the arithmetic mean value of each secondary index, we construct the dimension index based on the data of questionnaires. By least square and generalized linear regression model, we find that the influence of independent participation on the learning harvest is significantly positive, indicating that the daily party member education activities such as active participation and social practice can significantly improve the learning harvest.
Parameters optimization of SVM based on the swarm intelligence
Authors: Wang Yu-xin , Li Xue-zhen , Wang Zheng-yi
Published: January 2020

In view of the existing optimization method in the optimization of support vector machine (SVM) parameters of low searching efficiency and easy falling into the master problem, put forward a kind of modified pollinate flowers algorithm based on mutation strategy—MFPA algorithm, and applied to SVM parameter optimization problem, through the relevant test data sets from UCI, test results show that MFPA-SVM parameter optimization model of its classification performance is superior to the existing PSO-SVM model and the BA-SVM model.
A survey on key technologies of privacy leakage detection for Android platform
Authors: Yu Fu , Xuming Lv , Xiao Gao , Lin Liu , Jingzhao Luan
Published: January 2020

With the rapid advancement of the mobile internet, smartphones have been the essential tools for communication and entertainment. In recent years, Android, developing quickly, has been the largest mobile operating system in the world. However, due to mass private data generated by Android intelligent devices, privacy disclosure is a serious problem in security for the Android platform. Based on the relevant background, this thesis indicates the problems of privacy protection that the Android platform faces, summarizes the detecting techniques for privacy disclosure on the Android platform at the present stage, and introduces the research status and progress with the aim of offering a reference for relevant practitioners and researchers.
Deep Layer Aggregation with Cross Attention for Lane Detection
Authors: Lu Ming , Liu Duan , Sun Yongteng , Duan Hao
Published: January 2020

As an important part of driverless vehicle research and high-precision map making, lane detection technology needs to be improved urgently. However, it is difficult for current networks to obtain long-distance semantic information, which leads to the confusion of categories in narrow lanes. This paper adds cross attention based on deep layer aggregation to obtain long-distance semantic information, and constructs time series filter to filter in time domain, the method is simple and robust. Our encoder is based on resnet-50. According to the experimental results, slightly changing resnet-50 can achieve better results. Our method is evaluated on Baidu ApolloSpace land segmentation dataset, increases 3.4% relative to DeeplabV3+, and cross attention with time series filter contribute more than 1% mIoU accuracy.
Back-propagation Artificial Neural Network for Early Diabetic Retinopathy Detection Based On A Priori Knowledge
Authors: Wu Huiqun , Zhao Shenghui , Zhang Xiaofeng , Sang Aimin , Dong Jiancheng , Jiang Kui
Published: January 2020

Purpose: We aim to develop a back-propagation artificial neural network (BP-ANN) improved by a priori knowledge and to compare its efficacy with other methods in early diabetic retinopathy (DR) detection.

Methods: A total of 240 fundus images, composed of 120 early-stage DR and 120 normal images, were obtained with the same 45° field of view camera, with the macula at the center, as a cohort for further training. All retinal images were processed, and a priori knowledge features such as blood vessel width and tortuosity were semi-automatically extracted. An improved BP-ANN with a priori knowledge was developed, and its efficacy was compared with that of the traditional BP network and SVM. Besides, k-fold cross validation method was conducted to demonstrate the efficiency of the proposed methods. We also developed a graphical user interface of our proposed BP-ANN to aid in DR screening.

Results: Our 10 randomization and 5-fold cross validation results of SVM, traditional BP, and improved BP were compared. The results indicated that the BP-ANN with a priori knowledge can achieve better detection results. Besides, our results were also comparable with other reported state-of-art algorithms. During the training stage, the epoch in the improved BP-ANN was less than that in the traditional BP group (109 vs 254), indicating that the time cost was shorter when using our improved BP-ANN. Furthermore, the accuracy and epoch of both the traditional BP and our improved BP network obtained better performances when the number of hidden neurons was 20.

Conclusions: A priori knowledge-based BP-ANN could be a promising measure for early DR detection.

CCS: Information system→Expert system
Research on Key Technologies of Personalized Education Resource Recommendation System Based on Big Data Environment
Authors: Cheng Yunli , Bu Xu
Published: January 2020

This paper focuses on the construction of personalized education resource recommendation system and the research of key technologies in the big data environment, designs a personalized recommendation system architecture that can handle PB education resources, and implements an educational resource personalized recommendation system based on this architecture. A hybrid recommendation algorithm based on content and collaborative filtering is designed to meet the recommendation novelty requirements and solve the cold start problem of the recommendation system. Research a flexible, reliable, high-performance personalized recommendation system architecture that can store and process PB-level data and can be recommended in real time, research personalized recommendation engine, data pre-processing and data mining model construction, and implement the recommendation system. It is important to determine the availability of the system by performing related functions and performance verification.
Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension
Authors: Yue Yongzeng , Zhang Yuhong , Hu Xuegang , Li Peipei
Published: January 2020

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness; secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method.
The Facilitation of Modern Technics for English Pronunciation Class in Foreign Language Learning in China
Authors: Zhou Qian , Li Yonghong , Guo Lei
Published: January 2020

English is learned in China as a foreign language, the foreign language setting made it very difficult to learn the standard pronunciation. But the short comings can be greatly overcome by the proper use and facilitation of modern technics. This passage focused on what and how software and mobile phone Apps can be used in English class to improve the performance of English pronunciation.

CCS Concept

• Social and professional topics➝Adult education
Research on User Knowledge Acquisition and Application in Software Ecology
Authors: Qiuyu Ding , Fuhua Shang
Published: January 2020

In the process of software development, users play a crucial role. User knowledge plays a role of energy driver in the software ecology, and user knowledge plays a guiding and reference role in software development. From the perspective of knowledge chain, improving the acquisition rate and utilization rate of user knowledge can better complete the upgrading work of large software platform. Based on this starting point, this paper puts forward the idea of actively acquiring user behavior information, mining association rules of software platform components, and finally applying knowledge mapping technology to the knowledge representation of user knowledge and inherent knowledge of software platform, so as to strengthen the extensibility and continuity of knowledge. Experiments show that above methods can improve the acquisition rate and utilization rate of user knowledge, quickly locate relevant entities in the software to improve the speed and quality of large software platforms.
Research on Access Control Model Based on RBAC Model in Microservice Environment
Authors: Zhao Jiantao , Sun Jin
Published: January 2020

With the development of cloud platform technology, micro-service has become a very popular software development architecture mode. The micro-service architecture reduces the coupling between application modules, which is conducive to the development, deployment and operation of complex application systems, but also brings many system security problems. Access control model is an important guarantee. This paper discusses the basic requirements of access control in micro-service environment. Through the research of traditional access control technology, the limitations and shortcomings of access control in micro-service environment are analyzed, and an optimization model of access control based on RBAC is proposed.
Research on UAV image denoising effect based on improved Wavelet Threshold of BEMD
Authors: Pingjuan NIU , Xueru MA , Run MAO , Pan Jie , Wang Shan , Hao Shi , She Huanlin
Published: January 2020

Aiming at the noise generated by external or internal factors reduces the sharpness of unmanned aerial vehicle (UAV) images, an improved bi-dimensional empirical mode decomposition (BEMD) de-noising algorithm is proposed to solve the problem. Firstly, an intrinsic mode function (IMF) is obtained after BEMD decomposition of noisy image, and then the high frequency apply particle swarm optimization (PSO) after wavelet decomposition to the high-frequency IMF to obtain the optimal threshold filtering. As for the low frequency, it is filtered by the exponential attenuation threshold algorithm of wavelet semi-soft threshold. Finally, the filtered both high-frequency and low-frequency IMF are reconstructed after inverse wavelet transformation. This algorithm is applied to the processing of UAV images, and compared with the traditional UAV image denoising method and the advanced UAV image denoising method. Simulation results demonstrate that the proposed denoising method outperforms other de-noising methods in terms of peak signal-to-noise ratio and mean square error.
Study on Joint Regulation and Safety Supervision of Urban Rail Transit System
Authors: Cai Xiaolei , Gao Fan
Published: January 2020

The integration of uiban rail transit system is an important stage of urban rail construction. By analyzing the relationship between the urban rail subsystems, this paper studies the project, content and process of each electromechanical system. From the perspective of large scale system, the linkage technology and specifications of the electromechanical equipment are verified to ensure the overall coordination function of the system. In addition, the system joint supervision mode is introduced, and the whole process management is carried out to ensure the orderly and safe operation of the system. The debugging service process of EMU is analyzed emphatically. And the contents of the safety supervision work are also studied. From the perspective of risk analysis of motor vehicle commissioning, taking the risk hidden danger and prevention and control measures of a certain Beijing rail transit line as an example, the main points of safety supervision are studied. It can provide reference for urban rail transit industry system joint management.
Multi-empirical Discriminant Multi-Agent Reinforcement Learning Algorithm Based on Intra-group Evolution
Authors: Zhang Zhong-lei , Zheng Jiao-ling , Zou Chang-jie
Published: January 2020

In order to find the optimal target in the unknown environment more quickly, a multi-empirical discriminant multi-agent reinforcement learning algorithm based on intra-group evolution is proposed based on the deep deterministic policy gradient algorithm (DDPG). In the unknown environment, the agent can find the target faster by performing Information exchange, group genetics and other mechanisms. The comparison with the traditional reinforcement learning algorithm shows that the proposed algorithm is superior to the traditional reinforcement learning algorithm in solving time and solving accuracy, and can quickly and effectively find the optimal target in the environment.

CCS Concepts

•Computing methodologies → Multi-agent systems
Research on Performance Optimization of Web Application System based on JAVA EE
Authors: Cai Zengyu , Li Jingxiao , zhang Jianwei
Published: January 2020

With the development of informatization, the Web application system is being used by more and more enterprises and institutions. With the increase of the user group, the concurrent visits are increasing, which leads to the poor service quality of Web system. Several high-concurrency solutions existed, but they address only one aspect of the performance problem, and small and medium enterprises need to consider the cost. This paper will analyze the performance factors affecting the Web application system through multiple levels, aiming to build a Web application system with high availability, high concurrency, high expansion and less cost. Based on the factors affecting the performance of the Web application system, the project of the online mall system was optimized, and the service support of the Web application system was adjusted to make it more suitable for the system requirements. Besides, related SQL statements were optimized, Java code layer was optimized, user experience layer was optimized, and performance testing and analysis were completed.
Thinking on the Teaching Reform of Electronic Technology
Authors: Shengchang Lin
Published: January 2020

with the rapid development of electronic technology, this paper starts with the design and manufacture of a kind of equipment in the reform of electronic technology teaching, and develops an electronic product. The reason is that the memory of the elderly is declining year by year, and people often forget to bring things when they go out of the house. For example, cell phones, door keys, etc., more serious is to forget to close the electric kettle in use, electromagnets, gas switches and so on. In view of the above phenomenon, I designed a warning device to go out, from shallow to deep in teaching, case teaching, to enhance studentsundefined interest in learning. The product can give a reminder when you go out, check to see if you have everything in place, and remind you to turn off the electrical appliances and gas switches that are in use. So that people can work in peace of mind, happy home.
Analysis of the status quo and trend of agricultural economic management under the background of big data
Authors: Kun-Fa Lee , Hui-ni Chen , Ying Lu
Published: January 2020

With the global economic network, the collection and processing of big data information data is more and more important, and the development trend of big data makes appropriate adjustments and choices in the management of agricultural economy. The use of big data in the combination of agricultural economic management environment has increased the information value of agricultural economic management. On the basis of science, big data as a development economic forecast not only provides a powerful guiding role for agricultural production, but also provides an effective basis for the management and decision-making of agricultural production. Based on the analysis of the development status of big data agricultural economic management, this paper provides a new direction for the innovation and development of China’s agricultural economic management.
Tomato Pests and Diseases Classification Model Based on Optimized Convolutional Neural Network
Authors: Jia Shaopeng , Gao Hongju , Hang Xiao
Published: January 2020

In the field of agricultural information processing, automatic identification and diagnosis of common diseases of tomatoes play an important role. The emergence of deep learning can help people simplify the process of image feature extraction, reduce network complexity and improve recognition accuracy. This paper built the Inception-v3 model to identify and classify five classes of tomato pests and diseases. We collected some images of tomato diseases uploaded by farmers from the online diagnosis platform for crop pests and diseases. In the experiment, the combination of the moving average model and the regularization was used to adjust the parameters in the model and enhance the robustness of the model on the test data. Experiments showed that the Inception-v3 model based on regular expression and moving average optimization can effectively avoid regularization and has higher accuracy, which can reach 86.9%.
Research on personal credit scoring model based on multi-source data
Authors: Zhang Haichao , Zeng Ruishuang , Chen Linling , Zhang Shangfeng
Published: January 2020

In the Internet financial personal credit loan business, it is necessary to construct a credit scoring model for users, and the problems of unbalanced user categories, high data dimensions and sparse features make it difficult to model the credit situation of users. This paper adopts the idea of grouping modeling. It proposes an improved BIV value feature screening method and a weighted average model based on Logistic Regression, Random Forest and Catboost, which provides a set of solutions for user modeling in this scenario. The grouping modeling idea pre-groups the customers and reduces the feature sparsity problem. The improved BIV value shows the influence of each feature on the results and points out the mutation threshold. The oversampling method alleviates the category imbalance problem. AUC is used as the model result evaluation index, and the results show that the classification effect of the model is good. The results show that customers with a long history of credit history and a history of good credit behavior have lower credit risk.
A virtual examination evaluation system design and implementation of equipment maintenance
Authors: Qiu Lei , Xie Cun , Zhang Xuezhi , He Peng
Published: January 2020

This paper presents a design method of assessment system for equipment virtual maintenance training, introduces the framework of the system, the components of each module and the scoring basis of the assessment link, and puts forward the scoring weight ratio according to the characteristics of equipment. Finally, taking the equipment as an example, the virtual assessment system is implemented.
Competitiveness Analysis of Hebei Electric Power Industry Based on Factor Analysis and Cluster Analysis
Authors: Huang Yuansheng , Zhu Lifeng
Published: January 2020

Based on the “Diamond Model” which incorporates the factors of power industry, and starting from the industrial chain structure of power industry development, an evaluation system including three dimensions of power industry demand side, power generation side and power industry resource side is constructed. The cross-sectional data of 31 provinces in China in 2017 are selected, and factor analysis and cluster analysis are used. The Empirical Study on the competitiveness of Hebei electric power industry shows that the competitiveness of Hebei electric power industry is the fourth gradient among 31 provinces (cities and districts) in China, and the overall competitiveness of the electric power industry is weak. At the same time, factor analysis shows that the competitiveness of power industry on generation side is weak, while the competitiveness of upstream and downstream power market of power industry has advantages over other provinces, and the development coordination of the overall supply and demand structure of power industry is unbalanced, which hinders the development of the competitiveness of the whole power market. On the basis of the above analysis, this paper combines the strengths and weaknesses of Hebei electric power industry, and gives relevant suggestions for its development.
Practice of Rural Revitalization Planning Based on Baidu Index’s Attention to Behavior Tendency: A Case Study of Jinyi House in Gutian County, Fujian Province
Authors: Shan Ke , Hui Pan
Published: January 2020

At present, the era of network information explosion, people use search engines to obtain more comprehensive and detailed information while leaving relevant records. In spite of these network drawbacks, this paper mainly focuses on the network search engine, which has brought a new database and platform for the theoretical and practical research of social sciences. Based on Baidu Index, a popular statistical analysis platform which can reflect the facts more comprehensively, this paper analyses the trend of people’s attention and choice on rural revitalization. The most relevant key words of Baidu Index for Rural Revitalization are summarized and sorted out: culture and nostalgia. In response to the strategy of “cultural renaissance” put forward by our country, this paper constructs a cultural revitalization based on Baidu Index’s research on behavior tendency. Taking Golden Wing House in Gutian County, Fujian Province as an example, this paper explores the practical ways of cultural revitalization, hoping that this research can provide relevant rural revitalization construction units and managers with information. Reference.
Research on the application of Naive Bayes and Support Vector Machine algorithm on exercises Classification
Authors: Li Qi-na , Li Ting-hui
Published: January 2020

In this paper, exercises on electronic major courses of college are classified, further, compared with the accuracy of algorithms based on the Naive Bayes and Support Vector Machine (SVM) in the classification of exercises. Data sets from exercises related to the content on the course of Data Structure are selected. According to knowledge content of course and characteristics of exercises, judgment and multiple-choice questions are used in experimental data, and these exercises are divided into seven categories depending on content of chapters. Firstly, the package of Jieba participle is applied to segment text of exercises, and the proper nouns of knowledge content are identified by importing a custom dictionary. Because characteristic words of short text are quite sparse, weight method about TF-IDF is directly adopted to feature representation of text instead of feature selection. Finally, algorithms based on the Nave Bayes and SVM are employed to train and classify text of exercises. The experimental results show that algorithm based on SVM has lower error rate and higher classification accuracy than the Naive Bayes.
Water Depth Inversion based on Landsat-8 Date and Random Forest Algorithm
Authors: Zhang Jin , Li Shujun , Wang Mo
Published: January 2020

In view of the fact that the traditional water depth inversion model is susceptible to water quality and environmental factors, the water depth inversion model is constructed using the random forest algorithm using the high resolution remote sensing image of the Huangyan Island area and the corresponding measured water depth data. The influence of the number of training sets on the inversion of remote sensing water depth is explored. The results show that the inversion accuracy of random forest is higher, and for this method, the more features in the training set, the better the effect of remote sensing inversion.
Research on Science and Technology Information Dissemination Based on Homogeneous Networks
Authors: Guanhui Wang , Yu Chen
Published: January 2020

With the advancement of science and technology and the advent of the era of knowledge-based economy, knowledge sharing has become an important factor in the development of science and technology. This paper constructs a model of public sentiment information dissemination in the context of homogeneous networks, revealing the influence and control effect of the connection relationship between enterprise nodes in science and technology network on the dissemination of science and technology information. The alteration of critical thresholds helps to control the degree and speed of public opinion information dissemination by adjusting the parameters in the model.
Partial Discharge Pattern Recognition of Transformer Based on Deep Forest Algorithm
Authors: Guan Jie , Guo Minkai , Fang Sen
Published: January 2020

In order to improve the accuracy of pattern recognition of transformer partial discharge type, solving the current pattern recognition based on machine learning algorithm requires artificial extraction of description features, poor adaptability and low recognition accuracy. In this paper, the deep forest algorithm is introduced into the partial discharge pattern recognition of transformers. In this method, the partial discharge images collected by partial discharge inspection instrument are processed by gray-scale and bilinear interpolation as input of deep forest model. The feature extraction of PD images is realized by using multi-grained scanning structure, and the classification of PD is realized by using cascade forest structure as classifier. By setting up the partial discharge experiment platform of transformer, the algorithm is tested with the sample data obtained from the experiment, and the results show that the pattern recognition accuracy of this method is high, and the recognition accuracy increases with the increase of sample data.
Negative Effects of Complex Promotion Strategy in Online Shopping Evidence from Lab Experiment
Authors: Huang Zhanbing , Zhuli  , Gao Fangyuan
Published: January 2020

This study discusses the negative Effects of the complex promotion strategy on consumers. Based on the dual-systems theory, we conducted lab behavioral experiments to obtain real decision data with Mouselab, a decision-making tracking technology. A sample of 116 students in Shaanxi Province participated in the experiment. The results show: (1) Under the complex promotion strategy, the quality of consumer decisions deteriorates. (2) Under the complex promotion strategy, the cognitive cost of consumers increases.