bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Sensor Fault Detection and Isolation in Doubly Fed Induction Generator
Authors: Anitha Kumari S. , Priya C.
Published: September 2021

Fault Detection and Isolation (FDI) is gaining interest as a means to increase the reliability and availability of efficient systems. Measuring sensors used for data acquisition are more prone to failures. The main objective of this paper is to provide model based FDI for current sensor fault closed-loop controlled Doubly Fed Induction Generator (DFIG) model connected to power grid. The sensor fault was detected and isolated by two methods including Unknown Input Observer (UIO) on reduced order DFIG model and fuzzy logic. The results show improvement in computational speed during online monitoring when the fault isolation is performed on reduced order models.
Novel analysis of blocking probability along with topologies of WDM Passive Optical Network
Authors: Verma Dinesh Kumar , Garg Amit Kumar
Published: September 2021

A novel analysis of blocking probability has been studied along with topologies. It has been shown that the algorithm proposed offers the minimum probability of blocking. The performance of wavelength division multiplexing passive optical network (WDM-PON) has been studied for different values of loads. To analyze the blocking probability, conventional algorithms have been used i.e. random and first-fit wavelength assignment using different number of wavelengths. It elaborates the proposed WDM-PON network in order to serve all ONUs providing the bandwidth as required by each ONU or end user. Performance of the system has been investigated using the quality parameters like signal-to-noise ratio (SNR), bit error rate (BER) and eye diagram. The result elaborates the performance of proposed algorithm which offers the least blocking probability. At last, study introduces different challenges and aspects of future needs in formulating an effective WDM PON system.
Anti-Collision & Obstacle Notifier Using Machine Learning and Networking Technologies in VANET
Authors: Anitha D. , Anupama C.G. , Sinha A Aditya Kumar
Published: September 2021

Anti-Collision System for Avoidance of accident occurrence on road among the vehicle will be able to detect the object which is approaching the vehicle. And at the same time, it will also recognize the category of approaching objects to determine whether it comes in the category of obstacle or not. Apart from Object recognition, the system will also be able to determine the distance between the respective vehicle and the approaching object to the vehicle and if the object will come to the nearest distance to the vehicle, which is implemented as the danger distance for them, then the notification alarm will start to ring with the signature of warning signal to the vehicle driver site.

Due to this, the vehicle collision and the occurrence of road accidents among the vehicle will be reduced and also as the system is performing the recognition then the vehicle driver will also be able to see the color of the front traffic light on their display unit.
An Ultra-low Power IoT System for Indoor Air Quality Monitoring
Authors: Agbulu Pius G. , Joselin Retna Kumar G.
Published: September 2021

Air pollutions induce most deaths from heart-diseases, lung-cancer, and strokes. Therefore, a mechanism to create cognizance of air pollution amongst the habitats of any locality is indispensable. This work presents an Ultra-low-power IoT System (ULP-IoTS) for Indoor Air-Quality Monitoring. The developed ULP-IoTS provides instantaneous indoor atmospheric air quality readings on Carbone monoxide (CO), Carbon dioxide (CO2), Fine-particle (PM2.5), Temperature, and Humidity. The IoT-node integrates MICS-5524 and MQ-135 DHT-11 multi-sensors with ESP32 (ultra-low-power) microchip. The node architecture exploits ESP32 Deep-sleep, and Modem-sleep configurable modes to complement the Active-mode and aid optimal power-saving. The real-time readings from the ULP-IoT node is dispatched to an IoT-gateway that eventually post the pollutant-reading to an external IoT-server for user-retrieval on android-applications. The test-bed experimental results validate the proposed ULP-IoTS to guarantee long-term minimal power consumption and reliable indoor air-quality tracking.
Curve Approximation Models based on Statistical Distribution with Application to Photoplethysmography (PPG) Signal
Authors: Raj Remya , Selvakumar J. , Maik Vivek
Published: September 2021

Photoplethysmography (PPG) signal captures blood volume change in the arteries that carry blood. PPG signals are often used to check the cardiovascular health in patients. Health care automation have made more importance into study of PPG signal and its automatic prognosis and diagnosis. In this paper we aim to achieve motion artifact reduction using low rank minimization and PPG signal representation in mathematical form using statistical distribution models. The proposed approach has been tested for Gaussian, Bifurcated Gaussian, Exponentially broadened Gaussian and lognormal distributions. The accuracy of each distribution in modeling the PPG signal was also studied.
Investigation of Peak Detection Algorithms for Fiber Bragg Grating Interrogation based Sensing Systems for Temperature, Depth and Salinity Measurements
Authors: Rakshit Sampita , Dhanalakshmi Samiappan , Chakravartula Venkatesh , Nandini P. , Kumar R.
Published: September 2021

Fiber Bragg Grating (FBG) sensors are now one of the prominent and emerging technologies in in the field of optical sensing technology and are used for measurement of strain, depth, temperature and many other applications. This paper reviews the accuracy and stability parameters of four conventional peak seeking algorithms. These algorithms are used in for calculating the peak Bragg wavelength for three different scenarios in which the reflection spectrum of FBG sensors are calculated in our case we consider temperature at 25 C, depth at 6 cm and salinity at 25 PSU. The performance of each of the algorithms i.e., centroid method, Gaussian fitting, polynomial fitting and spline fitting are compared for the above parameters for the actual spectrum and the noisy spectrum on the basis of accuracy and stability and the results are reviewed..
An experimental study on cloud computing security issues and a framework for xml ddos attack prevention
Authors: Krishnamoorthy N. , Umarani S.
Published: September 2021

Cloud Computing (CC) i is the openness of PC framework assets, especially information stockpiling also, processing power, selective of direct exuberant oversight by the client. In most recent decade, major oddity have gotten known, which gives add more practicality to organizations other than clients. This has seen noteworthy advancement in its working and happen to widely received by either private or public segments. It very well may be seen that more number of associations are communicating their data to the cloud. Alternately, security is a significant caution for the CC administrations which is based on Internet association that makes it vulnerable to different kinds of assaults. In this paper, we do an evaluation concentrate on CC and tended to grouped kind of assaults and likely intimidation to this cutting-edge aptitude, just as stronghold means and clarification to such hit. Cloud Security(CS) is a composite relations of advances, controls, cycles, and approaches.
Case Study for Regression Testing using Genetic Algorithm
Authors: Saraswat Birendra Kumar , Parashar Binayak , Takuli Arun Kumar , Singh Samender
Published: September 2021

For quality-assured information engineering software assessment has become critical. Software quality is defined as all the features and proprieties of a software system that can meet certain criteria to test a software product to know how good it is. Software testing is a necessary process for software development, and automating data production helps reduce time and expense. The design of software tests in today’s industry is mostly focused on the experience of the experts, while the tools for test automation can be used only for pre-planned tests. The new investigation focuses on the safe, accurate and effective discriminatory regression testing. A selective algorithm for regression testing is considered secure and sound if the disclosing of test cases is not ignored. Precision that tests the sum of the algorithm moving through the test cases without alteration. The successful selection of these cases is one of the key issues in regression testing. Due to the utility of selective regression testing, an efficient algorithm is important due to the speed of test case selection. We use a formal language approach in this article, to explain the problems and to give tentative solutions. For the test case selection problem, we find the genetic algorithm. We demonstrate that this genetic algorithm that satisfies both secure and reliable standards take time and time in general exponentially. In addition, a secure algorithm requires the most severe case of O (n2) time and Ont) (location, in which n is as many statements as possible, and t is as many test cases as possible.
A review on impact of distributed generation penetration in deregulated electricity market having renewable energy sources in India
Authors: Maurya Bajrangi
Published: September 2021

Nowadays it is seen that, distributed generation (DG) system based on conventional energy sources and renewable energy sources (RESs), specially using solar energy, is playing an important role to match load demand across India. Energy policies are promoting renewable energy resources and in the coming time to increase the energy efficiency, DGs installation and RESs scheme will play a vital role. It can be observed that India’s load rate is increasing rapidly day by day. Unplanned management made without planning can adversely affect the grid and put a serious load demand on the power grid. Distributed generation systems can be very useful to avoid such problems, especially if we use renewable energy. In this paper, we have mentioned about how we can manage the load demand and how to meet the power supply of the people using the distributed generation. In this review, strength, stability and reliability of distributed generation (DG) system is discussed with different aspects including different scenarios. This analysis shows a major action on the power system planning and working of the grid system. Recent works are included in this review. Different steps have also been taken for the integration of DGs with various scenarios in electric power systems. The main objective of this review is, how to achieve better integration of flexible demand and demand response, so that demand side Management (DSM) with the help of distributed generation can be fulfilled and create grid proficiency. Different challenge in DG system has also been mention in this research.
Effect of Water Temperature on the Compressive Strength of Silica Fumes based Porous Concrete
Authors: Sharma Aniket Kumar , Pandey Shobhit , Jain Ayush , Shekhar Shreya
Published: September 2021

Pervious concrete is a zero droop solid, which comprises of coarse aggregates, water and different cementitious materials. As there is no fine utilized in the concrete so it’s at some point referred as no-Fine concrete. Also pertaining to its water draining capacity it is also known as called permeable concrete. Pervious concrete is special kind of high porosity solid which permits water from precipitation to either infiltrate into the ground water or to the other storage facility. Since it provides better friction to vehicle tyres and hence also prevents skidding. It utilized for asphalt since it gives a genuine presentation against sliding for vehicles in stormy days and a far superior sound retention property. In this examination the concrete supplanted with silica fumes with different mixing water temperature were tested, so as to arrive at an optimum level of workability and strength. Concrete Blocks of Standard Sizes were prepared and relieved at a standard time period as per IS recommendations. Distinctive solid blend extents, for example, OPC and SPC are set up to the check the compressive quality of pervious concrete. The outcomes show the Pervious Concrete containing fifteen percentage of silica smoke can accomplish maximum compressive at an age of twenty eight days of relieving individually and at low mixing water temperature 100C the workability and slum value enhances. With addition of more silica fume in mixer the value of permeability was decreasing.
Research Algorithm on the Wheat Phenotypic Feature Extraction based on Image Processing
Authors: Guo Xiaojuan , Cheng Jifu , Gao Guohong , An Jingliang
Published: September 2021

In order to solve the problems of manpower and long cycle in traditional manual measurement, a wheat phenotypic feature extraction and analysis system was designed and implemented based on the image processing technology. The system can analyze and estimate the phenotypic parameters of wheat leaf, plant and wheat ear in different growth stages. The paper introduces the estimation algorithm of wheat leaf and wheat ear characteristics in detail, including the following contents: image acquisition, image preprocessing, feature extraction, parameter data estimation, etc. the estimated data provide data basis and reference for wheat breeding experts.
Research on Mobile Robot Vision Navigation Algorithm
Authors: Guo Ling , Sun Chao
Published: September 2021

Robot navigation is one of the key technologies of indoor robots and it is the primary solution to the problem of intelligent robot. In this paper, the visual navigation problem of mobile robot was analyzed and the robot vision navigation algorithm based on monocular vision was proposed. The algorithm obtained three-dimensional information of obstacle through data fusion, builder a three-dimensional model of obstacle, and optimized the navigation path by using the heuristic best search algorithm, and realized the drawing of the robot navigation path.
Research on Algorithm of DRC Catalog Generation Based on Machine Learning
Authors: Jiang Yuan , Yang Wenhui , Miao Fang , Ni Yating , Huang Anqi
Published: September 2021

Aiming at the current logic and expansion issues in data governance caused by data scheduling in two directions: business retrieval and data processing. Data Oriented Architecture proposes a Registration Intermediate Library(RIL) and a Catalog Intermediate Library(CIL) to achieve separation of management and application. Among them, the business-oriented catalog intermediate library automatically classifies and labels data registration information through machine learning, and realizes the automatic generation of catalogs.

There are many text classification methods based on machine learning, but the model obtained through unbalanced data set training often has performance degradation. In order to solve this problem, category weights are introduced in the feature vector to reduce the influence of most samples on the model parameters. Experiments on THUCNews text classification data set show that the proposed method can effectively improve the performance of baseline system, solve the problem of unbalanced training data categories and solve the problem of automatic generation of DRC directory.
Research on air quality prediction method in Hangzhou based on machine learning
Authors: Fu Zhongjie , Lin Haiping , Huang Bingqiang , Yao Jiana
Published: September 2021

Air pollution has become the subject of many current environmental studies, and the quality of air is directly related to the quality of life and health of human beings. In this paper, the Bayesian network model is used to predict air quality in Hangzhou. Six air pollutants SO2, NO2, O3, CO, PM2.5 and PM10 are used as the evaluation factors of the model, and AQI value is the output of the model, and then the Bayesian network model is established. Finally, the model is used to predict air quality and compare with the actual value. The results show that the accuracy of air quality prediction is over 80%, and the predicted value is close to the actual value in most cases, and this shows that Bayesian network model has a certain practical value as a means of air quality prediction.
Dynamic scheduling mechanism for software-defined security resources based on multi-mode load balancing
Authors: Zhang Tao , Shao Zhipeng
Published: September 2021

Rather than the traditional rigid and decentralized security mechanism, software-defined security provides a flexible and centralized solution by extracting security mechanisms from the security hardware layer to the software layer, but the existing software-defined security mechanism is prone to the problem of high load on physical devices performing security functions leading to performance limitations. To address these problems, this paper proposes a dynamic scheduling mechanism for security resources based on load balancing, while designing the southbound interface of software-defined networks to be scheduled by the control plane of software-defined security for flows according to established policies and logical topologies. The mechanism can match the load resources required by the security components, thus avoiding the problem of overall performance degradation due to excessive load and long queuing time of individual devices.
Study on Dynamic Surface Visualization Simulation Based on GPU
Authors: Zhang Shinan , Pan Jun
Published: September 2021

In the visual simulation of battlefield geographic environment, the realistic water surface effect can increase the reality of battlefield and enhance the immersion of training personnel, but the fluctuation of water body and the optical characteristics of water surface will also lead to the increase of algorithm complexity and the increase of data quantity, which will affect the fluency of the system. With the improvement of computer graphics hardware performance, in order to reduce the pressure of CPU, many complicated graphics computing has been transferred from CPU to GPU gradually. This chapter’s dynamic surface real-time rendering algorithm will make full use of the programmable characteristics of GPU and powerful computing power, the surface reflection refraction texture sampling, bump mapping, texture movement and other operations are put into the GPU, in order to ensure the surface fidelity, improve the rendering speed.
Research on optimal utilization of demand side resources based on quantitative analysis
Authors: Zhu Qing , Wei Si-Ya , Li Xue-Ming , Meng Xian-Hai
Published: September 2021

Through quantitative analysis, the power grid, environment and user behavior are analyzed, and the influencing factors of resource operation on demand side are found, and the high related parameters of resource operation on demand side are formulated based on this. Based on the principle of relative gain matrix, the interaction model including grid, environment and user behavior on demand side resources is established to realize the optimal utilization of demand side resources.
Improved cat swarm optimization for permutation flow shop scheduling problem
Authors: Pei Xiaobing , Tang Yan
Published: September 2021

To minimize the maximum make span of permutation flow shop scheduling problem, this paper proposed a cat swarm optimization algorithm based on estimation of distribution algorithms. This article Take the cat swarm optimization as the framework, embedded in the Estimation Distribution Algorithm, in the search mode, mining the excellent gene link in the solution sequence by using the probability matrix to combination blocks, speed and position tracking mode using characteristics of cat swarm optimization updates in the cat in order to update the excellent solution sequence generator group. Finally, through the simulation test and result comparison of Carlier and Reeves standard example set, the good robustness and global searching ability of the algorithm are verified.
Design of TR module aging control system based on adaptive filtering algorithm
Authors: Jiang Lei , He Jinsong
Published: September 2021

As an important part of active phased array radar, the working stability of TR module has a direct impact on the detection performance of radar system. This paper designs a set of TR electrical aging test system, for batch component aging test at the same time. Based on the principle of spontaneous heating under full power operation, the test environment was designed. The design of wave control board circuit met the excitation requirements in the work of the components. Meanwhile, the adaptive filtering algorithm and classical control theory were combined to control the TR working temperature, which realized the requirements of stable aging test of batch TR module. This system can accurately monitor whether the TR component products fail in the process of electrical aging, and at the same time can more accurately control the working temperature of the tested products within the required temperature range. It also provides a technical basis for further analysis and improvement of TR component product performance parameters. This design method can also be applied to the aging screening of other electronic board cards or components with high reliability requirements.
Complex Document Layout Segmentation Based on An Encoder-Decoder Architecture
Authors: Yao Jia , Huang Linlin
Published: September 2021

In our work, we propose an end-to-end encoder-decoder network for complex document layout segmentation. The proposed multi-scale feature extraction network with two parallel branches is applied to further process the feature maps, where one branch enriches the multi-scale information of the feature maps by building feature pyramids, another branch is introduced to capture the dependencies between different locations and integrate long-range context information. Moreover, we merge the outputs of the two branches to enhance the feature representation so as to further improve segmentation accuracy. Experimental results on datasets of PubLayNet and DSSE-200 demonstrate the effectiveness of our proposed method, which yields pixel-wise accuracy of above 99%.
A Just-in-Time Mechanism for Computer System
Authors: Chen Qijian , Zhong Yuanchang , Liang Taohua
Published: September 2021

As the installed programs will consume increasing space of hard disk, the computer becomes more and more bulky and costly. However, it’s harder and harder to make a larger yet still light and cheap hard disk. Therefore, computers used in most occasions are suggested to cancel their hard disks, and have their operating system programs and the application programs ‘installed’ on internet, instead of being installed on local hard disk. The data is accessed via supper high speed internet when the program booting. The operating system additionally serves as an open platform for registering the application software as well as the traditional role. This mechanism will bring us many gains. First, the user will no longer be restricted by the limited storage space, and will literarily have a never discarded and unlimited storage space that is shared by his or her all intelligent terminals. Second, the computers can become lighter and therefore easier to carry. Third, the user can get the latest version program once he or she launches the program. Last, the program provider can take the initiative to push the newest message to its users easily if they have subscribed.
Research on synergic Control Algorithm and Collision Avoidance of unmanned aerial vehicle Formation
Authors: Dai Feiyang , Zhou Shaolei , Yan Shi , Liu Xuanbing , Hou Pengsen
Published: September 2021

The current study mainly researches the problem of leader-follower based formation cooperative maintenance of fixed-wing UAV. First, the fixed-wing UAV formation with the leader-follower structure is used as the research object, and the flight dynamics model is established; the formation synergic control law is designed for the follower and the leader in the formation, especially the follower’s control law is improved to realize the state tracking control of the follower by controlling the speed and roll Angle of UAV; as stated by the master-slave formation, the distributed topology is introduced to realize the information interaction between adjacent UAVs, and on this basis, a collision avoidance strategy has been added to prevent drones from colliding with each other in the formation. Finally, the formation control algorithm and collision avoidance strategy are simulated through data simulation, and the results prove that the UAV formation can effectively complete the formation task and avoid collisions within the formation.
Feature Purity: A Quantitative Evaluation Metric for Feature Extraction in Convolutional Neural Networks
Authors: Chen Wenjun , Cong Chao , Huang Liwen
Published: September 2021

The quantitative analysis of specific convolutional layer feature information in Convolutional Neural Networks (CNN) has received considerable critical attention in the field of deep learning. In this paper, we proposed an innovative measuring method based on the principle of information entropy to quantitatively analyze the performance of feature extraction in CNN. In the method, the feature purity was defined as the normalized entropy of activation histogram, which was calculated from all kernels in feature layer of CNN. Feature purities were evaluated respectively in different layers of CNN as the inspection for internal structure in specific classification models. At the same time, general degrees of purities among different models were compared with classification performances. The experiments were performed in models of AlexNet, VGG, ResNet, and SENet with different epochs trained by cifar10 and ImageNet1000 datasets. Additionally, a visualization evaluation was performed by grad-CAM method in the manners of the intra-model and inter-model purities. Experimental results showed a strong relationship between feature purity and the classification accuracy in each model. Additionally, the visualized feature map also demonstrated that salient region extracted in specified feature layer had good consistency with purity value. As a result, the proposed feature purity can serve as a quantitative metric representing the degree of feature extraction in CNN without the dependency of label or specific network structure; and has significant interpretability and versatility across models.
Improving Distant Supervised Relation Extraction using shortest dependency path
Authors: Luo Yang , Zhang Lijun , Yin Dawei , Peng Dezhong , Yang Ke
Published: September 2021

Distant supervised relation extraction is the problem of classifying the semantic relation between a pair of entities in a given sentence, where data is created by aligning unlabelled text with knowledge base automatically. Prior researches indicated that most of the sentences in the distant supervised relation extraction setting are very long and benefit from the attention over words. We note that the shortest dependency path between the entity pair in the syntax analysis tree of a sentence can help word based attention. In this paper, we propose a noval distant supervised neural relation extraction method, which makes use of the shortest dependency path to supervise the learning of word attention. Through extensive experiments on benchmark datasets, we demonstrate effectiveness of our model.
Deep learning based handwritten digit recognition in Android
Authors: Li Yiqing
Published: September 2021

Handwritten digit recognition refers to the recognition of handwritten digits written and printed on paper, and the recognition results are stored in the computing machine in the form of text. According to the characteristics of a handwritten digital image, based on the multi-layer perceptron method, this paper introduces the source and principle of handwritten digital image recognition in detail, and analyzes the steps, related functions and theories of handwritten digital image recognition in sequence. The proposed algorithm was validated on the handwritten dataset MNIST, and the model was evaluated and adjusted. Eventually, the trained model was migrated to Android.
An intelligent routing optimization strategy based on deep reinforcement learning
Authors: Zhou Xiong , Guo Hongyu
Published: September 2021

Finding the optimal strategy in network routing has always been a NP hard problem. Due to the complexity and dynamics of network traffic, the existing intelligent routing algorithms have poor generalization ability. Therefore, this paper proposes an intelligent routing strategy based on deep reinforcement learning, and with the help of SDN control can dynamically collect network traffic distribution information, can dynamically adjust the routing strategy. Compared with traffic engineering algorithms such as TCMP and DRL-TE, the end-to-end delay is optimized under different throughput.
CWTs: A Public Large-Scale Chinese Web Table Data Set
Authors: Wang Changlong , Ren Zhilong , Bi Siyun , Zhang Rong , Fu Qibin , Zhang Zhichang
Published: September 2021

As the world’s largest information sharing platform, web pages contain a large amount of table data. However, these table data emphasize the presentation of data to users in a more intuitive way, which cannot be understood by computers. How to obtain large-scale web table data and carry out in-depth applications has become the focus of data science. This paper designs an extraction framework for Chinese Web table data. This framework extracts Web table data containing Chinese from the Web and constructs an open Chinese Web table data set CWTs (Chines Web Tables). The data set is quantitatively analyzed in multiple dimensions such as scale and data distribution. The results show that the data set obtained in this paper can provide a preliminary data basis for table data query, question answering system, and knowledge base construction, at the same time provide an optional channel for building a large-scale machine-understandable Chinese knowledge base.
Research on Data Calibration of Air Quality
Authors: Feng Qiming
Published: September 2021

In recent years, the urban air pollution in our country has become more and more serious, which has aroused widespread concern of the general public and the scientific community. The micro air quality detector not only costs little, but also can real-time monitor the air quality of a certain area in a grid way, so it can be used as the supplement of national survey point data. Based on the canonical correlation analysis of the data, it is found that the concentration deviation of “two dust and four gas” is significantly related to the meteorological parameters, among which the concentration deviation of PM2.5, PM10, NO2 and O3 is greatly related to the factors of pressure and humidity, and it is also known that the correlation between concentration deviation and humidity is the largest. And the concentration deviation between self-built point and national survey point is modeled. The results of this study can provide a method for the completion of urban air quality data, and the research method can provide a reference for data mining.
Efficient Texture Mask Prefiltering in Global Volume Rendering
Authors: Lin Chengyu , Zhu Meizheng , Wang Jiarun
Published: September 2021

In the application of ray-casted volume rendering on digital earth, the data in the region of interest (ROI) need to be pre filtered: culling or highlighting. At present, there are few researches on this problem in the field of volume rendering. Referring to the image mask in the field of image processing, an efficient pre filtering transformation technology based on texture mask is proposed: the pre filtering region is transformed into a 0-1 mask array, which is uniformly described as a two-dimensional texture. The texture is transferred into GPU Shader, and the pre filtering operation is transformed into an efficient texture query based on GPU hardware to realize filtering. And high performance rendering is based on GPU. Based on the global meteorological data, the experiments of land culling and administrative region highlighting are carried out, and the experimental results verify the effectiveness of the technology. The overall rendering effect is good, reaching the level of smooth display: the rendering frame rate is not less than 24 frames / s.
Hybrid Data Mining Method of Telecom Customer Based on Improved Kmeans and XGBoost
Authors: Shi Yongge , Yan Shaoyun , He Meibin , Li Xiangjun
Published: September 2021

In order to mine specified telecom customers with special behaviours from vast voice communication records of Telecom company, a novel Hybrid Data Mining method of Telecom Customers (HDMTC) with special behaviours is proposed in this paper, which integrates Kmeans and XGBoost into one framework. First, AHP model helped to model the features of customers with special behaviours. Then, Due to semi-supervised methodology, Kmeans approach was improved by small amounts of tagged initial cluster centroids and weighted Euclidean distance. Improved Kmeans was utilized to tag the decision attribute of data to construct training dataset. After that, XGBoost model was trained via the training dataset. Finally, the efficiency of HDMTC was validated via real records from telecom company.
Some effective tricks are used to improve Soft Actor Critic
Authors: Qin Chenjie , Zhang Lijun , Yin Dawei , Peng Dezhong , Zhuang Yongzhong
Published: September 2021

The development of artificial intelligence is becoming more intelligent, and Reinforcement learning (RL) plays an important role. RL has been widely used in recommendation systems, smart cars, game AI, and investment transactions. Since the emergence of DQN, DRL improvement methods have been proposed continuously. Soft Actor-Critic (SAC) is the current state-of-the-art RL algorithm. In this paper, some tricks are used to improve SAC. Firstly, the baseline is added to the policy gradient algorithm to reduce variance and speed up training. This paper adopts a new method to implement it in SAC. Because the temperature coefficient is a super parameter, which is difficult to adjust, so I propose a novel method to set it. These tricks proved to be effective on the MUJOCO.
Determining which physical parameters are significant for heart disease
Authors: Li Zhuoning , Tao Dingxin , Zheng Jiaao , Zhu Chenhao
Published: September 2021

Heart disease currently, as one of the most important causes of death in the world which can take one’s life immediately, should be taken seriously. In 2019, there were almost 9 millions people died from this disease, and the mortality was still increasing. This study aims to warn people be more focus on their heart condition, and provide a further suggestion about whether they can speculate their heart disease at home. In order to test the hypothesis that the cardiac parameters examined at home can also demonstrates the correct heart condition, many different machine learning algorithms were used to predict the final heart disease accuracy by different data sets (from home and from hospital). Also, there would be spot diagram and Pierson correlation diagram to show the correlation between features and condition, and the correlation among the features. In the end, chi-square method was used to determine the influential degree of those factors which can cause the heart disease. The results of these illustrated that even though several important factors which could only be examined in the hospital rather than at home played the leading role of predicting heart disease, people could still try to predict or observe the heart condition at home, because accuracy difference of the predicting results were only 2% to 4%. Therefore, the result from home could be the reference of whether people should go to the hospital to do more advanced examinations.
Take full advantage of demonstration data in deep reinforcement learning
Authors: Zhang Yong-Xu
Published: September 2021

Deep reinforcement learning (DRL) algorithms have achieve a great breakthrough in many tasks, even though, they still suffer from the problems, such as random exploration and sparse reward. Recently, some reinforcement learning from demonstration (RLfD) methods have shown to be promising in overcoming these problems. However, they usually require considerable demonstrations and the demonstration data may not be optimal which means the absent of exploitation. To deal with it, we propose a novel algorithm in which we use a behaver cloning network BC to learning the demonstration data, and then use it to guide the reinforcement learning agent, when the agent learn and explore a better trajectory, the BC then learning the trajectory to make itself better, which will lead the BC network and the agent become better at the same time.
Object classification based on RGB-D features fusion and LLC coding
Authors: Hong Zhang
Published: September 2021

An object classification method based on RGB-D features fusion is proposed in this paper. Firstly, image features in terms of texture, size, shape, from the RGB image and the depth image are extracted; secondly, the improved K-means clustering method is used to cluster the different extracted features to obtain seven different visual dictionaries respectively; then, the LLC coding model is used to perform feature coding on the extracted visual features to obtain seven different image feature expressions; meanwhile, above seven features expression are fused using the linear cascade mode to obtain the fusion feature; finally, linear SVM performs classifier is used to realize the object classification based on fusion feature. Experimental results show that compared with some existing algorithm based on RGB-D, the accuracy of our proposed method is increased to 87.3%. The accuracy of object classification is significantly improved.
An improved IP scheme for compressible Navier-Stokes equation
Authors: Zhao Hui , Ma Mingsheng , Liu Fan
Published: September 2021

Based on the internal penalty discontinuous Galerkin method (IP-DG), an improved discretization method is proposed to solve the viscosity term of Navier-Stokes equation. The improved method can eliminate the calculation process of intermediate variables and variable gradients, and effectively improve the calculation efficiency. Because the calculation process of convection flux, viscous flux, and lifting operators can be performed at the current integration point, there is no need to calculate the derivative of the viscosity term with respect to the variable gradient, the improved method has very good local characteristics, and is easy to extend to other PDEs. The Couette flow verifies the design accuracy of the improved method, and the cavity flow problem proves the effectiveness of the method for viscous flow simulation. The example of the flow around a sphere shows that this method also has a good performance for three-dimensional problems.
Multi-dimensional data analysis technology of business application system based on Spark framework
Authors: Dong Changchao , Jiao Yanbin , Chen Youyong , Feng Lanxian
Published: September 2021

The current multidimensional data analysis technology uses the data approximation to obtain deep data information in the data cube, which requires repeated traversal of the full link data, resulting in low processing efficiency and unable to analyze a large amount of data in parallel. Therefore, a multi-dimensional data analysis technology based on Spark framework for business application system is proposed to optimize the defects of traditional technology. Spark technology is used to build a full-link data analysis framework, and the data warehouse logic is designed to build a full-link data warehouse. Genetic algorithm is used to mine the full-link data of the business system, and the data are stored in the data warehouse, and the data warehouse and the sequential structure neural network are established. In the sequential structure neural network, multi-dimensional data analysis is realized from multiple angles and aspects. The technical feasibility verification results show that the research technology shortens about 14.7% of the time on average, significantly improves the analysis efficiency and can conduct concurrent analysis on a large number of data, which is better than the application effect of traditional analysis technology.
Verifiable Access Technology of Hybrid Database in Distributed System Under Big Data
Authors: Jia Jun
Published: September 2021

Targeting at the problem that the existing hybrid database solutions can not simultaneously realize the client’s verification and access function to the encrypted data in the public cloud database and the cross database query function in the heterogeneous database environment, this paper designs an access and integration middleware to realize the client’s verification and access function to the heterogeneous hybrid database in the public cloud and the cross database query function. Access and integration middleware is composed of verification access component and heterogeneous database data integration method. The verification and access component reconstructs the protocol interaction process based on MySQL protocol, and encrypts and decrypts the encrypted data in the public cloud database through the existing hybrid database scheme, so as to realize the client’s verification and access to the encrypted data in the cloud database. The heterogeneous database data integration method unifies the data from different types of databases, and then integrates them into the same database, and uses this database for query operation to realize cross database query. This paper first analyzes the hybrid database system model, then designs from the aspects of MySQL protocol analysis, verification access, data integration, and finally analyzes the access and integration middleware from the aspects of functionality and security.
Formal Verification of ST Programs using CSP
Authors: Zhou Qibin , Cai Fangda , Yang Yang , Wu Changshun
Published: September 2021

Programmable Logic Controllers (PLCs) are embedded devices that are widely used in industrial control systems. PLC programs are written in special languages that are defined in the IEC 61131-3 standard, which includes two textual and three graphical programming languages. Structured text (ST) language is one of IEC 61131-3 standards, which is a high-level text language similar to PASCAL language. ST language features the following advantages: freely programming style, compact structure and strong data processing capabilities. Using ST language can be more convenient to realize the complex control programs. In this paper, we research and discuss formal verification of PLC program based on ST language, and show the conversion rules from ST language to the formal modeling language named CSP# (short for Communicating Sequential Programs). Finally, a case study is presented to demonstrate the procedure that PLC programs written in ST language are translated into CSP#, consequently, and the result of verification is given using the Process Analysis Toolkit (PAT).
Research on Early Warning of Hoist Failure based on Big Data and Parallel Simulation
Authors: Zhang Yuyan , Zhao Sihai , Li Dan
Published: September 2021

Aiming at the problems of easy fault diagnosis and difficult early warning of mine hoist, a parallel system architecture of hoist fault early warning based on big data is proposed, the structure of each subsystem of hoist is analyzed, and a parallel simulation system of hoist fault early warning is established; secondly, the Hadoop ecosystem of hoist is established on the virtual machine, and the massive data is mined by using clustering algorithm and association rule algorithm, so as to speed up the calculation speed and improve the reliability of early warning; finally, the safety state evaluation rules of hoist are proposed, and the system decision is made according to the fault early warning results. The experimental results show that it can achieve the purpose of fault prediction.
Numerical analysis and improvement of longitudinal moment pitch-up characteristics for civil aircraft
Authors: Cui Xingda , Zhou Zhu , Yu Yonggang , Yu Lei , Li Yalin , Zhou Tao
Published: September 2021

The longitudinal moment pitch-up ahead of the stall angle can decrease the available range of maximum lift coefficient. It has a major influence on take-off and landing performance, and safety of a civil aircraft. Numerical simulation by CFD (Computational Fluid Dynamics) is used to study the longitudinal moment characteristics of a civil aircraft with landing configuration in this paper. Combined with the flowfield post-processing interpolation technology, the reason of longitudinal moment pitch-up is analysed and discussed. According to the research results, the characteristics of longitudinal moment pitch-up are further improved by modification. The relevant research methods and results have some guiding significance for improving the moment characteristics of a civil aircraft.
An estimation method of underwater target spectrum line parameters based on convolutional neural network
Authors: Luo Zhaorui , Du Shuanping , Chen Yuechao
Published: September 2021

The spectrum line parameters of underwater target radiated noise are important basis for underwater target recognition. In this paper, the convolution neural network (CNN) is applied to estimate the spectrum line parameters. The low frequency analysis record (LOFAR) spectrum of radiated noise is used as input data, and a suitable convolution neural network is constructed to estimate the spectrum line parameters. The processing results of simulation data show that the estimation sensitivity of convolution neural network reaches 91.6% under the condition of low SNR. It proves the effectiveness of CNN in estimating spectrum line parameters of underwater targets.
Hardware implementation of the DFP algorithm using inexact line search
Authors: Su Kaikai , Guo Anhua
Published: September 2021

The Quasi-Newton method (QN) is widely used in solving large-scale optimization problems due to its high efficiency. However, this algorithm requires a large number of iterative calculations, which is more time-consuming to compute in software. The Davidon-Fletcher-Powell (DFP) algorithm has been implemented on field-programmable gate array (FPGA) in order to speed up the computation. The number of iterations in the online search part of the algorithm is excessive, which affects the speed of the calculation. In order to solve this problem, this paper proposes a hardware implementation of DFP algorithm using inexact linear search. The results show that the calculation speed of the DFP algorithm implemented in this paper on the software side and FPGA platform is increased by 7.59 times and 3.08 times, respectively. And resource consumption and accuracy have little effect on the results.
Design and implementation strategy of data migration system based on Hadoop platform
Authors: Wang Xiaoshu
Published: September 2021

This paper analyzes the Hadoop technical system related content, and combines the design points of the data migration system, including interface migration design, data synchronization design, data transmission process design, data consistency verification design, data storage scheme design. Through the research of data cleaning function, data conversion function, data loading function, data return function, unstructured data migration and other implementation strategies, the purpose is to improve the application value of data migration system, improve the application value of the collected data.