bluuebunny/crossref_metadata_2025 huggingface dataset:
UMAP Based Data Validity Evaluation for Artificial Intelligence Systems
Authors: Son Hanseong , Lim Hyemin
Published: March 2021

Although comparison is one of the most useful data validity evaluation method, it has a few potential drawbacks. One of them is that results from the training or the testing a machine learning model shall be obtained. Another one is that uncertainty due to the machine learning model itself may cause a difficulty in evaluating the data validity. In this paper, a new data validity evaluation method was proposed so that these drawbacks can be made up by the proposed method. The proposed method enables a data validity evaluation to be performed in data collection phase or data pre-processing phase. Uniform Manifold Approximation and Projection (UMAP) offers the methodological background to the proposed method.
Conceptual Basis of Cognitive Mimetics for Information Engineering
Authors: Saariluoma Pertti , Karvonen Antero , Kujala Tuomo
Published: March 2021

Intelligent information processing is topical in modern technology design and development. The fundamental idea was developed by Turing as he made the first conceptual models of information-processing computers. Though it has practically never been noticed, Turing’s work was a model of how to mimic human intelligent information processes and generate technologies, which can carry out intelligent tasks. The design method can be called cognitive mimetics as it imitates human information processes to design technologies and their applications. One can use cognitive mimetics even in solving techno-ethical problems. This is why we think that cognitive mimetics are vital as a method to generate intelligent information processes.
Decision-making System Based on The Ontology of The Choice Problem
Authors: Stryzhak Oleksandr , Horborukov V’yacheslav , Prychodniuk Vitalii , Franchuk Oleg , Chepkov Roman
Published: March 2021

The complexity of the decision-making process for experts in various topics is in the problem of initial processing of unstructured information. This problem of Big data requires the latest solutions that provide a structural reflection and integrative use of information descriptions under consideration by experts. It takes the process of information and analytical evaluation and decision-making beyond one subject area and raises questions about the ontological consideration of the problem itself. The ontological principles of such consideration are based on multiple hyperproperties of information for the realization of the categories of integration, systematicity and continuity, as well as implementation of structures and their functionality reflection and transformation. For this, there is the ontology of the problem of rational choice based on means of a ranking of alternatives on a set of indicators. It provides the creation of a system designed to solve the ranking problem based on the ontological model of the subject area. Hyperproperties of ontological representation of large amounts of information ensure the implementation of meta-tasks of its analysis, its structuring, synthesis, and support the process of rational choice. Such an approach creates the conditions for optimal processing of a Big Data.
The Design and Implementation of Quantum Finance-based Hybrid Deep Reinforcement Learning Portfolio Investment System
Authors: Qiu Yitao , Liu Rongkai , Lee Raymond S T
Published: March 2021

With the rapid development of Deep Learning (DL) and Reinforcement Learning (RL), Deep Reinforcement Learning (DRL) has become one of the leading trends in automatic trading. However, research of RL in portfolio investment has difficulties in distributing investment funds, controlling profit and loss, and exploring the unseen environment. This paper introduces an intelligent portfolio investment system based on the integration of DRL, Quantum Finance Theory (QFT). Our proposed system consists of two agents: 1) A trading agent based on Deep Deterministic Policy Gradient (DDPG) algorithm to generate continuous actions for investment weighting; and 2) A risk-control agent based on Policy Gradient (PG) algorithm produces discrete actions according to each day’s Quantum Price Levels (QPLs). One significant merit of integrating two intelligent agents is that they can cooperate to make more reasonable and stable fund distribution adjustments in the portfolio investment. The experimental results reflect the flexibility and robustness of our system, as it achieves considerable profits in back-tests consisting of various combinations of FOREX products.

(Source code is available at: https://github.com/772435284/QF-portfolio-investment-system)
Implementation of Multiple Precision Sparse Matrix-vector Multiplication on CUDA using ELLPACK Format
Authors: Isupov Konstantin , Babeshko Ivan , Krutikov Alexander
Published: March 2021

In this paper, we implement sparse matrix-vector multiplication (SpMV) kernels in the ELLPACK format on graphics processing units using arithmetic libraries supporting multiple precision on CUDA. We evaluate the performance of the developed kernels and also provide an optimized SpMV implementation in which multiple precision floating-point operations are split into several parts, each of which is executed as a separate kernel. Experimental evaluation with various matrices from real-world applications and at various levels of numeric precision shows that, in many cases, the optimized multiple precision SpMV performs better than the other implementations considered.
Optimization for Hot-film Anti-icing Structure by BPNN and GA
Authors: Liu Jie , Ke Peng
Published: March 2021

A coupled method combining the Back Propagation Neural Network (BPNN) and Genetic Algorithm (GA) was developed to optimize a 2D design of aero-engine inlet anti-icing structure, which has a cover on the film heating ejection slot. The optimal goal is to maximize the heating effectiveness which was used to assess the performance of hot-air film. The film-heating ejection angle and the cover opening angle were selected as the design variables to be optimized. The training and testing samples employed in BPNN were obtained by numerical simulation, after which the objective function of GA was predicted. With a given flow rate of bled air, the optimal values of the two design variables were achieved as 22.6° and 15.1°, respectively. Compared to the previous optimal result of other researchers, the heating performance was improved by 16.7% with rapid progress. The result of this study illustrates that this hybrid optimal method can meet the accuracy requirements with high time-efficiency for optimization problems in aeronautics engineering.
Thunder Signal Detection via Deep Learning
Authors: Zhang Han , Yan Biwu , Gu Shanqiang , An Chao , Li Jian , Wu Min , Wang Yu , Xu Heng
Published: March 2021

In this paper, a thunder signal detection method is proposed based on the deep learning framework. The recorded thunder signal is segment-wise acquired, stored and pre-processed. In each frame, we use Mel Frequency Cepstrum Coefficient (MFCC) to extract the features of the thunder signal, which is consistent with the frequency characteristics of human perception. We then use the MFCC features derived in each frame to form a 3-channel tensor data, which is used as the further input to the designed convolutional neural network (CNN). The goal of CNN is to classify the existence of thunder for a single data frame. To improve the robustness of CNN, we included other confusing signals that are similar to thunder signals in the training and testing datasets. On the testing dataset, our proposed method outperforms the state-of-art methods in terms of accuracy, sensitivity, and specificity. Our proposed deep-learning-based thunder detection method not only increases the real-time performance of the lighting location system with thunder signals but also further improves the accuracy of other sound alarm systems.
Crowd Density Estimation Based on Multi-Column Hybrid Convolutional Network
Authors: Guo Linlin , Zhou Weimin
Published: March 2021

This paper studies an accurate counting model for dealing with highly crowded people, multi-column hybrid convolutional neural network model. The model is mainly composed of three parts. The first part uses the first ten layers of VGG-16 convolutional network for image feature extraction. The middle layer is a dilated convolution with three rows of “jaggy” dilation rates, and each row uses the Resnet-block connection method, which is used primarily to perceive human head features of different sizes. Compared with a variety of image up-sampling ways, in the third part of the model, this paper tries to use a combination of bilinear interpolation and convolution to up-sample image features, and research shows that this method effectively reduces the model error. In this experiment, the average absolute error (MAE), mean square error (MSE) and average relative error (MRE) are used as evaluation indicators, and experiments on the ShanghaiTech dataset proves that the network works well.
A Dual Branch Attention Network for Multiple Degraded Image Restoration
Authors: Jia R M , Wang D , Li T
Published: March 2021

In this paper, we propose a novel dual branch attention network (DBA-Net), which can efficiently restore multiple degradation images. Most methods of image restoration focus on a single degradation factor, such as blur, noise, raindrop, etc. However, these methods frequently fail to real degraded images, because the real images usually contain different types of degradation. Our network backbone consists of two branches, the residual branch and the information distillation branch, which have different receptive fields. We design a gate module to choose useful feature maps from the two branches. Then, an improved multi-channel attention selection module is proposed to allow the network to learn more features of real results. On DIV2K datasets, which contains different degradation types (noise, blur, and compression loss) and diverse degradation levels (mild, moderate and severe), our results on PSNR and SSIM outperform other advanced algorithms. In visual performance, the texture of objects in DBA-Net images is more similar to the original image.
Questions and Answers on Legal Texts Based on BERT-BiGRU
Authors: Zhang Na-Na , Xing Yinan
Published: March 2021

Using text question and answer technology to improve the efficiency of judicial personnel in the process of case handling can greatly reduce labour costs. This paper proposes a hybrid neural network model that combines pre-training model BERT and Bi-GRU. The model first uses the pre-training model to learn powerful semantic capabilities, then combines Bi-GRU to learn the semantic information between the text and the question, finally gets the answer corresponding to the legal text. The experimental results on the CJRC data set show that compared with the basic baseline model, the algorithm in this paper can effectively improve the accuracy and F1 value.
A Comparison of Machine Learning Algorithms for Automatic Cloud Resource Scaling on a Multi-Tenant Platform
Authors: Lee Chun-Hsiang , He Zhengda , Li Zhaofeng , Lu Xu , Wang Jian , Wu Chao
Published: March 2021

In an online multi-tenant machine learning platform, the system manager would dynamically load the computing resource according to the tenant’s demand. With cloud computing services, the platform can rent or release computing resources dynamically to fulfill tenant usage which minimizes resource consumption and ensure scalability. Currently, many cloud-based services providers are using the rule-based, threshold auto-scaling mechanism. However, the rule-based method is not efficient, as the nature of availability and cost-reducing violate each other in this method, especially with the sudden increase or variation of the demand. In this paper, we compare several machine-learning-based predictive algorithms to build models based on the information of the system used to predict future usage demands. Decisions made based on this prediction save over 80% cloud resource consumption compared to the rule-based method.
Multi Information Fusion Diagnosis Method for Diesel Engine Based on Optimized ANFIS
Authors: Han Yuting , Fei Jingzhou , Wang Zhongwei
Published: March 2021

A novel diagnosis method based on optimized subtraction clustering ANFIS (adaptive neural fuzzy inference system) algorithm is proposed in order to improve the accuracy and reliability of diesel engine fault diagnosis. The improved analytic hierarchy process (AHP) and subtractive clustering algorithm are combined to form a new ANFIS network suitable for multi information fusion diagnosis. The initial clustering centers of subtractive clustering algorithm and reasoning rules of ANFIS are automatically optimized by AHP algorithm without relying on expert experience. The effectiveness of the novel algorithm is investigated on the example of multi information fusion diagnosis of diesel engine, and the results indicate that the proposed method can eliminate the disadvantages of more inference rules, slow convergence speed and low diagnostic accuracy of the conventional ANFIS algorithm under multiple input parameters, which means this new method can effectively improve the accuracy of diesel engine fault diagnosis with the advantages of more fusion parameters and less calculation.
An Improved Decision-Making Approach Based on Interval-valued Fuzzy Soft Set
Authors: Zhang Qiansheng , Sun Dongfang
Published: March 2021

Some operators and laws of the interval-valued fuzzy soft sets are first introduced in this paper. Then an improved decision-making approach is proposed by employing the comparable table of interval-valued fuzzy soft set. At last, one numerical example is given to demonstrate the effectiveness of the presented decision approach.
A Siamese Network Tracking Algorithm Based on Hierarchical Attention Mechanism
Authors: Zhang Hu , Hu Dong , Qiu Yingcan
Published: March 2021

A siamese network tracking algorithm based on hierarchical attention mechanism is proposed in this paper. In order to obtain more robust target tracking results, different layer features are fused effectively. In the process of extracting features, attention mechanism is used to recalibrate the feature map, and AdaBoost algorithm is used to weight the target feature map, which improves the reliability of the response map. Besides, the Inception module is also introduced which not only increases the width of the network and the adaptability of the siamese network to the scale, but also reduces the parameters and improves the speed of network training. Experimental results show that this method can effectively solve the impact of background clutter and improve the accuracy of tracking.
Real-time Railway Transit Management Based on Multi-Agent System (MAS)
Authors: Guo Yida , Zhang Cheng
Published: March 2021

Because of environmental concerns, increasing attention has been given to developing energy-efficient technologies. As an essential public transportation method, metro transit is facing increasing pressure. Disturbances may cause the offline-optimized timetable and speed trajectory to be invalid. The present paper proposes a multi-agent system (MAS) train control method, in which each train is controlled by an agent. Each train agent has a simulation platform and an optimization platform. The simulation platform collects information such as track slop, speed limitation, speed trajectories of neighboring trains from a few neighboring agents. The simulation platform sends a signal with the collected information to the optimization platform when it detects a disturbance. Afterward, the optimization platform performs energy-aimed optimization to the timetable and corresponding speed trajectory based on a combination of a trained Neuro Network and a Mixed Integer Linear Programming (MILP) model. The test result shows an encouraging balance in optimization time and accuracy. The case study result proves that the proposed approach could provide a more energy-efficient control strategy when disturbances occur.
Learning Collision Avoidance of Ship Manoeuvring based on Gated Recurrent Unit
Authors: Sun Wenli , Gao Xu
Published: March 2021

Unmanned surface vehicle (USV) has progressed quickly in recent decades, with widespread research and practical applications in academic and industry circles. Collision avoidance is a fundamental capability of USVs. It is extremely challenging to develop an ideal and sophisticated collision avoidance algorithm for USV in complex environments and practical offshore situations. However, a supervised learning method provides the USV a way of learning the process of avoidance and imitating a human pilot maneuvering to navigate in the real environment. This study analyzes the relative relationships and features of the own and target ships in the avoidance process firstly. And then a Gated Recurrent Unit (GRU) recurrent neural network model is constructed. Maneuvering commands during avoidance by human pilots are utilized as tags for training. Finally, the validity of method is proven by performing navigation experiments. In particularly, we also compare the effectiveness of GRU with Long Short-Term Memory (LSTM) network which is also a kind of recurrent neural network. The experimental results indicate that the proposed GRU model is better than the LSTM model, and the USV can autonomously navigate during the collision avoidance process by using the well-trained GRU model, reaching a level similar to that of human pilots.
Algorithm of Vehicle’s Data Cleaning and Monitoring
Authors: Li Hao , Ma Di , Yan Zhijiang , Fu Jie , Zeng Mengjie , Bao Wuzhida
Published: March 2021

The aim of this paper is to screen out abnormal data which is caused by human factors and natural factors. We proposed an algorithm of vehicle’s data cleaning and monitoring. First, the valid data is filtered out by an improved DBSCAN method, through data analysis, and then get the threshold range. After that, it screens out the abnormal data in the valid data through the threshold range. Finally, the abnormal data is classified and counted according to the factors which was stipulated by the enterprise. The results show that the proposed algorithm can simpler and faster to process the abnormal data than the other similar algorithm.
Long-Term Electricity Consumption Forecasting in China — Based on a Combined Model of KPCA and Linear Regression
Authors: Huang Zili , Zhang Haochen , Qiu Chenxi , Liu Jia
Published: March 2021

Total electricity consumption is a barometer of a country’s economy. Long-term forecasting of total electricity consumption in the whole society can effectively track a country’s economic development and monitor the implementation of energy conservation and emission reduction policies. How to effectively forecast the long-term total electricity consumption is an important topic in the academic and industrial fields. The combined model of kernel principal component analysis (KPCA) and linear regression (LR) proposed in this paper can accurately predict the changes in total electricity consumption over time, even if the sample size is small. Meanwhile, the model results have strong interpretability and practical value. Further, through the correlation analysis of principal components obtained from KPCA dimensionality reduction, this paper finds that the most important features affecting the total electricity consumption are the economy feature and production efficiency feature. Finally, this paper predicts that China’s total social electricity consumption will reach 1.83 trillion KWH in 2035, which is more optimistic than the prediction of Oxford experts, which is consistent with the reality that China has achieved an overall victory in the fight against COVID-19.
Effect of Pitch Control on the Performance of an Offshore Floating Multi-Wind-Turbine Platform
Authors: Bashetty Srikanth , Ozcelik Selahattin
Published: March 2021

The extraction of wind energy from more reliable deep-water offshore wind resources can be advanced by using a multi-wind turbine platform that can reduce mooring and installation costs. This paper describes the results of research aimed at designing a novel semi-submersible platform for placing multiple wind turbines. The offshore floating multi-wind-turbine platform (OFMWTP) hosts five 8 MW wind turbines which is proposed for installing on the gulf coast of the United States. The principal problems that have been addressed here include analyzing the effects of blade pitch control on the performance of the OFMWTP in the above-rated wind speed operating conditions. In this process, the adaptive control techniques vary the blade pitch angle for generating optimum power production and reduce platform motions. The coupled dynamics of the wind turbines with the platform are formulated considering the aerodynamic, hydrostatic, hydrodynamic, and mooring forces. In this study, the adaptive control algorithm designed in previous work is used for the blade pitch control. The wind speed distribution with 15% turbulence intensity and irregular waves with zero degrees incident wave angle is used for simulating the operating conditions. The performance of the adaptive control is compared to a baseline proportional-integral (PI) controller. Simulation results showed that the adaptive controller significantly improves the rotor speed regulation and reduces the fluctuations in generated output power under varying operating conditions.
Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control
Authors: Pritchard Michael , Weinberg Abraham Itzhak , Williams John A R , Campelo Felipe , Goldingay Harry , Faria Diego R
Published: March 2021

We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as part of a brain-computer interface (BCI) the cost of EEG equipment commonly prevents this approach from being adopted outside the lab. This study demonstrates as a proof-of-concept that multimodal classification can be achieved by using low-cost EMG and EEG devices in tandem, with statistical decision-level fusion, to a high degree of accuracy. We present multiple fusion methods, including those based on Jensen-Shannon divergence which had not previously been applied to this problem. We report accuracies of up to 99% when merging both signal modalities, improving on the best-case single-mode classification. We hence demonstrate the strengths of combining EMG and EEG in a multimodal classification system that could in future be leveraged as an alternative control mechanism for robotic prostheses.
MIL-Standards Verification of Battery Control for More Electric Aircraft Application
Authors: Russo Antonio , Canciello Giacomo , Cavallo Alberto
Published: March 2021

As a consequence of the increase of air traffic, the innovative topic of the More Electric Aircraft (MEA) has received increasing attention. In this paper, the control of a bidirectional DC/DC converter for battery management in the MEA framework is described. A detailed simulator and simulation campaign have been designed in order to verify the satisfaction of the MIL-STD-704F standard which regulates the behaviour of electric devices on-board the aircraft.
The Capacity Load Model of K-Uniform Hyper-Network based on Equal Load Distribution
Authors: Chen Yang , Ma Xiujuan , Ma Fuxiang , Liu Qian , Cheng Wenxin
Published: March 2021

Cascading failures occur frequently in many network systems. In this paper, we propose a hyper-network Capacity Load Model based on hyper-edge degrees load to explore the influence of cascading failures on some K-uniform hyper-networks. In simulation, we attack the hyper-edge which has largest hyper-edge degree. When the hyper-edge fails, its load will be equally distributed to adjacent hyper-edges. Through simulation analysis, we get the relationship between the capacity-load model parameters and the robustness of the k-uniform hyper-network. The results show the robustness of the hyper-networks is enhanced with the increase of its hyper-edge capacity. In addition, simulation result show that the robustness of K-uniform hyper-networks is strongest at α=1.
Research on the Electromagnetic Interference of Yanmenguan DC Grounding Electrode on West-East Transmission Gas Pipeline
Authors: Shi Weiguang , Wang Kesheng , Liu Wei , Li Pingwei , Cai Xinjing
Published: March 2021

There was a west-east gas transmission pipeline nearby the Yanmenguan DC grounding electrode. In order to assess the electromagnetic interference of Yanmenguan DC grounding electrode on the pipeline, an electromagnetic simulation model was established. The pipeline to earth potential was analyzed. The effects of insulation layers and insulation joints on the pipeline to earth potential were investigated. It can provide theorical support for the pratical operation of gas pipelines.
Calculation of Corrosion Depth of Wind Power Generators and AC Substations Induced by HVDC Grounding Electrode
Authors: Feng Lijun , Hao Chenglong , Zhao Yuting , Guo Dongqing , Cai Xinjing
Published: March 2021

At present, grounding potential rise is often used as the evaluation index to assess the electromagnetic intererence induced by HVDC grounding electrodes. It will lead to too stringent standards, for HVDC grounding electrodes are considered as long-term operation. Actuallly, HVDC grounding electrodes operates only in the test run stage, planned maintence and forced outage. In this paper corrosion depth is adopted as the evaluation index to assess the electromagnetic intererence. The method to calculate the corrosion depth of wind power generators and AC substation is described. The corrosion depth of windfarms and AC substation nearby Yanmenguan converter station is derived. The effects of material properties, distance and soil resistivity on corrosion depth are also studied.
Performance Degradation Prediction of IF Conversion Circuit Based on DE-GWO-SVM Model
Authors: Zhu Y , Fu M R , Bu X Z , Hu J
Published: March 2021

Due to the complex structure, nonlinearity and tolerance of IF conversion circuit, the failure probability of IF conversion circuit is greatly increased. To solve the above problems, a differential evolution adaptive grey wolf algorithm (DE-GWO) was proposed to optimize the parameters of support vector machine model, and the model was applied to the fault diagnosis of IF conversion circuit for the first time. Firstly, the energy feature of the output signal of IF conversion circuit was extracted by wavelet packet decomposition, which effectively reduced the dimension of feature vector. Secondly, the improved grey wolf algorithm was used to optimize the parameters of SVM model and establish DE-GWO-SVM fault diagnosis model; Finally, taking the IF conversion circuit as an example, the fault diagnosis experiment was carried out and compared with other methods. Comparison results show that the fault diagnosis accuracy rate of this method reaches 97.33%. Compared with the traditional methods, this method can better improve the fault diagnosis rate and shorten the diagnosis time.
Calculation of Electromagnetic Thrust for Long Primary Double Sided Linear Induction Motors with Secondary of Arbitrary Length
Authors: Zhang Qian , Liu Huijuan , Zhang Zhenyang , Song Tengfei
Published: March 2021

The expressions of the air gap flux density and secondary current density of long primary double-sided linear induction motor (LPDLIM) with arbitrary secondary length are derived by the numerical method, which with the secondary as the moving reference frame. The effects of the forward and backward components of end effects on the normal travelling wave and the secondary current density are analyzed respectively, and their temporal and spatial distributions are presented. And then the electromagnetic thrust and its ripple expression considering the longitudinal end effect of the air gap magnetic field and the components of the secondary current density are solved. The slip characteristics distribution of each electromagnetic thrust and thrust ripple components, and the contribution of end effect to resultant thrust and force ripple of the motor with arbitrary secondary length are investigated. Finally, the thrust characteristics of the LPDLIM with arbitrary length secondary are verified by the results of the FEM model. The calculation shows that the appropriate secondary length can reduce the thrust ripple of the LPDLIM.
A Survey on Recent Applications of Cloud Computing in Education: COVID-19 Perspective
Authors: Agrawal Somya
Published: March 2021

Due to the outbreak of coronavirus pandemic (COVID-19) in recent times, almost all countries have been affected in a drastic way, impacting delivery mode of education in world everywhere. Several schools, colleges and universities are under lockdown or have been forced to provide online education. To meet the existing challenges due to COVID-19, it has become highly critical that educational institutes become more efficient in virtual delivery of quality teaching services. In here, cloud computing provides an excellent platform for educators to improve their teaching practices and productivity. Cloud computing presents an ideal opportunity as it not only saves costs, but also power, due to the simultaneous usage of infrastructure by several stakeholders for the purposes of teaching, learning, and research. This is a short survey paper which explores the applicability of cloud computing within the context of educational settings. It also describes a few applications such as cloud rendering, gamification and collaborative e-learning technologies. In the end, a few challenges have also been presented.
Asynchronous Communication for Improved Data Transport in The Network
Authors: Zepeda S , Nuñez A
Published: March 2021

Currently there are two types of interaction in web systems mediated by synchronous and asynchronous communication, each of them with its programming and interaction paradigms. Although intuitively we can say that asynchronous communication is more efficient in data transport, there is not much research on this topic. We present a comparison of these two types of communication by requesting the same data from a server and receiving a client. The requests are through the traditional web forms and an experimental semantic interface, which only needs the data in plain text as a response from the server. The results found are of interest for the problem of excessive traffic on the network. This research can open new lines of investigation or generate new paradigms and programming models to improve communication between client and server.
An Intelligent Mobile Application Testing Experience Report
Authors: Tran Chi Thi Phuong , Valmiki Mohana Gudur , Xu Guoyan , Gao Jerry Zeyu
Published: March 2021

Artificial intelligence applications provide tremendous opportunities to improve human life and drive innovation. AI systems/applications which operate in a real-world environment have to encounter an infinite set of feasible scenarios. Conventional testing approach to test the AI application allows only limited testing and does not allow taking the different contexts into consideration and may lead to insufficient validation and characterization. Therefore, to ensure robustness, certainty and reliability of AI applications, the authors applied classification-based AI software testing framework and 3D decision tables to generate test cases. Moreover, the authors compared the quality assurance metrics (accuracy, correctness, reliability and consistency) of AI and non-AI functions in the AI mobile application scenario. Our results indicate and confirm that complete AI function validation is not possible with conventional testing methods, but AI software testing strategy proposed based on classification framework and 3D decision tables has a good effect.
A Fault Line Selection Technique for Single-Phase-to-Earth in network grounded with Arc Extinguishing Coil
Authors: Si Rui-Qi , Zhao Hu , Guo Jia-Dong , Shi Jia-Wei
Published: March 2021

For the fault line selection of single-phase-to-earth network, the instantaneous characteristic of the arc extinguishing coil is worth expecting. Focused on the transient characteristics in fault line selection technique, the sinusoid adaptive filter (SAF) and the sinusoid approximation of instantaneous signal (SAIS) are adopted in this paper. SAF is used to obtain the grounding moment, while SAIS is applied to obtain the direction and amplitude of the zero-sequence fundamental current when grounding happens. As for eliminating the influence of current transformer (CT) polarity, the compensation feature realized by SAIS is effectively adopted in selecting process. The techniques and methods in the paper have been applied to Chongqing Electric Power Co. for 5 years. In 5 years, hundreds of grounding faulted line has been chosen correctly, 6 wave records of which are shown in the paper.
Test Platform of LTE-U Communication System Based on FPGA
Authors: Zhang Rui , Shi Guchen , Jiang Wei , Chen Qiuxia
Published: March 2021

In order to solve the problem that the LTE communication system cannot be easily deployed, we design a test platform of LTE-U communication system based on FPGA. The test platform includes mobile terminals, base stations and base station networking system. It supports 5G unauthorized frequency band, WIFI and 100M network. A single base station can support up to 8 terminals with a distance of 1 km. It uses an omnidirectional antenna to communicate with the terminal and uses a gigabit ring network between base stations. In the process of mobile communication, we use RSSI (received signal strength indication) and bit error rate as the evaluation criteria and test the 32 terminals covered by the chain network and 4 base stations. The test platform can complete the LTE-U test well.
Computational-Efficient Iterative TDOA Localization Scheme Using a Simplified Multidimensional Scaling-Based Cost Function
Authors: Li Liming , Ding Baogang , Sun Rui , Jiang Wuyang
Published: March 2021

This work deals with source localization with time-difference-of-arrival (TDOA) measurements. Recently, a multidimensional scaling (MDS)-based iterative localization scheme is introduced in the literature, where an MDS-based cost function is defined as the norm of the difference matrix between two scalar product matrices. The minimizer of the MDS-based cost function which is computed by Newton’s iteration is considered as the estimate of the source position. However, the scalar product matrices of the MDS-based cost function are of high order, which need a lot of computations in each step of the Newton’s iteration. In this paper, a computational-efficient iterative localization scheme is proposed. In the proposed scheme, a simplified MDS-based cost function is constructed from two low-order matrices that are converted from the high-order scalar product matrices by a few steps of Lanczos iteration, and then the Newton’s iteration is applied to find the minimizer of the simplified MDS-based cost function. Simulation results show that the localization accuracy of the proposed scheme is nearly the same as that of the original scheme, whereas the computational complexity of the proposed scheme is about 20% as much as that of the original scheme.
Remote Sensing Camera Automatic Test System Based on Ethernet
Authors: Zhang Zhandong , Yang Luping , Zhang Xiaohong , Zheng Jun
Published: March 2021

With the rapid development of high-resolution remote sensing camera and the rapid increase of the number of remote sensors, telemetry data and image data have the characteristics of large amount of concurrency and high real-time. These characteristics bring great challenges to the test, interpretation and processing of massive data, which requires a lot of manpower and time resources, meanwhile, it is easy to cause repeated test work because of the errors occur during operations and interpretations. In order to improve work efficiency, test coverage, test accuracy, comprehensiveness, and human resources, developing automatic and intelligent test equipment is becoming much more particularly urgent and necessary. The test system proposed in this paper uses the on-line monitoring and diagnosis system to analyse the running state of the equipment in real time, find out the system fault in time, locate and eliminate it. The system can put forward the network protocol of data processing and storage, command control, real-time analysis and transmission of telemetry information and display the realization way of the test system workflow. It is expected that the system test efficiency will be increased by more than 5 times. In addition, the efficiency of fault diagnosis and data processing will be increased by more than 3 times. The automatic test system can solve the conflicts between test quality and human resources, moreover, the architecture patterns based on Ethernet is easy to expand and reconstruct.
The Research of an On-demand Threaded Interrupt Technology of Serial Port Based on the Power Edge Smart Device
Authors: She Wenkui , Yang Fan , Wang Shuang , Li Yuanyu
Published: March 2021

At present, the common problems facing the low-voltage power distribution system lie in plenty of equipment and wide range of power distribution. In order to address these problems, the overall architecture of the cloud-pipe-side-end is introduced through the IoT system in electric power distribution, where the end device is connected to the low-voltage power distribution system via the serial bus of the edge device and carries on data collection and real-time edge computing. As the core node, the edge device is required to have excellent real-time capability of timely detection and real-time response to the fault of various low-voltage power distribution facilities. In consideration of this, this paper proposes an on-demand serial port interrupt threading design to ensure the real-time performance of an electrical edge smart system. According to the experimental results, as the number and load of serial port devices increase, the real-time task dispatching rate continues to decline until the transformation, and the impact on the real-time task dispatching rate is limited after the threaded interrupt. That is to say, the method is effective in ensuring the dispatching rate of real-time tasks for the edge smart terminals.
Microbial Community Analysis based on Bipartite Graph Clustering of Metabolic Network
Authors: Zhang Chen , Deng Li
Published: March 2021

Microbial metabolism network is significant for the study of the microbial community, which is crucial for microbiome related diseases, such as inflammatory bowel diseases (IBD). In order to understand the difference of gut microbial communities between IBD patients and healthy people. Firstly, metabolic bipartite networks—microbes-compound graph are proposed and then built for healthy people and IBD patients respectively, which preserve more metabolic information than the traditional unipartite network. Secondly, with the use of the community detection of LPA in weighted bipartite graphs, the community modules of the two networks are obtained. Finally, two networks are compared to analyse the differences between healthy people and IBD people from several perspectives, such as NMI, centrality, clustering coefficient, species and compounds related to IBD disease, and cross-validation is performed to prove that all results are reliable and robust. The result shows that the gut microbial communities of healthy people and IBD patients are quite different, and the diversity and stability declined. From the clustering results, it can be judged that the distribution of disease-related bacteria changed.
The Throughput of TCP Over Wireless Loacal Area Networks
Authors: Liu J , Lu J , Wang J , Dang J
Published: March 2021

The TCP protocol performance over Wireless Local Area Networks with 802.11b is analysed in this paper. Based on the communication system consisting of a large number of source-destination TCP pairs, a model for a TCP connection is proposed. According to the principles of TCP and RTS/CTS Access mechanism, the normalized throughput and the utilization of time slots are conducted. Based on the relevant parameters at TCP and MAC layers, TCP performance are analysed, and factors which determine TCP performance are explicitly obtained.
Development of Requirements for The Content of a Digital Passport and Design Solutions
Authors: Donetskaya Julia V. , Gatchin Yuriy A.
Published: March 2021

The work presents the results of the development of requirements for the content of a digital passport and design solutions. The composition of each of them is listed, taking into account the specifics of the activities of a particular enterprise and the design tasks to be solved. On this basis, an execution sequence for creating and using a digital passport is proposed.
Design Procedures for The Analysis of The Components and Parameters of a Digital Passport
Authors: Donetskaya Julia V.
Published: March 2021

The design procedures for the analysis of the components and parameters of a digital passport is a main part of the development of design procedures for the analysis of design solutions. Its main idea is to clarify the models of components and their parameters, formed at the stage of creating a digital passport, and use them to find the desired solution. Further, the development of rules for the selection of digital passport components descriptions in the form of fuzzy variables is performed. As a result, for each component, a range of values is determined that is comparable to a similar range specified by the requirements for the design solution. The results obtained in this case are used in the development of a similarity criterion, as well as an algorithm for forming a design solution.
Twitter Sentiment Analysis on Coronavirus: Machine Learning Approach
Authors: Machuca Cristian R. , Gallardo Cristian , Toasa Renato M.
Published: March 2021

In machine learning, a fundamental challenge is the analysis of data to identify feelings using algorithms that allow us to determine the positive or negative emotions that people have regarding a topic. Social networks and microblogging are a valuable source of information, being mostly used to express personal points of view and thoughts. Based on this knowledge we propose a sentiment analysis of English tweets during the pandemic COVID-19 in 2020. The tweets were classified as positive or negative by applying the Logistic Regression algorithm, using this method we got a classification accuracy of 78.5%.
Electronic Contract Ledger System Based on Blockchain Technology
Authors: Ma Feng , Tang Ning , Xu Rui , Zhang Ziqian
Published: March 2021

The rapid development of the information Internet has promoted the development of electronic contracts, but user identities have been stolen and electronic contracts are easily tampered with. Seriously affected the fairness and security of online electronic transactions. For the main body of the transaction, how to confirm that the data identity of the transaction body has not been misused and the transaction electronic contract has not been tampered is the key problem which needs to be solved first. This paper designs the electronic contract platform by combining user identity authentication, encrypted transmission of electronic contracts and blockchain ledger storage. Ensure user identity uniqueness, contract transmission data integrity and non-tampering, and traceability of the signing process, thereby achieving security and fairness.
Blockchain-based Searchable Proxy Re-encryption Scheme for EHR Security Storage and Sharing
Authors: Yang Xiaodong , Li Xixi , Chen Aijia , Xi Wanting
Published: March 2021

The sharing of electronic health records (EHR) has shown significant advantages in the accurate diagnosis of patients and the development of medical institutions. However, due to the privacy and sensitivity of medical data, it is easy to cause security issues such as difficulty in data sharing among different medical institutions and easy leakage of data privacy. Because the blockchain has the characteristics of non-tampering, anonymity, and decentralization. We propose a blockchain-based searchable proxy re-encryption scheme for EHR security storage and sharing. First, we use blockchain and cloud server to store encrypted EHR together to prevent EHR from being tampered with and leaked. Secondly, we use a certificateless encryption and proxy re-encryption based on identity and type scheme as a data sharing protocol. Meanwhile, searchable encryption technology is used to generate a keyword index. Moreover, the proxy node is selected by the delegated proof-of-stake (DPOS) consensus algorithm, which ensures the privacy, immutability and security. It realizes the safe access of third-party data users to medical health data. Finally, security analysis and evaluation show that our scheme can resist identity disguise and replay attacks. In addition, it has stronger security and higher efficiency.
Forecasting of Credit Card Default Based on Incremental Random Forest
Authors: Gao Xiang , Wen Junhao , Zhang Cheng
Published: March 2021

In this paper, the incremental random forest algorithm is proposed for the classification and prediction problem of dynamically increasing data. Traditional batch machine learning algorithms perform modeling at one time and cannot allow newly generated samples to participate in learning, which leads to too much model deviation. This paper combines incremental learning with random forest and proposes incremental random forest. Applying this algorithm to the problem of predicting credit card customer default behavior can help banks control risks and reduce losses. It is important to conduct card issuance audits on card issuers and early warning of risks to cardholders. The algorithm performed better in the experiment of predicting the default behavior of credit card customers based on a batch of credit card holder data of a bank in Taiwan. Compared with random forest, decision tree, logistic regression, naive bayes, BP neural network, and support vector machine, it has relatively better performance in our experiment.
How Science and Inventing Play Together: Example of Averaging and Mixing Processes Design
Authors: Yakovis L M , Chechurin L S , Federa V D
Published: March 2021

The problem of poor systematic support of the stage of ideation in practical engineering design is addressed. Mathematical modelling of process’s dynamics is used to generate alternative architecture of a technological unit. The approach is applied to the design of mixing-averaging complex, where the analysis of statistical dynamics equations paves the road to new design concepts. The parametric analysis and optimization of new solution demonstrates potential benefits and savings. The study suggests an iterative scheme of conceptual design, where quantitative and qualitative design tools work together.