bluuebunny/crossref_metadata_2025 huggingface dataset:
Emperical Modeling of Growth Parameters in Cellulosimicrobium cellulans during Heavy Metal Tolerance
Authors: Pipil Nikhil Kumar , Chatterjee Sayan , Purty Ram Singh
Published: June 2020

In the present work we have studied the growth kinetics of Cellulosimicrobium cellulans under different metal stress. The strain used was Gram positive and non-pathogenic. Metal studied were Fe, Zn, Pb, Cu, Cd, and Ni. Growth curve of Cellulosimicrobium cellulans was studied under different concentrations of these metals. In the second part of our investigation we tried to established growth equation which can define the growth curve of Cellulosimicrobium cellulans. The three parameters μm, λ, Xm majorly contribute to growth equations. The changes in these parameters affect the location and duration of exponential or log phase t. Thus, we have chosen these factors primarily to model our system. In each case we have observed little deviation from the predicted equation at the maximum error percentage below 10% (α< = 10) R2<0.8. Next we have tried to model the cumulative effect of these metals simultaneously but our model has failed to reach an appreciable correlation with the experimental data set. In this case we may assume that more factors like mole fraction or partial molar volume of metal solution may have contributed to the growth profile.
Interpretation of Weil’s Theorem
Authors: Meng Feilong
Published: June 2020

This is an introductory paper based on general knowledge such as simplicial complex, nerves and their homotopy equivalents in the topology field. It focuses on the theoretical part of one of the four statements on nerve theorem of paper variations on the nerve theorem. One of many ideologies throughout this theorem is to conduct an approximating process on mathematical spaces with restrictions, which is the bridge from a theory to an application of Weil’s theorem and many others. Besides, work done by Kathryn Heal in her paper variations on the nerve theorem has proved the correctness and feasibility about Weil’s theorem. This paper aims to clarify her proof of Weil’s theorem and make it more easier for people to understand.
Analysis and Research of Sorting Algorithm in Data Structure Based on C Language
Authors: Zhu Zhi-Gang
Published: June 2020

In the process of learning the data structure, it is very necessary to master the sorting algorithm, and in the program design, the sorting algorithm is applied frequently. Based on the importance of sorting algorithms, this paper will carefully compare the characteristics of different algorithms, starting with the work efficiency, algorithm implementation, basic ideas, sorting methods and other aspects, and draw conclusions so as to better select sorting algorithms.
Research on Machine Learning and Its Algorithms and Development
Authors: Jin Wei
Published: June 2020

This article analyzes the basic classification of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It combines analysis on common algorithms in machine learning, such as decision tree algorithm, random forest algorithm, artificial neural network algorithm, SVM algorithm, Boosting and Bagging algorithm, BP algorithm. Through the development of theoretical systems, further improvement of autonomous learning capabilities, the integration of multiple digital technologies, and the promotion of personalized custom services, the purpose is to improve people’s awareness of machine learning and accelerate the speed of popularization of machine learning.
An Uncertain Tuple Density Clustering (UTDC) Algorithm for Uncertain Data
Authors: Li Chunhui , Zheng Linjiang
Published: June 2020

The massive uncertain data generated by network applications has potential value, and it is of great significance to carry out clustering analysis of uncertain data. However, the uncertainty of data brings a serious challenge to traditional clustering algorithms. There are some problems in the existing clustering algorithms for uncertain data. (1) Some algorithms have a lot of meaningless distance calculations when calculating the distance of uncertain objects, and then the calculation complexity of algorithms increases. (2) The data model in clustering algorithms results in the loss of data distribution information, and then the accuracy of the algorithms decreases. In this paper, we propose an uncertain tuple density clustering (UTDC) algorithm for uncertain data. Firstly, we extract the data distribution features of uncertain instances and construct uncertain tuples by introducing the cloud model, which realizes the pruning of clustering objects. Secondly, we apply the EW distance to the traditional density clustering algorithm DBSCAN, which completes the density clustering for uncertain data. The experimental results show that comparing with UK-Means and FDBSCAN, UTDC algorithm effectively reduces the computational complexity and improves the accuracy.
The Discussion on Application of Underground Pipeline 3D Information System in College’s Repair Project
Authors: Feifei Hu
Published: June 2020

A university campus has a high population density. When the construction workers repairs and reconstructs underground pipelines, they must complete the construction as soon as possible to reduce the impact on the work and study of teachers and students. The underground pipeline 3D information system can display the spatial location and attributes of the underground pipeline intuitively, providing the construction workers with operational guidance to ensure that the repair is carried out in an orderly manner.
An efficient and high-precision algorithm on parameters estimation of LFM signal
Authors: Ruan Hang , Zhang Shuxian
Published: June 2020

At present, prevalent algorithms on parameters estimation of linear frequency modulation (LFM) signal include the fractional Fourier transform (FrFT) method, of which the crucial procedure is to estimate the optimal rotation angle. The estimated value of the optimal rotation angle has a great influence on the estimation accuracy of LFM parameters, and it also affects the computational complexity of the algorithm. This paper proposes an algorithm on parameters estimation of LFM signal with high efficiency and precision. Based on the mathematical relationship between the optimal and any two rotation angles of FrFT, the optimal rotation angle is calculated through three FrFT transforms and interpolation, and the LFM signal parameters are estimated with high efficiency and precision. Simulation results verify the effectiveness of the proposed algorithm.
Three-dimensional reconstruction filtering algorithm based on single ray picture of pipe welds
Authors: Yang Xianbiao , Yang Chao , Yang Qingxu , Yue Xianqiang , Shi Renqiang , Shen Qiucheng , Wang Lihui
Published: June 2020

With constraints in single image, such as collinear equations and coplanarity, a real-scale object model can be obtained, and real scenes of regular objects can be reconstructed, by using three-dimensional reconstruction algorithms such as grid processing and interpolation fitting. A 3D reconstruction algorithm based on single pipe weld seam ray picture is proposed. Firstly, recognizing and segmenting the weld image, and using non-linear filtering to enhance the gray level of the image. Secondly, identifying the inner and outer edge positions of the weld, and performing threshold segmentation, morphological filtering, connected area detection, edge detection, line detection, image rotation, and inner wall area extraction. Thirdly, based on the image of the elliptical opening of the weld seam, the inverse perspective transformation is used to restore the formation of the weld seam root and the weld 3D scatter points. Finally, 3D point cloud is meshed, the function of point-to-surface is achieved, and a three-dimensional model of the welding tumor is generated. The experimental results demonstrate that the algorithm can accurately and effectively perform 3D reconstruction of the weld seam based on a single 2D pipe radiograph of the weld seam, and realize accurate identification of the weld nodules at the root of the pipe through-flow weld.
Random Forest Algorithm Based on GAN for Imbalanced Data Classification
Authors: Shu Qihui , Hu Tianyu , Liu Song
Published: June 2020

Data imbalance increases the difficulty of knowledge mining from data. Aiming at the problem of data imbalanced classification, a random forest algorithm based on GAN is proposed, which can effectively classify imbalanced data sets. The GAN model is used to generate a few class samples, which are mixed with the original data samples to form a new data set to reduce the imbalance of data. Then the new data set is divided into several data subsets with a balanced sample distribution. Finally, all the decision trees are collected to form the forest and the classification results are obtained. The random forest algorithm based on GAN improves the classification effect on minority classes, which makes the equilibrium rate of the dataset reach 50%. The algorithm in this paper has performed relevant experiments on multiple imbalanced datasets, the results show that the algorithm has good performance in data imbalance classification. The optimized algorithm after parallelization improves the running speed.
Least-squares method for the diffraction problem of strip gratings
Authors: Li Huimin , Wang Yujie , Zheng Enxi
Published: June 2020

In this paper, we focus on the diffraction problem of periodic strip gratings. A least-squares non-polynomial finite element method is proposed for this problem. Firstly, the computational domain is decomposed. Secondly, a local approximation space is defined by using non-polynomial functions in every subdomain. Then the least-squares finite element method is derived. Finally, numerical results are reported to show the effectiveness and convergence of the least-squares non-polynomial finite element method.
Orthodontic path planning method based on optimized artificial bee colony algorithm
Authors: Li Zhanli , Liu Tongxin , Li Hong-An , Sun Zhihao
Published: June 2020

Aiming at the orthodontic path planning problems, for example, the number is estimated by orthodontists based on experience, and the efficiency of tooth movement path planning on computer is low. This paper proposed a teeth movement path planning method based optimized artificial bee colony algorithm (ABC). At first, according to the age and gender of patients, the constraint amount of tooth movement is automatically determined. Second, by optimizing the observation bees select method, using both roulette and anti-roulette mechanisms replaced roulette mechanisms, this method can avoid the population from falling into local optimality and obtain better distribution. Then, the initial stage number is obtained through interpolation, and combined with the initial food source generation strategy in ABC. Finally, this paper uses the optimized ABC to plan the teeth movement path and obtain the actual amount of orthodontic stages. The experimental result shows that the optimized ABC has better distribution than basic ABC, and the proposed method is closer to clinical orthodontic, reduced the workload of the orthodontist. The data shows this method is more suitable for different patient situations, reduced the orthodontic stage number and the cost of teeth movement effectively.
Research on Error Correction Algorithm of Vehicle SINS and Odometer Integrated Navigation
Authors: Wu Meng , Yin Xun-Feng
Published: June 2020

In order to solve the problem of odometer small differential error and pulse calculation error and improve the precision of SINS and Odometer Integrated Navigation, this paper proposes an error correction algorithm of vehicle SINS and odometer integrated navigation, compares the calculated position of SINS with the calculated position of odometer as the measured value of integrated navigation within the fixed mileage range and presents a method to correct the truncation error of odometer counting. The simulation results show that these methods can quickly separate the sensor errors of the integrated navigation system, have the high positioning and orientation accuracy. Also, these methods can be applied in the practical vehicle SINS system.
Particle Swarm Optimization Algorithm for Container Deployment
Authors: Wu Libin , Xia Hongbin
Published: June 2020

In recent years, with the development of cloud computing, virtualization technology has received widespread attention. As a new representative of virtualization technology, containers have been widely used in software development, operation and maintenance, testing and other aspects, such as microservices and Docker Cloud. In cloud data centers, containers have gradually replaced virtual machines (VMs) as a new carrier for cloud tasks. However, with the increasing number of cloud products, the scale of tasks requested by users in cloud data centers continues to expand. The economic cost of tasks in the process of containerized deployment has become a concern of various cloud service vendors. The container deployment cost usually includes the data exchange cost, the image pull cost and the server energy cost. In order to solve the containerized deployment of application tasks in the container cloud environment with the lowest possible container deployment cost, this paper proposes a new cost calculation model in a container cloud environment, and then presents an improved particles swarm algorithm, namely a particle swarm optimization (PSO) algorithm for container deployment (CD-PSO) to provide the best solution for application task loading. Experimental results show that the proposed algorithm has a lower deployment cost than other scheduling algorithms.
Research status of mobile robot path planning based on genetic algorithm
Authors: Li Yanhui , Huang Zhonghua , Xie Ya
Published: June 2020

With its strong search ability and other characteristics, the genetic algorithm in mobile robot path optimization has been rapidly developed and applied, and has become one of the most successful algorithms to solve the mobile robot path planning problem. Firstly, this paper introduces the basic principle of genetic algorithm and gives a brief overview of its development and improvement strategies. Then, combining several representative improvement strategies, path planning of mobile robot is explained in detail. Finally, combining the theoretical and applied research results of genetic algorithm, the further development of the algorithm is summarized and prospected.
A real-time detection USV algorithm based on bounding box regression
Authors: Zhang Yihong , Wu Sen , Liu ZiHao , Yang Yijin , Zhu Di , Chen Qian
Published: June 2020

With the rapid development of unmanned surface vessels (USV) applications, USV are widely used in military intelligence collection, target monitoring, and shipping services. However, this also brings security issues to shipping and the country. For more efficient detection of USV, in this paper, an enclosing center distance Intersection over Union (E-CIoU) algorithm is proposed for real-time USV detection, where the normalized distance between center points of the smallest enclosing box and target box is combined with bounding box regression. Meanwhile, we designed a new USV data set. The extensive experiments on USV data set have demonstrated that the proposed approach has achieved better convergence speed during training and a significant accuracy prediction.
Automatic Detection of Pointer Automobile Instrument Based on Machine Vision
Authors: Lin Chuanwen , Chen Gang , Gao Xiang , Liu Zhenhua
Published: June 2020

Automotive instrument detection is usually performed manually. Factors such as human subjectivity and external environment have a great influence and are prone to errors. This paper proposes a method for automatic detection of pointer automobile instruments based on machine vision. Firstly, the common part of multiple instrument images with different pointer angles is extracted as the background image; secondly, based on the frame difference method, binarization, morphological operation and other techniques the instrument pointer is identified, and then the intersections of the pointer extension lines are used to determine the center point; thirdly, we extract the image of the scale position and calculate the scale template array; finally, the pointer value of each input instrument image is calculated based on the scale template array. Experiments show that this method can effectively solve the shortcomings of poor applicability and poor intelligence of existing algorithms, and can identify the value of the pointer accurately.
Music Recommendation System based on Fusion Deep Learning Models
Authors: Dong Yuchen , Guo Xiaotong , Gu Yuchen
Published: June 2020

With the growth of user popularity of online music platforms, the amount of music data and the demands on accurate recommendation system for music also increases. Therefore, an accurate and efficient recommendation system is urgently needed. In this paper, a music recommendation system based on fusion deep learning models is proposed. Both of the audio information and lyrics data are originally exploited. A supervised training method is applied to train this model with annotated data. In this paper, the result of the test dataset shows that the proposed model can achieve more than 90% accuracy performance on average.
CapsNets algorithm
Authors: ZHang Ping , Wei Ping , Han SHuhuan
Published: June 2020

Capsule networks (CapsNets) is a commonly used neural network structure, especially in computer vision research., which may have a significant impact on deep learning. Convolutional neural networks (CNNs) have reached superhuman level in various computer classification tasks such as classification, parsing, object detection, semantic segmentation, and instance segmentation. Traditional deep learning tends to deteriorate when the relevance of detection objects changes. For this kind of situation, this paper improves a new network, namely the capsule network(CapsNets), the network can achieve a higher detection rate in the hostile environment.
New Mode of Distance Learning in Primary Schools in the Environment of Multimedia Computer Assisted Instruction
Authors: Gao Xiaoyan
Published: June 2020

with the Rapid Development of Science and Technology in China, Modern Information Technology Has Been Deepened and Popularized in China. At the Same Time, It Has Also Developed to a Certain Extent in the Classroom Teaching of Primary School Language. At the Same Time, the Education Reform Has Made Great Changes in the Teaching Methods of Education, and the Teaching Requirements for Each Stage Have Gradually Improved, Especially the Pupils Who Are Extremely Malleable. At the Same Time, Because of the Development of Science and Technology, Teachers Gradually Found Ways to Improve the Efficiency of Primary School Teaching: Traditional Education Combined with Modern Technology. the Development of Technology Has an Ever-Increasing Impact on Society. Therefore, in Primary School Teaching, Multimedia Technology and Traditional Media Teaching Are Combined.
Research on Path Planning of Robot Based on Artificial Intelligence Algorithm
Authors: Yan Cheng
Published: June 2020

the Task of Robot Path Planning Based on Artificial Intelligence Algorithm is to Determine an Optimal Strategy, with the Goal of Maximizing the Return. When the Robot is in a New State, It Needs to Select an Action from the Action Set and Execute It. in Order to Improve the Quality of the Algorithm, It is Necessary to Apply the Search Strategy to the Action Selection. in the Path Planning System of the Robot, One of the Most Critical Links is the Realization of Directional Reference Vehicle Scheduling. Generally, Artificial Intelligence Algorithm is the Most Widely Used in Robot Path Planning. in This Paper, an Artificial Intelligence Algorithm is Used to Optimize the Vehicle Scheduling Problem. the Path Variable of the Problem Can Be Defined Correspondingly, Which Originates from the Planning Node. the Sequence of Sending Points Reached by Completing Other Sending Requirements is Called the Legitimate Sub Path, While the Corresponding Traversal Path Does Not Include the Sequence of Several Legitimate Sub Paths of Repeated Planning Points. the Algorithm Can Significantly Improve the Efficiency of Multi-Robot System, Reduce the Number of Explorations and Speed Up the Process of Convergence.
Car Plate Detection Based on Yolov3
Authors: Shen Siyuan , Wang Lidan , Duan Shukai , He Xin
Published: June 2020

An intelligent car plate detection method can make the travel more convenient and efficient. However, traditional methods are reasonably effective under the specific circumstances or strong assumptions only, and there are few databases for car plate detection. Therefore, a novel real-time car plate detection method based on improved Yolov3 has been proposed. In order to select the more precise number of candidate anchor boxed and aspect ratio dimensions, the K-Means algorithm is utilized. To solve the short of the available car plate database, a car plate database which has 6668 pictures has been established. As shown in the experimental results, the method which is proposed by this paper is better than original Yolov3. Thanks to the car plate database, the proposed method obtained better results even in the situation of inclination, too bright or too dark, different weather and so on.
The general formula of entropy and similarity measures for hesitant fuzzy linguistic term sets
Authors: Yuan Xiujiu , Huang Lin , Zhao Xuejun , Zheng Mingfa
Published: June 2020

In order to quantitatively characterize the uncertainty of hesitant fuzzy linguistic information, the entropy and similarity measures of hesitant fuzzy linguistic term sets are studied. First, the axiomatic definitions of entropy and similarity measures of hesitant fuzzy linguistic term sets are given, and general formulas of entropy and similarity measures for hesitant fuzzy linguistic term sets are proposed. Moreover, the algorithms for generating entropy and similarity of hesitant fuzzy linguistic term set are given. Then the relationship between entropy and similarity of hesitant fuzzy linguistic term set is studied, and the general formula of entropy based on similarity is proposed. These lay the theoretical foundation for flexible selection of entropy and similarity in multi-attribute decision making.
Legs Components Micro-Doppler Estimation of Human Target Based on Doppler Radar
Authors: Lei Chengxi , Ding Yipeng
Published: June 2020

The radar based human micro-motion research is a new technology rapidly developed in recent years. Radar detection of human target has advantages that other sensors do not have. The radar system is not affected by light or weather condition, which makes it possible to achieve detection in various environments. Besides, targets hidden behind obstacles can be detected, because electromagnetic waves can penetrate barriers of a certain thickness. Doppler frequency shift caused by the modulation of electromagnetic waves by the swing of hands and legs during human motion can be obtained by various signal processing methods. Separation and estimation of multi-components signals is a prerequisite for studying micro-motion characteristics of human target. Because there are a lot of aliasing in the time-frequency diagram of component with each other, separation of multi-components signals is a difficult but urgent problem at present. In addition, kinematic parameters of human legs play a key role in distinguishing human and animal targets. Therefore, in order to extract human leg signal and estimate its frequency accurately, a signal separation method based on Bezier curve fitting is proposed in this paper. Because human radar echo signals are often non-stationary time-varying signals, we need to process the collected signals with time-frequency transforms. The accuracy of this method for extracting components of legs is precise, and it has met the accuracy requirement of Micro-Doppler research. The simulation and experimental results prove the effectiveness of this method.
Research on Calibration Method of Multi-camera System without Overlapping Fields of View Based on SLAM
Authors: Feng Mingchi , Jiang Panpan , Liu Yibo , Wang Jingshu
Published: June 2020

The basis of multi-camera system measurement is the calibration of the multi-camera system. Although multi-camera system calibration has achieved certain results, there are still problems such as tedious calibration process and high calibration cost. In this paper, we propose a practical calibration method for multi-camera systems without overlapping fields of view. Firstly, a global 3D model of the scene is reconstructed using Simultaneous Localization and Mapping (SLAM). Then, each camera is calibrated using the 2D-3D correspondence between the images acquired by the multi-camera system and the 3D model. Finally, the external parameters of the multi-camera system are calibrated. In the process of using SLAM to build the model, the graph optimization is used to improve the accuracy of the modeling, and the accuracy of the calibration depends on the accuracy of the modeling. After several experiments, the experimental results show that the method is feasible.
Single-station Passive Reconnaissance Target Location Based on Intelligent Technology
Authors: He Kun , Liu XuBo , Guo KaiFeng , Lan ChunYan , Liu Yi
Published: June 2020

At present, passive reconnaissance target location usually adopts multi-station direction-finding cross or multi-station time difference method, which has the disadvantages of complex scenes, large resource consumption, long time consumption and low positioning accuracy. In this paper, intelligence technology is used to perform machine learning and predictive evaluation of target radiated power in battlefields, and combined with the electromagnetic propagation model to complete the estimation of target approximate position of single-station passive reconnaissance. The results show that after training with a certain sample, the method can quickly complete the estimation of the target position, and has high positioning accuracy.
Design of lightweight pedestrian detection network in railway scenes
Authors: Han Chuanyi , Zhu Jinlei , Li Feng , Wan Shengkai , Chen Houjin
Published: June 2020

The deployment of many advanced pedestrian detection applications is largely hindered by the high computational cost of deep convolutional neural networks (CNNs). In this paper, we propose a two-step pruning method to design a lightweight pedestrian detection network in railway scenes. The first step is feature pyramid network (FPN) pruning, which utilizes the characteristic of pedestrian in railway scenes and the FPN structure in YOLOv3. The second step is regular channel pruning, which utilizes network slimming knowledge and is an accelerator-friendly pruning strategy. Our two-step pruning method gives about 88% reduction in parameters and about 74% reduction in computing complexity with comparable detection accuracy.
CSDA: A novel attention-based LSTM approach for code search
Authors: Ren Leiming , Shan Shinmin , Wang Kai , Xue Kun
Published: June 2020

Previous studies have proposed semantic-based approaches for code search over large-scale codebases, which has bridged the gap in understanding the semantics between natural language and source-code language. However, these studies either failed to determine an effective method for semantic representations or lacked the distinction of semantic features. In this study, we propose a novel attention-based LSTM neural network known as CSDA (Code Search based on Description Attention), which can effectively improve the code search performance. The proposed model can focus on different parts of a semantic feature when numerous aspects of a source code snippet are used as input. As opposed to assigning the same weight to different parts of the semantic vector, CSDA takes the semantics of natural language descriptions into account, so that the subtle differences hidden in the code snippet can be discriminated and associated with the corresponding queries. We compare CSDA with the existing state-of-the-art approach CODEnn, which uses a jointly embedding technique for code search. Our experimental evaluation demonstrates that CSDA outperforms previous methods and achieves superior code search performance to CODEnn, with higher success rates and mean reciprocal ranks. This study provides significant insights into the use of semantic representation methods in deep learning-based code search approaches.
Research and Optimization of Electromagnetic Compatibility of a Hardened Computer
Authors: Li Chao , Wang Tao , Gao Tongguo
Published: June 2020

A hardened computer is an army vehicle equipment. During the testing of electromagnetic compatibility, the RE102 electromagnetic radiation test item appeared multiple points exceeding the standard. In view of this problem, the electromagnetic environment of a reinforced computer is analyzed, and it is found that there are adverse factors that cause electromagnetic leakage in the aspects of the case aperture, cable shielding, power protection, and grounding methods. After optimization, the device passed the RE102 test smoothly. The radiated emission level of the improved equipment has decreased significantly, and the radiated disturbance suppression measures adopted are easy to implement and universal, which provides a reference for subsequent research and the development of similar equipment.
Application of PLC in Automatic Tapping Machine Control System
Authors: Liu Hengjuan
Published: June 2020

Aiming at traditional equipment’s problems, such as low tapping efficiency, poor reliability, a set of special automatic tapping machine control system is designed. The system can tap five workpieces at the same time by using Siemens S7-200 series CPU226 AC/DC/Relay PLC technology. The system will carry out technical reform of traditional equipment. The paper describes the control system scheme, work flow chat, I/O wiring diagram and program design of PLC. The operation results meet the use requirements. The system has the characteristics of good maneuverability, high operation safety and high production efficiency, which can greatly reduce the production cost.
QoS Dynamic Perception Scheduling Strategy for Edge Intelligent Computing
Authors: Hou Wanwan , Sun Depeng , Sheng Mengxue
Published: June 2020

Edge intelligent computing devices are often deployed in some extreme environments, where the transmission network bandwidth is low or the network environment changes greatly. Therefore, the traditional queue scheduling algorithms cannot guarantee the QoS of edge intelligent computing. WF2Q+ allocates bandwidth according to a fixed weight, which causes real-time data flow delay to increase when the network is unstable. The dynamic perception scheduling strategy proposed in this paper is to dynamically change the weight of WF2Q+ by dynamically sensing the backlog length of the queue. At the same time, combined with the queue scheduling algorithm of PQ, this algorithm can prioritize the transmission of real-time data with certain fairness. In addition, the token bucket algorithm is used to limit the sending rate of the device and prevent network congestion caused by burst data injection into the network. After experimental simulation, the improved algorithm can achieve good results on the delay index.
Elderly care system based on cloud platform internet of things
Authors: Zhou Changhu , Xia Huiwen , Lv Xiaotian , Tan Meifang , Xiong Yuhang
Published: June 2020

Against the background of a gradual increase in the elderly population, the care system for the elderly appears extremely important. The nursing system based on the cloud platform Internet of Things mainly includes the design of the lower computer, the debugging of the upper computer and the design of wireless communication. The design of the lower computer uses STM32, pulse detector, accelerometer, gyroscope and other modules. And with the cooperation of the host computer and the communication module, it can realize health monitoring functions such as medicine warehouse switch detection, elderly heart rate monitoring and fall detection. In addition, family members can also use the mobile phone APP to control connected devices around the elderly, such as smart pill boxes, to remind them to take medicine. There is no doubt that the design will greatly reduce family stress.
Application of Multisim Simulation Software in Teaching of Analog Electronic Technology
Authors: Li Zhengdong , Li Xiuling , Jiang Decai , Bao Xingzong , He Yan
Published: June 2020

Analog electronic technology is a core basic course of electronic specialty in colleges and universities. It has the characteristics of complex theory, abstract content, difficult to explain by teachers and difficult to understand by students. This article mainly discusses some typical applications of Multisim virtual simulation software in the teaching of analog electronic technology courses, and analyzes the impact on classroom teaching methods. The practice shows that the application of simulation software in the teaching of simulation electronic technology can solve the problems of insufficient experimental teaching site and potential safety hazards. It can help students to understand abstract theory and master related knowledge. It can help to organize lively and efficient classroom teaching and promote the quality and efficiency of classroom teaching.
Needle-tissue interaction force study during simulated regional anaesthesia
Authors: Zhang Shengli , Graeme  , George  , Qiao Panpan , Huang Zhihong , Xia Chunming
Published: June 2020

Needle insertion usually performed with the aid of an Ultrasound (US) image in the regional anaesthesia. Getting the position of the needle tip from US requires the technical expertise and skilled anaesthetist. To detect where the needle tip is around the nerve during the needle insertion, a modified block needle attached with a force sensor is used to feedback the amount of counter force from the needle tip. This paper is to investigate whether needle force under ultrasound guidance can guide anaesthesiologists in identifying the relative position of the needle tip and nerve The goal of our research was to use the visual needle tip force to guide the anaesthetist to detect the relative positions between the needle tip and nerve.
A Traditional Chinese Medicine Prescription Recommendation method based on Mutual Information Clustering
Authors: Qin Yao , Ma Zherui
Published: June 2020

Traditional Chinese medicine (TCM) data is the main knowledge resource of TCM, which contains a wealth of clinical experience knowledge. Machine learning has made remarkable achievements in natural language processing. As the carrier of TCM knowledge and information stored in the form of text, using machine learning method to study these TCM data can save a lot of manpower cost, improve the objectivity of TCM, promote TCM related knowledge better, and have certain guiding significance for the research of TCM human engineering experiment. This paper proposes a recommendation algorithm based on mutual information clustering. Its core idea is calculating mutual information between two symptoms, and set symptom “relatives and friends group”, after getting the symptom clustering results of mutual information, then combine the clustering results and search algorithm to achieve the effect of recommendation and filtering. Experimental results show that the proposed method is effective.
Research on hotel online sales forecast model based on improved WaveNet
Authors: Duan Dongmei
Published: June 2020

In recent years, with the rapid development of the Internet in China, online transactions have grown greatly. For example, OTAs with a large number of hotels have accumulated a large amount of hotel data and user consumption data. And the online sales of hotels is the basis and core of revenue management. Time series prediction has always been one of the main application fields of machine learning algorithm. From the classical traditional time series prediction methods to long-term and short-term memory networks and closed-loop neural networks, the prediction ability is constantly improving. With the development of deep neural networks, convolution neural networks show superior performance in the prediction of time series. This paper proposes a new prediction model based on the improved WaveNet using not only the parameters of historical sales and hotel property, but also the parameters of holiday time and time position in the prediction range, which are processed by serialization. Simulation results are presented in details in this paper, where these results indicate the effectiveness of the proposed forecasting tool as an accurate technique.
Automatic shutdown system for unmanned air-conditioned room
Authors: Tan Meifang , Chen Zhenyu , Lv Xiaotian , Yu Maochang , Guo Jiabin , Fan Qin
Published: June 2020

With the development of society and economy, air conditioning has also become an integral part of our lives and it allows us to enjoy warmth in winter and cool in summer. However, there is also a phenomenon that people do not turn off the air conditioner due to personal savings awareness or anxious to go out. This paper intends to design a system where the air-conditioning room is automatically turned off if there is no one in half an hour. Pyroelectric sensor is used to detect the presence of people in the room, and wind speed sensor detects whether the air conditioner is on. If the room is empty and the air conditioner is not turned off for more than half an hour, the system is used to automatically turn off the air conditioner. It can reduce some waste of resources, it can also reduce certain environmental pollution, and realize economic and social benefits.
Application on Development of Modern Service Industry in China Based on Artificial Intelligent Model Optimized by TANSAFOA
Authors: Wang Tian , Lin Jianbang
Published: June 2020

Modern service industry is one of the industries that widely applies artificial intelligent (AI) in the era of big data. The development of modern service industry plays a very important role in industrial transformation. The emergence of AI technology improves the effectiveness of data mining, and more and more scholars suggest a series of optimization methods to lift the prediction ability. In this study, tangent fruit fly optimization algorithm with step adjust (TANSAFOA) is proposed and it is used to optimize multivariate adaptive regression splines (MARS) and back propagation neural network (BPNN) to construct a prediction model of business performance. The result shows TANSAFOA can effectively optimize the prediction model and the BPNN model optimized by TANSAFOA has higher prediction performance than MARS. TANSAFOA BPNN-model is the most appropriate prediction model for modern service industry in China.
Airborne Communication Computer Based on PowerPC+ SRIO Architecture
Authors: Gao Tongguo , Lyu Meimei , Yang Hongbin
Published: June 2020

In order to break through the transmission capacity limitation of airborne computers, an airborne communication computer based on PowerPC+ SRIO Architecture is designed according to the wide application of the SRIO bus and the high performance of the PowerPC processor. In terms of optimization design, the computer adopts a redundant fault-tolerant system and improves the SRIO register initialization and address mapping method. The experimental results show that the transmission speed of the airborne communication computer based on PowerPC+ SRIO architecture reaches 81% of the theoretical value, and 16KBytes data can be transmitted in real time within 1μs.
Deformable Feature Pyramid Network for Aluminum Profile Surface Defect Detection
Authors: Wang Juan , Meng ZhaoHui
Published: June 2020

Surface defect detection is an important part in the process of aluminum production. The goal of a complete defect detection task is to realize the specific category and precise location of each defect in the image. Due to the similarity between surface defects and background, as well as the large diversity and difference in the appearance of the defects, this task is still challenging for applying this task in practice. In order to solve these problems, a deep learning based aluminum profile defect detection network is proposed. In order to realize strong classification ability, the deep convolutional neural network is used to generate feature graphs at each stage. On this basis, the variable feature pyramid module (DFP) is proposed, and deformable convolution is added to the feature layer to make the feature layer have the ability to adapt to defect deformation. Based on these multi-layer features, the size of anchor box was customized by Kmeans clustering algorithm, and then the region extraction network (RPN) was used to generate the region of interest (ROIs). For each ROI, a detector, consisting of a classifier and a bounding box regressor, produces the final detection results. Finally, we used a defect detection dataset of aluminum profiles to train and evaluate our approach. Through two sets of ablation experiments, the effectiveness of the introduced module is proved. Finally, we reached 76.92 mAP in aluminum profile data set.
Research on object detection technology for human detection
Authors: Niu Yanling , Meng Zhaohui
Published: June 2020

There are many classical application scenarios in computer vision, and human detection in dense scenarios is a classical problem. Its goal is to find all the people in an image or each frame of an video, as well as their positions and sizes, and frame them in a rectangular box. In recent years, deep learning has been applied to the field of computer vision, laying a solid foundation for human detection based on deep learning. Aiming at the crowdhuman dataset, based on faster-RCNN and feature pyramid network (FPN), this paper studies the training effect. In order to detect small objects, a multi-scale feature fusion network (MFFN) was proposed for feature extraction. According to the serious occlusion of dataset, a double-branch structure was proposed to improve the detection accuracy. These two structures combine to form a new network, we call it MDN, namely multi-feature fusion and double-branch network. Experiments show that these methods are helpful to improve the detection accuracy.
A Study on the Influencing Factors of Conflicts Between Motor and Non-motor Vehicles in the Turning Lanes of Signalized Intersection
Authors: Wei Fulu , Ma Xueshi , Guo Yongqing , Li Qingyin , Cai Zhenggan
Published: June 2020

In order to reveal the influencing factors of the conflicts between the right-turning motor vehicles and the straight non-motor vehicles at the signalized intersection, the unmanned aerial vehicle (UAV) was used to collect the field data. And various methods such as manual statistics, video detection, deep learning were employed to analyze the data to obtain the traffic volume and the trajectory of pedestrians and vehicles. Pearson correlation analysis, 2-tailed significance test and other methods were used to examine the influences of traffic volume, speed, traffic control factors on traffic conflicts. The results show that (1) the correlation coefficient between the traffic volume of non-motor vehicles and the conflict frequency is 0.842 (close to 1), which indicates that the traffic flow of non-motor vehicle has a significant impact on the conflict frequency; (2) the speeds of right-turning vehicles at intersections have no significant impact on the conflict frequency (P = 0.071 > 0.05); (3) the setting of traffic markings is a key factor (P = 0.000 > 0.05), which can be seen in the traffic volume at intersections (not controlled by signals), and the improvement of traffic control elements can effectively reduce the frequency of conflict between motor and non-motor vehicles (MNV).
Study on 3D quantization and optimization of the acoustic package of vehicle powertrain by energy boundary element method
Authors: Wu Congbing , Yu Ping , Zhou Hongxia
Published: June 2020

Automobile noise has become the first source of environmental noise pollution, and its radiated power reaches 75% of the total environmental noise, while powertrain noise is the main source of automobile noise. With the problem of noise reduction of automobile powertrain, a 3D grid positioning technology, based on energy boundary element method, was proposed to quantify the noise source of automobile powertrain, and the generalized sound holography was applied to obtain the noise cloud map and analyze the noise propagation path, so as to determine the acoustic package material and the optimal location. Through the optimization experiment and simulation of 4 schemes, the acoustic package of automobile powertrain with the best effect was obtained, and the insertion loss of 6.7db could be obtained at 7m of the powertrain. Therefore, the results have important engineering application values for automobile noise reduction.
Design and Performance Simulation of Modern Automobile & Automatic Navigation Control System
Authors: Xiaoping Lei
Published: June 2020

This Study Focuses on the Composition, Performance, Application Field and Performance Simulation of Automotive & Automatic Navigation Control System, and Based on the Control Concept of the Specific Scheme, the Design Execution Leader is Used to Study the Redesign of the System Feedback. Finally, the Digital-Analog Simulation System is Applied to Improve the Control System, in Order to Provide a Feasible Reference for the Design and Performance and Improvement of the Modern Automobile Automatic Navigation Control System.
Research and Application of Mechanical Product Design Process Based on QFD and TRIZ Integration
Authors: Weijie Jia
Published: June 2020

with the Increasingly Fierce Competition in Science and Technology, the Competitiveness of Enterprises Depends on the Level of Products, and the Level of Products Depends on the Design of Products. Therefore, the Design Quality of Products Will Directly Affect Product Performance, Product Development Cycle and Economic Benefits, and Even the Development of National Economy. in Order to Achieve Product Innovation and Meet the Needs of Customers, This Paper Studies How to Integrate QFD and TRIZ to Achieve Mechanical Product Innovation Design. through the Research, It is Found That QFD Can Only Obtain Customer Demand, Not Create Demand, Only Find Conflict, Not Solve Conflict, and TRIZ Can Solve These Two Problems Well. Therefore, This Paper Takes HoQ as the Skeleton Framework, Integrates QFD and TRIZ in the Demand side of HoQ and the Roof to Guide the Product Design Process in Reality, Which Can Realize the Complementary Advantages, Overcome the Shortcomings of Only Relying on a Single Design Theory to Guide the Design Process, and Has Practical Significance.
Application of Wireless Positioning Technology in Risk Management and Control of Substation Operation Site
Authors: Feng Junjie , Shi Junfeng , Gao Lei , Huang Hongwei
Published: June 2020

the Construction of Smart Grid Puts Forward Higher Requirements for the Safe Operation of Substation, But There Are Still Some Hidden Dangers of Human Factors in the Current Operation of Substation. the Safety Management System of Substation Operation Based on Wireless Positioning Technology Can Avoid the Influence of Human Factors by Technical Means. Based on the Analysis of the Special Requirements of Operation Safety Management in Substation Site, It is Pointed out That Css Broadband Wireless Positioning Technology is Suitable for Substation Application Because of Its High Positioning Accuracy, Strong Anti-Interference Ability and Reasonable Price. the Work Flow and Application of the Substation Operation Safety Management System Are Described.