bluuebunny/crossref_metadata_2025_split huggingface dataset:
System-on-a-chip (SoC) FPGA-based Hardware Acceleration for Green Crop Segmentation
Authors: Li Shan , Zhang Zhibin , Zheng Yuan 
Published: September 2019

Abstract
With the development of artificial intelligence technology, agricultural robot plays a significantly important role for agricultural intelligence. Crop segmentation is a critical and fundamental pre-processing step for agricultural robot navigation and crop growth monitoring. Although there are several crop segmentation methods, they cannot meet the online real-time requirement for agricultural robot. In view of this, a real-time crop segmentation system implemented on a SoC-FPGA is first proposed in this paper, which contains image loading module, segmentation algorithm module and image display module. As a core module, segmentation algorithm module consists of greenness identification, thresholding operation and morphological operation. Its hardware implementation adopted a parallel pipeline architecture to enhance real-time performance. The proposed system is implemented on a Xilinx Zynq-7000 FPGA and the experimental results showed that it can achieve a frame rate of 60 frames per second for the image resolution of 1920×1080 with the average accuracy of 91%.
A Load Balancing Vertical Handoff Algorithm Considering QoS of Users for Heterogeneous Networks in Power Communication
Authors: Shao Gang , Wu Wenqin , Yin Lei , Ding Chenyang 
Published: September 2019

Abstract
With the increase of the number of users and the complexity of services, only single communication network cannot meet all its requests in power communication networks. Therefore, heterogeneous networks are an effective solution. Vertical handoff (VHO) is an important step in the process of heterogeneous network convergence. An appropriate handoff algorithm can improve the quality of service (QoS) of users. In order to solve the problem of network congestion caused by a large number of users connecting to partial networks in heterogeneous networks, a vertical handoff algorithm based on load balance is proposed. Among the networks that can meet users’ requirements, the most load-balancing network is obtained using optimal algorithm. In addition, to consider users’ requirements and ensure the QoS of network service, we use analytic hierarchy process (AHP) to weight different networks. This algorithm can take full use of the various network resources, so that the distribution of the load among networks is average. The simulation results show that the proposed algorithm can select the network according to the user’s demands and each network has close network utilization under the algorithm.
Research on Evaluation Method of LDA Topic Model in Mail Classification
Authors: Gong Hechen , You Fucheng , Lai Shuren 
Published: September 2019

Abstract
The LDA topic model is a document generation model, which is an unsupervised machine learning technique that can be used in keyword extraction, topic classification, and so on. The main purpose of this paper is to effectively evaluate the number of optimal topics of the LDA topic model in the message subject classification and extract the importance of the topic, so that the LDA topic model can be highly readable after subject classification. Therefore, this paper proposes a keyword matching and subjective statistical value word comparison (KM-SSVW) for the subject classification of emails, the keyword matching technique in this method uses the TF-IDF technique. This method can accurately evaluate the extracted keywords and optimize the number of topics. The data in this article is mainly from the mail gate, a total of 7,000 messages that Hillary communicates with others. The empirical results show that the proposed method of keyword matching and subjective statistical value word comparison has a good effect on subject quantity optimization and subject word readable evaluation. However, there are still some limitations in this paper. In the experiment, new methods are not validated for other types of data sets, such as microblog short text, XML documents, and WeChat public platform articles.
Intrusion Detection Method Based on Improved K-Means Algorithm
Authors: Meng Chun , Lv Yuanyuan , You Long , Yue Yuchen 
Published: September 2019

Abstract
Data mining technology has a good application in the field of intrusion detection. For the problem that K-Means algorithm is difficult to process high-dimensional data, local optimal solution, and cannot determine K value, this paper proposes an improved K-Means algorithm. Firstly, the PCA algorithm is used to reduce the dimension of the data set, and then the Outlier detection is used to eliminate the Outliers that have a great influence on the final clustering result. Then, the initial clustering center point is selected based on the distance to avoid the local optimal solution. Finally, K- is used. The Means algorithm performs clustering to obtain an intrusion cluster. The experimental results show that compared with the common data mining-based intrusion detection algorithm, the proposed method has a good performance in detection rate and false positive rate, and its performance has also improved.
Deep Belief Network based Coal Mine Methane Sensor Data Classification
Authors: Wu Xuefeng , Zhao Zhikai , Wang Li 
Published: September 2019

Abstract
Gas explosion is the main hazard which affects the safety of coal mine production. One way to solve the problem is to predict the dangerous levels of methane concentration using the sensor time series from hazard monitoring systems. In this paper we proposed our method based on DBN to classify the dangerous levels of methane concentration. A multilayer RBM network is built to reconstruct the methane sensor data and depth characteristics of methane disaster classification are extracted. Then a BP network is used for classification learning. We test our method on a real coal mine methane sensor data, and contrast with two classical methods, SVM and KNN. The experiments results showed that our proposed deep feature learning methods could achieve better performance than the two classical shallow methods.
Research on the Falling Detection System for Elderly-assistant and Walking-assistant Robot
Authors: Wei Xiaojuan 
Published: September 2019

Abstract
With the development of living standards, the problems caused by aging population have become more and more serious. At the same time, with the growth of age, the body function of the elderly is degenerating and the body flexibility is reducing, which leads to frequently occurring of falling in elderly. The research data shows that falls are the most common and unpredictable problem for old people, which are even life-threatening. Therefore, a falling detection system used in the walking-assistant robot is proposed to make the walking-assistant robot better service during the outdoor walking. Firstly, a general design plan of the falling detection for old people, which is used in walking-assistant robot, is introduced through the analysis of falling mechanism of elderly. Then, a acceleration sensor ADXL345 and the detection module of touch pressure sensors are used to collect data of acceleration and hand touch force of users. Next, the feature extraction is carried out on the above data, and the falling detection is delivered by time domain feature representation and recognition algorithm. At last, the test system is built for test verification, and the results show that the falling detection system has a stable and reliable performance and high detection accuracy rate, reaching 99.05%. It can detect the falling of elderly effectively.
Multi-Person Pose Estimation via Learning Feature Integration
Authors: Hu Xiaomei , Tian Jiahao , Qiao Huixiang , Wang Minjie 
Published: September 2019

Abstract
Human pose estimation is a fundamental but challenging task in computer vision. Although batch normalization is widely used for deep learning, feature extraction in deep convolutional neural networks (DCNN) is still not well explored. In this work, we propose a pose encoding module (PEM) to enhance the learning ability and generalization ability of feature extraction. Given input images, PEM integration instance normalization and batch normalization, combining them in an appropriate way to learn to capture and eliminate appearance changes while maintaining the distinction between learning features can be seen as an integration and adjustment of global information. In addition, we use a simple and efficient up-sampling strategy to recover high-resolution representations for predicting more accurate human keypoint heatmaps, which has achieved better performance than the average network in recent missions. We studied our approach on a standard benchmark for human pose estimation.
Research on Visual Algorithm of Road Garbage Based on Intelligent Control of Road Sweeper
Authors: Min Haitao , Zhu Xuhan , Yan Beizhan , Yu Yuanbin 
Published: September 2019

Abstract
In order to reduce drivers’ labor intensity during the operation of road sweeper, the development of road operation feature recognition algorithm based on visual perception technology is of great significance, especially in the intelligent control of the cleaning devices and the improvement of energy saving effect. This paper summarizes the development status and trends of road sweepers in domestic and foreign, proposes intelligent road sweeper operation power control system, and designs key technologies. Taking the type of garbage and the coverage of garbage as the target of feature recognition, the corresponding visual perception and recognition algorithm was developed, and the algorithm was tested and analysed. The recognition rate and recognition effect reached the expected.
Research on Bearing Stress Characteristics of Stacking Robot Base under New Engineering Background
Authors: Duan Peng , Li Yulong , Zhou Wenyu , Liu Jun 
Published: September 2019

Abstract
Under the background of the continuous development of new engineering, more and more researches are aiming at new cutting-edge technologies, among which industrial robots have attracted much attention. Based on typical trajectory and a given load condition of the palletizing robot, the dynamics analysis and calculation was accomplished by using the dynamics analysis and calculation module of Adams software under the condition that the palletizing robot was working normally, then obtained the dynamic load variation when rolling bearing was running, furthermore, the contact stress change rule was obtained by using the finite element analysis calculation method, and the rule was regard as the basis of rolling bearing fatigue life analysis and evaluation. Judging from the results of analysis and calculation, the contact stress distribution of rolling bearing under the combined effect of both external load and dynamic load, which was similar to the phenomenon of commonly used rolling bearing stress fatigue destruction, this method can effectively overcome the deficiencies of traditional rolling bearing fatigue life calculation method that only consider static load, and reference for relevant engineering and technical personnel.
Deep Generate Residual Similar Feature Networks for Image Super-Resolution
Authors: Li Xian , Zhang Jiahuan , Wang Minjie , Xu Gang 
Published: September 2019

Abstract
In this paper, Driven by advanced convolutional neural networks, we present a deep generate residual similar feature networks to improve super-resolution performance. Many researches have found that many CNN network training of SR requires skill and computing equipment. The input low-resolution features and CNN intermediate features contain rich similar feature maps. The different upscaling factor is used for needing the different model, which increase computational complexity. For the problem raised above, we proposed the generate residual feature (GRF) module to generate more high-frequency information in the residual feature. Each generated residual structure contains the short skip connection and long skip connection. Furthermore, we extract the similar feature in the residual feature by considering the interrelation among residual feature. Qualitative and quantitative assessments on benchmark datasets shows that we use different methods to achieve the same effect as best results of SR, while we make the network light weighted. Meanwhile, Our experiments shows that the pedestrian detection in the monitoring scene has achieved good results.
Research on Parameter Identification Algorithm of Updraft Based on Energy Recursion of Unmanned Aerial Vehicle
Authors: Zhang Ge , Tian Yingxin , Xi Boqi , Yi Guoxing , Xu Zeyuan 
Published: September 2019

Abstract
In this paper, the problem of UAV utilizing updraft energy near flight path is modeled, simulated and analyzed. Firstly, a simplified updraft model is established according to the characteristics of updraft. Then, according to the energy trend of UAV in the updraft, the recursive algorithm of UAV energy and the information acquisition model of UAV in the updraft are designed. Based on Nelder-Mead minimum search algorithm, an updraft parameter identification scheme is designed and simulated. It can be concluded that according to the above design scheme, UAV can basically identify the parameters of the updraft region, and achieve a certain identification accuracy, which provides a reference for future research.
Research on Power Control Strategy for 35kV Medium Voltage Transformerless PCS
Authors: Yang Bo , Li Guanjun , Li Dan , Yu Haojie 
Published: September 2019

Abstract
The continuous growing generating capacity of new energy power has brought more and more serious impact on the stability of electrical power system. As an important method to compensate the fluctuating power of new energy plant, highly efficient large-scale power storage system has played a more and more important role in helping stabilizing electrical power system. In this paper, a topological structure of 35kV transformerless power storage system is designed, and an active and reactive decoupling control system against this system is proposed. Concerning the application scenario of primary frequency regulation, an active support frequency response control technology for power grid with changing the droop coefficients based on three response modes is put forward. Finally, the control strategy designed is simulated and verified by using PSCAD system. The simulation result shows that the system and control strategy can do very well in active support frequency response control for power grid.
Research on Shape Optimization of Marine Magnetometer Based on Adjoint Method
Authors: Hu Xiaomei , Zhang Yiqi , Chai Jianfei , Ali Mubarak 
Published: September 2019

Abstract
There are many magnetic objects underwater, such as pipelines/pipelines, optical cables, cables, etc. Ocean magnetometer can locate and identify them. It is very important for the detection of underwater magnetic objects whether the marine magnetometer can be in a relatively stable state when it is moving underwater. Accordingly, shape optimization of marine magnetometer based on adjoint method is proposed. Firstly, a fluid region model is established to simulate the underwater motion of the marine magnetometer, and pressure and velocity maps are obtained. Then, the shape of the marine magnetometer is optimized by adjoint optimization technology. The simulation results show that the shape optimization of the ocean magnetometer makes the drag reduction rates of the front of the transponder part, the towed part and the side of the probe part become 40%, 35% and 27%, and the effect of optimization is obvious.
Target Threat Assessment Based on Dynamic Bayesian Network
Authors: Wang Xin , Zuo Jialiang , Yang Rennong , Zhang Zhenxing , Yue Longfei , Liu Huiliang 
Published: September 2019

Abstract
Conventional threat assessment model based on Bayesian Network is a reasoning process in static environment, which is difficult to deal with a large number of broken air combat data in a complex dynamic battlefield environment. Motivated by this fact, a Dynamic Bayesian Network-based threat assessment model is established, and a theoretical method based on Expectation Maximization to deal with missing data is proposed. Finally, simulations are presented to verify the effectiveness of the proposed structure.
Research on Optimization of Association Rules Mining Algorithm
Authors: Li Ningning , Lu Zhaoxia 
Published: September 2019

Abstract
The association rules mining algorithm is always one of the specialists most concerned topics in the research of database knowledge. It is widely used in many fields in society. This article is based on association rule mining algorithm to put forward the relevant strategies for the majorization to make the mining algorithm get better application. With the development of the society, social information becomes richer and richer, people also become more and more interested in the unknown world. Sustainably improve the automation and intelligentization of the tools for processing information can continually improve the abilities in many ways and get more useful information from the huge database. So the appearance of the data mining makes it possible. It can fetch some hidden, unknown and useful information and knowledge from the huge practical application data. And the association rule is a main mode in research of data mining. It can fetch target data by finding the dependency between fields of a given condition from different data fields. As a result, the optimization of association rules mining algorithm is of quite importance in fetching target data.
Design and Implement of a General Zone Controller for Intelligent Building Platform System
Authors: Shi Long , Zhang Yewei , Li Jing 
Published: September 2019

Abstract
There are several problems of traditional building monitoring and management system, such as separation and isolation of sub-systems, lack of linkage control, low compatibility, difficulties in extending, etc. A design of intelligent building platform system architecture, which is compatible with existing building systems, has been put forward after the research of intelligent building technology. Combined with the requirements of modern intelligent building, a general zone controller with powerful processing capabilities, rich peripheral interface, multiple communication protocol and communication protocol has been designed and impleted. The zone controller can support the connection and linkage of various sub-systems, devices and sensors in different zone of the intelligent building. An intelligent conference management system of intelligent building system has been realized based on the zone controller.
Research on Image Processing Methods in Pavement Crack Extraction
Authors: Zhang Zhihua , Zhang Xinxiu , Yang Shuwen , Yang Jun 
Published: September 2019

Abstract
Since the acquired road images are affected by weather and lighting factors, a large amount of noise information is contained. Therefore, it is necessary to denoise the image. Based on the acquired large number of images, the best method is selected through comparison to three image compression methods; after compressed, the image gray-scale processing method based on YUV is selected through comparison; then, the image enhancement processing method based on the frequency domain transformation is selected, and the image denoising process is performed by comparing three denoising methods, the best effect is for median filter; finally, the two image stretching methods are compared, by using the custom function adjstra method, the better image stretching effect can be achieved. So, the image processing method selected by the invention can enlarge the useful information of the image more effectively, and simultaneously, the influence of image noise is greatly reduced.
Residual Cigarette Recycle System Spectrum Switching Based on Markov Model
Authors: Sun Shunkai , Li Jie , Xu Qi , Zhu Jiule , Cao Weilin , Chen Haitao , Liu Ruidong , Hu Wangdi 
Published: September 2019

Abstract
In the process of communication of residual cigarette recycle system, due to the residual cigarette recycling bin WIFI switch with the signal strength, signal quality, distance of WIFI and other reasons. In order to keep the residual cigarette recycling bin in a non-inductive switching state, WIFI needs to adopt seamless Switching technology [1]. In which cognitive radio automatic networking (CRAN) is one of the core technologies, in order to suppress cognitive users from interfering with authorized users, it is necessary to reduce WIFI switching times as much as possible. Advance a cognitive wireless dynamic WIFI switching method based on detection of the WIFI chanel idle time (PWCIT). The theory is based on a known-state sequence hidden Markov [2] model (KSS-HMM). The selection method of the alternative WIFI is given, and the optimal channel is selected for data transmission by comparing the date transmission throughput of each candidate WIFI. The simulation results show that compared with the traditional channel selection method and the random channel [3] selection, the method can significantly reduce the number of WIFI handovers and improve the throughput of cognitive [4] users.
The Vertex Degree Distance of One Vertex Union of Two Cycles
Authors: Guo Haili 
Published: September 2019

Abstract
One vertex union of two cycles is a kind of multi-cycle graphs. In this paper, the vertex degree distances of one vertex union of two cycles are calculated, and on this basis, the maximum and minimum of vertex degree distances are studied. The results show that the maximum vertex degree distances of one vertex union of two cycles is at the common intersection vertex and the minimum at the adjacent vertices of common intersection vertex.
An Improved Particle Swarm Optimization Based on Hormone Modulation Mechanism
Authors: Xiao Chuncai 
Published: September 2019

Abstract
The manufacture of polyacrylonitrile carbon fiber (PCF) contains a group of workshop technologies with very complexity. A pivotal technology is the coagulation of the PCN in production of fiber which has a great many arguments connecting with one another. In this paper, an PID neural networkregulate strategy with improved particle swarm optimization (PIDNNHPSO) on account of the endocrine hormone regulate theory of human body is presented, and used to the coagulation tank of PCF manufacture. Three crucial arguments including liquid high, temperature, concentration of water are extracted. Experimental consequences reveal that the PIDNNHPSO can promptly response to the alternating quantity of regulate arguments, perfectly cut down the coupling effect and make reposeful adjustion without fluctuation, which has a exceed property than traditional regulate programmes.
Virtual Hospital with Real-Time Image Diagnosis
Authors: Mihertie Habtamu Demeke , Gao Ying 
Published: September 2019

Abstract
Recently, deep learning models such as deep convolutional neural networks have shown great success in different imaging tasks in general and in detection of diabetic retinopathy in particular. An automated detection system is undoubtedly a great help for the screening stage of diabetic retinopathy for further treatment to reduce the burden on the public health system. In this paper, a general framework for automated detection virtual environment is developed as a virtual hospital using a convolutional neural network (CNN) approach as imaging end. Image classification is done in two stages. The best model being 87.12% accurate for the first stage on more than 53000 images which is better than previous works tested only on very less amount of test data. Using this model, a standalone application is generated with extra features for user interaction to be used by the patient in the virtual hospital when he presses a key on the keyboard.