bluuebunny/crossref_metadata_2025_split huggingface dataset:
The visualization of 3D radiation dose field in virtual environment
Authors: Chen Qiyi , Zhang Jiawan 
Published: March 2021

Abstract
Avoiding the harm caused by nuclear radiation is an important topic in the field of nuclear physics. For nuclear radiation protection, it is necessary to show the distribution of radiation dose in the power plants. In this paper, we propose the methods combining ray-casting volume rendering and geometry model fusion to visualize the 3D nuclear radiation dose field. We implement a nuclear radiation dose field virtual simulation system based on Unity3D, combined with virtual reality to help workers conduct nuclear radiation operations and training. Moreover, we plan the maintenance path of the workers based on the NEAT algorithm, thus providing necessary radiation protection measures. The experimental results and evaluation show the efficiency of our virtual radiation dose visualization and simulation system.
Research on Interactive Device Design of Anti-noise Pollution Based on Processing Programming Language
Authors: Jiaqi Li 
Published: March 2021

Abstract
The art design of interactive device is based on Arduino, an open-source technology and Processing, a programming language, whose application enriches the interactivity and expansibility of the device, and improves its visual effect and experience. With the rapid development of social economy, urban noise pollution has become increasingly severe, which has been yet to arouse enough public attention. Therefore, the research enables people to more intuitively experience the impact of urban noise through the interactive device, and realizes the interaction between the device and people through the communication between Arduino and Processing, so that people can deeply understand the severity and danger of urban noise pollution, thus calling for public’s higher awareness of noise prevention.
Design of Portable Universal Tester for Aircraft Pylon and Launcher Based on IPC
Authors: Dejun Zhang , Wei Zhang , Le Qi 
Published: March 2021

Abstract
To solve the problem of displacement survey fleet aircraft pylon and launcher, universal testing equipment is designed. The system is based on industrial computer architecture, the system through the software to control signal analog circuit with accuracy, emission, such as excitation signal, transmission device by signal feedback to complete various suspension test of the internal circuit. It has been proved by use that the test based on IPC is versatile, the operation interface is clear and easy to be used. The use of the test improved the test efficiency and the maintenance reliability, meet the requirement testing of the pylon/launcher.
A Computer Vision System for Missing Tablets Detection
Authors: Zhao Xuhan 
Published: March 2021

Abstract
In this paper, we established a computer vision system for missing tablets detection in pharmaceutical industry during the manufacturing process. The images of blister pack with tablets in RGB color format were acquired by using a CCD industrial color camera on the industrial assembly line in real time. In order to reduce the effect of environmental brightness, the obtained RGB format images were transformed into HSI color format. According to the threshold of the hue and saturation, the image segmentation was carried out. Then, the tablets could be identified and determined. The Canny edge detection and Hough circle detection was used to mark the locations of tablets. Based on the locations and number of circles, the location of the missing tablets was obtained. This computer vision system could boost efficiency, reduce the cost of labor, and guarantee the safety of drug consumers.
Modelling of Pilotage Service Portfolio for e-navigation and its application in approaching port
Authors: Zhang Jie , Zhang Yingjun 
Published: March 2021

Abstract
Based on the objectives and requirements of the pilotage service portfolio, using the design standards of HGDM organization, the nodes, activities and certification modules of ships entering and leaving the port are analysed, and the S-100 standard model of pilotage data is established. According to the characteristics and actual needs of pilotage, the technical design mode of C/S (client/server) mode of pilotage service portfolio is determined; the application example of pilotage service portfolio is verified by the service mode of tide riding opportunity in Ningbo waters for large container pilotage.
Analysis of Maritime Traffic Safety Based on Intrinsic Safety Theory
Authors: Lili Jiang , Minglu Ma 
Published: March 2021

Abstract
Strengthening intrinsic safety of maritime traffic is essential for further modernization of the maritime traffic safety governance system and the governance capabilities. This paper studies the connotations of the intrinsic safety of maritime traffic, then analyzes the composition of the intrinsic safety system of maritime traffic and explores how to achieve intrinsic safety of the system factors such as crew, ships, navigation environment and safety management by referring to a typical case, and finally proposes maritime regulatory measures based on the intrinsic safety of maritime traffic and the long-term maritime management model for ensuring the intrinsic safety of maritime traffic.
Research on the Importance of Data Enhancement Technology in Power Document Understanding
Authors: Gao Ming , Wang Jiayan , Zhang Wenfei , Wang Dehui , Peng Zheng , Lu Hongzhi 
Published: March 2021

Abstract
With the rise and development of the Internet and the artificial intelligence boom, natural language processing has been greatly developed in various fields and industries, including power industry. Intelligent document understanding, as a sub field of natural language understanding, uses artificial intelligence technology to enable machines to have natural language understanding ability. It has always been the focus of researchers and industry, and it is also the core problem of intelligent semantic interaction. As a common method to expand the sample set, data enhancement technology is an important text and image processing technology. Its core purpose is to use limited data and produce value equivalent to that from much more data. This technology has been widely used in various fields of deep learning. This paper reviews the development of data enhancement technology, lists and discusses five kinds of text enhancement technology paths and their corresponding representative technologies, including back translation, random word replacement, non core word replacement, text enhancement based on context information, and text enhancement technology based on generative language model. It also analyzes the effectiveness of text enhancement technology in the aspects of regularization, transfer learning, improving model robustness, manifold and so on, to achieve the goal of improving the accuracy and effectiveness of document understanding in power industry.
Mobile Phone Remote Controlled Wastewater Treatment Equipment Used in Tribe
Authors: Chang Hsiao-Ming , Lin Chun-You , Chiu Mao-Chun 
Published: March 2021

Abstract
This study developed the data acquisition module which involves electronics control hardware and mobile phone APP software, the module could control and monitor wastewater treatment equipment by the APP software running on mobile phone for wastewater treatment in the tribe of eastern Taiwan. The APP user of the tribe can keep abreast of the situation of wastewater treatment and discharge to maintain the water resource with a good water quality.
Development of 2D MXene for Energy Storage
Authors: Fang Mengqi 
Published: March 2021

Abstract
The recently discovered large-scale two-dimensional (2D) early transition metal carbides and carbonitrides have captured people’s attention as hot topics, called MXenes. MXene is a two-dimensional layered solid, linked by strong metals, ions and covalent bonds, e.g. Ti2AlC, Ti3AlC2 and Ta4AlC3, usually by selectively etching the A element from the MAX phase to produce MXene. MXene has a an array of applications in all aspects cause its outstanding electrical conductivity, excellent specific surface area, good hydrophilicity and stability. In this article, the experimental and theoretical developments are reviewed in their synthesis, physicochemical properties and potential applications. MXene is expected to be applied in electrochemical energy storage and also provides current applications in batteries and super capacitor.
Actor-Critic Tracking with Precise Scale Estimation and Advantage Function
Authors: Wang Chuyao , Ling Yuchen 
Published: March 2021

Abstract
In this work, a deep reinforcement learning (DRL) method is proposed to address the problem of real-time object tracking. The adopted framework in this paper is based on the ‘Actor-Critic’ tracker (ACT), since ACT only considers the scale change instead of regression object boundary, which cannot adapt the object size variation. To this end, the ACT method is improved by using a more reasonable action space, which contains a left-top and right-bottom corner coordinates. Precise shape estimation is given by regressing the variation of width and height, respectively. Furthermore, to speed up the whole training and tracking process, the Advantage Function (AF) is adopted, and its performance is compared with ACT, ACT with improved action space (IAS), and ACT with IAS and AF. This method is tested on the OTB100 dataset to validate its effectiveness.
Research on the Application of Financial Robot under the Background of Next Generation Information Technology -- Taking Sinochem International as an example
Authors: Li Xun 
Published: March 2021

Abstract
With the development of the times and the progress of science and technology, the computerized accounting system can no longer meet the problem between the increasing human cost of most companies and the backward high-end talent pool. The transformation of the financial work system is imminent. The emergence of financial robots is undoubtedly an opportunity for the transformation of corporate financial work systems. It has attracted the attention of enterprises due to its unique process automation advantages. To mitigate above issue, this paper analyzes the development, application, and impact of financial robots in enterprises based on a brief introduction to the meaning and development of financial robots, and proposes relevant suggestions for introducing financial robots.
Generation of Handwritten Numbers Using Generative Adversarial Networks
Authors: Qin Zhaoxiang , Shan Yuntao 
Published: March 2021

Abstract
As a promising generative modeling method, Generative Adversarial Networks are a deep-learning-based generative model, in which two networks, namely the generative network and the discriminant network, contest with each other in a game during which the generator “learns” and get trained to be able to fool the discriminator of believing a specific image is (superficially) authentic. It is widely used to generate pictures based on existing data. Based on the MNIST data set, this paper studies and analyzes the application of Generative Adversarial Networks in the generation of handwritten digital images. In addition, by studying the Deep Convolutional Generative Adversarial Network and the Conditional Generation Adversarial Network model, we compared the connections and differences between these three models in training the MNIST dataset to generate handwritten digits algorithms, and gave an optimization method for generating handwritten digits.
Long span ranging system based on aircraft vision
Authors: Sun Boyang , Sun Jianmei 
Published: March 2021

Abstract
At present, the transmission line in the field has a large potential safety hazard, and professionals often encounter difficulties in patrol inspection and distance measurement. In view of the current situation of safe distance between equipment in power grid construction, this paper proposes a long-span distance measurement method based on aircraft vision, which can automatically achieve high-precision distance measurement between equipment, and ensure personal safety, avoid equipment failure and other problems. Then the long-span ranging technology based on aircraft vision is analyzed, and the research and application of the given technology are discussed.
Bone Age Estimation with X-ray Images Based on EfficientNet Pre-training Model
Authors: Hao Guoyao , Li Yifei 
Published: March 2021

Abstract
Human bones have different characteristics in different development stages, so the estimation of bone age can reflect the growth and development level of individuals relatively accurately. Bone age estimation aims to predict the biological age of children, which plays an important role on the diagnosis of some pediatric endocrine diseases. Tradition methods are carried out by doctors, and it is not effective in accuracy and speed. To this end, we proposed a deep-learning based method for bone age estimation. Based on the training set of more than 10000 X-ray images of hand bones from Radiological Society of North America (RSNA), this paper studies the processing, segmentation, feature extraction of X-ray hand bone images by using computer image processing and artificial intelligence learning methods, and uses convolution neural network to process samples and analyze them automatically. The main research work and achievements are as follows: (1) Pre-processing of X-ray hand bone image, unifying the size and cutting, reducing the image area without hand bone; (2) The gray-scale image is transformed into a three-channel image, and pre-processing by EfficientNet of ImageNet. Then convolution neural network is used to learn the features of X-ray hand bone image and evaluate it automatically. Finally, the network is evaluated by the minimum mean square error, so that the minimum mean square error is as close as possible to the minimum value. Through the neural network, the bone age from X-ray hand bone image can be quickly judged, and then it can be applied to clinical research.
Foreground detection of human targets with abnormal behavior of mechanical operators
Authors: Luo Hongqi , Liu Zhixin , Liu Xia 
Published: March 2021

Abstract
Complexity of mechanical production environment has brought a certain degree of interference to human target recognition, so it is necessary to preprocess the target video sequence before target detection. The advantages and disadvantages of various detection methods are compared. Research results show that the inter frame difference method has strong adaptability and the target contour is discontinuous. The optical flow method is suitable for the case of lens motion, which is complex in calculation and poor in noise resistance. ViBe algorithm has good effect in obtaining the target contour, clear edge and less holes, and the operation efficiency is not high. The background subtraction method basically retains the features needed by the moving foreground target behavior recognition and detection, and the processing speed is very fast, which can meet the requirements of real-time processing.
Dynamic risk assessment of oil and gas leakage in heating furnace: A DBT-DBN approach
Authors: Wang Mingliang , Zhang Mingguang 
Published: March 2021

Abstract
The heating furnace has dynamic fluctuations of hazard factors in the actual circumstance and is the core equipment of hydrocracking unit, which is the main equipment of petrochemical enterprises. Owing to the limitation of conventional risk analysis approaches, a dynamic risk analysis approach based on dynamic Bow-tie model(short for DBT) and dynamic Bayesian network(short for DBN) was proposed. Taking the failure accident of oil and gas leakage of heating furnace as a case study, the timing sequence of equipment failure of heating furnace is analyzed first, the risk evolution process is obtained, and the dynamic tie model of accident is established. By mapping the dynamic tie model to the dynamic Bayesian network, the risk factors of oil and gas leakage in heating furnace are deduced in two-way reasoning. The dynamic risk trend of heating furnace was predicted eventually, the probability of oil and gas leakage caused by heating furnace is about 1.26E-07 after two year. Through diagnostic reasoning, 6 key factors and 3 accident paths were determined. The results are in accordance with the actual situation, proving that DBT-DBN model can effectively reflect the dynamic characteristics of accident scenarios and provide reference for the risk assessment of petrochemical enterprises.
Research on Service Quality Evaluation Model of Medical Institutions Based on Dempster-Shafer Evidence Theory— Taking Jiangsu Province, China as an Example
Authors: Yu Benhai , He Chuang 
Published: March 2021

Abstract
From the perspective of patients and medical insurance agencies, the paper used the fuzzy rough set method to select the indexes and established the service quality evaluation index system of medical institutions. Using Dempster-Shafer evidence theory to synthesize the subjective weights and the objective weights, the combined weights of the evaluation indexes were obtained. Combining grey relational analysis and Dempster-Shafer evidence theory to establish a regional medical service level evaluation model in an asymmetric environment. The model can accurately identify the quality of medical service in various regions. We can compare and analyze the service quality of medical institutions in various regions from different dimensions, and provide a theoretical basis for each region to formulate policies related to the rational allocation of medical resources.
Research on the Application of System Engineering Theory in Graphic Creative Design
Authors: Zhang Hongqiong 
Published: March 2021

Abstract
“System engineering” is one of the most important academic terms contemporarily. It is to make reasonable use of all resource conditions, optimize the configuration of the structure, and conduct process management to scientifically use resources. Through the practical process of research-based sketching and design transformation, the article uses plant ecological structure as the original experimental sample, and based on system thinking, explains the deconstruction, reorganization and final visual presentation of graphics from multiple dimensions of overall, three-dimensional, structural and dynamic. Explore and put forward a comprehensive hierarchical system model of “objective-deconstruction-reorganization-optimization and integration” to optimize graphic design behavior patterns and improve the effectiveness of design strategies.
Application of Knowledge Service in Integrated Supply Chain of Agricultural Products based on Knowledge Graph
Authors: Yang Bo , Mengying Yang 
Published: March 2021

Abstract
Based on the review of the existing research, this paper summarizes the application of knowledge services in supply chain. Using CiteSpace software for visualization analysis, it can be found that there is a lack of cooperation between research authors and institutions of agricultural products integrated supply chain, but the research content is relatively relevant. “Internet+” and “fresh agricultural products supply chain” will be the frontier hot spots of agricultural products integrated supply chain in the future. Therefore, enterprises need to combine some big data technology to further integrate and optimize the agricultural product supply chain.
Network Risk Propagation Model Based On Boundary-Center-Point-Search Algorithm
Authors: Ping Guo 
Published: March 2021

Abstract
Because the power CPS network with community structure has the unique structural characteristics of internal nodes and boundary nodes, the boundary nodes are more likely to cause cascading failures when attacked, which plays an extremely important role in network risk fault propagation. In this paper, the Boundary node is regarded as an important immune object, and these key node targets are quickly identified to take immune measures. A Boundary center-point-search (BCS) algorithm is proposed. According to the disadvantage of the original risk propagation model which ignores the fact that normal nodes can get immunity through artificial means, a risk propagation model is proposed. Compared with other local immune algorithms, the proposed BCS immune algorithm not only can accurately identify the boundary center points between communities, but also has a faster computational speed, which can provide reference for risk propagation control and prevention in large-scale complex networks with incomplete structural information.
Emotional Response Generation in Multi-Turn Dialogue
Authors: Jinyao Yang , Wu Chunhua 
Published: March 2021

Abstract
The research on emotional response generation is one of the directions in dialog system research. However, most of the researches on emotional dialog generation focus on the expression of expected emotion in single round dialogue. In this paper, we propose a method to model the emotion in multi-turn dialogue. Our model bases on a proposed model: Emotional Chatting Machine, and solves the problem of ECM model that which emotion should be used to the response. We compared the effects of various models and finally chose LSTM to model emotions in multi-turn dialogue. Experiments show that the performance of the dialogue system can be effectively improved by modelling emotion in multi-turn dialogue.
An Overview of Entity Relation Extraction
Authors: Xie Yixuan 
Published: March 2021

Abstract
Plenty of digital text is generated and shared in nowadays life, which constitutes many unstructured text resources. An automatically information extraction method is highly in demand as a large amount of useful information can be stored and output through it. In this paper, several basic concepts about entity relation extraction are introduced, including word embedding, positional embedding and convolutional neutral networks. Several types of relation extraction are also discussed: supervised relation extraction, relation extraction using distant supervision and relation extraction using few-shot approach. In addition, the existing challenges and problems are discussed, like overlapped triples, wrong labeled data and the difficulties that few-shot learning approaches are now facing.
Implementation of Personalized Recommendation Algorithm based on Mahout
Authors: Yang Deguo , Shi Qing 
Published: March 2021

Abstract
In the Information Age, the success of companies and individuals increasingly depends on how quickly and efficiently they can turn massive amounts of data into actionable information. This paper first introduces the definition of recommendation algorithm and the process of collaborative filtering recommendation algorithm, then introduces the classification of algorithms in the Apache Mahout Framework, finally, the principles of user-based personalized recommendation algorithm and project-based personalized recommendation algorithm in Mahout framework are described and implemented respectively.
Lung Nodule Classification in CT Images Using 3D DenseNet
Authors: Zhang Ge , Lin Lan , Wang Jingxuan 
Published: March 2021

Abstract
Lung cancer is the main malignant tumour affecting the health of residents in China. Automatically discriminating benign and malignant pulmonary nodules can facilitate the early detection of lung cancer, which reduces lung cancer mortality. The rising quantity of public available lung CT datasets made it possible to use deep learning approaches for lung nodules malignancy classification. Unlike most of the previous models that focused on 2D convolutional neural nets (CNN), here we explore the use of the DenseNet architecture with 3D filters and pooling kernels. The performance of the proposed nodule classification was evaluated on publicly available LUNA16 dataset, a subset of lung image database consortium and image database resource initiative dataset (LIDC/IDRI). It achieved a 92.4% classification accuracy. The proposed method provides an independent module with encouraging prediction accuracy that can be easily incorporated with a lung cancer computer-aided diagnosis system.