bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel ABC Score Predicts Mortality in Non-ST-Segment Elevation Acute Coronary Syndrome Patients Who underwent Percutaneous Coronary Intervention
Authors: Zheng Ying-Ying , Wu Ting-Ting , Gao Ying , Guo Qian-Qian , Ma Yan-Yan , Zhang Jian-Chao , Xun Yi-Li , Wang Ding-Yu , Pan Ying , Cheng Meng-Die , Song Feng-Hua , Liu Zhi-Yu , Wang Kai , Jiang Li-Zhu , Fan Lei , Yue Xiao-Ting , Bai Yan , Zhang Zeng-Lei , Dai Xin-Ya , Zheng Ru-Jie , Chen You , Ma Xiang , Ma Yi-Tong , Zhang Jin-Ying , Xie Xiang
Published: October 2020

Objective In the present study, we aimed to establish a novel score to predict long-term mortality of non-ST-segment elevation acute coronary syndrome (NSTE-ACS) patients who underwent percutaneous coronary intervention (PCI).

Methods A total of 2,174 NSTE-ACS patients from the CORFCHD-ZZ study were enrolled as the derivation cohort. The validation cohort including 1,808 NSTE-ACS patients were from the CORFCHD-PCI study. Receiver operating characteristic analysis and area under the curve (AUC) evaluation were used to select the candidate variables. The model performance was validated internally and externally. The primary outcome was cardiac mortality (CM). We also explored the model performance for all-cause mortality (ACM).

Results Initially, 28 risk factors were selected and ranked according to their AUC values. Finally, we selected age, N-terminal pro-B-type natriuretic peptide, and creatinine to develop a novel prediction model named “ABC” model. The ABC model had a high discriminatory ability for both CM (C-index: 0.774, p < 0.001) and ACM (C-index: 0.758, p < 0.001) in the derivation cohort. In the validation cohort, the C-index of CM was 0.802 (p < 0.001) and that of ACM was 0.797 (p < 0.001), which suggested good discrimination. In addition, this model had adequate calibration in both the derivation and validation cohorts. Furthermore, the ABC score outperformed the GRACE score to predict mortality in NSTE-ACS patients who underwent PCI.

Conclusion In the present study, we developed and validated a novel model to predict mortality in patients with NSTE-ACS who underwent PCI. This model can be used as a credible tool for risk assessment and management of NSTE-ACS after PCI.
Ophthalmology Clinic for the Homeless in San Francisco: Experiences and Findings
Authors: Nnamani Silva Ogonna N. , Hennein Lauren , Ramanathan Saras
Published: October 2020

Background Homelessness is a growing area of concern for communities around the world and homeless individuals are often not connected to healthcare and, consequently, this population is often left out of crucial studies that influence the care provided at public hospitals. The main aim of this study is to examine the prevalence of eye disease, demographics, and common comorbidities of patients who utilized the student-run, institution-sponsored ophthalmology clinic for homeless individuals across a 2-year period. A secondary aim was to elucidate the role that student-run clinics can play in connecting the homeless population to healthcare and community services.

Methods A retrospective cohort study examined individuals who utilized a student-run, free ophthalmology clinic at two homeless shelters in an urban community for 2 years, from 2017 to 2019.

Results A volunteer sample consisting of 123 residents at two homeless shelters who signed up to receive an eye examination at the free eye clinic. The average age of participants was 51 years, with 74% identifying as male, and the largest number of patients identified as African-American/Black (33%) and White (27%). The most prevalent eye disease was refractive error at a prevalence rate of 70% with presbyopia and myopia accounting for 77% and 42% of the refractive error diagnoses, respectively. A visually significant cataract was the second most prevalent pathology, which was diagnosed in 13% of patients. Diabetic retinopathy had a prevalence rate of 11%. Glaucoma had a prevalence rate of 20%. When stratifying the prevalence of glaucoma by race/ethnicity, Black/African-Americans had a prevalence rate of 30% and Asians had a prevalence rate of 29%.

Conclusion This study demonstrated a high prevalence of eye disease among the homeless population highlighting the major role that social determinants of health play in patient outcomes. Student-run ophthalmology clinics provide rich educational experiences, and most importantly, connect homeless populations to essential ophthalmologic care.
Novel Dual Therapy: A Paradigm Shift in Anticoagulation in Patients of Atrial Fibrillation Undergoing Percutaneous Coronary Intervention
Authors: Pradhan Akshyaya , Bhandari Monika , Vishwakarma Pravesh , Sethi Rishi
Published: October 2020

Patients with atrial fibrillation (AF) on long-term oral anticoagulation (OAC) either have underlying coronary artery disease or suffer from acute coronary syndromes necessitating a percutaneous coronary intervention (PCI). In such a scenario, an amalgamation of antiplatelet and antithrombotic therapy (conventionally called as “triple therapy”) is obligatory for preventing coronary ischemia and stroke. But such ischemic benefits are accrued at the cost of increased bleeding. We also now know that bleeding events following PCI are related to increased mortality. Balancing the bleeding and ischemic risks is often a clinical dilemma. With the advent of novel oral anticoagulants (NOAC's) with preserved efficacy and attenuated bleeding rates, anticoagulation in AF is undergoing paradigm shift. The spotlight is now shifting from conventional triple therapy (vitamin-K antagonist + dual antiplatelet therapy [VKA + DAPT]) to novel dual therapy (NOAC + single antiplatelet therapy [SAPT]) in situation of anticoagulated AF patients undergoing PCI. Such a strategy aims to ameliorate the higher bleeding risk with conventional VKA's while retaining the ischemic benefits. In this review, we briefly discuss the need for combination therapy, trials of novel dual therapy, strategies for mitigating bleeding, the current guidelines, and the future perspectives in AF undergoing PCI with stent(s).
Melatonin Administration from 2000 to 2020 to Human Newborns with Hypoxic-Ischemic Encephalopathy
Authors: D'angelo Gabriella , Cannavò Laura , Reiter Russel J. , Gitto Eloisa
Published: October 2020

Hypoxic-ischemic encephalopathy (HIE) is the main cause of long-term neurodevelopmental morbidity in term born infants worldwide. Melatonin is a hormone with antioxidant and anti-inflammatory effects that make it a promising molecule for the treatment of perinatal asphyxia. Probably, the synergistic use of hypothermia associated with melatonin treatment may improve the neurological outcome in infants with HIE. In the past 20 years, the efficacy of melatonin in reducing oxidative stress has been demonstrated in animals; however, clinical trials with sufficient sample size of newborns are lacking to date. Since in 2000 we were among the first to study the neuroprotective properties of melatonin on infants, in this review, we want to summarize the advantages and limitations of the investigations conducted to date.

Key Points
CHD7 regulates cardiovascular development through ATP-dependent and -independent activities
Authors: Yan Shun , Thienthanasit Rassarin , Chen Dongquan , Engelen Erik , Brühl Joanna , Crossman David K. , Kesterson Robert , Wang Qin , Bouazoune Karim , Jiao Kai
Published: October 2020

Significance

Mutation of
CHD7
causes multiple developmental disorders, including CHARGE syndrome, in which conotruncal anomalies are the most prevalent form of heart defects. Our study provides mouse genetic evidence demonstrating that CHD7 cell-autonomously regulates cardiac neural crest cell (cNCC) development to promote normal conotruncal morphogenesis, clarifying a long-standing controversy. Furthermore, through comprehensive in vitro and in vivo analyses, we reveal that CHD7 acts in cNCCs through both nucleosome remodeling and recruiting histone modifying enzymes to target loci. Our study provides fundamental insights regarding the etiology of CHD7-related congenital cardiovascular disorders. Importantly, our data suggest that patients carrying truncation mutations versus missense mutations will likely display different molecular alterations; these patients might therefore require personalized therapeutic interventions.
Reduction of interfacial thermal resistance of overlapped graphene by bonding carbon chains*
Authors: Huang Yuwen , Feng Wentao , Yu Xiaoxiang , Deng Chengcheng , Yang Nuo
Published: October 2020

Exploring the mechanism of interfacial thermal transport and reducing the interfacial thermal resistance are of great importance for thermal management and modulation. Herein, the interfacial thermal resistance between overlapped graphene nanoribbons is largely reduced by adding bonded carbon chains as shown by molecular dynamics simulations. And the analytical model (phonon weak couplings model, PWCM) is utilized to analyze and explain the two-dimensional thermal transport mechanism at the cross-interface. An order of magnitude reduction of the interfacial thermal resistance is found as the graphene nanoribbons are bonded by just one carbon chain. Interestingly, the decreasing rate of the interfacial thermal resistance slows down gradually with the increasing number of carbon chains, which can be explained by the proposed theoretical relationship based on analytical model. Moreover, by the comparison of PWCM and the traditional simplified model, the accuracy of PWCM is demonstrated in the overlapped graphene nanoribbons. This work provides a new way to improve the interfacial thermal transport and reveal the essential mechanism for low-dimensional materials applied in thermal management.
Quantum quenches in the Dicke model: Thermalization and failure of the generalized Gibbs ensemble*
Authors: Su Xiao-Qiang , Zhao You-Quan
Published: October 2020

Quantum quenches in the Dicke model were studied both in the thermodynamic limit and the finite systems. For the integrable situation in the thermodynamic limit, the generalized Gibbs ensemble can effectively describe the energy-level occupations for the quench within the normal phase, but it fails for the quench to the superradiant phase. For the finite systems which are considered non-integrable, the post quench systems were studied by comparing with the thermal ensembles. The canonical ensembles are directly available for the quench within the normal phase. With the increasing of the target coupling strength over the equilibrium phase transition critical point, sudden changes take place for the effective temperature and the distance to the thermal ensembles. The thermalization was also studied by comparing with the results of the microcanonical ensembles.
Frequency Hopping Signal Sorting Algorithm Based on Global Feature of Ambiguity Function
Authors: Wu Jixiang , Wang Shaobo , Yang Yinsong , Li Hongguang , Yu Xinyong
Published: October 2020

Aiming at the problem that the traditional feature parameters are difficult to meet the fastness and effectiveness of frequency-hopping signal sorting recognition, a feature extraction method based on ambiguity function theory is proposed. The algorithm applies the ambiguity function theory to the frequency hopping signal sorting and identification. Firstly, the ambiguity function of the single-hop signal is calculated, then the AFMR slice of the signal is extracted by the PSO algorithm, and the global features of the AFMR slice are extracted to form the feature vector. The results of FCM clustering experiments show that the extracted feature parameters have higher sorting accuracy and can adapt to the larger dynamic signal-to-noise ratio.
Review of Web Authentication
Authors: Wang Zikai , Sun Wei
Published: October 2020

In the server situation of web security, a large number of Web sites and users are under the threat of network attacks. Fortunately, a complete authentication system can effectively protect them from some attacks. With the help of the authentication system, web sites can identify their users correctly and then decide whether to provide the corresponding services. The web authentication system is mainly constructed by the credential and the authentication strategy, the credential symbolizes identity and the authentication strategy judges the authenticity of identity. This paper will analyse and discuss some related literature and research findings of credentials and authentication strategies in the world, grasp overall direction, and look forward to the development of web authentication.
Research on Attack Identification Method and Device Method Based on Random Forest Algorithm
Authors: Yu Yang , Xiao Shengyuan , Chen Rui , Du Yu , Ding Kai , Zhu Xiaofan , Zhou Guoqi , He Shengguo , He Hongyan , Huang Zhiwei , Gao Xiaoquan , Yao Hao , Xi Wei , Xian Qing , Fu Yang , Ding Qing
Published: October 2020

Because the energy storage system has the function of stabilizing the voltage and frequency of the grid, when the energy storage system is connected to the grid, it is necessary to obtain its operating status information in a timely and accurate manner. The operating status data of the energy storage system is uploaded to the higher-level dispatch center via the energy storage coordination control device. However, with the intelligence and informationization of the power grid, various cyber attack methods have emerged endlessly. There are various security risks in the way of information interaction based on the network. Attackers can analyze the communication protocols within them, or implement Eavesdropping attacks, Dos attacks, and tampering with sensitive data, etc., causing the energy storage coordination control device to obtain the wrong data sent by the dispatch center. The wrong data causes the energy storage system to output the wrong power, which may have the opposite effect when adjusting the voltage and frequency of the power grid, causing an imbalance in the voltage and frequency of the power grid, resulting in great economic losses, and even causing casualties due to equipment failure. Attackers can also gradually invade the upper-level dispatch center by using the energy storage coordination control device as a springboard. In view of the above problems, this paper uses a random forest-based network attack detection module is used to make real-time judgments on all data flows of the energy storage coordination control device. That is, when the energy storage coordination control device is subjected to a network attack, all data flows of the energy storage coordination control device pass a trained random forest attack detection model, thereby implementing attack detection on real-time data flows.
Transmission Efficiency of Licklider Transmission Protocol (LTP) and Bundle Protocol (BP) in DTN Architecture
Authors: Wang Ding , Yu Hongzhi , Ma Jun
Published: October 2020

From Near-Earth orbit (NEO) to Earth-Sun Lagrangian points will continue to be a majority of Near Earth missions ranging from Near-Earth orbit (NEO) to Earth-Sun Lagrangian points will continue to be a majority of future space missions. A few works have been done with delay/disruption tolerant networking (DTN) technology for Near-Earth orbit (NEO)-space center and deep space communications and provided feasibility for its adoption in NEO-space and deep space missions. [1] However, no much work has been done to fully evaluate the transmission efficiency of DTN in such an environment, especially in the presence of long link disruption, data corruption and lost rate, and link asymmetry. In this paper, we present an experimental performance evaluation of DTN architecture and protocol stack, with Licklider transmission protocol (LTP) serving as a convergence layer adapter (CLA) underneath bundle protocol (BP), in typical NEO-satellite system and deep space communication infrastructures accompanied by a very long link delay, various packet corruption and lost rates, and channel rate symmetry and asymmetry. In addition, we present an experimental research of the transmission efficiency of DTN custody transfer with long link delay. The experiment was conducted by performing realistic file transfers over a virtual server-based test-bed.
Application of BP Neural Network Algorithm in Classification of Red Wine Grades
Authors: Gao Sheng , Fang Jie , Miao Ning
Published: October 2020

The rapid development of society promotes the improvement of people’s living standard. Red wine has entered thousands of households, at the same time, high-quality red wine is more and more loved by people. As an important standard to distinguish the quality of red wine, the grade of red wine plays an important reference role in the evaluation of red wine. Therefore, accurate and efficient classification of red wine grade is particularly important. In this study, the BP neural network model is established, and the MIV algorithm is introduced to screen the chemical properties of red wine, further optimizing the BP neural network. The optimized BP neural network is applied to the classification of red wine grades to complete the efficient classification of red wine grades. The experimental results show that the correct rate of red wine classification can be effectively improved by using the red wine data screened through MIV algorithm. This meets the requirements of accuracy in classification of red wine grades.
Transformation from AADL to Colored Petri Nets for Dynamic Reconfiguration Process
Authors: Liu Lei , Zhao Wei , Jiang Zeyong , Wang Shihai
Published: October 2020

Although integrated modular avionics (IMA) provides many advantages such as the reduced weight and higher efficiency for system operations, safety problems in IMA dynamic reconfiguration process come up due to its complexity to analyze. Architecture Analysis and Design Language (AADL) has advantage to model for embedded systems. However, it is quite limited to employ AADL for analyzing system safety. This paper, seeks to translate the AADL models of IMA dynamic reconfiguration process into colored petri net (CPN), which have advantages to simulate and evaluate embedded systems. Then, the colored petri net (CPN) is proposed for the analysis for AADL models, because CPN can describe the properties and behaviors of dynamic reconfiguration process well. Moreover, it is essential to make a transformation from the AADL models to the CPN models. This work will benefit the subsequent analysis of IMA dynamic reconfiguration process. Finally, a case study is provided for effectiveness the transformation rules.
Research on the Classification of Aviation Safety Reports Based on Text and Knowledge Graph
Authors: Zhang Wenfang , Shi Hongwei , Yang Yang , Luo Yonghui
Published: October 2020

The existing automatic classification methods of aviation safety reports are mainly traditional machine learning classification algorithms, which have the disadvantages of relying on manual feature extraction and selection, unable to consider domain information and dealing with complex dependencies, which limits the improvement of classification accuracy. Therefore, this paper proposes an automatic classification algorithm for aviation safety reports that combines text and knowledge graph. The algorithm uses the knowledge triples with rich relationships as the background knowledge in the aviation safety field into the word2vec word vector training process so that the words can not only learn context information in training but also learn the complex semantics between words in the aviation field information, so that the trained word vectors can express richer semantic features, thereby improving the classification effect. Through a series of comparative experiments, the results show that the algorithm has the highest F1-score value and has a classification accuracy rate of up to 91.4%.
Research on Attack Detection Method of Photovoltaic Grid-connected Interface Device Based on Gradient Boosting Decision Tree
Authors: Yao Hao , He Shengguo , Chen Rui , Chen Yuansheng , Zheng Yucheng , Ding Kai , Zhu Xiaofan , Zhou Guoqi , Huang Zhiwei , Xiao Shengyuan , He Hongyan , Pan Junjun , Xi Wei , Yu Yang , Ning Baifeng
Published: October 2020

Photovoltaic grid-connected interface devices are an important class of smart devices in microgrids. The authenticity and reliability of the data they acquire, as well as the safety and stability of operation, are related to the safe and reliable operation of the entire microgrid system. However, in the context of microgrid intelligence and informatization, information / network attacks will become the norm, making network-dependent information interaction methods subject to various security risks. The photovoltaic grid-connected interface device involves an open operating environment and is extremely vulnerable to network attacks. The attack information will occupy the space or resources of the photovoltaic grid-connected interface device, making the photovoltaic grid-connected interface device unable to respond to other important requests or instructions in a timely manner, and in severe cases, will cause the device to be paralyzed and affect the normal system operation. Aiming at the above problems, this paper presents an attack detection method based on the gradient-upgraded decision tree model, and gives a detailed design process of attack detection model of the photovoltaic grid-connected interface device. That is, the important data flow in the photovoltaic grid-connected interface device is used as the input of the gradient-upgraded decision tree model, and then the gradient-upgraded decision tree model detect or classify flows, finally, intercept the data flow with attack behavior and give a warning prompt, and forward data without attack behaviors normally.
An Automatic Optical Inspection Algorithm of Capacitor Based on Multi-angle Classification and Recognition
Authors: Fang Jian , Shang Lina , Xiong Kai , Gao Guangchun , Zhang Cui
Published: October 2020

There are many plug-in components in PCB circuit production. The AOI of these components has always been a difficult problem because of the large deviation of manual installation position of these components. In this paper, an AOI algorithm based on multi-angle classification and recognition is proposed for the plug-in polar capacitors. The algorithm combines traditional image comparison method with feature recognition method, and uses AdaBoost classifier based on Haar-like feature to recognize and classify multi-angle polar capacitors. Experimental results show that the algorithm proposed in this paper can effectively detect three kinds of defects: capacitor missing, capacitor polarity error and capacitor size mismatch.
Automatic Lung Segmentation in CT Images Using Dilated Convolution Based Weighted Fully Convolutional Network
Authors: Wu Yuchao , Lin Lan
Published: October 2020

Lung cancer is one of the primary lung malignant tumour with the fastest increasing morbidity and mortality and the greatest threat to people’s health and life. Early detection of lung cancer can significantly increase patients’ chance of survival. Lung parenchymal segmentation is an essential pre-processing step for analysing thoracic computed tomography(CT) images. Conventional methods for lung segmentation rely on user generated features, and do not segment lung parenchymal with juxta-pleural nodules accurately. Deep learning has outperformed other methods in image classification and target recognition tasks. In this study, a new dilated convolutional based weighted fully convolutional network (FCN) has been proposed for the segmentation of lung parenchyma to minimize the juxta-pleural nodule issue. The effectiveness of this method was verified by experiments on 173,694 diagnosis CT images of lungs and their corresponding segmentation maps. The Dice similarity coefficient and pixel accuracy achieved are 0.9702 and 0.9833 respectively. The experiment results show that the proposed method can provide more accurate and robust results than traditional FCN.
The Summary of Deep Learning in the Field of Weather Forecast Research
Authors: Bai Yanrong , Wang Juan , Wang Xiaoting , Li Hui
Published: October 2020

This article investigates application of deep learning in the field of meteorology at home and abroad. summarized of deep learning and several deep learning network models widely used in meteorological field, It focuses on research and summary application of deep learning in Meteorological Department of China, and also summarized the future development trend of deep learning in weather forecast application.
A Novel Image Feature Extraction Algorithm Based on the Fusion AutoEncoder and CNN
Authors: Xu Ke , Long Wenhan , Sun Yuan , Lin Yichao
Published: October 2020

Deep learning method has very excellent ability of image feature extraction. In order to get rid of disadvantages of traditional methods require a priori knowledge, this paper proposed an image feature extraction algorithm based on the fusion AutoEncoder and convolutional neural networks (CNN). The method introduces a fast sparsity control technique to AutoEncoder and utilizes AutoEncoder to train the basic elements of image and initialize the convolution kernel of CNN. Meanwhile, the algorithm adds filtering mechanism to the CNN network to keep the sparsity of output characteristics. The results of experiments point out that this method has achieved good performance on the Minist handwritten digital library and the Yale face database. Furthermore, the advanced experimental outcomes indicate that the feature extraction model included the filtering technique is more effective than the model without filtering mechanism by using cross-validation with T Test.
Magnetic Anomaly Feature Extraction Using the Tunable Q-factor Wavelet Transform Based on Non-convex Overlapping Group Shrinkage
Authors: Hu Jie , Guo Baolong , Liao Nannan , Yi Zhong , Zhang Shaohua , He Wangpeng
Published: October 2020

Extraction of the magnetic anomaly signal is one of the difficulties in the magnetic anomaly detection as the weak features extracted are easily disturbed by strong background noise. To address this problem, a sparse feature extraction method based on the tunable Q-factor wavelet transform and overlapping group shrinkage is developed in this paper. Compared with the traditional wavelet transform, the proposed method has excellent characteristics, which can flexibly tune the Q-factor according to the oscillation characteristics of the useful features. In this way, the sparsity of extracted features can be induced more effectively. In addition, the non-convex overlapping group shrinkage can effectively extract weak features from signals with group property, enhancing the extraction accuracy of features. The practical experiment verifies the effectiveness of the proposed method in the magnetic anomaly detection.
Research on the Method of Transforming Vague to Fuzzy Value
Authors: Li Yan , Cheng Bihong
Published: October 2020

In the study of the transformation of Vague sets to Fuzzy sets, the key is to judge the tendency of the Vague degree. To determine the tendency of Vague degree, it is necessary to make decision on the information of the uncertainty. Therefore, the transformation of the Vague set to the Fuzzy set can be regarded as the decision maker’s risk decision under the uncertain conditions. Risk preference of decision-makers is a key decision parameter when decision makers make risk decision. In this paper, the weight of risk - income weighed weight is introduced into the transformation model. Risk preference of decision-makers is well reflected in the transformation results. The membership degree of the Fuzzy set is decomposed into two parts: the true membership value of the Vague set, the value of the risk and the value of the tendencies. The results show that it is necessary and meaningful to consider the risk preference of the decision-makers. The thinking mode of dealing with this kind of problem can be changed from “point” thinking to “face” thinking. The fuzziness and risk preference of decision-makers are presented to the decision-makers in the form of a two-dimensional decision map. The transformation model is to help decision makers to fully understand the facts. The role of the transformation model is to assist decision makers in making scientific decisions rather than making decisions for decision makers.
Electric Vehicle Charging Fault Diagnosis Based on BP Neural Network
Authors: Zhang Yuanxing , Li Taoyong , Zhao Shuaishuai , Yan Xiangwu , Zhang Jing , Diao Xiaohong , Li Bin
Published: October 2020

With the popularization and application of electric vehicles, the problem of charging failure is exposed frequently. In order to effectively and accurately determine the type of charging failure and find out the cause of the failure, a method for identifying the charging failure using neural network is proposed. By collecting charging data and using neural network for training, the failure results are obtained through continuous optimization, and compared with the results of preliminary judgment, the superiority and feasibility of BP neural network are verified.
Research on KNN Algorithm of Charging Device Based on Attack Recognition Method
Authors: Huang Zhiwei , Yan Xichao , Wang Qiangang , Chen Yuansheng , Wu Wenju , Zhan Jiewen , Chen Rui , Ding Kai , Zu Lianxing , Xu Aijun , Su Zongzhou , Chen Dianjun , Lin Sajia , Xu Peng , Wang Chong
Published: October 2020

With the continuous improvement of the level of economic development and the increasingly serious environmental problems, new energy is gradually being respected by people, and more and more electric vehicles have been put on the market and gradually used widely. The development of electric vehicles has led to the development of charging devices. However, traditional charging devices operate in an open environment, and communication with other devices depends on information interaction with the network. With the development of network information today, network attacks are ubiquitous, and there are various risks in network information interaction. As long as there is a security loophole in the charging device, an attacker can use various means to attack the device and even invade the inside of the power grid, which directly affects the safe and stable operation of the power grid. Aiming at the potential threats in operation, a research on the nearest neighbor algorithm of charging device K based on attack recognition method is proposed here to improve the information security and operation reliability of charging device.
Research on Land Battlefield Situation Distribution Network Based on BRN
Authors: Chen Juan , Zheng Xueqiang , Peng Laixian , Xu Zhengqin , Li Aijing
Published: October 2020

In order to meet the requirements of battlefield complex electromagnetic environment and situation distribution instantaneity, the Communication network protocol in land battlefield needs to achieve low delay, high scalability and link robustness. This paper analyzes the development and problems of the situation distribution network in the land battlefield. Based on the analysis of the autonomous cooperative communication mechanism, the basic principle of the BRN protocol based on the communication mechanism is introduced, and the research fields based on this technology in the situation distribution network are proposed, so as to further improve the performance of the situation distribution network in the land battlefield.
Traffic Flow Prediction Method Based on Deep Learning
Authors: Jiang Luofeng
Published: October 2020

Accurate traffic flow forecasts provide an important data basis for traffic management departments. This paper proposes a traffic flow prediction model based on deep learning, which combines Convolutional Neural Network (CNN), Long Short-term Memory (LSTM) and Support Vector Regression (SVR) features: use CNN neural network to mine the spatial characteristics of traffic flow, and then input the time series features captured by LSTM neural network into the SVR model for traffic prediction. The actual traffic flow data of intersections in Mianyang City are selected to verify the CNN-LSTM-SVR hybrid model, and compare it with the CNN model, LSTM model, and SVR model. The results show that the proposed prediction model has higher prediction accuracy.
Study on Pragmatic Equivalence of Computer Aided Translation
Authors: Chen Fang
Published: October 2020

The development of technology provides people more opportunities to access to translation softwares. Translation softwares bring convenience and efficiency. This paper aims to discusses the pragmatic equivalence of computer-aided translation software in translation.
Prediction of Traffic Flow on Highway Ramp in Scenic Area
Authors: Du Chuanhong , Liu Licai , Shi Shuaishuai , An Jiao
Published: October 2020

Through the phase space reconstruction technology, it is proved that there are chaotic behaviors on highway ramp. The establishment of a traffic flow prediction model can also solve the impact of unexpected accidents or delayed management. Short term traffic flow prediction is also an important prerequisite for the intelligent traffic management system. The wavelet transforms neural network can be used to realize the short-term prediction of the traffic flow on the highway ramp.
Non-Crypto Authentication for Smart Grid Based on Edge Computing
Authors: Liu Wei , Song Jianfang , Wu Haitao , Lei Wenxin , Huang Fuquan , Hou Wenjing , Liang Honghao , Wen Hong
Published: October 2020

As a new computing architecture, edge computing has gradually penetrated into the smart grid and provide real time monitoring and control. However, as a new type of node arranged in the network, its own security protection has become an incredibly challenging task. Security authentication is the first line of defence for security protection. Due to its fast and light-weight characteristics, physical layer channel information access authentication technology for non-cryptographic packets is a potential identification method for the smart grid terminal access under the edge computing system, in which the real time response is incredibly important. This paper aims to investigate the physical layer authentication method compared with the traditional upper layer authentication technology, and an authentication scheme suitable for smart grid is proposed.
Bone Age Prediction Method Based on Convolutional Neural Network
Authors: Hu Bin , Shang Lidan , Cheng Feng , Sheng Hai , Pan Jiayan , Yin Dong
Published: October 2020

How to accurately observe whether children are developing normally has always been one of the focuses of medical research. Based on the convolutional neural network, this paper proposed a bone age prediction method. Firstly, the hand bone data set was constructed. Secondly, the prior frame required by the neural network was obtained by K-means clustering. Finally, fine-tuning, training, detecting and cropping were performed to achieve bone age classification. The experimental results show that our method achieves the smallest average error and has strong practicability.
Research on Integrated Algorithm Based on Convolutional Neural Network for Rice Disease Identification
Authors: Liu Zixi , Zheng Xingqiang , Xiao Zihan , Bao Junyu , Shuang Liang , Sheng Fang , Jie Lirui , Yu Tanzhou , Qiu Zhangyan
Published: October 2020

Rice is the staple food in many Asian countries. The effective intelligent identification of rice diseases is helpful to improve the industrialized management of rice, reduce the input of manpower and improve the efficiency. In 2018, China’s first intelligent identification and service system for rice pests and diseases was released, but its average identification rate is only 80%. This paper takes 4 diseases of rice as the research object, adopts supervised learning, uses convolutional neural network to extract the characteristics of each disease, and integrates the extracted characteristics with AdaBoost algorithm to improve the accuracy. According to the research quantity and the characteristics of the research object, a 10-layer convolutional neural network and three convolution kernels are designed to miss the important information of the image as much as possible. According to the method in this paper, the correct recognition rate of the four diseases of Bacterial Blight, Flax Spot, Sheath Blight and Leaf Sheath Spot was 95.64% on average, which is higher than 5.56% and 14.69% respectively when the convolution neural network and AdaBoost algorithm are stable. Therefore, compared with the classical convolutional neural network and AdaBoost, the integrated algorithm of convolutional neural network proposed in this paper has a higher accuracy in rice disease research.
Joint Proportional Task Offloading and Resource Allocation for MEC in Ultra-dense Networks with Improved Whale Optimization Algorithm
Authors: Zeng Yanling , Chen Wen , Tang Zhangbin , Wu Jiaxing
Published: October 2020

Computationally intensive and latency sensitive applications put forward strict requirements on mobile user equipments (UEs) to provide the required computation capacity and delay. Mobile edge computing (MEC) in ultra-dense network (UDN) is considered as a promising solution that can reduce the computation limitations of UEs and extend their battery life by computation offloading. But the dense deployment of UEs, and the limited resources of small cell base stations (SBSs), which imposes more delays on computation offloading in MEC environment. In this paper, using the characteristics of UDN, we study the computation offloading management scheme based on the weighted sum of task completion time and energy consumption in MEC system. Then, we propose a computation offloading framework and formulate joint task offloading and resource allocation problem in UDNs to minimize overall cost consumption. Based on Genetic algorithm (GA) and Whale optimization algorithm (WOA), we adopt a heuristic algorithm named as GAWOA to solve this problem. Numerical results show that the algorithm has a fast convergence speed and better performance than other benchmark algorithms.
Modeling of Reef Distributed Reconnaissance and Early Warning System Based on DoDAF
Authors: Wang Yan , Chen Jian , Na Chenxu , Li Zhicheng , Gao Song
Published: October 2020

In view of the new requirements of the reconnaissance and early warning system in the complex battlefield environment, a distributed architecture design method for the early warning and reconnaissance of reefs is proposed. This paper presents the design process of DoDAF based distributed reconnaissance and early warning architecture framework for reefs, constructs the architecture model of specific operational activities by using TD-CAP software, describes the relationship between various combat platforms from different perspectives through different views, and evaluates the consistency and operational effectiveness of the distributed detection and early warning system. The evaluation results show that the model has good integrity and consistency, and the combat effectiveness is significantly improved compared with the non distributed reconnaissance and early warning system.
Multi-PaaS Oriented IoT Dual Middleware Model in 5G Era
Authors: Huang Shuai , Hu Shejiao , Hou Zhengfeng , Niu Zhao , Wu Lin
Published: October 2020

The PaaS platform based on cloud computing technology provides a large number of professional and flexible services in the fields of intelligent voice and computer vision. The large-scale application of 5G technology enables the software and hardware of the Internet of Things to use the PaaS service to improve the intelligence level of the system and reduce development costs. However, the problem of service invocation and service selection in the process of combining the IoT system with multiple PaaS platforms directly affects the interaction between the IoT perception layer and the application layer. This paper proposes a 5G multi-PaaS-oriented IoT dual middleware model. The unified communication protocol DMCP is designed. DMCP implements unified parsing of messages based on variable-length data items, which reduces the protocol conversion between the perception layer terminal device and multiple PaaS platforms. Complexity and high communication efficiency. Designed the PaaS service selection algorithm DMPS, DMPS comprehensively according to the equipment demand preferences and the overall performance of PaaS services, to achieve accurate service selection in the multi-PaaS platform at the application layer. Finally, examples and comparative tests verify the feasibility and effectiveness of the proposed model, protocol and algorithm.
Architecture Design of Application Specific Power Chip Security for Power Terminal in Smart Grid
Authors: Lv Zhining , Tang Tao , Du Yu , Wang Huiqin , Zeng Xiangjun , Liu Dongqi
Published: October 2020

Power terminal based on chip is the development demand of smart grid. Aiming at the problems faced by the current power terminal based on chip, an architecture design scheme, which includes power security subsystem (PSS) and application special power system (ASPS) is proposed in this paper for power terminal in smart grid. Then, concrete subsystem architectures of the PSS and ASPS are designed and described. The PSS provides data encryption and decryption, identity authentication and other security services while ASPS can improve the efficiency of data parallel execution and processing efficiency of power terminal. Therefore, the proposed architecture design can solve the problem of security and efficiency better for power terminal in smart grid.
Concept and Key Technologies of Intelligent Logistics
Authors: Wu Xingze , Mai Jiaming , Zhou Jiaqi , Jiang Meilian , Wang Kun
Published: October 2020

Intelligent Logistics is the forefront of the logistics industry, which is quite different from traditional logistics. This article will define the Intelligent Logistics from four perspectives: the definition, characteristics, differences with traditional logistics and key technologies.
Research on Pedestrian Detection Algorithm Based on Deep Learning
Authors: Xu Zhengqin , Zhao Wendong , Peng Laixian , Chen Juan
Published: October 2020

Pedestrian detection is a kind of computer vision technology which is used to judge whether there is a pedestrian in a given image or video and estimate its position accurately. Pedestrian detection technology is widely used in intelligent security monitoring, driverless and robot fields. Pedestrian detection technology has nearly 30 years of development. Although it has greatly improved in accuracy and speed, there are problems such as lighting, pedestrian individual differences, occlusion and multi-scale pedestrian targets in the actual application scenarios, which lead to more missed and false detection in pedestrian detection. The algorithm is not robust enough, which makes the actual deployment difficult.
Comparative Study of Short-term Forecasting Models of Online Car-hailing Demand
Authors: Chang Shiyu , Liu Zhili , Kuang Dongyu , Zhao Hui
Published: October 2020

Based on the time sequence of the online car-hailing data and the change trend characteristics of the online car-hailing data, the short-term prediction of the demand for online car-hailing is carried out. Established the short-term prediction model of the online car-hailing demand, which is based on ARIMA, single feature LSTM and multi feature LSTM respectively. The optimal parameters and structure of the model are determined by using the actual demand data of drip-drip taxi platform, and the comparative experiments are carried out. The experimental results show that the multi feature LSTM model is the best.
Intelligent Pipelining Method and Its Application in C-TSP Problem
Authors: He Dingjia , Peng Hui
Published: October 2020

At present, researches on intelligent optimization problems such as China travel salesman problem (C-TSP) have obtained research results such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Simulated Annealing Algorithm (SA), etc., but the single algorithm also exposes practical problems such as low efficiency and accuracy in the solving process. In order to improve the present situation effectively, this paper proposed an intelligent optimization pipeline method based on the idea of industrial production line flow. This method solved the C-TSP problem by combining the genetic simulated annealing (GA-SA), ant colony optimization simulated annealing (ACO-SA) algorithm pipeline. Experiments show that the efficiency of this method is improved by 4%-10%.
Research on the Teaching Reform of Equipment Guarantee in Special Environment
Authors: Deng Bin , Hu Yamin , Wang Jingjing , Hu Yahui
Published: October 2020

The special topic of equipment guarantee under special environment is one of the special topics in a certain course of a major in the college. Combined with the characteristics of the project, the problems in the teaching of the project are analyzed. In view of the existing problems such as the thin teaching content, the limited teaching resources and the single teaching methods, this paper puts forward the specific teaching reform measures for the special equipment guarantee topic in the special environment from four aspects: reconstructing the teaching content system, improving the teaching methods, enriching the teaching resources and improving the teachers’ teaching ability.
A Text Classification Method Based on the Merge-LSTM-CNN Model
Authors: Wang Kunfu , Zhang Pengyi , Su Jian
Published: October 2020

An MLCNN (Merge-LSTM-CNN) based text classification model combining a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is proposed due to CNN’s deficiency in obtaining context dependency in text and feature loss of the deep neural network in terms of text character extraction. First, the vector representation of input text is realized through word embedding, then full-text semantics are integrated by extracting the local features of the text through a three-layer CNN. Meanwhile, LSTM is used to store the features of historical information in the text to obtain its context-related semantics. Second, input vectors are integrated with the outputs of CNN in each layer respectively to allow the reuse of original features.
A Study on the Three-dimensional Transformation Strategy of Guide Interpretation of the Site of Haihunhou State in Han Dynasty in Nanchang City under the Background of Artificial Intelligence
Authors: Tan Jun , Hu Min
Published: October 2020

With the further development of Artificial Intelligence technology, its influence in all walks of life has gradually increased. AI is playing an irreplaceable role in translation industry. Based on the theory of eco-translatology, this paper will analyze texts of the machine interpretation and human interpretation in Haihunhou Archeological Sites in Nanchang, and discuss how to use the three-dimensional transformation strategy to improve the interpretation quality in the specific interpretation environment.
Computer Network Information Security Protection Based on Virtual Private Network
Authors: Shi Baohui
Published: October 2020

In order to better realize the protection and management of computer network information security, the virtual private network technology introduced has played an important role. This paper summarizes the characteristics and common techniques of virtual private network technology, analyzes the specific application of virtual private network technology in the field of network information security technology, and summarizes the application effect, so as to provide reference for similar research.
RUL Prediction by LSTM Model with Bayesian Parameter Optimization for Turbine Engines
Authors: Hu Shuli , Zhang Shunong , Xu Xing
Published: October 2020

Effectively predicting the remaining useful life (RUL) of a product is significant for reasonable reliability planning and maintenance activities. The long-short term memory (LSTM) model, which belongs to deep learning methods, was applied for RUL prediction of turbine engines, and a parameter optimization method with Bayesian theory was studied.