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A Federated Named Entity Recognition Model with Explicit Relation for Power Grid

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Computers, Materials & Continua 2023年第5期75卷 4207-4216页

作者： Jingtang Luo Shiying Yao Changming Zhao Jie Xu jim feng State Grid Sichuan Economic Research Institute ChengduChina School of Computer Science Chengdu University of Information TechnologyChengduChina School of Information and Communication Engineering University of Electronic Science and Technology of China ChengduChina Amphenol Global Interconnect Systems San JoseCA 95131USA

The power grid operation process is complex,and many operation process data involve national security,business secrets,and user ***,labeled datasets may exist in many different operation platforms,but they cannot be directly shared since power grid data is highly *** to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart ***,this paper proposes federated learning named entity recognition method for the power grid field,aiming to solve the problem of building a named entity recognition model covering the entire power grid process training by data with different security *** decompose the named entity recognition(NER)model FLAT(Chinese NER Using Flat-Lattice Transformer)in each platform into a global part and a local *** local part is used to capture the characteristics of the local data in each platform and is updated using locally labeled *** global part is learned across different operation platforms to capture the shared NER *** local gradients fromdifferent platforms are aggregated to update the global model,which is further delivered to each platform to update their global *** on two publicly available Chinese datasets and one power grid dataset validate the effectiveness of our method.

关键词： Power grid named entity recognition federal learning

Classification of Arrhythmia Based on Convolutional Neural Networks and Encoder-Decoder Model

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Computers, Materials & Continua 2022年第10期73卷 265-278页

作者： Jian Liu Xiaodong Xia Chunyang Han Jiao Hui jim feng School of Computer&Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Satellite Navigation Center Beijing100093China The Institute of NBC Defense Chinese PLA ArmyBeijing102205China Amphenol Global Interconnect Systems San JoseCA 95131US

As a common and high-risk type of disease,heart disease seriously threatens people’s *** the same time,in the era of the Internet of Thing(IoT),smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of ***,the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of *** this paper,we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network(CNN)and Encoder-Decoder *** model uses Long Short-Term Memory(LSTM)to consider the influence of time series features on classification ***,it is trained and tested by the MIT-BIH arrhythmia ***,Generative Adversarial Networks(GAN)is adopted as a method of data equalization for solving data imbalance *** simulation results show that for the inter-patient arrhythmia classification,the hybrid model combining CNN and Encoder-Decoder model has the best classification accuracy,of which the accuracy can reach 94.05%.Especially,it has a better advantage for the classification effect of supraventricular ectopic beats(class S)and fusion beats(class F).

关键词： Electroencephalography convolutional neural network long short-term memory encoder-decoder model generative adversarial network

Automated Machine Learning for Epileptic Seizure Detection Based on EEG Signals

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Computers, Materials & Continua 2022年第10期73卷 1995-2011页

作者： Jian Liu Yipeng Du Xiang Wang Wuguang Yue jim feng University of Science and Technology Beijing Beijing100083China Hwa Create Co. LtdBeijing100193China Amphenol Global Interconnect Systems San JoseCA 95131US

Epilepsy is a common neurological disease and severely affects the daily life of *** automatic detection and diagnosis system of epilepsy based on electroencephalogram(EEG)is of great significance to help patients with epilepsy return to normal *** the development of deep learning technology and the increase in the amount of EEG data,the performance of deep learning based automatic detection algorithm for epilepsy EEG has gradually surpassed the traditional hand-crafted ***,the neural architecture design for epilepsy EEG analysis is time-consuming and laborious,and the designed structure is difficult to adapt to the changing EEG collection environment,which limits the application of the epilepsy EEG automatic detection *** this paper,we explore the possibility of Automated Machine Learning(AutoML)playing a role in the task of epilepsy EEG *** apply the neural architecture search(NAS)algorithm in the AutoKeras platform to design the model for epilepsy EEG analysis and utilize feature interpretability methods to ensure the reliability of the searched *** experimental results show that the model obtained through NAS outperforms the baseline model in *** searched model improves classification accuracy,F1-score and Cohen’s kappa coefficient by 7.68%,7.82%and 9.60%respectively than the baseline ***,NASbased model is capable of extracting EEG features related to seizures for classification.

关键词： Deep learning automated machine learning EEG seizure detection

Auxiliary Diagnosis Based on the Knowledge Graph of TCM Syndrome

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Computers, Materials & Continua 2020年第10期65卷 481-494页

作者： Yonghong Xie Liangyuan Hu Xingxing Chen jim feng Dezheng Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Amphenol Global Interconnect Systems San JoseCA 95131USA

As one of the most valuable assets in China,traditional medicine has a long history and contains pieces of *** diagnosis and treatment of Traditional Chinese Medicine(TCM)has benefited from the natural language processing *** paper proposes a knowledge-based syndrome reasoning method in computer-assisted *** method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning *** to this reasoning path,we could infer the path from the symptoms to the syndrome and get all possibilities via the relationship between symptoms and ***,this study applies the Term Frequency-Inverse Document Frequency(TF-IDF)idea to the computer-assisted diagnosis of TCM for the score of syndrome ***,combined with symptoms,syndrome,and causes,the disease could be confirmed comprehensively by voting,and the experiment shows that the system can help doctors and families to disease diagnosis effectively.

关键词： Knowledge graph reinforcement learning auxiliary diagnosis inference path

NOMA-Based Cooperative Relaying Transmission for the Industrial Internet of Things

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Computers, Materials & Continua 2022年第12期73卷 6513-6534页

作者： Yinghua Zhang Rui Cao Lixin Tian Rong Dai Zhennan Cao jim feng Dawning Information Industry Co. Ltd.Beijing100193China Dawning Information Industry Chengdu Co. Ltd.Chengdu610041China Amphenol Global Interconnect Systems San JoseCA95131US

With the continuous maturity of the fifth generation(5G)communications,industrial Internet of Things(IIoT)technology has been widely applied in fields such as smart *** smart factories,5G-based production line monitoring can improve production efficiency and reduce costs,but there are problems with limited monitoring coverage and insufficient wireless spectrum resources,which restricts the application of IIoT in the construction of smart *** response to these problems,we propose a hybrid spectrum access mechanism based on Non-Orthogonal Multiple Access(NOMA)cooperative relaying transmission to improve the monitoring coverage and spectrum *** there are a large number of production lines that need to be monitored in smart factories,it is difficult to realize real-time monitoring of all production lines due to insufficient wireless ***,we divide the production lines into high priority and low priority,and introduce cognitive radio technology to increase the number of monitoring production *** order to better describe the wireless fading channel environment in the factory,the two-wave with diffuse power(TWDP)channel is discussed to simulate the real factory environment and the outage probability of the secondary production line data transmission is derived in the proposed *** with the traditional mechanism,the proposed transmission mechanism can ensure the continuity of the secondary transmission,greatly reduce the outage probability of the secondary transmission,and improve the efficiency of the monitoring of the production lines.

关键词： Relay IIoT outage probability NOMA TWDP beam-forming

Comprehensive Information Security Evaluation Model Based on Multi-Level Decomposition Feedback for IoT

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Computers, Materials & Continua 2020年第10期65卷 683-704页

作者： Jinxin Zuo Yueming Lu Hui Gao Ruohan Cao Ziyv Guo jim feng School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China School of Information and Communication Engineering Beijing University of Posts and Telecommunica-tionsBeijing100876China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijing100876China Amphenol Global Interconnect Systems San JoseCA 95131USA

The development of the Internet of Things(IoT)calls for a comprehensive in-formation security evaluation framework to quantitatively measure the safety score and risk(S&R)value of the network *** this paper,we summarize the architecture and vulnerability in IoT and propose a comprehensive information security evaluation model based on multi-level decomposition *** evaluation model provides an idea for information security evaluation of IoT and guides the security decision maker for dynamic ***,we establish an overall evaluation indicator system that includes four primary indicators of threat information,asset,vulnerability,and management,*** also includes eleven secondary indicators of system protection rate,attack detection rate,confidentiality,availability,controllability,identifiability,number of vulnerabilities,vulnerability hazard level,staff organization,enterprise grading and service continuity,***,we build the core algorithm to enable the evaluation model,wherein a novel weighting technique is developed and a quantitative method is proposed to measure the S&R ***,in order to better supervise the performance of the proposed evaluation model,we present four novel indicators includes residual risk,continuous conformity of residual risk,head-to-tail consistency and decrease ratio,*** results show the advantages of the proposed model in the evaluation of information security for IoT.

关键词： IoT information security quantitative evaluation safety score residual risk

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急性特发性视神经炎患者的心理问题分析

国际眼科杂志 2010年第8期10卷 1556-1557页

作者：秦文娟毛俊峰张付峰魏世辉中南大学湘雅医院手术室中国湖南省长沙市410008 中南大学湘雅医院眼科中国湖南省长沙市410008 中南大学湘雅医院神经内科中国湖南省长沙市410008 中国人民解放军总医院眼科中国北京市100853

目的:探讨急性特发性视神经炎患者存在的精神心理问题。方法:收集中南大学湘雅医院眼科、神经内科2007-05/2009-10诊治的急性特发性视神经炎患者239例,采用90项症状自评量表(symptomchecklist90,SCL-90)对这些患者的心理健康状况进行调... 详细信息

目的:探讨急性特发性视神经炎患者存在的精神心理问题。方法:收集中南大学湘雅医院眼科、神经内科2007-05/2009-10诊治的急性特发性视神经炎患者239例,采用90项症状自评量表(symptomchecklist90,SCL-90)对这些患者的心理健康状况进行调查分析。结果:急性特发性视神经炎患者239例的总症状指数为1.25±0.48,其中双眼患者的总症状指数高于单眼患者,最佳矫正视力0.3者。此组患者的阳性项目数为19项。9个分量表中,因子分高于正常值的有:人际关系敏感、抑郁、焦虑和敌对,以抑郁和焦虑最常见。结论:许多急性特发性视神经炎患者存在一定的精神心理问题,以抑郁和焦虑最常见。医护人员应重视对此类患者的心理干预治疗。

关键词：急性特发性视神经炎心理障碍 90项症状自评量表

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水平管油水两相流持率仪响应特征试验研究

石油天然气学报 2010年第1期32卷 88-91页

作者：刘军锋郭海敏王界益于天芳柴新辉油气资源与勘探技术教育部重点实验室(长江大学) 湖北荆州434023 中国石油西部钻探工程有限公司测井公司新疆克拉玛依834000

准确获取井筒中各相持率是生产测井产出剖面精确解释的关键步骤之一,水平管中因普遍存在层流导致常规居中测量的持率仪器响应产生纵向片面性。电容阵列仪是一种采用多探头覆盖全井眼信息采集的新型流体识别技术,研究基于电容阵列仪、放... 详细信息

准确获取井筒中各相持率是生产测井产出剖面精确解释的关键步骤之一,水平管中因普遍存在层流导致常规居中测量的持率仪器响应产生纵向片面性。电容阵列仪是一种采用多探头覆盖全井眼信息采集的新型流体识别技术,研究基于电容阵列仪、放射性流体密度计和电容持水率计的室内模拟试验,分析了水平管油水两相流动型态和电容阵列仪的持率确定方法,详细研究了3种不同持率测量仪器随流体流动型态变化的响应规律,提出了水平管油水两相流持率参数优化选取的方案。

关键词：生产测井电容阵列仪水平管油水两相持水率

Incineration of kitchen waste with high nitrogen in vortexing fluidized-bed incinerator and its NO emission characteristics

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Journal of Environmental Sciences 2013年第9期25卷 1841-1846页

作者： feng Duan Chiensong Chyang Jiaruei Wen jim Tso School of Metallurgy and Resource Anhui University of Technology Department of Chemical Engineering Chung Yuan Christian University Research and Development Center for Environmental Technology Chung Yuan Christian University

Some municipal solid waste （MSW） can be used as the fuel. Combustion of MSW with high nitrogen content is successfully conducted in a lab-scale vortexing fluidized-bed incinerator （VFBI）. Pigskin with 16.5 wt.% nitrogen content was used to simulate the high nitrogen content kitchen waste, and silica sand was used as the bed material. The effects of operating conditions, such as the bed temperature, freeboard temperature, excess oxygen ratio, and static bed height on the CO and NO concentrations at the exit of combustor and cyclone were investigated. The experimental results show that the freeboard temperature is the most important factor for CO emission. The order of operating conditions impact on the NO emission is：（1） excess oxygen ratio; （2） bed temperature; （3） freeboard temperature; and （4） static bed height. Utilizing cyclone can significantly reduce the CO emission concentration when the CO concentration released from the freeboard is higher than 50 ppm. On the other hand, the cyclone has no significant effect on the NO emission. Despite having high nitrogen content, a low conversion from fuel-N to NO was attained. Compared with other types of combustors, VFBI reduces the CO and NO emission concentrations much better when burning MSW with high nitrogen content.

关键词： incineration high nitrogen content vortexing fluidized bed pollutant emission