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Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

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Computer Systems Science & Engineering 2023年第4期45卷 715-736页

作者： Kandasamy Sellamuthu Vishnu Kumar Kaliappan Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407Tamil NaduIndia

Pesticides have become more necessary in modern agricultural ***,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the *** to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to *** residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly *** residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in *** is an obligation to provide a warning about this amount of pesticide use in *** technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using complex event processing(CEP)by processing given spatial and sequential *** analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and ***,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.

关键词： Pesticide contamination complex event processing recurrent neural network Q learning multi residual level and contamination level

Digital Twin Framework and Its Application to Power Grid Online Analysis

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CSEE Journal of Power and Energy Systems 2019年第3期5卷 391-398页

作者： Mike Zhou Jianfeng Yan Donghao Feng China Electric Power Research Institute Beijing 100085China Kedong Electric Power Control System Co. Ltd.Beijing China

Digital twin(DT)framework is introduced in the context of application for power grid online *** the development process of a new power grid real-time online analysis system,an online analysis digital twin(OADT)has been implemented to realize the new online analysis *** OADT approach is presented and its prominent features are *** presentation,discussion,and performance testing are based on a large-scale grid network model(40K+buses),exported directly from the EMS system of an actual power grid.A plan to apply the OADT approach to digitize power grid dispatching rules is also outlined.

关键词： complex event processing digital twin inmemory computing machine learning neural network model parallel computing power grid online analysis

A New Solution Architecture for Online Power System Analysis

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CSEE Journal of Power and Energy Systems 2018年第2期4卷 250-256页

作者： Mike Zhou Jianfeng Yan State Grid Electric Power Research Institute Beijing 100192China China Electric Power Research Institute Beijing 100192China

The current DSA system used in the dispatching control centers in China is a near real-time analysis system with response speed in the order of *** on a review of the state-of-the-art in online analysis and discussion of distributed data processing and computation architecture patterns,a new online analysis architecture is *** primary goal of the new architecture is to increase the online analysis response speed to the order of seconds.A reference implementation of the proposed online analysis architecture to validate the feasibility of implementing the architecture and some performance testing results are presented.

关键词： CEP complex event processing DSA EMS in-memory computing power grid modeling power system online analysis

Semantic complex event Detection System of Express Delivery Business with Data Support From Multidimensional Space

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International English Education Research 2013年第12期 197-200页

The express delivery induslry of China is relatively backward in the automation degree of critical business processes. The basic reason is that the business-related supporting data, which is scattered in the multidimensional space, is difficult to utilize and process. This paper proposes an automatic data acquisition fi-amework to resolve such difficulty, which synthetically utilize intelligent inemet of things （IoT）, semantic web and complext event processing （CEP） technology. We also implement a SCEP prototype system with the capability of real-time detecting complex business events on the goods sorting line, which adopts a detection method consisting of four stages. The simulation results show that the system has good performance and feasible enough to deal with the complex business which need data support fTom multidimensional space.

关键词： interact of things semantic web complex event processing express delivery industry domain ontology detection method

Study on Internet of Things application for Highspeed Train Maintenance,Repair and Operation（MRO）

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Study on Internet of Things application for Highspeed Train ...