Intelligent network-based early warning systems

Abstract. In this paper we present an approach for an agent-based early warning system (A-EWS) for critical infrastructures. In our approach we combine existing security infrastructures, e.g. firewalls or intrusion detection systems, with new detection approaches to create a global view and to determine the current threat state

Research on key architecture and model of coal mine water hazard intelligent early warning system

In order to ensure the safe production of mine threatened by water hazard, speed up the intelligent process of mine water hazard prediction and early warning technology, and improve the effect of mine water hazard prediction and early warning, based on the research status of water hazard mechanism and monitoring and early warning at home and abroad, four types of key technical issues for constructing water hazard monitoring and intelligent early warning systems are analyzed. The complexity of early warning requirements and data access standards, the classification and spatio-temporal matching of multi-source heterogeneous big data information, the intelligent processing and analysis of water hazard big data information, and the timeliness of early warning and intelligent decision information release are discussed in detail. From the perspective of early warning system resource integration and data drive, water hazard warning resources are divided into information collection resources and computing resources, water hazard warning big data information is divided into static source information and dynamic monitoring information, and data processing is divided into basic geological model data processing, numerical processing and Computational simulation and information fusion data processing divide coal mine disaster early warning into primary monitoring parameter early warning, intermediate index grading early warning, and advanced intelligent model early warning. The key technical architecture of an intelligent warning system for coal mine water hazards is proposed and analyzed. A software service architecture that meets the technical requirements is proposed, including infrastructure layer, data resource layer, application support layer, business application layer, and user presentation layer. Based on the water hazard warning construction process, a Gated Recurrent Unit algorithm warning model for water hazard monitoring data is proposed, and the network structure of the warning model is given. The forward calculation, backward propagation calculation, and weight gradient calculation methods of the warning model are studied. The classification of different types of perception data access, storage, encoding, models, construction and testing of intelligent deep learning models, and technical paths for warning information release are analyzed. It provides a reference for the intelligent construction of coal mine water hazard early warning

Exhaustive Search-based Model for Hybrid Sensor Network

A new model for a cluster of hybrid sensors network with multi sub-clusters is proposed. The model is in particular relevant to the early warning system in a large scale monitoring system in, for example, a nuclear power plant. It mainly addresses to a safety critical system which requires real-time processes with high accuracy. The mathematical model is based on the extended conventional search algorithm with certain interactions among the nearest neighborhood of sensors. It is argued that the model could realize a highly accurate decision support system with less number of parameters. A case of one dimensional interaction function is discussed, and a simple algorithm for the model is also given.Comment: 6 pages, Proceeding of the International Conference on Intelligent & Advanced Systems 2012 pp. 557-56

Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner

Early warning fault detection using artificial intelligent methods

This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper

A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China

This paper combines artificial neural networks (ANN), fuzzy optimization and time-series econometric models in one unified framework to form a hybrid intelligent early warning system (EWS) for predicting economic crises. Using quarterly data on 12 macroeconomic and financial variables for the Chinese economy during 1999 and 2008, the paper finds that the hybrid model possesses strong predictive power and the likelihood of economic crises in China during 2009 and 2010 remains high.Computational intelligence; artificial neural networks; fuzzy optimization; early warning system; economic crises