Using the KISO's Framework to Integrate Knowledge Sources

In complicated application environment such as CIMS (Computer Integrated Manufacture System) enterprise, it will bring great benefits to integrate distributed knowledge sources. But the difficulties of knowledge sharing and reuse seriously encumbrance the implementation of knowledge integration. In this paper, we describe how a framework of knowledge-integrated system based on ontology (KISO) can be used to support integrating distributed knowledge sources.

An ethics assessment list for geoinformation ecosystems: revisiting the integrated geospatial information framework of the United Nations

To achieve sustainable development goals,georeferenced data and geographic information systems play a crucial role.Yet,the way in which these data and systems are summoned upon rests on positivist assumptions which overlook both epistemological and ethical concerns.This is epitomized by the integrated geospatial information framework(IGIF)of the United Nations,which,from the perspective of sustainable development,aims to provide guidance for the management of geoinformation and related tools,considering these as mirrors of the physical world.In this respect,the article has three main goals.First,it delivers an epistemological and ethical critique of the IGIF,by highlighting its internal tensions.Second,it suggests how the IGIF and similar geoinformation initiatives can benefit from an ethical reflection that allows to conduct georeferenced practices in a fair(er)way.Third,it designs an ethics assessment list for self-evaluating the ethical robustness of geoinformation initiatives as ecosystems.

Electric Vehicle Charging Simulation Framework Considering Traffic,User,and Power Grid

The traffic and user have significant impacts on the electric vehicle(EV)charging load but are not considered in the existing research.We propose a novel integrated simulation framework considering the traffic,the user,and power grid as well as the EV traveling,parking and charging based on cellular automaton(CA).The traffic is modeled by the traffic module of the proposed framework based on CA,while the power grid and user are modeled in the EV charging module.The traffic flow,user’s charging preference,user’s charging satisfaction,and the total supply capability(TSC)in the surveyed region are considered in the proposed framework.Two cases are carried out to show the interactions between the user and power grid.It is shown that the proposed framework can accurately simulate the interactions among traffic situation,user's behavior and TSC,which are significantly lacking in the existing research.The proposed framework is scalable in considering additional interrelated elements.

Development of a research platform for BEPCⅡ accelerator fault diagnosis

Background BEPCⅡis an electron-positron collider with a beam energy of 1.89 GeV and luminosity of 1×10^(33)cm^(-2)s^(-1).Being a complex accelerator,fault diagnostics both in an accurate manner and in time are often challenging during its operation.Purpose The fault diagnosis research platform is capable of locating and resolving faults on time,thus can largely improve the operation of BEPCⅡby reducing downtime due to machine fault and consequently satisfying the demand for fault diagnosis of a complex system.Methods Making full use of the existing large-scale hardware foundation,abundant data resources and previous experiences,a fault diagnosis research platform was built by applying data mining,machine learning and other related algorithms to carry out specific fault analysis on the entire system.Conclusion The fault diagnosis research platform has been developed with implemented functions of data query,statistics and analysis.Partial fault diagnoses of the three subsystems have been realized.The 4W1 power supply fault has been successfully discovered,and effective solutions have been subsequently provided.

Joint Design of Clustering and In-cluster Data Route for Heterogeneous Wireless Sensor Networks

A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes.In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that,we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.

Machine learning in building energy management: A critical review and future directions

Over the past two decades,machine learning(ML)has elicited increasing attention in building energy management(BEM)research.However,the boundary of the ML-BEM research has not been clearly defined,and no thorough review of ML applications in BEM during the whole building life-cycle has been published.This study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research directions.An integrated framework of ML-BEM,composed of four layers and a series of driving factors,is proposed.Then,based on the hype cycle model,this paper analyzes the current development status of ML-BEM and tries to predict its future development trend.Finally,five research directions are discussed:(1)the behavioral impact on BEM,(2)the integration management of renewable energy,(3)security concerns of ML-BEM,(4)extension to other building life-cycle phases,and(5)the focus on fault detection and diagnosis.The findings of this study are believed to provide useful references for future research on ML-BEM.