• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3880-3899
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• 2019

Distributive desynchronization algorithms based on pulse-coupled oscillator (PCO) models have been proposed for achieving collision-free wireless transmissions. These algorithms do not depend on a global clock or infrastructure overheads. Moreover, they gradually converge to fair time-division multiple access (TDMA) scheduling by broadcasting a periodic pulse signal (called a 'firing') and adjusting the next firing time based on firings from other nodes. The time required to achieve constant spacing between phase neighbors is estimated in a closed form or via stochastic modeling. However, because these algorithms cannot guarantee the completion of desynchronization in a short and bounded timeframe, they are not practical. Motivated by the limitations of these methods, we propose a practical solution called PD-DESYNC that provides a short and deterministic convergence time using a flag firing to indicate the beginning of a cycle. We demonstrate that the proposed method guarantees the completion of desynchronization within three cycles, regardless of the number of nodes. Through extensive simulations and experiments, we confirm that PD-DESYNC not only outperforms other algorithms in terms of convergence time but also is a practical solution.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.219-235
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• 2019

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.222-229
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• 2022

Although stochastic programming and feedback control approaches could efficiently mitigate the overdue risks caused by inherent uncertainties in ground conditions, the lack of formal representations of planners' rationales for resource allocation still prevents planners from applying these approaches due to the inability to consider comprehensive resource allocation policies for hard rock tunnel projects. To overcome the limitations, the authors developed an ontology that represents the project duration estimation rationales, considering the impacts of ground conditions, excavation methods, project states, resources (i.e., given equipment fleet), and resource allocation policies (RAPs). This ontology consists of 5 main classes with 22 subclasses. It enables planners to explicitly and comprehensively represent the necessary information to rapidly and consistently estimate the excavation durations during construction. 10 rule sets (i.e., policies) are considered and categorized into two types: non-progress-related and progress-related policies. In order to provide simplified information about the remaining durations of phases for progress-related policies, the ontology also represents encoding principles. The estimation of excavation schedules is carried out based on a hypothetical example considering two types of policies. The estimation results reveal the feasibility, potential for flexibility, and comprehensiveness of the developed ontology. Further research to improve the duration estimation methodology is warranted.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1069-1078
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• 2018

Demand response (DR) programs give opportunity to consumers to manage their electricity bills. Besides, distribution system operator (DSO) is interested in using DR programs to obtain technical and economic benefits for distribution network. Since small consumers have difficulties to individually take part in the electricity market, an entity named demand response provider (DRP) has been recently defined to aggregate the DR of small consumers. However, implementing DR programs face challenges to fairly allocate benefits and payments between DRP and DSO. This paper presents a procedure for modeling the interaction between DRP and DSO based on a bilevel programming model. Both DSO and DRP behave from their own viewpoint with different objective functions. On the one hand, DRP bids the potential of DR programs, which are load shifting and load curtailment, to maximize its expected profit and on the other hand, DSO purchases electric power from either the electricity market or DRP to supply its consumers by minimizing its overall cost. In the proposed bilevel programming approach, the upper level problem represents the DRP decisions, while the lower level problem represents the DSO behavior. The obtained bilevel programming problem (BPP) is converted into a single level optimizing problem using its Karush-Kuhn-Tucker (KKT) optimality conditions. Furthermore, point estimate method (PEM) is employed to model the uncertainties of the power demands and the electricity market prices. The efficiency of the presented model is verified through the case studies and analysis of the obtained results.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.157-163
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• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 120 buildings that KEPCO headquarter and branch offices use. K-BEMS system is composed of PV, battery, and hybrid PCS. In this system, ESS, PV, lighting is used to save building energy based on demand prediction. Currently, neural network technique for short past data is applied to demand prediction, and fixed scheduling method by operator for ESS charging/discharging is used. To enhance this system, KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. As the result of this project, we developed new real-time highly reliable building demand prediction technique with error free and optimized automatic ESS charging/discharging technique. Through several field test, we can certify the developed algorithm performance successfully. So we will describe the details in this paper.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.145-154
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• 2004

The growing demand for customer-responsive, made-to-order manufacturing is stimulating the need for improved dynamic decision-making processes in commercial fleet operations. Moreover, the rapid growth of electronic commerce through the internet is also requiring advanced and precise real-time operation of vehicle fleets. Accompanying these demand side developments/pressures, the growing availability of technologies such as AVL(Automatic Vehicle Location) systems and continuous two-way communication devices is driving developments on the supply side. These technologies enable the dispatcher to identify the current location of trucks and to communicate with drivers in real time affording the carrier fleet dispatcher the opportunity to dynamically respond to changes in demand, driver and vehicle availability, as well as traffic network conditions. This research investigates key aspects of real time dynamic routing and scheduling problems in fleet operation particularly in a truckload pickup-and-delivery problem under various settings, in which information of stochastic demands is revealed on a continuous basis, i.e., as the scheduled routes are executed. The most promising solution strategies for dealing with this real-time problem are analyzed and integrated. Furthermore, this research develops. analyzes, and implements hybrid algorithms for solving them, which combine fast local heuristic approach with an optimization-based approach. In addition, various partitioning algorithms being able to deal with large fleet of vehicles are developed based on 'divided & conquer' technique. Simulation experiments are developed and conducted to evaluate the performance of these algorithms.