• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.57-64
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• 2019

In this paper, we study a polynomially solvable case of the inverse interval graph coloring problem. Given an interval graph associated with a specific interval system, the inverse interval graph coloring problem is defined with the assumption that there is no proper K-coloring for the given interval graph, where K is a fixed integer. The problem is to modify the system of intervals associated with the given interval graph by shifting some of the intervals in such a way that the resulting interval graph becomes K-colorable and the total modification is minimum with respect to a certain norm. In this paper, we focus on the case K = 1 where all intervals associated with the interval graph have length 1 or 2, and interval displacement is only allowed to the righthand side with respect to its original position. To solve this problem in polynomial time, we propose a two-phase algorithm which consists of the sorting and First Fit procedure.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.18-18
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• 2018

상수관망 시스템(Water Distribution System, WDS)은 이용자에게 양질의 상수도를 공급하기 위해 구축된 사회기반시설물로써, 정수된 물이 사용처에 도달하기까지 송수과정에서 발생 가능한 수질저하를 고려해야 한다. 일반적으로 정수장에서 염소처리를 한 후, 도달시간을 고려한 시스템 내 잔류 염소농도를 유지함으로써 수질저하를 예방한다. 여기서 상수도 내 잔류 염소농도는 미생물 번식 및 관내 부식물 등 다양한 생물 화학적 오염을 효과적으로 예방하는 반면, 과다할 경우 이용자의 음용성을 저해할 수 있어 시스템 전반에 걸쳐 염소농도의 적절한 관리가 요구된다. 특히, 상수관망에서는 공급경로 및 공급량에 따라 각 수요처의 도달 염소농도가 다르게 분포할 수 있으므로, 시설운영자는 균등하고 적절한 염소농도를 유지하기 위해 추가적인 염소 재투입시설을 설치하여 함께 관리하고 있다. 이 때, 염소투입 시설의 운영계획은 EPANET과 같은 상수관망 해석모형의 수질모의를 바탕으로 수립된다. 그러나 일반적으로 수질모의는 수리해석과는 달리 긴 시간이 소요되는 단점이 존재한다. 본 연구에서는 이러한 단점을 개선하기 위해, 특정 네트워크의 수질모의 결과를 학습시킨 인공신경망(ANN) 모형을 구축하고 이를 이용하여 상수관망 수질모의 계산시간을 단축하고자 하였다. 여기서 ANN모형의 학습은 EPANET을 통해 미리 선정된 다양한 염소 투입지점의 염소 투입농도와 용수 공급량 자료, 그리고 주요 관측지점에서 측정된 염소농도자료를 이용하였다. 학습된 ANN모형을 EPANET 수질모의 결과와 비교 및 검증을 실시한 결과, 사전에 소요된 학습시간을 제외하면 수질모의 소요시간 측면에서 큰 개선효과를 보였으며, 대표지점에서의 수질모의 결과가 유사하였다. 추가적으로, 본 연구에서는 학습된 ANN모형과 최적화 알고리즘인 GA(Genitic Algorithm)를 연계하여 상수관망에서의 염소 재투입 스케줄링을 최적화하는 프로그램을 개발함으로써, 안전하고 경제적인 상수관망의 수질운영에 기여하고자 하였다.

• Journal of the Korean Society for Railway
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• v.18 no.6
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• pp.619-629
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• 2015

The train conflict resolution problem refers to an adjustment of the train operation schedule in order to minimize the delay propagation when a train conflict is predicted or when a train conflict occurs. Previous train studies of train conflict resolutions are limited in terms of the size of the problems to be solved due to exponential increases in the variables. In this paper, we propose a train conflict resolution model using a time-space network to solve the train conflict situation in the operational phase. The proposed model adjusts the size of the problem by giving only the dwell tolerance in the time-space network only for stops at the station after a train conflict. In addition, the proposed model can solve large problems using a path flow variable. The study presents a train delay propagation analysis and experimental results of train conflict resolution assessments as well using the proposed model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.71-76
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• 2021

Recently, as the size of IoT data grows, the memory power consumption of real-time systems increases rapidly. This is because real-time systems always place entire tasks in memory, which increases the demand of DRAM significantly. In this paper, we adopt emerging fast storage media and move a certain portion of real-time tasks from DRAM to storage. The part of tasks in storage are, then, loaded into memory when they are actually used. We incorporate our memory/storage power-saving into the dynamic voltage/frequency scaling of processors, thereby optimizing power consumptions in CPU and memory simultaneously. Specifically, the proposed technique aims at minimizing the CPU idle time and the DRAM memory size by determining appropriate voltage modes of CPU and the swap ratio of memory, without violating the deadlines of all tasks. Through simulation experiments, we show that the proposed technique significantly reduces the power consumption of real-time systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3172-3193
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• 2022

Cognitive radio-simultaneous wireless information and power transfer (CR-SWIPT) has attracted much interest since it can improve both the spectrum and energy efficiency of wireless networks. This paper focuses on the resource sharing between a point-to-point primary system (PRS) and a multiuser multi-antenna cellular cognitive radio system (CRS) containing a large number of cognitive users (CUs). The resource sharing optimization problem is formulated by jointly scheduling CUs and adjusting the transmit power at the cognitive base station (CBS). The effect of accessing CUs' spatial channel correlation on the possible transmit power of the CBS is investigated. Accordingly, we provide a low-complexity suboptimal approach termed the semi-correlated semi-orthogonal user selection (SC-SOUS) algorithm to enhance the spectrum efficiency. In the proposed algorithm, CUs that are highly correlated to the information decoding primary receiver (IPR) and mutually near orthogonal are selected for simultaneous transmission to reduce the interference to the IPR and increase the sum rate of the CRS. We further develop a spatial correlation-based resource sharing (SC-RS) strategy to improve energy sharing performance. CUs nearly orthogonal to the energy harvesting primary receiver (EPR) are chosen as candidates for user selection. Therefore, the EPR can harvest more energy from the CBS so that the energy utilization of the network can improve. Besides, zero-forcing precoding and power control are adopted to eliminate interference within the CRS and meet the transmit power constraints. Simulation results and analysis show that, compared with the existing CU selection methods, the proposed low-complex strategy can enhance both the achievable sum rate of the CRS and the energy sharing capability of the network.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.540-545
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• 2023

AESA radar is able to instantaneously and adaptively position and control the beam, and this enables to have interleaved mode in modern airborne AESA radar which can maximize situational awareness capability. Interleaved mode provides two or more modes simultaneously, such as Air to Air mode and Sea Surface mode by time sharing technique. In this interleaved mode, performance degradation is inevitable, compared with single mode operation, and effective resource allocation is the key component for the success of interleaved mode. In this paper, we identified performance evaluation items for each mode to analyze interleaved mode performance and proposed effective resource allocation methodology to achieve graceful performance degradation of each mode, focusing on detection range. We also proposed beam scheduling techniques for interleaved mode.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1749-1773
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• 2024

Recent advances in deep neural networks (DNNs) have greatly improved the accuracy and universality of various intelligent applications, at the expense of increasing model size and computational demand. Since the resources of end devices are often too limited to deploy a complete DNN model, offloading DNN inference tasks to cloud servers is a common approach to meet this gap. However, due to the limited bandwidth of WAN and the long distance between end devices and cloud servers, this approach may lead to significant data transmission latency. Therefore, device-edge collaborative inference has emerged as a promising paradigm to accelerate the execution of DNN inference tasks where DNN models are partitioned to be sequentially executed in both end devices and edge servers. Nevertheless, collaborative inference in heterogeneous edge environments with multiple edge servers, end devices and DNN tasks has been overlooked in previous research. To fill this gap, we investigate the optimization problem of collaborative inference in a heterogeneous system and propose a scheme CIS, i.e., collaborative inference scheme, which jointly combines DNN partition, task offloading and scheduling to reduce the average weighted inference latency. CIS decomposes the problem into three parts to achieve the optimal average weighted inference latency. In addition, we build a prototype that implements CIS and conducts extensive experiments to demonstrate the scheme's effectiveness and efficiency. Experiments show that CIS reduces 29% to 71% on the average weighted inference latency compared to the other four existing schemes.

• Journal of Bio-Environment Control
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• v.6 no.3
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• pp.205-215
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• 1997

An accurate transpiration model for greenhouse tomato crop, which is liable to transpiration depression and yield loss because of low solar radiation and high humidity, could be an efficient tool for the optimum control of greenhouse climate and for the optimization of Irrigation scheduling. The purpose of this study was to develop transpiration model of greenhouse tomato and to carry out the experimental verification. The formulas to calculate the canopy transpiration and temperature simultaneously were derived from the energy balance of canopy. Transpiration and microclimate variables such as net radiation, solar radiation, humidity, canopy and air temperature, etc. were simultaneously measured to estimate parameters of model equations and to verify the suggested model. Leaf boundary layer resistance was calculated as a function of Nusselt number and stomatal diffusive resistance was parameterized by solar radiation and leaf-air vapor pressure deficit. The equation for stomatal diffusive resistance could explain more than 80％ of its variation and the calculated stomatal diffusive resistance showed good agreements with the measured values in situations independent of which the constants of the equation were estimated. The canopy net radiation calculated by Stanghellini's model with slight modification agreed well with the measured values. The present transpiration model, into which afore-mentioned component equations were assembled, was found to predict the canopy temperature, instantaneous and daily transpiration with considerable accuracy in greenhouse climates.

• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.2
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• pp.174-182
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• 2002

The object-oriented design methods and their CASE tools are widely used in practice by many real-time software developers. However, object-oriented CASE tools require an additional step of identifying tasks from a given design model. Unfortunately, it is difficult to automate this step for a couple of reasons: (1) there are inherent discrepancies between objects and tasks; and (2) it is hard to derive tasks while maximizing real-time schedulability since this problem makes a non-trivial optimization problem. As a result, in practical object-oriented CASE tools, task identification is usually performed in an ad-hoc manner using hints provided by human designers. In this paper, we present a systematic, schedulability-aware approach that can help mapping real-time object-oriented models to multi-threaded implementations. In our approach, a task contains a group of mutually exclusive transactions that may possess different periods and deadline. For this new task model, we provide a new schedulability analysis algorithm. We also show how the run-time system is implemented and how executable code is generated in our frame work. We have performed a case study. It shows the difficulty of task derivation problem and the utility of the automated synthesis of implementations as well as the Inappropriateness of the single-threaded implementations.