• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5622-5632
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• 2011

This paper presents the simulation design and analysis of Integrated Logistics System(ILS) which is operated by using the AGV(Automated Guided Vehicle). To maximize the operation performances of ILS with AGV, many parameters should be considered such as the number, velocity, and dispatching rule of AGV, part types, scheduling, and buffer sizes. We established the design of experiment in a way of Orthogonal Array in order to consider (1)maximizing the throughput; (2)maximizing the vehicle utilization; (3)minimizing the congestion; and (4)maximizing the Automated Storage and Retrieval System(AS/RS) utilization among various critical factors. Furthermore, we performed the optimization by using the simulation-based analysis and Evolution Strategy(ES). As a result, Orthogonal Array which is conducted far fewer than ES significantly saved not only the time but the same outcome when compared after validation test on the result from the two methods. Therefore, this approach ensures the confidence and provides better process for quick analysis by specifying exact experiment outcome even though it provides small number of experiment.

• Journal of Industrial Convergence
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• v.16 no.1
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• pp.21-26
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• 2018

The Seaborne Trade Volume accounts for 97% of the total. This means that the port operation management system can improve port efficiency, reducing operating costs, and the manager who manages all operations at the port needs to check and respond quickly when delays of work and equipment support is needed. Based on the real-time location information confirmation of yard automation equipment used the existing system GPS, the real-time location information confirmation system is a GPS system of the tablet, rather than a port operation system that monitors location information for the entered information, depending on the completion of the task or the start of the task. Network configurations also reduce container processing delays by using commercial LTE services that do not have shading due to containers in the yard also reduce container processing delays. Trough introduction of smart devices using Android or IOS and container processing scheduling utilizing artificial intelligence, we will build a minimum delay system with Smart Device usage of container processing applications and optimization of container processing schedule. The adoption of smart devices and the minimization of container processing delays utilizing artificial intelligence are expected to improve the quality of port services by confirming the processing containers in real time to consumers who are container information demanders.