• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.68-77
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• 2018

Regarding demand response (DR) by residential users (R-users), the users try to reduce electricity costs by adjusting their power consumption in response to the time-varying price. However, their power consumption may be affected not only by the price, but also by user convenience for using appliances. This paper proposes a methodology for appliance scheduling (AS) that considers the user convenience based on historical data. The usage pattern for appliances is first modeled applying the copula function or clustering method to evaluate user convenience. As the modeling results, the comfort distribution or representative scenarios are obtained, and then used to formulate a discomfort index (DI) to assess the degree of the user convenience. An AS optimization problem is formulated in terms of cost and DI. In the case study, various AS tasks are performed depending on the weights for cost and DI. The results show that user convenience has significant impacts on AS. The proposed methodology can contribute to induce more DR participation from R-users by reflecting properly user convenience to AS problem.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.4
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• pp.113-118
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• 2017

As the demand for industrial robots and Automated Guided Vehicles (AGVs) increases, higher performance is also required from them. Fuzzy controllers, as part of an intelligent control system, are a direct control method that leverages human knowledge and experience to easily control highly nonlinear, uncertain, and complex systems. This paper uses a $LabVIEW^{(R)}-based$ fuzzy controller with gain scheduling to demonstrate better performance than one could obtain with a fuzzy controller alone. First, the work area was set based on forward kinematics and inverse kinematics programs. Next, $LabVIEW^{(R)}$ was used to configure the fuzzy controller and perform the gain scheduling. Finally, the proposed fuzzy gain scheduling controller was compared with to controllers without gain scheduling.

• Journal of the Korean Institute of Navigation
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• v.20 no.1
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• pp.27-46
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• 1996

Vessles in the world merchant fleet generally operate in either liner or bulk trade. The supply and the demand trend of general cargo ship are both on the ebb, however, those trend of tankers and containers are in slight ascension. Oil tankers are so far the largest single vessel type in the world fleet and the tanker market is often cited as a textbook example of perfect competition. Some shipping statistics in recent years show that there has been a radical fluctuation in spot charter rate under easy charterer's market. This implys that the proper scheduling of tankers under spot market fluctuation has the great potential of improving the owner's profit and economic performance of shipping. This paper aims at developing the TS-DSS(Decision Support System for Tanker Scheduling) in the context of the importance of scheduling decisions. The TS-DSS is defined as the DSS based on the optimization models for tanker scheduling. The system has been developed through the life cycle of systems analysis, design, and implementation to be user-friendly system. The performance of the system has been tested and examined by using the data edited under several tanker scheduling scenarios and thereby the effectiveness of TS-DSS is validated satifactorily. The authors conclude the paper with the comments on the need of appropriate support environment such as data-based DSS and network system for succesful implementation of the TS-DSS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.551-569
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• 2024

With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2490-2495
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• 2005

The scheduling of plant should be determined based on the product demands correctly forecasted by reasonable methods. However, because most existing forecasting packages need user's knowledge about forecasting, it has been hard for plant engineers without forecasting knowledge to apply forecasted demands to scheduling. Therefore, a forecasting module has been developed for plant engineers without forecasting knowledge. In this study, for the development of the forecasting module, an automatic method using the ARIMA model that is framed from the modified Box-Jenkins process is proposed. And a new method for safety inventory determination is proposed to reduce the penalty cost by forecasting errors. Finally, using the two proposed methods, the web-based automatic module has been developed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2268-2283
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• 2013

Scalable video coding can handle peer heterogeneity of P2P streaming applications, but there is still a lack of comprehensive studies on how to use it to improve video playback quality. In this paper we propose a capacity aware scalable video coding mechanism for P2P on demand streaming system. The proposed mechanism includes capacity based neighbor selection, adaptive data scheduling and streaming layer adjustment, and can enable each peer to select appropriate streaming layers and acquire streaming chunks with proper sequence, along with choosing specific peers to provide them. Simulation results show that the presented mechanism can decrease the system's startup and playback delay, and increase the video playback quality as well as playback continuity, and thus it provides a better quality of experience for users.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.484-492
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• 2016

Smart grid (SG) technology is now elevating the conventional power grid system to one that functions more cooperatively, responsively, and economically. When applied in an SG the demand side management (DSM) technique can improve its reliability by dynamically changing electricity consumption or rescheduling it. In this paper, we propose a new SG scheduling scheme that uses the DSM technique. To achieve effective SG management, we adopt a mixed pricing strategy based on the Rubinstein-Stahl bargaining game and a repeated game model. The proposed game-based pricing strategy provides energy routing for effective energy sharing and allows consumers to make informed decisions regarding their power consumption. Our approach can encourage consumers to schedule their power consumption profiles independently while minimizing their payment and the peak-to-average ratio (PAR). Through a simulation study, it is demonstrated that the proposed scheme can obtain a better performance than other existing schemes in terms of power consumption, price, average payment, etc.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.203-205
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• 1999

The unit commitment is an important problem of production scheduling which determines the generating unit to in service(on/off) during scheduling period, to meet system demand and reserve requirement at minimum cost. This paper presents an box type crossover to improve searching ability of GA, to solve unit commitment problem. Satisfactory results are obtained by GA with the proposed crossover operator.

• Journal of the Korean Society of Industry Convergence
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• v.3 no.2
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• pp.167-178
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• 2000

Software RAID(Redundant Arrays of Inexpensive Disks) is defined as a storage system that provides capabilities of hardware RAID, and guarantees high reliability as well as high performance. In this paper, we propose an enhanced disk scheduling algorithm and a scheme to guarantee reliability of data. We also design and implement software RAID by utilizing these mechanism to develop a storage system for multimedia applications. Because the proposed algorithm improves a defect of traditional GSS algorithm that disk I/O requests arc served in a fixed order, it minimizes buffer consumption and reduces the number of deadline miss through service group exchange. Software RAID also alleviates data copy overhead during disk services by sharing kernel memory. Even though the implemented software RAID uses the parity approach to guarantee reliability of data, it adopts different data allocation scheme. Therefore, we reduce disk accesses in logical XOR operations to compute the new parity data on all write operations. In the performance evaluation experiments, we found that if we apply the proposed schemes to implement the Software RAID, it can be used as a storage system for small-sized video-on-demand servers.