• 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.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.569-589
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• 2018

Cloud computing, also known as "country as you go", is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders' number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software's profitability. Our axis of research is the load balancing between a data center's virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels ('odd levels' or 'even levels') in ascending order based on the meta-heuristic "Bat-algorithm". The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1724-1737
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• 1999

Multimedia data service applications have to store and manipulate LOBs(unstructured large objects) composing multimedia data. As such, the tertiary storage devices such as an optical disk jukebox and a tape library that consist of a number of platters (the disks in case of an optical disk jukebox and the cartridge tapes in case of a tape library) have been considered essential for the storage system of a DBMS in order to efficiently support storage and management of vary large volume of data. Since the latency with tertiary storage is too long, the schemes for efficient retrieval of LOBs out of tertiary storage need to be investigated. In this paper, we investigated the tertiary I/O Considering the performance characteristics of the LOBs, we proposed various I/O scheduling heuristic algorithms that reduce latency in query processing with LOB retrieval from tertiary storage, and evaluated their performance through a detailed simulation.

• 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 Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.159-167
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• 2015

We consider a wafer lot transfer/release planning problem from semiconductor wafer fabrication facilities to probing facilities with the objective of minimizing the deviation of workload and total tardiness of customers' orders. Due to the complexity of the considered problem, we propose a two-level hierarchical production planning method for the lot transfer problem between two parallel facilities to obtain an executable production plan and schedule. In the higher level, the solution for the reduced mathematical model with Lagrangian relaxation method can be regarded as a coarse good lot transfer/release plan with daily time bucket, and discrete-event simulation is performed to obtain detailed lot processing schedules at the machines with a priority-rule-based scheduling method and the lot transfer/release plan is evaluated in the lower level. To evaluate the performance of the suggested planning method, we provide computational tests on the problems obtained from a set of real data and additional test scenarios in which the several levels of variations are added in the customers' demands. Results of computational tests showed that the proposed lot transfer/planning architecture generates executable plans within acceptable computational time in the real factories and the total tardiness of orders can be reduced more effectively by using more sophisticated lot transfer methods, such as considering the due date and ready times of lots associated the same order with the mathematical formulation. The proposed method may be implemented for the problem of job assignment in back-end process such as the assignment of chips to be tested from assembly facilities to final test facilities. Also, the proposed method can be improved by considering the sequence dependent setup in the probing facilities.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.64-73
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• 2019

The manufacturing companies under Make-To-Order (MTO) production environment face highly variable requirements of the customers. It makes them difficult to establish preemptive production strategy through inventory management and demand forecasting. Therefore, the ability to establish an optimal production schedule that incorporates the various requirements of the customers is emphasized as the key success factor. In this study, we suggest a process of designing the simulation model for establishing production schedule and apply this model to the case of a flat glass processing company. The flat glass manufacturing industry is under MTO production environment. Academic research of flat glass industry is focused on minimizing the waste in the cutting process. In addition, in the practical view, the flat glass manufacturing companies tend to establish the production schedule based on the intuition of production manager and it results in failure of meeting the due date. Based on these findings, the case study aims to present the process of drawing up a production schedule through simulation modeling. The actual data of Korean flat glass processing company were used to make a monthly production schedule. To do this, five scenarios based on dispatching rules are considered and each scenario is evaluated by three key performance indicators for delivery compliance. We used B2MML (Business To Manufacturing Markup Language) schema for integrating manufacturing systems and simulations are carried out by using SIMIO simulation software. The results provide the basis for determining a suitable production schedule from the production manager's perspective.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.230-240
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• 2020

To develop a virtual world-based immersive tutoring system, we would like to develop a simulation in the spatial aspect to maximize the diversity and realism of the situation. This implementation requires the modeling of virtual space as well as the knowledge and intelligent thinking functions of virtual humans. First, information structures are needed to simulate the hierarchical and multifaceted composition of space and the corresponding knowledge of virtual humans. Specifically, four structures for 2.5D spatial distribution expression, complex spatial relationship expression, object expression, and temporal and spatial representation of events are developed respectively. It then uses these expressed knowledge to develop the spatial thinking function of virtual humans needed to make spatial movement. In general, events have a chain effect on adjacent or connected objects through force, resulting in a variety of situations and reflected in the planning of the next action by the virtual humans involved. For this purpose, the development of events according to historical trends is recorded on the representation structure of time and space. It embodies typical events to demonstrate the feasibility of independent behavior in complex spaces among virtual people.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• Journal of Korean Clinical Nursing Research
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• v.28 no.1
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• pp.1-12
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• 2022

Purpose: The purpose of this study is to investigate the association between the nurse staffing level and the patient mortality using Korean National Health Insurance data. Methods: The data of 1,068,059 patients from 913 hospitals between 2015 and 2016 were analyzed. The nurse staffing level was categorized based on the bed-to-nurse ratio in general wards, intensive care units (ICUs), and hospitals overall. The x² test and generalized estimating equations (GEE) multilevel multivariate logistic regression analyses were used to explore in-hospital mortality and 30-day mortality after admission. Results: The in-hospital mortality rate was 2.9% and 30-day mortality after admission rate was 3.0%. Odd Ratios (ORs) for in-hospital mortality were statistically lower in general wards with a bed-to-nurse ratio of less than 3.5 compared to that with 6.0 or more (OR=0.72, 95% CI=0.63~0.84) and in ICUs with a bed-to-nurse ratio of less than 0.88 compared to that with 1.25 or more (OR=0.78, 95% CI=0.66~0.92). ORs for 30-day mortality after admission were statistically lower in general wards with a bed-to-nurse ratio of less than 3.5 compared to that with 6.0 or more (OR=0.83, 95% CI=0.73~0.94) and in ICUs with a bed-to-nurse ratio of less than 0.63 compared to that with 1.25 or more (OR=0.85, 95% CI=0.72~1.00). Conclusion: To reduce the patient mortality, it is necessary to ensure a sufficient number of nurses by improving the nursing fee system according to the nurse staffing level.