• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.4
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• pp.73-93
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• 2012

In recent years, service delivery systems employing a self-service approach have been rapidly spreading. Since a self-service system provides a lower product price, it attracts more customers. However, some system managers are still hesitant to accept a self-service system, because there is no systematic model to predict its performance. Therefore, this research attempts to provide a systematic and quantitative model to predict the performance of a self-service system, focused specifically on a self-service gas station. Under this model, the traditional queuing theory was adopted to describe the general self-service process, but it is also assumed that some changes occur in both the customer arrival rate and the service performance rate. In particular, the price elasticity was introduced to capture the change in the customer arrival rate, and the existence of learning effect and helpers were assumed to design the changed service performance rate. Under these assumptions, a simulation model for a self-service gas station is established, and three performance measurements, such as average number of customers, average waiting time, and Utilization are observed, depending on the changes in price difference and helper-operating time. In this research, the optimal operation strategy for price differentiation and helper-operating time is proposed in accordance with the level of the customer learning rate. Although this research confines the scope of the study to the self-service gas station model, the results of this research can be applied to any type of self-service system.

• IE interfaces
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• v.23 no.4
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• pp.311-318
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• 2010

In the daily multi-reservoir operating problem, monthly storage targets can be used as principal operational guidelines. In this study, we tested the use of a simple back-propagation Artificial Neural Network (ANN) model to derive monthly storage guideline for daily Coordinated Multi-reservoir Operating Model (CoMOM) of the Han-River basin. This approach is based on the belief that the optimum solution of the daily CoMOM has a good performance, and the ANN model trained with the results of daily CoMOM would produce effective monthly operating guidelines. The optimum results of daily CoMOM is used as the training set for the back-propagation ANN model, which is designed to derive monthly reservoir storage targets in the basin. For the input patterns of the ANN model, we adopted the ratios of initial storage of each dam to the storage of Paldang dam, ratios of monthly expected inflow of each dam to the total inflow of the whole basin, ratios of monthly demand at each dam to the total demand of the whole basin, ratio of total storage of the whole basin to the active storage of Paldang dam, and the ratio of total inflow of the whole basin to the active storage of the whole basin. And the output pattern of ANN model is the optimal final storages that are generated by the daily CoMOM. Then, we analyzed the performance of the ANN model by using a real-time simulation procedure for the multi-reservoir system of the Han-river basin, assuming that historical inflows from October 1st, 2004 to June 30th, 2007 (except July, August, September) were occurred. The simulation results showed that by utilizing the monthly storage target provided by the ANN model, we could reduce the spillages, increase hydropower generation, and secure more water at the end of the planning horizon compared to the historical records.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.648-658
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• 2018

Regarding the fact that wind power forecast accuracy is gradually improved as time is approaching, this paper proposes a two-stage rolling dispatch approach based on model predictive control (MPC), which contains an intra-day rolling optimal scheme and a real-time rolling base point tracing scheme. The scheduled output of the intra-day rolling scheme is set as the reference output, and the real-time rolling scheme is based on MPC which includes the leading rolling optimization and lagging feedback correction strategy. On the basis of the latest measured thermal unit output feedback, the closed-loop optimization is formed to correct the power deviation timely, making the unit output smoother, thus reducing the costs of power adjustment and promoting wind power accommodation. We adopt chance constraint to describe forecasts uncertainty. Then for reflecting the increasing prediction precision as well as the power dispatcher's rising expected satisfaction degree with reliable system operation, we set the confidence level of reserve constraints at different timescales as the incremental vector. The expectation of up/down reserve shortage is proposed to assess the adequacy of the upward/downward reserve. The studies executed on the modified IEEE RTS system demonstrate the effectiveness of the proposed approach.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.143-151
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• 2018

In this paper, a novel hybrid configuration combining a phase-shift full-bridge (PSFB) and a half-bridge resonant LLC converter is proposed for the On-Board Charger of Electric Vehicles (EVs). In the proposed converter, the PSFB converter shares the lagging-leg switches with half-bridge resonant converter to achieve the wide ZVS range for the switches and to improve the efficiency. The output voltage is modulated by the effective-duty-cycle of the PSFB converter. The proposed converter employs an active reset circuit composed of an active switch and a diode for the transformer which makes it possible to achieve zero circulating current and the soft switching characteristic of the primary switches and rectifier diodes regardless of the load, thereby making the converter highly efficient and eliminating the reverse recovery problem of the diodes. In addition an optimal power sharing strategy is proposed to meet the specification of the charger and to optimize the efficiency of the converter. The operation principle the proposed converter and design considerations for high efficiency are presented. A 6.6 kW prototype converter is fabricated and tested to evaluate its performance at different conditions. The peak efficiency achieved with the proposed converter is 97.7%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1743-1750
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• 2003

DC motor requires a rotor speed sensor for accurate speed control. The speed sensors such as resolvers and encoders are used as a speed detector, but they increase cost and size of the motor and restrict the industrial drive applications. So in these days, many papers have reported in the sensorless operation of DC motor〔3­5〕. This paper presents a new sensorless strategy using neural networks〔6­8〕. Neural network has three layers which are input layer, hidden layer and output layer. The optimal neural network structure was tracked down by trial and error, and it was found that 4­16­1 neural network structure has given suitable results for the instantaneous rotor speed. Also, learning method is very important in neural network. Supervised learning methods〔8〕 are typically used to train the neural network for learning the input/output pattern presented. The back­propagation technique adjusts the neural network weights during training. The rotor speed is gained by weights and four inputs to the neural network. The experimental results were found satisfactory in both the independency on machine parameters and the insensitivity to the load condition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.12
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• pp.1085-1092
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• 2012

Mobile communication systems define user state transition mechanisms in order to manage radio resources and battery power efficiently. In the state transition mechanism, a state with a higher energy consumption inherently offers a shorter access delay, so there is a tradeoff between the energy and delay performances. In this paper, we analyze the user state transition mechanism of UMTS by considering the bursty traffic attributes of mobile applications. We perform a numerical evaluation for both the energy consumption and the activation delay by Markov modeling of the state transition mechanism, and investigate their tradeoff relationship as functions of operational parameters. The resulting energy-delay tradeoff curves clearly show an achievable performance bound of the user state transition mechanism and also offer an optimal operation strategy to minimize the energy consumption while guaranteeing the delay requirement.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.77-88
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• 2006

The purpose of this study is to Propose the time dependent K-least time path algorithm applicable to a real-time based operation strategy in multi-modal transportation network. For this purpose, we developed the extended method based on entire path deletion method which was used in the static K-least time path algorithm. This method was applied to time dependent K-least time path algorithm to find k least time paths in order based on both time dependant mode-link travel time and transfer cost In particular, this algorithm find the optimal solution, easily describing transfer behavior, such as walking and waiting for transfer by applying a link-based time dependent label. Finally, we examined the verification and application of the Proposed algorithm through case study.

• Korean Chemical Engineering Research
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• v.60 no.4
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• pp.525-534
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• 2022

In this study, we investigated the applicability and optimal operating strategy of a closed-loop pressure retarded membrane distillation (PRMD) for brackish water desalination. For effective operation with net power generation, high temperature of heat source over 90 ℃ and feed flow rate at 0.6 kg/s are recommended. At 3 g/L of feed concentration, the average permeate flux and net energy density showed 8.04 kg/m²/hr and 2.56 W/m², respectively. The average permeate flux and net energy density were almost constant in the range of feed concentration from 1 to 3 g/L. Compared to the case with seawater feed, the PRMD with brackish water feed showed higher average permeate flux and net energy density. Thus, PRMD application using brackish water feed can be more effective than that using seawater feed in terms of power generation.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.4
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• pp.462-472
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• 2023

We investigated the physio-chemical and geochemical parameters in the spraying shellfish aquacultures (Yeoja and Gangjin Bay) to establish the systematic strategy for effective environmental management. Spatial variation of each parameter showed partially significant difference (P<0.05) between Yeoja and Ganjin Bay, inferring the discriminative progress (i.e., accumulation and degradation) of the autochthonous organic matter within the aquaculture environments. We additionally integrated various properties (e.g., water/sediment quality, natural hazard, and biological health) which may affect the biological growth within the aquaculture habitats based on the biogeochemical cycles related to environmental components and aquaculture species. We used a screening approach (i.e., one out-all out; OOAO) which can permit the assessment of the health levels of aquaculture species, the scoring for other parameters (seawater, sediment, and natural hazard) as three levels (excellent, moderate and poor) depending on the complex interactive properties occurring in the aquaculture environments. Actual, discriminative scores obtained via our case studies may confirm that these stepwise processes are effectively evaluated for optimal health conditions within the aquaculture habitats. Thus, this approach may provide valuable insights for effective environmental management and sustainable growth of aquaculture operation.