• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.56-63
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• 2007

In this paper, an eye tracking method is presented using a neural network (NN) and mean-shift algorithm that can accurately detect and track user's eyes under the cluttered background. In the proposed method, to deal with the rigid head motion, the facial region is first obtained using skin-color model and con-nected-component analysis. Thereafter the eye regions are localized using neural network (NN)-based tex-ture classifier that discriminates the facial region into eye class and non-eye class, which enables our method to accurately detect users' eyes even if they put on glasses. Once the eye region is localized, they are continuously and correctly tracking by mean-shift algorithm. To assess the validity of the proposed method, it is applied to the interface system using eye movement and is tested with a group of 25 users through playing a 'aligns games.' The results show that the system process more than 30 frames/sec on PC for the $320{\times}240$ size input image and supply a user-friendly and convenient access to a computer in real-time operation.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.3 s.49
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• pp.13-20
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• 2006

Substations in electric power transmission network systems (EPTS) operate using several transformers in parallel to increase the efficiency in terms of stability of energy supply. We present a seismic reliability assessment method of EPTS considering the parallel operation of transformers. Two methods for damage state model are compared in this paper: bi-state and multi-damage model. Simulation results showed that both models yielded similar network reliability indices and the reliability indices of the demand nodes using hi-state model exhibited higher damage probability. Particularly, the corresponding EENS (Expected Energy Not Supplied) index was significantly larger than that of the multi-damage state.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.293-299
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• 2018

The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.

• Journal of Distribution Science
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• v.15 no.2
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• pp.79-90
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• 2017

Purpose - Cargo Distribution O2O Business Model is the form of business that connects the cargo and empty cargo-truck based on mobile online platform. In Korean cargo distribution market, FIN(Freight Information Network) is the only model that represents O2O Business Model. The purpose of this paper is investigating the recognition of driver who is the only source of income toward cargo distribution O2O Business Model, and based on the investigated recognition of trucker, suggesting strategic implication. Research design, data, and methodology - PESTLE methodology which is massive environment analysis, and 5 Forces Model when analyze the present and future of cargo distribution O2O business market of industrial structure analysis were used as investigation methodology. Also structured questionnaire was used for trucker's recognition investigation. Based on collected 196 structured effective questionnaires organized with 26 questions were analyzed using statistics package. Results - 51.3% of responded driver is non-differentiated, deprofessionalize form that transport all types of cargo. 95.4% recognize cargo distribution O2O Business Model, FIN is needed, especially during back-hall(94.7%). As a payment method, monthly due is preferred(73%), but it is also needed to pay annual due and pay whenever cargo and cargo-truck are connected(24.5%). Trucker prefer FIN operation corporation which has rich supply(85.2%), and is liberal in supply in any domestic area(75.5%). Conclusions - First, 91% was the member of FIN, and 95% of non-member recognized FIN is needed. 83% of them has the intent to be the member of FIN. Second, besides of monthly due as payment method of FIN, 25% has positive recognition toward new payment method. The new payment method means paying annual due and pay whenever cargo and cargo-truck are connected. Third, because of information imbalance about the cargo and cargo-truck among, operators whose business goal is FIN, it was investigated that transportation fee is low and commission charge of broker is high. The core of Korean Cargo Distribution O2O Business Model, FIN, is online platform that matches cargo and cargo-truck. Therefore, FIN operator should minimize the amount of single transportation of trucker. This study suggests the development of shipper using FIN, diversify distribution channel, suggesting backhaul toward trucker as solution to FIN operator.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3D
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• pp.383-389
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• 2011

The purpose of this paper is to analyze the blind spot for public transportation by level of service(LOS) in public transportation supply service. For the purpose, we proposed indices of service coverage area, service frequency and hours of service and analysis method for them. Service coverage area analysis is to decide station location and network design for the maximum beneficiary area. Moreover, we can use the service frequency is for the maximum service frequency decision and hours of service is for the minimum operation hours. The results of this study are applied to the plan of minimum supply service, minimum service frequency and minimum operation hours for city and national public transportation plan.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3104-3123
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• 2022

The development of Vehicular Ad hoc Network (VANET) has greatly improved the efficiency and safety of social transportation, and the routing strategy for VANET has also received high attention from both academia and industry. However, studies on dynamic matching of routing policies with the message types of VANET are in short supply, which affects the operational efficiency and security of VANET to a certain extent. This paper studies the message types in VANET and fully considers the urgency and reliability requirements of message forwarding under various types. Based on the diversified types of messages to be transmitted, and taking the diversified message forwarding strategies suitable for VANET scenarios as behavioral candidates, an adaptive routing method for the VANET message types based on reinforcement learning (RL) is proposed. The key parameters of the method, such as state, action and reward, are reasonably designed. Simulation and analysis show that the proposed method could converge quickly, and the comprehensive performance of the proposed method is obviously better than the comparison methods in terms of timeliness and reliability.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.331-338
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• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.176-185
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• 2003

The SCM is on the effective treatment solution that has process schedule, material supplying, inventory management for perfect product these days. The SCM is innovative process activity to attain effective whole. It depends on the structure of approach because supplying network is consist of organization, budgeting, responsibility and authority. The major objective of the thesis was to propose the integration model and structure method technique of the SCM. Thus, to implement strategy model, firstly, it is determined repairs and non-effectiveness of supplying network. secondly, to set up future vision and goal, it is considered success factor of supplying net. thirdly, no gaps introduce between present and future of supplying net fourthly as above-mentioned consequence, Alternative is to set up integration and implementing model according to enterprise administration strategy .

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2335-2341
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• 2009

High technique growth of modem times and high industrial facility in consequence of buildings demand for electric power of an extensive scale with stability supply and maintenance of high quality. But, power system always have risk of network contingency. When power system break out disturbance, it circumstantially happen like uncontrolled loss of load developing from of cascading. Severely which would be raised wide area blackout, plan to prevent, which make stability through a little of load shedding and multi-level UnderVoltageLoadShdding should work. This paper presents target, sensitivity of bus voltage have choose appropriating load shedding location and load shedding decision making logic with considering rate of change of voltage have studied multi-level under voltage load shedding scheme. Calculation of rate of change of voltage applied method of least square. As a result, we are studied an dynamic analysis of 2008 summer peak data. We have been known that network analysis is a little development and developing UnderVoltageLoadShedding scheme.