• Journal of Korea Society of Industrial Information Systems
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• v.8 no.4
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• pp.1-7
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• 2003

This study is concerned with the operational methods of Quality Management employed by the small and medium-sized companies in Gang won-do to improve their power of competition. This study is particular, studied the consortium-participating companies on their Plans of Quality Management as to how they carry them out in order to advance to the first-class business enterprises. The results of this study showed that the of shortage manpower and funds that the local small and mediumsized companies commonly face played a significant role as major obstacles in implementing their Quality Management programs. The study results also suggest that it is necessary to promote some periodic manpower train- ing programs in cooperation with local universities, to utilize professors and experts to build management strategies that can bring and secure the power of competition, and to construct a system of educational-industrial cooperation, as ways leading these companies to the 21s1 century's type of business enterprise.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.05a
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• pp.74-77
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• 2000

The main objective of this paper is to design and test a new type of polymer ZnO surge arrester for AC power system of railroad vehicles. Metal oxide surge arrester for most electric power system applications, electric train and must not have explosive breakage of the housing to minimize damage to other equipment when subjected to internal high short circuit current. When breakdown of ZnO elements in a surge arrester occurs due to flashover, fault short current flows through the arrester and internal pressure of the arrester rises. The pressure rise can usually be limited by fitting a pressure relief diaphragm and transferring the arc from the inside to the outside of the housing. However, there is possibility of porcelain fragmentation caused by the thermal shock. pressure rise, etc. Non-fragmenting of the housing is the most desired way to prevent damage to other equipment. The pressure change which is occurred by flashover become discharge energy. This discharge energy raises to damage arrester housing and arrester housing is dispersed as small fragment. Therefore, the pressure relief design is requested to obstruct housing dispersion. The main research works are focused on the structure design by finite element method, pressure relief of module, and studies of performance of surge arrester for electric railway vehicle.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.58-65
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• 2009

Recently, the Korea Tilting Express(TTX), "HANVIT 200", KRRI(Korea Railroad Research Institute) developed runs for test on the conventional railroad, Honam line, Center line, Chunk-buk line and Tae-baek line. In this paper, we measured the service voltage while TTX ran on the conventional railroad and analyzed the condition of voltage. For measuring voltage directly, we installed PT under the pantograph and measured the voltage of the primary winding. In addition, we record situations that other trains pass by TTX and environments while TTX ran. And then we analyzed the condition of voltage using the records and CBEMA curve. In result, over 110[%] voltage of rated voltage(25[kV]) often occurred and the voltage of some place continued over 110[%] for about 200[s]. Especially, the voltage condition of Tae-baek line is the worst. The results in this paper can help to stabilize the power device and power converter of TTX.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.459-462
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• 2010

Response to nerve stimulation platform for implementing measures to detect finger movement has been functioning as an important factor. This stimulated finger on the nerve and muscle responses would vary. In other words, the finger movement of the muscle response to nerve stimulation and sensing Actuator for the H/W development is needed. In addition, a low power embedded CPU based on the top was used. H/W configuration portion of the isolation power, constant current control, High impedance INA, amplifier parts, and the stimulus mode and the Micro-control the status of current, AD converter Low Data obtained through the processing system is implemented.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.31-38
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• 2022

This paper applies an expert independent unsupervised neural network learning-based multivariate time series data analysis model, MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder), and to overcome the limitation, because the MCRED is based on Auto-encoder model, that train data must not to be contaminated, by using learning data sampling technique, called Subset Sampling Validation. By using the vibration data of power plant equipment that has been labeled, the classification performance of MSCRED is evaluated with the Anomaly Score in many cases, 1) the abnormal data is mixed with the training data 2) when the abnormal data is removed from the training data in case 1. Through this, this paper presents an expert-independent anomaly diagnosis framework that is strong against error data, and presents a concise and accurate solution in various fields of multivariate time series data.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.51-65
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• 2024

Accurate electrical load forecasting is important to the effective operation of power systems in smart grids. With the recent development in machine learning, artificial intelligence-based models for predicting power demand are being actively researched. However, since existing models get input variables as numerical features, the accuracy of the forecasting model may decrease because they do not reflect the semantic relationship between these features. In this paper, we propose a scheme for short-term load forecasting by using features extracted through the large language models for input data. We firstly convert input variables into a sentence-like prompt format. Then, we use the large language model with frozen weights to derive the embedding vectors that represent the features of the prompt. These vectors are used to train the forecasting model. Experimental results show that the proposed scheme outperformed models based on numerical data, and by visualizing the attention weights in the large language models on the prompts, we identified the information that significantly influences predictions.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.225-232
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• 2013

In this paper, the study was conducted on the subject of the hybrid electric vehicles used by the military, and optimized the propulsion system for fuel economy considering energy supply to the mission equipments. For the analysis of the vehicles, a method based on the geometry and some assumptions was applied with basic vehicle dynamics. The sources of energy supply in the military hybrid electric vehicles are an engine, a battery and an ultra-capacitor. The optimal operation point among an engine, a battery and an ultra-capacitor can be found by minimizing energy consumption of driving power train and mission equipments. In the study, it was possible to find the optimal propulsion system by comparing fuel efficiency of the vehicles during the driving cycle.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.144-151
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• 2005

This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.12
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• pp.1095-1102
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• 2016

This paper presents the results of the design, fabrication and tether test for a gross weight 20 kg tilt-duct VTOL aerial robot. The tilt-duct vehicle, a tri-ducts air-vehicle, which composed of two main tilt ducts for thrust and an aft-fan for pitch attitude control, has been developed as an aerial platform. The research on the air vehicle has been focused on the hover characteristics and controllability to improve the thrust and stability. The tether test for measuring various performance of vehicle and evaluating controllability have been carried out to figure out effects of modified main-prop linkage, actuator, duct configuration and control surfaces.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1489-1503
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• 2018

Spectrum reuse has attracted much concern of researchers and scientists, however, the dynamic spectrum access is challenging, since an individual secondary user usually just has limited sensing abilities. One key insight is that spectrum usage forecasting among secondary users, this inspiration enables users to obtain more informed spectrum opportunities. Therefore, spectrum usage forecasting is vital to cognitive radio networks (CRNs). With this insight, a spectrum usage forecasting model for the occurrence of primary users prediction is derived in this paper. The proposed model is based on auto regressive enhanced primary user emergence reasoning (AR-PUER), which combines linear prediction and primary user emergence reasoning. Historical samples are selected to train the spectrum usage forecasting model in order to capture the current distinction pattern of primary users. The proposed scheme does not require the knowledge of signal or of noise power. To verify the performance of proposed spectrum usage forecasting model, we apply it to the data during the past two months, and then compare it with some other sensing techniques. The simulation results demonstrate that the spectrum usage forecasting model is effective and generates the most accurate prediction of primary users occasion in several cases.