• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
• /
• pp.562-564
• /
• 2002

Noise reduction and a simplification of a precision measurement system has been performed by changing analog output mode of a load cell into digital output mode. Usually, analog output signal of a few $\mu V$ from a load cell are amplified by amp and acquired by A/D converter. If the distance from a load cell to a DAS(Data Acquisition System) increases, more noise signals are mixed. So, a microprocessor has been integrated into a load cell so that the amplification and A/D conversion of output signals could be done in close proximity to the lode cell for the reduction in mixing of noise. Obtained data from the load cell like this manner are transferred to a computer with digital values(of TTL level). To simplify the configuration of a multi-channel DAS, RS-485 communication system has used for data transfer.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
• /
• pp.2142-2143
• /
• 2006

Today any data storage system cannot satisfy all of these conditions, however holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanically actuating part therefore fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this research, to reduce errors of binary data stored in holographic data storage system, a new method for bit error reduction is suggested. Firstly, find fuzzy rule to use test bed system for Element of Holographic Digital Data System. Secondly, make fuzzy rule table using DNA coding method. Finally, reduce prior error element and recording digital data. Recording ratio and reconstruction ratio show good performance.

• 대한산업공학회지
• /
• 제36권3호
• /
• pp.193-202
• /
• 2010

The recommender system is a typical software solution for personalized services which are now popular in e-commerce sites. Most of the existing recommender systems are based on customers' explicit rating data on items (e.g., ratings on movies), and it is only recently that recommender systems based on implicit ratings have been proposed as a better alternative. Implicit ratings of a customer on those items that are clicked but not purchased can be inferred from the customer's navigational and behavioral patterns. In this article, a dimensionality reduction (DR) technique is newly applied to the implicit rating-based recommender system, and its effectiveness is assessed using an experimental e-commerce site. The experimental results indicate that the performance of the proposed approach is superior or at least similar to the conventional collaborative filtering (CF)-based approach unless the number of recommended products is 'large.' In addition, the proposed approach requires less memory space and is computationally more efficient.

• 전기학회논문지
• /
• 제59권7호
• /
• pp.1232-1236
• /
• 2010

In this Paper, optimal capacity of energy storage and amount of $CO_2$ reduction in Jeju is calculated. Based on electricity demand data of Je-Ju from 2006 to 2007, the estimation electricity demand from 2009 to 2018 is performed. To calculate the amount of maximum $CO_2$ reduction and energy storage capacity in Jeju, the 4th power supply planning and IPCC guideline are used. Finally, Optimal capacity of energy storage and the amount of $CO_2$ reduction are showed.

• 전기학회논문지
• /
• 제58권10호
• /
• pp.1902-1910
• /
• 2009

This paper presents a graphical windows-based program for the education and training for novel network reduction. The object of developed simulator is to provide users with a simple and useable tool for gaining an intuitive feel for power system analysis. The developed simulator consists of the main module (MMI,GUI), the location marginal price module (LMP), the clustering module and network reduction module. Each module has a separate graphical and interactive interfacing window. The developed simulator needs with the PSS/E input data format, generator cost function, location information. Line admittances of reduced network was determined by using the power flow method(Newton-Raphson). So line flow of reduced network is almost same to original power system. Results of reduced network are compared on the window in the tabular format. Therefore, the developed simulator can be utilized as a useful tool for effective education and training for power system analysis.

• 한국통신학회논문지
• /
• 제25권7B호
• /
• pp.1320-1325
• /
• 2000

A very simple and effective peak power reduction scheme for a downlink OFDM-CDMA system is proposed using the relationship between peak-to-average power ratio (PAPR) and out-of-phase autocorrelation. Since power spectrum and autocorrelation function are Fourier transform pair, the PAPR property of the sequences can be estimated by the out-of-phase autocorrelation function of the spreading sequences. Thus, by scrambling the spread data in the frequency domain, we can reduce the sidelobe energy of autocorrelation, and at last, suppress PAPR in the proposed OFDM-CDMA system.

• 대한전기학회논문지:전력기술부문A
• /
• 제48권2호
• /
• pp.107-114
• /
• 1999

This paper presents an application of artificial neural networks(ANN) to assess the dynamic security of power systems. The basic role of ANN is to provide assessment of the system's stability based on training samples from off-line analysi. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of ANN is a mapping of the pre-fault, fault-on, and post-fault system conditions into the CCT's. In previous work, a feed forward neural network is used to learn this mapping by using the generation outputs during the fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault considered. In order to enhance the speed of security assessment, the bus data and line powers are used as the input data of the ANN in thil paper. Test results show that the proposed neural networks have the reasonable accuracy and can be used in on-line security assenssment efficiently.

• Journal of Multimedia Information System
• /
• 제6권4호
• /
• pp.225-234
• /
• 2019

Deep learning has become increasingly popular in both academic and industrial areas nowadays. Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks. However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks. We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model. Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information. We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.

• 한국가시화정보학회지
• /
• 제19권1호
• /
• pp.62-67
• /
• 2021

Selective catalytic reduction(SCR) is an exhaust gas reduction device to remove nitro oxides (NOx). SCR operation of ship can be controlled through valves for minimizing economic loss from SCR. Valve in SCR-high pressure (HP) system is directly connected to engine exhaust and operates in high temperature and high pressure. Long-term thermal deformation induced by engine heat weakens the sealing of the valve, which can lead to unexpected failures during ship sailing. In order to prevent the unexpected failures due to long-term valve thermal deformation, a failure prediction system using autoregressive integrated moving average (ARIMA) was proposed. Based on the heating experiment, virtual data mimicking temperature range around the SCR-HP valve were produced. By detecting abnormal temperature rise and fall based on the short-term ARIMA prediction, an algorithm determines whether present temperature data is required for failure prediction. The signal processed by the data collection algorithm was interpolated for the failure prediction. By comparing mean average error (MAE) and root mean square error (RMSE), ARIMA model and suitable prediction instant were determined.

• 산업기술연구
• /
• 제29권B호
• /
• pp.151-155
• /
• 2009

In this paper, we implement a spatial data management system which is based on GIS technology. GIS technology is tightly related with spatial information and will be important method for future information-oriented society. The implemented system collects and manages spatial data. In the implementation, we use PostgreSQL DBMS. We also implement magnification, reduction, movement and search functions in the system.