• Journal of the Korean Solar Energy Society
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• v.28 no.6
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• pp.64-69
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• 2008

In this study the thermal performance and economic analysis program of solar heating system for district heating was developed. This program, named SOLAN-DHS and based on TRNSYS, consisted of four modules like as user's interface for system input/output, library, and utilities and a calculating engine. SOLAN-DHS simplifies user's input data through the database of most system engineering data including weather data of 17 areas in Korea. Five different types of solar systems which can be applicable to district heating system were presented in this program. Due to the user-friendly layout, all design parameters can be changed quickly and easily for the influence on system efficiency. The reliability of SOLAN-DHS was finally verified by the experiments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.6
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• pp.36-41
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• 2002

We consider the Fuzzy clustering which is devised for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. The researches carried out in this field before show that the Fuzzy clustering concept is involved so much that for a certain set of data, the main purpose of the clustering cannot be attained as desired. Thus we propose a new objective function, named as Fuzzy-Entroppy Function in order to satisfy the main motivation of the clustering which is classifying the data clearly. Also we suggest Mean Field Annealing Algorithm as an optimization algorithm rather than the ISODATA used traditionally in this field since the objective function is changed. we show the Mean Field Annealing Algorithm works pretty well not only for the new objective function but also for the classical Fuzzy objective function by indicating that the local minimum problem resulted from the ISODATA can be improved.

• ETRI Journal
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• v.27 no.6
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• pp.818-821
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• 2005

Recently, MPEG has opened activity to standardize scene representation for lightweight applications such as in mobile phones. The standard is named lightweight applications scene representation (LASeR) and can be applied to improve and make efficient rich media applications and services on mobile devices. In this standard, we proposed an efficient type codec for point data to maximize the bit efficiency of LASeR. In this paper, we describe the new method and the test results of the proposed scheme.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.130-133
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• 2000

In this paper, a new design methodology named FNNN(Fuzzy Polynomial Neural Network) algorithm is proposed to identify the structure and parameters of fuzzy model using PNN(Polynomial Neural Network) structure and a fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Handling), and uses several types of polynomials such as linear, quadratic and modified quadratic besides the biquadratic polynomial used in the GMDH. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. Each node of the FPNN is defined as fuzzy rules and its structure is a kind of neuro-fuzzy architecture Several numerical example are used to evaluate the performance of out proposed model. Also we used the training data and testing data set to obtain a balance between the approximation and generalization of proposed model.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.307-314
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• 2000

정보기술이 발달하면서 자료의 흔적들이 체계화된 데이터베이스에 저장이 되고, 더불어 데이터베이스의 규모는 점점 커지고 있다. 데이터 마이닝은 이런 방대한 자료의 분석을 통해, 그 속에 숨어있는 의미를 찾는 과점이라고 될 수 있다. 본 논문에서는 대우정보시스템(주)서 개발된 사용자지향 데이터 마이닝 도구인 XM-Tool/Miner의 개발을 대상으로 하고 있다. 개발된 XM-Tool/Miner은 문제 중심적 마이닝 도구를 목표로 하였으며, 대표적인 마이닝 알고리즘을 적용하였고, 또한 사용의 편이성에 초점을 맞추었다. 더 나아가 데이터 마이닝 기법뿐만 아니라 데이터의 샘플링과 성능향상을 통하여 방대한 데이터로부터 다양한 지식탐사가 가능해지고, 발견된 규칙 또는 지식의 유용성 측정을 통하여 업무 분야의 특성에 따라 효과적으로 반영되며 의사 결정 및 CRM마케팅, 동향분석 및 예측 등에 유용한 정보를 추출하는 도구로 사용할 수 있을 것이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.90-90
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• 2011

Long-term nonstationary oscillations (NSOs) are commonly observed in hydrological and climatological data series such as low-frequency climate oscillation indices and precipitation dataset. In this work, we present a stochastic model that captures NSOs within a given variable. The model employs a data-adaptive decomposition method named empirical mode decomposition (EMD). Irregular oscillatory processes in a given variable can be extracted into a finite number of intrinsic mode functions with the EMD approach. A unique data-adaptive algorithm is proposed in the present paper in order to study the future evolution of the NSO components extracted from EMD.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4706-4726
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• 2019

The paper is devoted to the detailed description of the distributed system for gathering data from Windows-based workstations and servers. The research presented in the beginning demonstrates that neither a solution for gathering data on attacks against Windows based PCs is available at present nor other security tools and supplementary programs can be combined in order to achieve the required attack data gathering from Windows computers. The design of the newly proposed system named Colander is presented, too. It is based on a client-server architecture while taking much inspiration from previous attempts for designing systems with similar purpose, as well as from IDS systems like Snort. Colander emphasizes its ease of use and minimum demand for system resources. Although the resource usage is usually low, it still requires further optimization, as is noted in the performance testing. Colander's ability to detect threats has been tested by real malware, and it has undergone a pilot field application. Future prospects and development are also proposed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.244-247
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• 2020

Data augmentation has been helpful in improving the performance in deep learning, when we have a limited data and random erasing is one of the augmentations that have shown impressive performance in deep learning in multiple domains. But the main issue is that sometime it loses good features when randomly selected region is erased by some random values, that does not improve performance as it should. We target that problem in way that good features should not be lost and also want random erasing at the same time. For that purpose, we introduce new augmentation technique named Intra-Class Random Erasing (ICRE) that focuses on data to learn robust features of the same class samples by randomly exchanging randomly selected region. We perform multiple experiments by using different models including resnet18, VGG16 over variety of the datasets including ESC10, UrbanSound8K. Our approach has shown effectiveness over others methods including random erasing.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.121-127
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• 1997

Multilayer feedforward networks may be applied to identify the deterministic relationship between input and output data. When the results from the network require a high level of assurance, consideration of the stochastic relationship between the input and output data may be very important. Variance is one of the effective parameters to deal with the stochastic relationship. This paper presents a new algroithm for a multilayer feedforward network to learn the variance of dispersed data without preliminary calculation of variance. In this paper, the network with this learning algorithm is named as a variance learning neural network(VALEAN). Computer simulation examples are utilized for the demonstration and the evaluation of VALEAN.

• Journal of Astronomy and Space Sciences
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• v.23 no.3
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• pp.269-288
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• 2006

We revised the OTFTOOL which was developed in Five College Radio Astronomy Observatory (FCRAO) for the On-The-Fly (OTF) observation. Besides the improvement of data resampling function of conventional OTFTOOL, we added a new SELF referencing mode and data pre-reduction function. Since OTF observation data have a large redundancy, we can choose and use only good quality samples excluding bad samples. Sorting out the bad samples is based on the floating level, rms level, antenna trajectory, elevation, $T_{sys}$, and number of samples. And, spikes are also removed. Referencing method can be chosen between CLASSICAL mode in which the references are taken from the OFFs observation and ELLIPSOIDAL mode in which the references are taken from the inner source free region (this is named as SELF reference). Baseline is subtracted with the source free channel windows and the baseline order chosen by the user. Passing through these procedures, the raw OTF data will be an FITS datacube. The revised-OTFTOOL maximizes the advantages of OTF observation by sorting out the bad samples in the earliest stage. And the new self-referencing method, the ELLIPSOIDAL mode, is very powerful to reduce the data. Moreover since it is possible to see the datacube at once without moving them into other data reduction programs, it is very useful and convenient to check whether the data resampling works well or not. We expect that the revised-OTFTOOL can be applied to the facilities of the OTF observation like SRAO, NRAO, and FCRAO.