• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 1993년도 추계학술발표회논문집
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• pp.51-54
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• 1993

In this paper, it is proposed the the SCADA(Supervisory control and Data Acquisition) system in Apartment complex. The proposed SCADA system make use of the computer CRT(Cathod RAy Tube), which automatically observe the Electrical Facility, Elvator Facility, Fire Alarm Facility and process in the real-time data. In order to improve the hardware performance and the information process, the SCADA system composed of master-slave topology and decentralized the supervisory Facility. This system is expected the retrenchment of construction expenditure and the level-up of supervisory execution.

• 대한의용생체공학회:의공학회지
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• 제10권2호
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• pp.157-164
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• 1989

Three-dimensional imaging and manipulation of CT data are becoming increasingly important for deterRing the complex structure and pathologies. Octree which is a hierarchical data model is used to reconstruct three- dimensional objects from CT scans. Orthogonal cross sections are displayed by traverse the octree partially. Arbitrary oblique planes are derived by intersecting the square region of plane and cubic volume of octal node. Thia method enables the display of multi-structured complex organ ann the realization by personal computer.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.191-194
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• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Also, the results of prediction are not a good performance so far, especially in the high-level ozone concentration. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.827-832
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• 2007

Several partial orders on integral partitions have been studied with many applications such as majorizations, capacities of quantum memory and embeddabilities of matrix algebras. In particular, the embeddability, stable embeddability and strong-stable embeddability problems arise for finite dimensional irreducible modules over a complex simple Lie algebra L. We find a sufficient condition for an L-module strong-stably embeds into another L-module using formal character theory.

• 한국환경과학회지
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• 제14권2호
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• pp.141-156
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• 2005

The importance of atmospheric conditions for the assessment of an air pollution situation has been demonstrated by their influence on the various compartments of an air pollution system, comprising all stages from emission to effects. Especially, air pollutants dispersion phenomenon are very sensitive according to wind data. But the discussions of how to apply representative meteorological data in air pollution dispersion model are not frequent in Korean environmental assessment processes. In this study, we investigated the difference of air pollutants dispersion phenomenon using U.S EPA ISCLT3 model according to applying the different meteorological data observed at two points for Seongseo industrial complex of Daegu. Two points are the spot site of Seongseo industrial complex and Daegu meteorological observatory. The winds speed of the spot site were smaller than those of Daegu meteorological observatory. In the winter season, the differences came to about $64\%$ for the period$(I\;February\;2001\~31\;January\;2002)$. Wind directions were also fairly different at two points. The air pollutants dispersion phenomenon estimated from our numerical experiments were also fairly different owing to the meteorological conditions at two points.

• 한국지역사회생활과학회지
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• 제27권3호
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• pp.465-475
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• 2016

This study was conducted to provide basic data regarding the safety of Hwaseong for women. Data regarding sexual violence awareness and damages were collected from 514 women aged 20 to 65 living in Hwaseong. The results were then compared with national survey data from the Ministry of Gender Equality and Family in 2010 and 2013. Hwaseong is a wide city composed of an urban and urban-rural complex. Data were analyzed to identify regional differences between urban and urban-rural complexes and educational differences between below college graduates and above university graduates. The ratios of awareness of sexual violence behavior, laws, and services were somewhat lower than the 2013 national research ratios. Second, women in the urban-rural complex showed a higher awareness of sexual violence behaviors and higher level of sexual violence myths. Third, the tendencies of sexual violence damages were similar to the 2013 national research. Fourth, women with higher education showed a higher level of sexual violence myths and a higher ratio of sexual violence damage.

• 센서학회지
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• 제16권5호
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• pp.361-368
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• 2007

Small size real-time ECG signal analysis function by QRS-complex detection was put into sensor nodes. Wireless sensor nodes attached on the patient’s body transmit ECG data continuously in normal u-healthcare system. So there are heavy communication traffics between sensor nodes and gateways. New developed platform for real-time analysis of ECG signals on sensor node can be used as an advanced diagnosis and alarming system for healthcare. Sensor node does not need to transmit ECG data all the time in wireless sensor network and to server PC via gateway. When sensor node detects suspicion or abnormality in ECG, then the ECG data in the network was transmitted to the server PC for further powerful analysis. This system can reduce data packet overload and save some power in wireless sensor network. It can also increase the server performance.

• 자원리싸이클링
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• 제29권1호
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• pp.62-69
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• 2020

산업단지 내 입주기업들의 제조공정에서는 미세먼지 생성 원인물질인 질산화물(NOx), 황산화물(SOx), 휘발성 유기화합물(VOCs) 등이 다양한 형태로 배출되고 있다. 본 연구에서는 효과적인 산업단지 미세먼지 저감을 위해 산재해 있는 공공데이터를 활용하여 산업단지별 특성을 분석하고 미세먼지 감축 기술과 매칭하여 미세먼지를 감축할 수 있는 최적화 감축 방안을 제시하였다. 데이터를 기반으로 한 산업단지 별 맞춤형 기술 및 설비 적용은 미세먼지 전구물질을 공정에서 사전에 감축함으로써 산업단지 미세먼지 뿐만 아니라 제조업 미세먼지 감축을 위한 효과적인 대안이 될 것이다.

• Clinical and Experimental Pediatrics
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• 제65권5호
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• 산업경영시스템학회지
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• 제46권2호
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• pp.1-12
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• 2023

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.