• Proceedings of the KSRS Conference
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• v.1
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• pp.487-490
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• 2006

Ocean Color Monitor (OCM) onboard the Indian Remote Sensing Satellite IRS-P4 has been used to retrieve chlorophyll concentration in the Bay of Bengal and the Arabian Sea using a bio-optical algorithm. Cloud masking and atmospheric corrections have been performed before applying mapping function to derive chlorophyll concentration from IRS-P4 OCM data. We have retrieved chlorophyll concentration from OCM, and MODIS during the summer and winter season along the eastern and western coast of India at every 1 degree latitude at increasing distance (25, 50, 100, 150 and 200km) away from the coast as well as near river mouths for the period 2000-2003. We have also studied spatial and temporal dynamics of monthly MODIS Aqua (for period July 2002-April 2004). The seasonal dynamics of chlorophyll concentration over the Bay of Bengal and the Arabian Sea have been discussed using OCM and MODIS for both the coastal region and the open sea.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.490-495
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• 2009

An approach to 3D visualization of multiple sound sources has been developed with the application of a moving array technique. Frequency-domain beamforming algorithm is used to generate a beam power map and the sound source is modeled as a point source. When a conventional delay and sum beamformer is used, it is considered that 2D distribution of sensors leads to have deficiency in spatial resolution along a measurement distance. The goal of moving an array in this study is to form 3D array aperture surrounding multiple sound sources so that the improved spatial resolution in a virtual space can be expected. Numerical simulation was made to examine source localization capabilities of various shapes of array. The 3D beam power maps of hemispherical and spherical distribution are found to have very sharp resolution. For experiments, two sound sources were placed in the middle of defined virtual space and arc-shaped line array was rotated around the sources. It is observed that spherical array show the most accurate determination of multiple sources' positions.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.5
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• pp.490-495
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• 2015

As flying robots are becoming popular, there are increased needs to use themforsuch purposes as parcel delivery, serving in restaurants, and stage performances. To control flying robots such as quad copters, localization is essential. In order to properly position flying robots, many techniques are in development, including IR (infra-red)-based systemswhich catch markers on a flying robot in order that it can position itself. However, this technique demonstrates only short coverage. Furthermore, localization from inertial sensors diverges as time passes. For this reason, this paper suggests a TWR (two-way ranging) based positioning technique. Despite the weaknesses in currently available TWR system, this paper suggests a self-positioning and outlier detection technique in order to provide reliable position information with a faster update rate. The self-positioning system sends a shorter message which reduces wireless traffic. By detecting and removing outlier measurements, a positioning result with better accuracy is acquired. Finally, this paper shows that the suggesting system detects outlierssequentially from less than half the number of anchors in localization system according to the degree of outlier in measurement and the noise level. By performing an outlier algorithm, better positioning accuracy is acquired as shown in the experimental result.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.283-287
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• 2010

Stochastic simulation of hydrologic data has been widely developed for several decades. However, despite the several advances made in literature still a number of limitations and problems remain. In the current study, some stochastic simulation approaches tackling some of the existing problems are discussed. The presented models are based on nonparametric techniques such as block bootstrapping, and K-nearest neighbor resampling (KNNR), and kernel density estimate (KDE). Three different types of the presented stochastic simulation models are (1) Pilot Gamma Kernel estimate with KNNR (a single site case) and (2) Enhanced Nonparametric Disaggregation with Genetic Algorithm (a disaggregation case). We applied these models to one of the most challenging and critical river basins in USA, the Colorado River. These models are embedded into the hydrological software package, Pros and cons of the models compared with existing models are presented through basic statistics and drought and storage-related statistics.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.490-495
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• 1999

In this paper. a learning method VoD-EBP for neural networks is proposed, which learn patterns by error back propagation. Based on Voronoi diagram, the method initializes the weights of the neural networks systematically, wh~ch results in faster learning speed and alleviated local optimum problem. The method also shows better the reliability of the design of neural network because proper number of hidden nodes are determined from the analysis of Voronoi diagram. For testing the performance, this paper shows the results of solving the XOR problem and the parity problem. The results were showed faster learning speed than ordinary error back propagation algorithm. In solving the problem, local optimum problems have not been observed.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.488-490
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• 2003

This paprer presents the evlauation method for transmission marginal loss factors(MLFs) considering the electrical distance. Generally, MLFs are represented as the sensitivity of transmission losses, which is computed from the change of generation by the change of the load. MLFs are classified as load-focused MLFs and generator-focused MLFs. The existing evaluation method for generator focused MLFs has the limit not reflecting the characteristic of power systems since the method has been introduced the assumption which the output of a generator is supplied to all of the load buses on the power system. Therefore, to overcome the limit of evaluation method for generator-focused MLFs, we have applied the process, which it approximately can find the load buses that supplied a generator to the method. We have applied the proposed method to the simple 5-bus system because the proposed method is not analytic but the hybrid method incorporated the Kirschen and Bialek's algorithm to the existing analytic method to find the load buses supplied by a generator.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.480-490
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• 2019

Nowadays, Electronic Medical Record (EMR) has just implemented at few hospitals for Outpatient Department (OPD). OPD is the diversified data, it includes demographic and diseases of patient, so it need to be clustered in order to explore the hidden rules and the relationship of data types of patient's information. In this paper, we propose a novel approach for unsupervised clustering of patient's demographic and diseases in OPD. Firstly, we collect data from a hospital at OPD. Then, we preprocess and transform data by using powerful techniques such as standardization, label encoder, and categorical encoder. After obtaining transformed data, we use some strong experiments, techniques, and evaluation to select the best number of clusters and best clustering algorithm. In addition, we use some tests and measurements to analyze and evaluate cluster tendency, models, and algorithms. Finally, we obtain the results to analyze and discover new knowledge, meanings, and rules. Clusters that are found out in this research provide knowledge to medical managers and doctors. From these information, they can improve the patient management methods, patient arrangement methods, and doctor's ability. In addition, it is a reference for medical data scientist to mine OPD dataset.

• Fashion & Textile Research Journal
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• v.23 no.4
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• pp.480-490
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• 2021

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.71-71
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• 2020

최근 기후변화의 영향으로 극심한 가뭄과 홍수가 발생하고 기온 또한 꾸준히 상승하고 있으며, 이러한 변화에 대응하기 위해 전 세계에서 이산화탄소를 줄이고 국제 에너지 시장을 재구성하려는 시도가 꾸준히 이루어지고 있다. World Energy Outlook(2012)에 따르면 특히 에너지 시장에서 개발도상국의 수력분야 개발투자가 2035년까지 15,490억 달러에 이를 것으로 전망됨에 따라 국내에서 해외 수력발전사업에 적극적으로 나서고 있다. 그러나 국내와는 달리 댐 건설의 사전조사에 필요한 자료가 없거나 구축하는데 문제가 있어 손쉽게 구할 수 있는 자료로 사전에 수력발전 댐 적지를 조사할 수 있는 기술의 개발이 필요하다. 따라서 본 연구에서는 수력발전용 댐 위치 결정을 위한 예비 적지 분석 알고리즘을 개발하고, 분석 알고리즘에 위성영상자료인 30m 해상도의 ASTGTM(ASTER Global Digital Elevation Model)와 500m 해상도의 MCD12Q1(MODIS/Terra Aqua Land Cover) 토지피복자료를 사용하고자 한다. 예비 적지 분석 알고리즘은 DEM의 전처리, 하천망생성, 유역분할과 지형정보를 고려한 자동적지탐색과 댐 건설시 수몰면적에 따른 보상면적 산정 알고리즘을 포함하고 있으며 Python기반의 오픈소스 GIS로 구현되었다. 적지산정은 DEM으로부터 낙차, 도달시간, 내용적곡선과 같은 지형정보와 토지피복도를 통한 보상면적을 기반으로 순위를 매겨 사용자에게 최적의 위치들을 표출한다. 본 연구의 결과는 향후 해외 수력 댐 적지 예비분석 및 해외 수력산업 진출을 지원할 수 있을 것으로 기대된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.490-492
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• 2016

Pancreatic ultrasound imaging is used to diagnose pancreatic hyperplasia, pancreatic steatosis, pancreatic cancer and the like. If the diagnosis of pancreatic steatosis is pancreatic parenchyma echo shades splashes spleen than in the pancreas ultrasound it determines that the fat is deposited. And research on ultrasound imaging of pancreatic cancer but is actively conducted research studies on pancreatic steatosis is insufficient In addition, pancreatic steatosis is often an error in accordance with the diagnostic criteria are vague and subjective diagnosis of the artisan. This study was a quantitative analysis using the feature value extracting a feature of an image extracted by applying a parameter to the algorithm GLCM image of the normal and pancreatic fat. Setting a region of interest ($5{\times}5pixel$) in the mild 89 case, moderate 89 case, severe 89 case, total image 267 case using GLCM algorithm, and using the Autocorrelation, Sum average, Sum of squares, Sum varience 4 kinds parameter in each image It was analyzed.