• Title/Summary/Keyword: 가중치 매개변수 연구

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A Performance Analysis by Adjusting Learning Methods in Stock Price Prediction Model Using LSTM (LSTM을 이용한 주가예측 모델의 학습방법에 따른 성능분석)

  • Jung, Jongjin;Kim, Jiyeon
    • Journal of Digital Convergence
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    • v.18 no.11
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    • pp.259-266
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    • 2020
  • Many developments have been steadily carried out by researchers with applying knowledge-based expert system or machine learning algorithms to the financial field. In particular, it is now common to perform knowledge based system trading in using stock prices. Recently, deep learning technologies have been applied to real fields of stock trading marketplace as GPU performance and large scaled data have been supported enough. Especially, LSTM has been tried to apply to stock price prediction because of its compatibility for time series data. In this paper, we implement stock price prediction using LSTM. In modeling of LSTM, we propose a fitness combination of model parameters and activation functions for best performance. Specifically, we propose suitable selection methods of initializers of weights and bias, regularizers to avoid over-fitting, activation functions and optimization methods. We also compare model performances according to the different selections of the above important modeling considering factors on the real-world stock price data of global major companies. Finally, our experimental work brings a fitness method of applying LSTM model to stock price prediction.

Development of Real-time Inflow Forecasting Models for Securing Reservoir Conservation Storage (이수용량 확보를 위한 실시간 저수지 유입량 예측모형의 개발)

  • Jang, Su-Hyung;Yoon, Jae-Young;Ahn, Jae-Hyun;Kim, Won-Seock;Yoon, Yong-Nam
    • Proceedings of the Korea Water Resources Association Conference
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    • 2005.05b
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    • pp.821-825
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    • 2005
  • 본 연구에서는 홍수조절 용 저수지의 예비방류 시행을 충분히 효과적으로 시행하고 강우종료 후에도 충분한 이수용량이 확보되도록 실시간 강우자료를 이용한 저수지 유입량 예측모형을 개발하였다. 사전예보(기상청 등)에 의한 총 예상강우량과 선행강우량, 현재 저수지 수위를 입력자료로 저수지 유입 총량과 수위변화량을 계산하여 홍수조절 응 저수지의 초기수위저하 및 하류 하도의 홍수방어를 사전에 대비할 수 있는 자료를 제시하였다. 또한, 유역을 하나의 통합시스템으로 구성하고 실제 강우가 시작되면 매시간 현시간 이후 강우가 중단된다는 가정 하에 현시점까지의 우량주상도를 통합시스템에 적용하여 이후 저수지 유입량을 예측하였다. 무한천 예당저수지에 적용하였으며 통합시스템의 구성은 저수지유역을 10개 소유역으로 분할하고 소유역별 홍수유출량은 Clark의 유역추적법, 하도구간은 Muskingum의 하도홍수추적 방법으로 계산되도록 하였다. 그리고 홍수유출시스템 내에는 강우관측소별 티센가중치에 따라 소유역별 평균강우량이 자동으로 입력되도록 하였으며, 예측정확도를 위해 현시간 이전까지 매시간마다 저수지의 수위변동과 실제 방류량으로부터 실측유입량을 산정하여 모형의 매개변수가 자동 보정되도록 하였다. 1995년 8월 23일$\~$8월 26일과 1999년 8월 2일$\~$8월 4일의 집중호우에 대하여 적용한 결과 모형의 예측정확도는 신뢰수준에 있었으며, 이와 같은 자료는 장수형 등(2005)이 제시한 효율적 저수지 운영관리 시스템과 하나로 통합되어 하류 하도의 통수능력을 고려한 홍수방어능력을 극대화한 예비방류의 시행과 강우종료 후에도 이수용량에는 손실이 없는 저수지의 관리방안의 지침이 되는데 효율적이라 판단되었다. 방법을 개발하여 개선시킬 필요성이 있다.>$4.3\%$로 가장 근접한 결과를 나타내었으며, 총 유출량에서도 각각 $7.8\%,\;13.2\%$의 오차율을 가지는 것으로 분석되어 타 모형에 비해 실유량과의 차가 가장 적은 것으로 모의되었다. 향후 도시유출을 모의하는 데 가장 근사한 유출량을 산정할 수 있는 근거가 될 것이며, 도시재해 저감대책을 수립하는데 기여할 수 있을 것이라 판단된다.로 판단되는 대안들을 제시하는 예비타당성(Prefeasibility) 계획을 수립하였다. 이렇게 제시된 계획은 향후 과학적인 분석(세부평가방법)을 통해 대안을 평가하고 구체적인 타당성(feasibility) 계획을 수립하는데 토대가 될 것이다.{0.11R(mm)}(r^2=0.69)$로 나타났다. 이는 토양의 투수특성에 따라 강우량 증가에 비례하여 점증하는 침투수와 구분되는 현상이었다. 경사와 토양이 같은 조건에서 나지의 경우 역시 $Ro_{B10}(mm)=20.3e^{0.08R(mm)(r^2=0.84)$로 지수적으로 증가하는 경향을 나타내었다. 유거수량은 토성별로 양토를 1.0으로 기준할 때 사양토가 0.86으로 가장 작았고, 식양토 1.09, 식토 1.15로 평가되어 침투수에 비해 토성별 차이가 크게 나타났다. 이는 토성이 세립질일 수록 유거수의 저항이 작기 때문으로 생각된다. 경사에 따라서는 경사도가 증가할수록 증가하였으며 $10\% 경사일 때를 기준으로 $Ro(mm)=Ro_{10}{\times}0.797{\times}e^{-0.021s(\%)}$로 나타났다.천성 승모판 폐쇄 부전등을 초래하는 심각한 선천성 심질환이다. 그러나 진단 즉시 직접 좌

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Designing a Blockchain-based Smart Contract for Seafarer Wage Payment (블록체인 기반 선원 임금지불을 위한 스마트 컨트랙트 설계)

  • Yoo, Sang-Lok;Kim, Kwang-Il;Ahn, Jang-Young
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.27 no.7
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    • pp.1038-1043
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    • 2021
  • Guaranteed seafarer wage payment is essential to ensure a stable supply of seafarers. However, disputes over non-payment of wages to seafarers often occur. In this study, an automatic wage payment system was designed using a blockchain-based smart contract to resolve the problem of seafarers' wage arrears. The designed system consists of an information register, a matching processing unit, a review rating management unit, and wage remittance before deploying smart contracts. The matching process was designed to send an automatic notification to seafarers and shipowners if the sum of the weight of the four variables, namely wages, ship type/fishery, position, and license, exceeded a pre-defined threshold. In addition, a review rating management system, based on a combination of mean and median, was presented to serve as a medium to mutually fulfill the normal working conditions. The smart contract automatically fulfills the labor contract between the parties without an intermediary. This system will naturally resolve problems such as fraudulent advance payment to seafarers, embezzlement by unregistered employment agencies, overdue wages, and forgery of seafarers' books. If this system design is commercialized and institutionally activated, it is expected that stable wages will be guaranteed to seafarers, and in turn, the difficulties in human resources supply will be solved. We plan to test it in a local environment for further developing this system.

Flood Risk Estimation Using Regional Regression Analysis (지역회귀분석을 이용한 홍수피해위험도 산정)

  • Jang, Ock-Jae;Kim, Young-Oh
    • Journal of the Korean Society of Hazard Mitigation
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    • v.9 no.4
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    • pp.71-80
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    • 2009
  • Although desire for living without hazardous damages grows these days, threats from natural disasters which we are currently exposed to are quiet different from what we have experienced. To cope with this changing situation, it is necessary to assess the characteristics of the natural disasters. Therefore, the main purpose of this research is to suggest a methodology to estimate the potential property loss and assess the flood risk using a regional regression analysis. Since the flood damage mainly consists of loss of lives and property damages, it is reasonable to express the results of a flood risk assessment with the loss of lives and the property damages that are vulnerable to flood. The regional regression analysis has been commonly used to find relationships between regional characteristics of a watershed and parameters of rainfall-runoff models or probability distribution models. In our research, however, this model is applied to estimate the potential flood damage as follows; 1) a nonlinear model between the flood damage and the hourly rainfall is found in gauged regions which have sufficient damage and rainfall data, and 2) a regression model is developed from the relationship between the coefficients of the nonlinear models and socio-economic indicators in the gauged regions. This method enables us to quantitatively analyze the impact of the regional indicators on the flood damage and to estimate the damage through the application of the regional regression model to ungauged regions which do not have sufficient data. Moreover the flood risk map is developed by Flood Vulnerability Index (FVI) which is equal to the ratio of the estimated flood damage to the total regional property. Comparing the results of this research with Potential Flood Damage (PFD) reported in the Long-term Korea National Water Resources Plan, the exports' mistaken opinions could affect the weighting procedure of PFD, but the proposed approach based on the regional regression would overcome the drawback of PFD. It was found that FVI is highly correlated with the past damage, while PFD does not reflect the regional vulnerabilities.

Business Application of Convolutional Neural Networks for Apparel Classification Using Runway Image (합성곱 신경망의 비지니스 응용: 런웨이 이미지를 사용한 의류 분류를 중심으로)

  • Seo, Yian;Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.24 no.3
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    • pp.1-19
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    • 2018
  • Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.