• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.3
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• pp.115-125
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• 2013

The systematic management of river is difficult due to various environmental factors such as season and terrain deformation. Especially, river terrain are rapidly changing by natural and anthropogenic factors such as torrential rain during the summer and river development projects. Thus in this conditions, building the advanced river management system is an essential condition to support the ongoing management of survey data and to acquire data regularly through river terrain survey in order to maintain an active river. The need to build an efficient system have been increased through the enhancement and advancement of River Management Geographic Information Systems(RIMGIS). In this study, database design system and Riverbed Change Data Management Program was developed for systematic management of new river terrain survey data and the efficient use of river data dynamic changes. The key features are construction of river survey data, cross and longitudinal section monitoring and analysis of riverbed change data. Maintenance tasks which can be utilized in river-based architecture was constructed. The expected results are to be able to manage river systematically, and utilization of river topographic survey data efficiently for river maintenance work.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.599-605
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• 2004

기존의 구축된 GIS 컴포넌트 혹은 개발 중이거나 향후개발을 목표로 설계단계에 있는 컴포넌트들의 최종 목표는 재사용성과 상호운용성의 가능성 여부이다. 하지만 컴포넌트 개발에 있어 시스템 개발환경의 다양성으로 인하여 그 재활용성은 생각만큼 쉬운 작업이 아니며, 특히 공간정보를 다루고 있는 GIS(Geographic Information System)분야에서의 GIS 컴포넌트 재활용은 전 세계의 산재한 각 데이터형의 포맷, 개발 환경, 운영환경을 고려하여 볼 때 시급한 일임에도 불구하고 그에 대한 노력이 상당히 미진한 실 정 이 다. 본 논문에서는 GIS 애플리케이션을 보다 효율적이고 유용하게 개발하기 위하여 GIS 컴포넌트의 개발과 관리에 이르는 전 과정을 관리 감독할 수 있는 COGIS(Component Oriented Geographic Information System)을 제안하고, COGIS 프로세스의 가이드라인이며 GIS 컴포넌트의 기능적인 면을 정의하기 위한 GCA(GIS based Component Architecture) 아키텍처를 제안하였다. 아울러 GIS 컴포넌트의 메타데이터를 분류 및 정의하여 GIS 컴포넌트의 비 기능적면을 제시하고 이를 이용하여 웹 기반 GIS 컴포넌트 등록/검색 에이전트 시스템을 개발하였으며 기존 GIS 컴포넌트 재사용 및 확장, 신규 컴포넌트의 등록, 검색이 가능하도록 한다. 사례연구로 웹 상에서 산불 발생 위험지수 표출을 위한 GIS 공간 분포도 작성이 쉽게 이루어지도록 2FDRV.avx와 2FDRC.exe 컴포넌트를 개발하였으며, COGIS 프로세스의 컴포넌트 관리방법을 통하여 여러 관련 컴포넌트를 조합함으로써 웹 기반 산불위험지수예보시스템을 구축하였다.입력 근거의 확보’, ‘갱신주체별 역할의 정의 및 유지관리 기준의 설정’, ‘분야별업무 특성을 고려한 관련 기준의 마련 및 타 시스템과 연계되는 항목을 고려한 절차 정의’ 등에 대한 다양한 접근을 시도하였다. 본 연구에서 제시하는 유지관리 모델을 기반으로 각 지자체별로 적절한 컨설팅이 진행되고 이에 따라 담당자의 실천이 이루어진다면 지자체 GIS의 투자대비 효과에 대한 기대는 이상이 아닌 현실로 다가오게 될 것이다.가오게 될 것이다. 동일하게 25%의 소유권을 가지고 있다. ？스굴 시추사업은 2008년까지 수행될 계획이며, 시추작업은 2005년까지 완료될 계획이다. 연구 진행과 관련하여, 공동연구의 명분을 높이고 분석의 효율성을 높이기 위해서 시료채취 및 기초자료 획득은 4개국의 연구원이 모여 공동으로 수행한 후의 결과물을 서로 공유하고, 자세한 전문분야 연구는 각 국의 대표기관이 독립적으로 수행하는 방식을 택하였다 ？스굴에 대한 제1차 시추작업은 2004년 3월 말에 실시하였다. 시추작업 결과, 약 80m의 시추 코아가 성공적으로 회수되어 현재 러시아 이르쿠츠크 지구화학연구소에 보관중이다. 이 시추코아는 2004년 8월 중순경에 4개국 연구팀원들에 의해 공동으로 기재된 후에 분할될 계획이다. 분할된 시료는 국내로 운반되어 다양한 전문분야별 연구에 이용될 것이다. 한편, 제2차 시추작업은 2004년 12월에서 2005년 2월 사이에 실시될 계획이다. 수백만년에 이르는 장기간에 걸쳐 지구환경변화 기록이 보존되어 있는 ？스굴호에 대한 시추사업은 후기 신생대 동안 유라시아 대륙 중부에서 일어난 지구환경 및 기후변화를 이해함과 동시에 이러한 변화가 육상생태계 및 지표지질환경에 미친 영향을 이해하는데 크게 기여할 것이다.

• Journal of Information Technology and Architecture
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• v.10 no.1
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• pp.79-91
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• 2013

This paper proposes a systematic methodology that could be used to decide which one shows the best performance among space filling curves (SFCs) in applying lower-dimensional transformations to histogram sequences. A histogram sequence represents a time-series converted from an image by the given SFC. Due to the high-dimensionality nature, histogram sequences are very difficult to be stored and searched in their original form. To solve this problem, we generally use lower-dimensional transformations, which produce lower bounds among high dimensional sequences, but the tightness of those lower-bounds is highly affected by the types of SFC. In this paper, we attack a challenging problem of evaluating which SFC shows the better performance when we apply the lower-dimensional transformation to histogram sequences. For this, we first present a concept of spatial locality, which comes from an intuition of "if the entries are adjacent in a histogram sequence, their corresponding cells should also be adjacent in its original image." We also propose spatial locality preservation metric (slpm in short) that quantitatively evaluates spatial locality and present its formal computation method. We then evaluate five SFCs from the perspective of slpm and verify that this evaluation result concurs with the performance evaluation of lower-dimensional transformations in real image matching. Finally, we perform k-NN (k-nearest neighbors) search based on lower-dimensional transformations and validate accuracy of the proposed slpm by providing that the Hilbert-order with the highest slpm also shows the best performance in k-NN search.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.225-232
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• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.4
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• pp.383-392
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• 2000

Currently, multiprocessors are evaluated almost exclusively with scientific applications. Commercial applications are rarely explored because it is difficult to obtain the source codes of commercial DBMS. Even when the source code is available, such as for POSTGRES, understanding the source code enough to perform detailed meaningful performance evaluations is a daunting task for computer architects.To evaluate multiprocessors with commercial applications, we have developed our own DBMS, called EZDB. EZDB is a parallelized DBMS, loosely inspired from POSTGRES, and running on top of a software architecture simulator. It is capable of executing parallel programs written in SQL. Contrary to POSTGRES, EZDB is not intended as a prototype for a production-quality DBMS. Its purpose is to easily run and evaluate the performance of commercial applications on multiprocessor architectures. To illustrate the usefulness of EZDB, we showed the cache performance data collected for the TPC-B benchmark on a shared-memory multiprocessor. The simulation results showed that the data structures exhibited unique sharing characteristics and that their locality properties and working sets were very different from those in scientific applications.

• The Journal of the Korea Contents Association
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• v.12 no.8
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• pp.411-419
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• 2012

In case that R&D results from R&D projects are well managed and archived, the research institutes can transfer the valuable technologies related to R&D results with some costs to corporations. However, it is still difficult to maintain and reuse R&D results because they are managed by each person or each department and not integrated between R&D results. Therefore, the government should undertake to manage R&D results overall by collecting meta data and distribute analyzed information from meta data. Each researching institute also makes an efforts to manage R&D results focusing on their reusing. For this purpose, in this paper, we present a process to manage R&D results; insert meta data of R&D results to the system, upload files of R&D results to the database of the system, inquire, and use meta data of R&D results. Based on the process, we design a system architecture for managing R&D results. In addition, it should be mainly considered to design a global schema for integrating R&D results into one database. The system shows detailed information on R&D results and provides R&D results conveniently to users. We expect that we may reduce the cost of reusing R&D results and improve the quality of R&D results with designing efficiently a process and a global schema of R&D result management system.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.1
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• pp.15-23
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• 2009

Microprocessors, which need correct arithmetic operations, have been the subject of in-depth research in relation to soft errors. Of the existing microprocessor devices, the memory cell is the most vulnerable to soft errors. Moreover, when soft errors emerge in a memory cell, the processes and operations are greatly affected because the memory cell contains important information and instructions about the entire process or operation. Users do not realize that if soft errors go undetected, arithmetic operations and processes will have unexpected outcomes. In the field of architectural design, the tool that is commonly used to detect and correct soft errors is the error check and correction code. The Itanium, IBM PowerPC G5 microprocessors contain Hamming and Rasio codes in their level-2 cache. This research, however, focuses on huge server devices and does not consider power consumption. As the operating and threshold voltage is currently shrinking with the emergence of high-density and low-power embedded microprocessors, there is an urgent need to develop ECC (error check correction) circuits. In this study, the in-output data of the level-2 cache were analyzed using SimpleScalar-ARM, and a 32-bit H-matrix for the level-2 cache of an embedded microprocessor is proposed. From the point of view of power consumption, the proposed H-matrix can be implemented using a schematic editor of Cadence. Therefore, it is comparable to the modified Hamming code, which uses H-spice. The MiBench program and TSMC 0.18 um were used in this study for verification purposes.