• Proceedings of the KIEE Conference
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• summer
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• pp.85-87
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• 2004

Supervisory control and data acquisition (SCADA) systems are essential parts of power system which employ a wide range of computers and communication technologies. The traditional SCADA system is mainly for information exchange in only one company, and the information is provided only to the operator or administrator. But in the deregulated environment, we need much more information, which can be exchanged among different companies. With the rapid development of internet, we can use it to access information easily. This paper proposes web technologies to be applied in power system in order to display some important information through accessing data from database, and to realize the real time control of the substation. The functions of SCADA system will be implemented by a set of Web-based components. The monitoring and control of standard 154[kV] substation model is already realized in the laboratory test. The Web-based SCADA system is able to provide sufficient information and control for pow or system through an efficient and economical way.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.325-335
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• 2006

This paper evaluates and maps the quality of life in the Atlanta, Georgia metropolitan area in 2000. Three environmental variables from Landsat TM data, four socioeconomic variables from census data, and a hazard-related variable from toxic release inventory (TRI) database were integrated into a geographic information system (GIS) environment for the quality of life assessment. To solve the incompatibility problem in areal units among different data, the four socioeconomic variables aggregated by zonal units were spatially disaggregated into individual pixels. Principal components analysis (PCA) was employed to integrate and transform environmental, socioeconomic, and hazard-related variables into a resultant quality of life score for each pixel. Results indicate that the highest quality of life score was found around Sandy Springs, Roswell, Alphretta, and the northern parts of Fulton County along Georgia 400 whereas the lowest quality of life score was clustered around Smyma of Cobb County, the inner city of Atlanta, and Hartsfield-Jackson International Airport. The results also reveals that normalized difference vegetation index (NDVI) and relative risk from TRI facilities are two versatile indicators of environmental and socioeconomic quality of an urban area in the United States.

• Safety and Health at Work
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• v.7 no.2
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• pp.102-110
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• 2016

Background: This work presents an experimental and modeling study of the electromagnetic environment in the vicinity of a high voltage substation located in eastern Algeria (Annaba city) specified with a very high population density. The effects of electromagnetic fields emanating from the coupled multi-lines high voltage power systems (MLHV) on the health of the workers and people living in proximity of substations has been analyzed. Methods: Experimental Measurements for the Multi-lines power system proposed have been conducted in the free space under the high voltage lines. Field's intensities were measured using a referenced and calibrated electromagnetic field meter PMM8053B for the levels 0 m, 1 m, 1.5 m and 1.8 m witch present the sensitive's parts as organs and major functions (head, heart, pelvis and feet) of the human body. Results: The measurement results were validated by numerical simulation using the finite element method and these results are compared with the limit values of the international standards. Conclusion: We project to set own national standards for exposure to electromagnetic fields, in order to achieve a regional database that will be at the disposal of partners concerned to ensure safety of people and mainly workers inside high voltage electrical substations.

• Journal of Platform Technology
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• v.6 no.4
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• pp.26-33
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• 2018

In this paper, we propose an image retrieval algorithm for real-time processing and design it as hardware. The proposed method is based on the classification of BoWs(Bag of Words) algorithm and proposes an image search algorithm using bit stream. K-fold cross validation is used for the verification of the algorithm. Data is classified into seven classes, each class has seven images and a total of 49 images are tested. The test has two kinds of accuracy measurement and speed measurement. The accuracy of the image classification was 86.2% for the BoWs algorithm and 83.7% the proposed hardware-accelerated software implementation algorithm, and the BoWs algorithm was 2.5% higher. The image retrieval processing speed of BoWs is 7.89s and our algorithm is 1.55s. Our algorithm is 5.09 times faster than BoWs algorithm. The algorithm is largely divided into software and hardware parts. In the software structure, C-language is used. The Scale Invariant Feature Transform algorithm is used to extract feature points that are invariant to size and rotation from the image. Bit streams are generated from the extracted feature point. In the hardware architecture, the proposed image retrieval algorithm is written in Verilog HDL and designed and verified by FPGA and Design Compiler. The generated bit streams are stored, the clustering step is performed, and a searcher image databases or an input image databases are generated and matched. Using the proposed algorithm, we can improve convenience and satisfaction of the user in terms of speed if we search using database matching method which represents each object.

• ETRI Journal
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• v.43 no.1
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• pp.109-119
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• 2021

Self-recovery is a tamper-detection and image recovery methods based on data hiding. It generates two types of data and embeds them into the original image: authentication data for tamper detection and reference data for image recovery. In this paper, a region-based scalable self-recovery (RSS) method is proposed for salient-object images. As the images consist of two main regions, the region of interest (ROI) and the region of non-interest (RONI), the proposed method is aimed at achieving higher reconstruction quality for the ROI. Moreover, tamper tolerability is improved by using scalable recovery. In the RSS method, separate reference data are generated for the ROI and RONI. Initially, two compressed bitstreams at different rates are generated using the embedded zero-block coding source encoder. Subsequently, each bitstream is divided into several parts, which are protected through various redundancy rates, using the Reed-Solomon channel encoder. The proposed method is tested on 10 000 salient-object images from the MSRA database. The results show that the RSS method, compared to related methods, improves reconstruction quality and tamper tolerability by approximately 30% and 15%, respectively.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.989-998
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• 2014

Recently volcanic eruptions and activities occurring in many parts of world have become a common global concern to many countries. The severity of these Volcanic disasters, such as of Mt. Eyjafjallajokull in Iceland and Mt. Merapi in Indonesia, have caused damages and causalities reaching astronomical levels. The infrastructure is categorized into 18sections that appropriately reflecting the survey data collected from various government agents, current inhabitant and engineers to accumulate a database on the priorities and preferences of restoring and reconstructing many kinds of infrastructure and facilities. The survey data was collect by using the "Likert 5 Scale Method" which emphasized the importance and priority of reconstruction and restoration for the specific facilities and infrastructures. The data was corrugated, organized and used in plotting and planning a strategic recovery agenda. The survey results were analyzed and verified to ensure the validity and reliability of the data by using chi-square test. This paper presents that recovery period and recovery cost to the total damage of infrastructure and facilities were used to make a recovery network with implemented construction management method. The research is expected that a more efficient and prompt recovery protocol and recovery plan can be executed and can be use as a reference and database.

• Archives of design research
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• v.15 no.1
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• pp.191-202
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• 2002

As family types have been diverse, patterns of living and living space became diverse as much as users are. Therefore, it is needed to provide various remodeled design of living space corresponding to changes of users'living patterns, and to provide these remodeling process to users directly on the web. In this paper, use scenario for the Internet-based Virtual Apartment Remodeling Simulator is researched as an export system to remodel space in accordance with users diverse lifestyle paradigm and the website is developed. The study consists of four parts. First, the general concept of remodeling, including the range and types of remodeling, are defined, and the misleading terms in this field are reviewed and organized by secondary research Second, fixed factors and variable factors are differentiated in the complex building for residence and business that was decided as a basic building type in this study. Third, there needed a database for consulting, final material, pre-estimation real estimation for simulation of remodeling. This database was introduced along with floor plan and elevation. Finally, the remodeling simulator is presented by the case study developed on the web. The system structure and use scenario are also presented. In order to present and inspect design alternatives, prototype was produced. The Final simulator was enhanced by defeating problems regarding interface efficiency and missing information of existing online site.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.79-86
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• 2005

In this paper, we propose a new LINF(Linear Independent Non-negative Factorization) algorithm for real-time face recognition systea This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction Part we applied subtraction image, the detection of eye and mouth region , and normalization method, and then in the face recognition Part we used LINF in extracted face candidate region images. The existing recognition system using only PCA(Principal Component Analysis) showed low recognition rates, and it was hard in the recognition system using only LDA(Linear Discriminants Analysis) to apply LDA directly when the training set is small. To overcome these shortcomings, we reduced dimension as the matrix that had non-negative value to be different from former eigenfaces and then applied LDA to the matrix in the proposed system We have experimented using self-organized DAIJFace database and ORL database offered by AT(')T laboratory in Cambridge, U.K. to evaluate the performance of the proposed system. The experimental results showed that the proposed method outperformed PCA, LDA, ICA(Independent Component Analysis) and PLMA(PCA-based LDA mixture algorithm) method within the framework of recognition accuracy.