• Journal of Electrical Engineering and Technology
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• 제3권3호
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• pp.391-400
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• 2008

Multi-pulse topology of converters using elementary six-pulse GTO - VSC (gate turn off based voltage source converter) operated under fundamental frequency switching (FFS) control is widely adopted in high power rating static synchronous compensators (STATCOM). Practically, a 48-pulse ($6{\times}8$ pulse) configuration is used with the phase angle control algorithm employing proportional and integral (PI) control methodology. These kinds of controllers, for example the ${\pm}80MVAR$ compensator at Inuyama switching station, KEPCO, Japan, employs two stages of magnetics viz. intermediate transformers (as many as VSCs) and a main coupling transformer to minimize harmonics distortion in the line and to achieve a desired operational efficiency. The magnetic circuit needs altogether nine transformers of which eight are phase shifting transformers (PST) used in the intermediate stage, each rating equal to or more than one eighth of the compensator rating, and the other one is the main coupling transformer having a power rating equal to that of the compensator. In this paper, a two-level 48-pulse ${\pm}100MVAR$ STATCOM is proposed where eight, six-pulse GTO-VSC are employed and magnetics is simplified to single-stage using four transformers of which three are PSTs and the other is a normal transformer. Thus, it reduces the magnetics to half of the value needed in the commercially available compensator. By adopting the simple PI-controllers, the model is simulated in a MATLAB environment by SimPowerSystems toolbox for voltage regulation in the transmission system. The simulation results show that the THD levels in line voltage and current are well below the limiting values specified in the IEEE Std 519-1992 for harmonic control in electrical power systems. The controller performance is observed reasonably well during capacitive and inductive modes of operation.

• 시스템엔지니어링학술지
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• 제19권2호
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• pp.1-17
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• 2023

This study aimed to examine the characteristics of ESG activities among major domestic companies in the carbon-emitting industry compared to industries with lower emissions, as ESG has emerged as a significant agenda across various industries. Departing from the traditional focus on the "why" of ESG, which primarily centers around financial performance, this research sought to uncover the "how" of effective ESG management in domestic companies. The analysis involved studying the sustainability reports of 124 companies using the Global Reporting Initiative (GRI) indicators and comparing high-emitting and non-high-emitting industries. The findings revealed industry-specific patterns in companies' ESG activities, providing valuable insights for future ESG evaluations and assessments. Furthermore, the advancement of rating analysis methods holds implications for ESG rating agencies and financial authorities in terms of policy-making.

• 한국지능시스템학회논문지
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• 제19권2호
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• pp.242-247
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• 2009

지능형 추천 시스템은 사용자의 요청에 응답하는 수동적인 시스템이 아닌 사용자가 원하는 서비스를 제안하는 시스템으로서 최근 콘텐츠 서비스 분야에 많이 개발되고 있다. 이러한 지능형 추천 시스템은 콘텐츠 개인화 서비스에 응용되고 있으며 대표적인 추천기법으로 내용기반과 협업적 필터링 기법이 있다. 본 연구에서는 협업적 필터링 및 퍼지 시스템을 이용하여 추천 시스템의 기반 기술인 예측 시스템을 제안하였다. 제안한 예측 시스템은 사용자의 과거 영화평가 정보를 바탕으로 영화에 대한 평가점수를 예측한다. 영화평가 예측시스템의 성능은 영화 평가점수의 실제값과 예측값의 오차를 RMSE(root mean square error) 방법으로 계산한 후 기존의 영화평가 시스템 RMSE 값과 비교하여 평가하였다. 본 연구를 통해 제안한 영화평가 예측시스템이 추천 시스템의 기반 기술로서 활용이 가능하고 다른 멀티미디어 컨텐츠 서비스 추천에도 응용이 가능할 것으로 기대한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5321-5334
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• 2019

Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on "place" and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.533-538
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• 2005

An outline of an Artificial Neural Network (ANN) model for bridge condition rating and the results of a pilot study are presented in this paper. Most BMS implementation systems involve an extensive range of data collection to operate accurately. It takes many years to effectively implement a BMS using existing methodologies. This is due to unmatched data requirements. Such problems can be overcome by adopting the ANN model presented in this paper. The objective of the proposed model is to predict bridge condition ratings using historical bridge inspection data for effective BMS operation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.331-334
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• 2006

Original Support Vsctor Machines (SVMs) by Vapnik were used for binary classification problems. Some researchers have tried to extend original SVM to multiclass classification. However, their studies have only focused on classifying samples into nominal categories. This study proposes a novel multiclass SVM model in order to handle ordinal multiple classes. Our suggested model may use less classifiers but predict more accurately because it utilizes additional hidden information, the order of the classes. To validate our model, we apply it to the real-world bond rating case. In this study, we compare the results of the model to those of statistical and typical machine learning techniques, and another multi class SVM algorithm. The result shows that proposed model may improve classification performance in comparison to other typical multiclass classification algorithms.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.717-723
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• 2019

Smart systems and services aim to facilitate growing urban populations and their prospects of virtual-real social behaviors, gig economies, factory automation, knowledge-based workforce, integrated societies, modern living, among many more. To satisfy these objectives, smart systems and services must comprises of a complex set of features such as security, ease of use and user friendliness, manageability, scalability, adaptivity, intelligent behavior, and personalization. Recently, artificial intelligence (AI) is realized as a data-driven technology to provide an efficient knowledge representation, semantic modeling, and can support a cognitive behavior aspect of the system. In this paper, an integration of AI with the smart systems and services is presented to mitigate the existing challenges. Several novel researches work in terms of frameworks, architectures, paradigms, and algorithms are discussed to provide possible solutions against the existing challenges in the AI-based smart systems and services. Such novel research works involve efficient shape image retrieval, speech signal processing, dynamic thermal rating, advanced persistent threat tactics, user authentication, and so on.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.129-136
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• 2020

Augmented reality (AR) technology assists construction workers by superimposing additional virtual information onto their real worksite environments. Ideally, this provides them with a better understanding of their tasks and hence boosts task performance. However, the additional information that AR places in users' field of view could limit their ability to understand what is going on in their surroundings and to predict how conditions may change in the near future. AR-assisted systems on construction sites could therefore expose their users to safety risks due to disturbance from the system. Hence, it is important to understand how AR-assisted systems can block users' understanding of their immediate environments, and in turn, how worksite safety in the construction industry could be improved through better design of such systems. This preliminary research conducted a laboratory experiment that simulated rebar inspection tasks and compared the situational awareness of AR users against that of subjects using traditional paper-based inspection methods, as measured by the Situation Awareness Rating Technique. Based on the results, we discuss the safety impact of head-mounted AR-assisted displays on situational awareness during construction tasks.

• 산업기술연구
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• 제30권B호
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• pp.11-16
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• 2010

To provide a development strategy on the method which assesses a potential risk of nationwide emerging IT infrastructure in planning and design phase, and to classify the assessment result into 5 levels is the goal of this research. The development strategy provided in this paper could improve a benefit-cost-ratio of investments on emerging IT infrastructure. With a premature assessment of the potential risks of a nationwide emerging IT infrastructure which needs astronomical amount of public funds, it could show a way of systematic investments on security systems and improve a benefit-cost-ratio of investments on emerging IT infrastructure. Also, this approach might improve the safety of nationwide IT infrastructure. It could identify and provide an optimized solution for the potential risks of nationwide IT infrastructure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.542-558
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• 2023

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.