• Spatial Information Research
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• v.19 no.1
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• pp.107-116
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• 2011

Due to development of environment of wireless Internet, location based services on the basis of spatial data have been increased such as real time traffic information as well as CNS(Car Navigation System) to provide mobile user with route guidance to the destination. However, the current application adopting the file-based system has limitation of managing and storing the huge amount of spatial data. In order to supplement this challenge, research which is capable of managing large amounts of spatial data based on embedded database system is surely demanded. For this reason, this study aims to suggest the lossless compression technique by using the hybrid approach of BytePacking and Lempel-Ziv which can be applicable in DBMS so as to save a mass spatial data efficiently. We apply the proposed compression technique to actual the Seoul and Inchcon metropolitan area and compared the existing method with suggested one using the same data through analyzing the query processing duration until the reconstruction. As a result of comparison, we have come to the conclusion that suggested technique is far more performance on spatial data demanding high location accuracy than the previous techniques.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.593-602
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• 2005

Need for machine-understandable web (Semantic web) is increasing in order for users to exactly understand Web information resources and currently there are two main approaches to solve the problem. One is the Topic map developed by the ISO/IEC JTC 1 and the other is the RDF (Resource Description Framework), one of W3C standards. Semantic web supports all of the metadata of the Web information resources, thus the necessity of interoperability between the Topic map and the RDF is required. To address this issue, several conversion methods have been proposed. However, these methods have some problems such as loss of meanings, complicated structure, unnecessary nodes, etc. In this paper, a new method is proposed to resolve some parts of those problems. The method proposed is called NOWL (NO structural Warp and semantics Loss). NOWL method gives several contributions such as maintenance of the original a Topic map instance structure and elimination of the unnecessary nodes compared with the previous researches.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1665-1680
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• 1997

The Entity-Relationship (E-R) model is widely used not only to increase understanding between user and designer, but also to model the relationship of real world data appropriately when designing database system in many application areas. It should be then transformed into an Object-Oriented database model which gives good merits to represent and manipulate data efficiently. Therefore, a method of transforming an E-R model into an Object-Oriented database model should be studied, but without losing any semantics of concept for the E-R model. This paper not only deals with transformation rules taking as input the elements of E-R model and delivering the elements of an Object-Oriented database model, but also improves the concept of generalization and aggregation inheritance. The paper also presents a method of transformation of relationship depending on these rules. The proposed method that obtains Object-Oriented database schema from an E-R model with preserving the properties of the E-R model is shown with examples. The method presented is able to be used to the logical database design.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.102-116
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• 2006

Electronic catalogs (or e-catalogs) hold information about the goods and services offered or requested by the participants, and consequently, form the basis of an e-commerce transaction. Catalog management is complicated by a number of factors and product classification is at the core of these issues. Classification hierarchy is used for spend analysis, custom3 regulation, and product identification. Classification is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. However, product classification has received little formal treatment in terms of underlying model, operations, and semantics. We believe that the lack of a logical model for classification Introduces a number of problems not only for the classification itself but also for the product database in general. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eClass, however, have a lot of limitations to meet these requirements for dynamic features of classification. In this paper, we try to understand what it means to classify products and present how best to represent classification schemes so as to capture the semantics behind the classifications and facilitate mappings between them. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes. And describe the semantic classification model, which satisfies the requirements for dynamic features oi product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph. We believe the model proposed in this paper satisfies the requirements and challenges that have been raised by previous works.

• Proceedings of the Korea Database Society Conference
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• 1994.09a
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• pp.311-333
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• 1994

$\textbullet$ 1970년대 이후 국가가 주도한 수출 드라이브 정책에 힘입어 세계사에서 유래를 찾아볼 수 없는 경제 성장을 이룩하여 왔으나 반면 한국경제는 내수시장이 좁은 관계로 국내 정제의 대외 의존도가 심화되는 결과를 초래했음. $\textbullet$ 한국의 교역량은 세계적인 수준에 있음. ▶ 1993년 기준 해운 : 340,531,100톤 (99.5%) 1993년 기준 항공 : 246,176톤 ( 0.5%) (교역량의 대부분을 해운이 차지하고 있음) ▶ 국민 총생산량 : 230조억 ▶ 총물류비용 : 33조원 수송비용 : 13조원 보관비용 : 8조원 포장비용 : 5조원 하역비용 : 3조원 정보비용 : 2조원 기타비용 : 2조원 ▶ 물류 VAM 구축시 3조원 절감(물류비용의 9%추정) $\textbullet$ 해운이 교역량의 대부분을 차지하고 있지만 관련업계의 정보화가 되어있지 않아 선적기일의 지연이나 선복/화물의 적기확보 곤란 등에 따른 물류비용손실 이 막대함. 따라서 신속 정확한 국제운송물류정보를 통한 물류비용 절감 및 신규 영업 개발에 막대한 공헌을 할 수 있음. $\textbullet$ 해운시장의 개방에 따라 외국의 우수한 선박회사/정보사들이 한국시장에 참여하게 됨에 있어서 열악한 한국의 관련업체에서는 큰 타격을 입을 것이 확실한 바 적절한 대처 필요 $\textbullet$ 당사가 구축하고 있는 해운 및 물류관련 Database는 수출관련 기업체 (상대적으로 정보의 부족함을 느끼는 중소기업체)에 도움을 주어 기업의 경쟁력 강화를 통하여 경제의 활성화에 기여할 것으로 기대.(중략)

• Proceedings of the Korean Information Science Society Conference
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• 2007.10d
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• pp.176-179
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• 2007

센서 네트워크는 제한된 에너지를 가지는 작은 노드들로 구성이 된다. 제한된 자원을 가지는 센서 네트워크에서 저 전력에 대한 연구는 핵심 분야로써, 다양한 연구가 진행 중이다. 센서 네트워크에서 에너지 효율을 높이는 방법들이 많이 연구되고 있다. 센서 네트워크에서 가장 큰 에너지 손실을 가져오는 부분은 통신 부분이며, 질의 기반 센서 네트워크에서는 질의를 배포하게 될 때 에너지 소비의 주요 요소들이 발생하게 된다. 따라서, 본 논문은 베이스스테이션으로 다중 질의를 요청할 때 질의 합성을 통해 전달되는 질의 수를 최소화하여 질의의 배포에 따른 에너지 소비 요소를 줄이는데 그 목적이 있다. 질의는 조건절에서 특정 속성에 대해서 판별하는데, 본 논문에서 제안하는 방법은 속성에 따라 질의를 합성하여 원본 질의의 개수보다 적게 하는 것이다. 제안 알고리즘을 통해 합성된 질의가 센서 노드에게 전해지는 과정에서 데이터의 송수신 횟수 및 overhead를 줄일 수 있으며, 또한 센서 노드는 합성된 질의로 센싱 횟수를 줄 일 수 있어 에너지 효율을 높일 수 있다. 본 논문에서 제안한 방법과 일반적인 방법을 비교하기 위해 에너지 소모 절감 예를 살펴봤다.

• Journal of Korean Society for Geospatial Information Science
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• v.1 no.2 s.2
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• pp.105-112
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• 1993

The conventional method for solving transportation problems were mainly based on numerical methods, where the understanding of outputs is not easy. Some difficulties come from the seperation of three key steps-the preparation of input for transportation traffic simulation, and model output interpretation. GSIS can help to eliminate some of thoses difficulties by combining graphics, database, and transportation plaining models. As pilot study, this study shows an application of Geovision GSIS to TRANPLAN transportation planning model that is based on four-step travel demand forecasting procedure. Accrued benefits and procedure are presented.

• Journal of the Society of Naval Architects of Korea
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• v.38 no.3
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• pp.107-116
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• 2001

Any small leakages in the submarines could lead to the serious consecutive damages since it operates under high water pressure. Such leakages including damages of pipe and hull itself eventually could incur humane casualties and loss of expensive properties as well as the loss of combat capabilities. In this paper, knowledge based decision system is developed. The domain knowledge is acquired from document of submarine design, design expertise and interview with operator. Damages of hull itself and pipes are treated and decision is recommended whether to stay in the sea or to be blowing, for instance.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.35-43
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• 2022

Makeup is the most common way to improve a person's appearance. However, since makeup styles are very diverse, there are many time and cost problems for an individual to apply makeup directly to himself/herself.. Accordingly, the need for makeup automation is increasing. Makeup transfer is being studied for makeup automation. Makeup transfer is a field of applying makeup style to a face image without makeup. Makeup transfer can be divided into a traditional image processing-based method and a deep learning-based method. In particular, in deep learning-based methods, many studies based on Generative Adversarial Networks have been performed. However, both methods have disadvantages in that the resulting image is unnatural, the result of makeup conversion is not clear, and it is smeared or heavily influenced by the makeup style face image. In order to express the clear boundary of makeup and to alleviate the influence of makeup style facial images, this study divides the makeup area and calculates the loss function using HoG (Histogram of Gradient). HoG is a method of extracting image features through the size and directionality of edges present in the image. Through this, we propose a makeup transfer network that performs robust learning on edges.By comparing the image generated through the proposed model with the image generated through BeautyGAN used as the base model, it was confirmed that the performance of the model proposed in this study was superior, and the method of using facial information that can be additionally presented as a future study.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.536-543
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• 2023

Speaker diarization, which labels for "who spoken when?" in speech with multiple speakers, has been studied on a deep neural network-based end-to-end method for labeling on speech overlap and optimization of speaker diarization models. Most deep neural network-based end-to-end speaker diarization systems perform multi-label classification problem that predicts the labels of all speakers spoken in each frame of speech. However, the performance of the multi-label-based model varies greatly depending on what the threshold is set to. In this paper, it is studied a speaker diarization system using single-label classification so that speaker diarization can be performed without thresholds. The proposed model estimate labels from the output of the model by converting speaker labels into a single label. To consider speaker label permutations in the training, the proposed model is used a combination of Permutation Invariant Training (PIT) loss and cross-entropy loss. In addition, how to add the residual connection structures to model is studied for effective learning of speaker diarization models with deep structures. The experiment used the Librispech database to generate and use simulated noise data for two speakers. When compared with the proposed method and baseline model using the Diarization Error Rate (DER) performance the proposed method can be labeling without threshold, and it has improved performance by about 20.7 %.