• Journal of Information Processing Systems
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• 제15권1호
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• pp.1-6
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• 2019

Recently, big data and artificial intelligence (AI) based on communication systems have become one of the hottest issues in the technology sector, and methods of analyzing big data using AI approaches are now considered essential. This paper presents diverse paradigms to subjects which deal with diverse research areas, such as image segmentation, fingerprint matching, human tracking techniques, malware distribution networks, methods of intrusion detection, digital image watermarking, wireless sensor networks, probabilistic neural networks, query processing of encrypted data, the semantic web, decision-making, software engineering, and so on.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.113-121
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• 2014

A subsea oil production system must be safely operated for 20-30 years after being installed. Because of the severe conditions of the subsea environment, such as extreme high pressure, low visibility, the possibility of unexpected impact by any object, and corrosion by seawater, subsea oil production systems should be monitored by subsea data logger systems and remotely operated vehicles to check for abnormal vibration and leakage to prevent a catastrophic accident. Because of the severity of subsea environmental conditions and the dominance of a few companies in the market, many people have thought that it would be difficult to develop a subsea data logger system. The primary objectives of the study described in this paper were to analyze existing subsea data logger systems to establish the requirements for a subsea data logger system, implement a prototype subsea data logger system, and conduct a test of the prototype subsea data logger system.

• 한국CDE학회논문집
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• 제9권2호
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• pp.167-174
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• 2004

As the networks connect the world, enterprises tend to move manufacturing activities into virtual spaces. Since different applications use different data terminology, it becomes a problem to interoperate, interchange, and manage electronic data among different systems. According to RTI, approximately one billion dollar has been being spent yearly for product data exchange and interoperability. As commercial CAD systems have brought in the concept of design feature for the sake of interoperability, terminologies of design feature need to be harmonized. In order to define design feature terminology for integration, knowledge about feature definitions of different CAD systems should be considered. STEP (Standard for the Exchange of Product model data) have attempted to solve this problem, but it defines only syntactic data representation so that semantic data integration is unattainable. In this paper, we utilize the ontology concept to build a data model of design feature which can be a semantic standard of feature definitions of CAD systems. Using feature ontology, we implement an integrated virtual database and a simple system which searches and edits design features in a semantic way. This paper proposes a methodology for integrating modeling features of CAD systems.

• 한국멀티미디어학회논문지
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• 제12권6호
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• pp.777-784
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• 2009

Recently, the importance of geoCRM (geographic Customer Relationship Management) systems are growing rapidly. So, result of the recognition that their applications extend well beyond the traditional CRM systems with the advent of ubiquitous environment and generalized location based services. A majority of traditional CRM systems are either incapable of managing spatial data or are not user-friendly when doing so. On the other hand, the geoCRM systems can be built as providing the geographic-based functions about CRM, including spatial and market analyses and the visualization of customer data, etc. However, it lacks the specific model and implementation of the geoCRM systems, being caused by the incomprehension of needs, the absence of related standards and the difficulties of development, and so on. In this paper, we develop a new spatial analysis supporting system that to enhance productivity through the convenient use and management of spatial data. The functionality provided by our system includes a set of analysis functions based on data mining techniques which allow a user to affect powerful transformation on spatial data. Particularly, both spatial data and non-spatial attributes can be efficiently handled as an object through our OODBMS.

• 한국정보통신학회논문지
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• 제10권12호
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• pp.2212-2220
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• 2006

최근 기업들은 레거시 시스템들간의 데이터 상호 운용하기 위해 기업들은 이미 보유하고 있는 레거시 업무와 EAI 시스템을 도입하고 있다. 협업적인 거래 환경에서의 EAI 시스템은 유기적으로 통합하고 공유함으로서 효율적인 검색을 기대할 수 있다. 그러나 기존 레거시 시스템은 특정 목적에 따라 설계단계부터 상호 운용성을 고려하지 않고 독자적으로 관리되므로 EAI는 표준기술 적용이 어려우므로 별도의 전용 EAI 솔루션을 도입해야 한다. 이러한 문제를 해결하기 위해 데이터 통합을 이용하여 메타데이터 레지스트리를 이용한다. 그러나 메타데이터의 다양한 타입과 의미론적 명세, 데이터 이질성 문제, 이기종간의 시스템 이질성에 대한 문제도 야기된다. 따라서 본 논문에서는 웹 환경에서 데이터 상호운용을 위한 XMDR(eXtended Meta-Data Registry) 기반의 검색 시스템을 제안한다.

• 한국CDE학회논문집
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• 제18권3호
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• pp.234-242
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• 2013

Different CAD systems are used in ship outfitting design on different usage and purpose. Therefore, data exchanges between CAD systems are required from different formats. For data exchange, boundary representation standard formats such as IGES and ISO 10303 (STEP) are widely used. However, they present only B-rep representation. Because of different CAD systems have their own geometry format, data exchanges with design intend are difficult. Especially, Tribon and PDMS use primitives for express their geometry in ship outfitting design. However, Tribon primitives are represented their parameter by values that are non-parametric. Therefore, data size of catalogue library is bigger than different CAD system using parametric primitive representation. And that system has difficulty on data reprocessing. To solve that problem, we discuss about shape DB which contains design parameters of primitive for exchange Tribon primitives. And geometry data exchange between Tribon and Shape Database that defines based on PDMS scheme are specified using primitive mapping that can represent design intend.

• 인터넷정보학회논문지
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• 제21권6호
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• pp.33-39
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• 2020

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• BMB Reports
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• 제41권3호
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• pp.184-193
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• 2008

Living organisms are comprised of various systems at different levels, i.e., organs, tissues, and cells. Each system carries out its diverse functions in response to environmental and genetic perturbations, by utilizing biological networks, in which nodal components, such as, DNA, mRNAs, proteins, and metabolites, closely interact with each other. Systems biology investigates such systems by producing comprehensive global data that represent different levels of biological information, i.e., at the DNA, mRNA, protein, or metabolite levels, and by integrating this data into network models that generate coherent hypotheses for given biological situations. This review presents a systems biology framework, called the 'Integrative Proteomics Data Analysis Pipeline' (IPDAP), which generates mechanistic hypotheses from network models reconstructed by integrating diverse types of proteomic data generated by mass spectrometry-based proteomic analyses. The devised framework includes a serial set of computational and network analysis tools. Here, we demonstrate its functionalities by applying these tools to several conceptual examples.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권9호
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• pp.485-490
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• 2006

According to the computerization of railway signaling systems, the interface link between the signaling systems has been replaced by the digital communication channel. At the same time, the importance of the communication link is more pronounced than before. In this paper, new Network-based protocol between railway signaling and SCADA (Supervisory Control and Data Acquisition system) has designed and the overview of designed protocol is briefly represented. And also this paper addresses analysis of newly designed train control systems. Fame error rates of the data transmissions are calculated and compared for the two cases that the CTC (Centralized Traffic Control)/SCADA has an extra data transmission error control (CRC16) besides the inherent error control of the Ethernet and that the CTC/SCADA has no extra data transmission error control. With simulation results it has been verified that the additional error control code contributes to lowering the frame error rate. It will be expected to increase the safety, reliability and efficiency of maintenance of the signaling systems by using the designed protocol for railway signaling system.