• 한국축산식품학회지
• /
• 제33권4호
• /
• pp.463-473
• /
• 2013

Through the development of efficient data collecting technologies like RFID, and inter-enterprise collaboration platforms such as web services, companies which participate in supply chains can acquire visibility over the whole supply chain, and can make decisions to optimize the overall supply chain networks and processes, based on the extracted knowledge from historical data collected by the visibility system. Although not currently active, the MeatWatch system has been developed, and is used in part for this purpose, in the imported beef distribution network in Korea. However, the imported beef distribution network is too complicated to analyze its various aspects using ordinary process analysis approaches. In this paper, we suggest a novel approach, called RFID-based supply chain process mining, to automatically discover and analyze the overall supply chain processes from the distributed RFID event data, without any prior knowledge. The proposed approach was implemented and validated, by using a case study of the imported beef distribution network in Korea. Specifically we demonstrated that the proposed approach can be successfully applied to discover supply chain networks from the distributed event data, to simplify the supply chain networks, and to analyze anomaly of the distribution networks. Such novel process mining functionalities can reinforce the capability of traceability services like MeatWatch in the future.

• International Journal of Computer Science & Network Security
• /
• 제22권4호
• /
• pp.203-208
• /
• 2022

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
• /
• pp.509-511
• /
• 2004

This paper outlines the development of image processing system, which will allow the general users in Government and Public organizations easily to use and apply KOMPSAT EOC images in their own business. The system includes an import/export module of EOC image distributed in Hierarchical Data Format (HDF) file and various image processing analysis modules. Especially, the image mosaic and subset functions are designed to use EOC image as an image map, generating the Ortho-image module. To update the various spatial data with EOC image, some essential modules such as change detection by pattern recognition, overlay between images and vector data, and modification of vector data are implemented in the system. The system is developed based on the user request analysis of government agency, and suited for more efficient use of satellite image in public applications. Such system is expected to contribute to practical application of KOMPSAT-2 that will be launched in 2005. Further efforts will be made to accommodate the KOMPSAT -2 MSC data.

• 대한산업공학회지
• /
• 제40권1호
• /
• pp.34-42
• /
• 2014

SCADA and DCS that have performed automatic control and monitoring activities increase the productivity of enterprise in industries. In such systems, although their performance had been improved, there are still many deficiencies in predictive maintenance which can foresee the risk of any kinds of accidents. Because the data acquisition systems of main facilities are being distributed throughout the whole plant and therefore, integration of data obtained from the systems is very difficult. Accordingly, techniques that acquire meaningful information from the gathered data through realtime analysis still need to be improved. This paper introduces a developed facility monitoring system which can predict equipment failure and diagnose facility status through big data analysis to improve equipment efficiency and prevent safety accidents.

• International Journal of Computer Science & Network Security
• /
• 제21권6호
• /
• pp.71-76
• /
• 2021

Education is one of the pillars of sustainable development. For this reason, the discovery of useful information in its process of adaptation to new challenges is treated with care. This paper aims to present the initiation of a process of exploring the data collected from the results obtained by Romanian students at the BBaccalaureate (the Romanian high school graduation) exam, through data mining methods, in order to try an in-depth analysis to find and remedy some of the causes that lead to unsatisfactory results. Specifically, a set of public data was collected from the website of the Ministry of Education, on which several classification methods were tested in order to find the most efficient modeling algorithm. It is the first time that this type of data is subjected to such interests.

• Journal of information and communication convergence engineering
• /
• 제20권2호
• /
• pp.96-102
• /
• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• 지능정보연구
• /
• 제10권1호
• /
• pp.65-75
• /
• 2004

네트워크로 연결된 환경에서 PIN 번호를 이용해 사용자의 신분을 증명하고 인증하는 방식이 일반적으로 활용되고 있다. 그러나, 아이디나 비밀번호가 해킹을 통해 유출되면 금전적인 피해뿐만 아니라 개인의 사생활까지도 침해받게 된다. 본 논문에서는 아이디나 비밀번호가 유출될 염려가 없는 안전한 인증방식으로 얼굴인식을 채택하였다. 또한, 2-Tier 간의 인증방식이 아닌 점점 분산화 되어 가는 네트워크 시스템을 고려해 3-Tier이상의 분산된 환경에서 원격으로 신분을 증명하고 인증할 수 있는 시스템을 제안하였다. 본 인증시스템의 얼굴인식 알고리즘으로는 최근 분류(Classification)와 특징추출(Feature Extraction)에서 빠른 속도와 정확성을 보이는 SVM(Support Vector Machine)과 PCA를 이용해 얼굴 특징을 분석하고, 분산된 환경에서 인공지능 기법을 활용해 인식속도 및 정확성을 높일 수 있는 분산형 인공지능 얼굴인증 모듈을 구현하였다.

• 한국전자거래학회:학술대회논문집
• /
• 한국전자거래학회 2001년도 International Conference CALS/EC KOREA
• /
• pp.615-624
• /
• 2001

Internet technology opens up another domain for building future CAD/CAM environment. The environment will be global, network-centric, and spatially distributed. In this paper, we present a new approach to network-centric virtual prototyping (NetVP) in a distributed design environment. The presented approach combines the current virtual assembly modeling and analysis technique with distributed computing and communication technology fur supporting virtual prototyping activities over the network. This paper focuses on interoperability, shape representation, and geometric processing for distributed virtual prototyping. STEP standard and CORBA-based interfaces allow the bi-directional communication between the CAD model and virtual prototyping model, which makes it possible to solve the problems of interoperability, heterogeneity of platforms, and data sharing. STEP AP203 and AP214 are utilized as a means of transferring and sharing product models. In addition, Attributed Abstracted B-rep (AAB) is introduced as 3D shape abstraction for transparent and efficient transmission of 3D models and for the maintenance of naming consistency between CAD models and virtual prototyping models over the network.

• 한국컴퓨터정보학회논문지
• /
• 제11권3호
• /
• pp.49-56
• /
• 2006

P2P 분산 시스템의 가장 큰 특징은 해당 피어들이 항상 온라인 상태일 것이라는 보장이 없다는 것이다. 즉 P2P 시스템을 이용할 때에는 해당 피어로부터 파일을 다운로드받다가 다운로드 되지 않는 경우가 발생하게 된다. 이를 해결하기 위한 연구의 대부분은 재전송이라는 방법에 의존하고 있다. 이는 P2P 시스템의 성능 저하의 원인이 되므로 이에 대한 해결책이 필요하다. 본 연구에서는 해당 P2P 시스템을 이용하는 사용자의 평균 이용 시간대의 분석 자료를 자원 제공자 선택의 기준으로 적용하여 자원 전송 보장성을 높이고, 또한 인기도 높은 자원에 대해서 자료 전송 기회를 높여주는 역할의 기존의 분산 객체 리플리케이션 기법들과의 조합에 의한 분산 객체 전송률이 향상된 검색 알고리즘을 제안한다.

• Journal of Ecology and Environment
• /
• 제46권3호
• /
• pp.259-272
• /
• 2022

Background: The Korean Peninsula exhibits a characteristic graded floral distribution, with northern (Manchurian flora) and southern (China-Japan-Korea flora) lineage species coexisting according to climatic and topographical characteristics. However, this distribution has been altered by climate change. To identify ecosystem changes caused by climate change and develop appropriate measures, the current ecological status of the entire Korean Peninsula should first be determined; however, analysis of the current floral distribution in North Korea has been hampered for political reasons. To overcome these limitations, this study constructed a database of floral distributions in both South and North Korea by integrating spatial information from the previously established National Ecological Survey in South Korea and geocoding data from the literature on biological distributions published in North Korea. It was then applied to analyze the current status and distribution characteristics of Manchurian and China-Japan-Korea plant species on the Korean Peninsula. Results: In total, 45,877 cases were included in the Manchurian and China-Japan-Korea floral distribution database. China-Japan-Korea species were densely distributed on Jeju-do and along the southern coast of the Korean Peninsula. The distribution density decreased as the latitude increased, and the distributions reached higher-latitude regions in the coastal areas compared with the inland regions. Manchurian species were distributed throughout North Korea, while they were densely distributed in the refugia formed in the high-elevation mountain regions and the Baekdudaegan in South Korea. In the current distribution of biomes classified according to the Whittaker method, subtropical and endemic species were densely distributed in temperate seasonal forest and woodland/shrubland biomes, whereas boreal species were densely distributed in the boreal forest biome Korean Peninsula, with a characteristic gradation of certain species distributed in the temperate seasonal forest biome. Factor analysis showed that temperature and latitude were the main factors influencing the distribution of flora on the Korean Peninsula. Conclusions: The findings reported herein on the current floral distribution trends across the entire Korean Peninsula will prove valuable got mitigating the ecological disturbances caused by ongoing climate change. Additionally, the gathered flora data will serve as a basis for various follow-up studies on climate change.