• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2014년도 제49차 동계학술대회논문집 22권1호
• /
• pp.209-210
• /
• 2014

최근 요식업계의 성장과 기업화 과정을 통하여 POS 시스템 사용이 일반화되었으며 관련 데이터 생산량이 급증하고 있다. 이러한 경향에 따라 종래와 다른 영업정보의 분석 생산을 위한 빅 데이터 기반의 요식업계 POS 데이터 분석시스템으로, 지역별 업계 영업현황 분석정보를 제공함으로써, 요식업계는 물론 정책 생성에 유용한 정보를 제공하는 시스템을 설계 구현하여 제안한다.

• Journal of Information Processing Systems
• /
• 제14권6호
• /
• pp.1464-1479
• /
• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• 한국지능시스템학회논문지
• /
• 제23권5호
• /
• pp.379-384
• /
• 2013

해상교통관제센터(Vessel Traffic Service, VTS)에서 항해중인 선박의 효과적인 관리를 위해 선박에 영향을 주는 외력에 대한 평가가 필요하다. 해상교통 빅데이터는 크게 선박 제원 및 통항정보 등 선박에 의하여 수집되는 정보가 있으며, 다른 하나는 해역에 관련된 바람, 파고, 조류흐름의 외력정보가 있다. 본 연구에서는 이러한 해상교통 빅데이터를 활용하여 선박에 영향을 주는 외력영향을 평가하는 방법에 대해 제안한다. 해상교통 빅데이터를 활용하기 위하여 바람, 파도, 조류 정보, 대수속력(Speed Over Water, SOW)에 대한 정보로 구성되는 해역외력코드(Waterway External Force Code)를 사용하였다. 해역외력코드를 데이터베이스로 하여 그 결과로서 선박에 작용하는 외력영향을 추정하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권4호
• /
• pp.1931-1953
• /
• 2017

The reverse skyline query plays an important role in information searching applications. This paper deals with continuous reverse skyline queries in sensor networks, which retrieves reverse skylines as well as the set of nodes that reported them for continuous sampling epochs. Designing an energy-efficient approach to answer continuous reverse skyline queries is non-trivial because the reverse skyline query is not decomposable and a huge number of unqualified nodes need to report their sensor readings. In this paper, we develop a new algorithm that avoids transmission of updates from nodes that cannot influence the reverse skyline. We propose a data mapping scheme to estimate sensor readings and determine their dominance relationships without having to know the true values. We also theoretically analyze the properties for reverse skyline computation, and propose efficient pruning techniques while guaranteeing the correctness of the answer. An extensive experimental evaluation demonstrates the efficiency of our approach.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2013년도 제48차 하계학술발표논문집 21권2호
• /
• pp.153-154
• /
• 2013

토목 및 건설, 건축 등의 현장에서 많이 사용되는 가설기기들은 기계의 자체적인 기계고장 뿐만 아니라 야외 현장의 환경에 따른 기후의 변화에도 고장이 발생할 수 있다. 이러한 고장들을 사후약방문의 형식으로 고장이 발생하는 경우에만 수리 후 사용한다면 시간적/경제적으로 많은 손실이 있을 것이다. 그러나 가설기기들의 종류별 기기적 특징을 미리 시스템화하여 발생할 수 있는 고장을 사전에 방지하고 예방한다면 불필요한 손실을 미연에 막을 수 있다. 따라서 본 논문에서는 가설기기들과 관련된 각종 빅 데이터를 이용하여 피로도를 예측하여 고장이 발생하기 전에 사전에 예방할 수 있는 시스템을 제안한다.

• International Journal of Computer Science & Network Security
• /
• 제21권3호
• /
• pp.145-152
• /
• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Journal of Information Technology Applications and Management
• /
• 제24권4호
• /
• pp.187-196
• /
• 2017

It is urgent to prepare countermeasures for traffic congestion problems of Korea's metropolitan area where central functions such as economic, social, cultural, and education are excessively concentrated. Most users of public transportation in metropolitan areas including Seoul use the traffic cards. If various information is extracted from traffic big data produced by the traffic cards, they can provide basic data for transport policies, land usages, or facility plans. Therefore, in this study, we extract valuable information such as the subway passengers' frequent travel patterns from the big traffic data provided by the Seoul Metropolitan Government Big Data Campus. For this, we use a Hadoop (High-Availability Distributed Object-Oriented Platform) to preprocess the big data and store it into a Mongo database in order to analyze it by a sequential pattern data mining technique. Since we analysis the actual big data, that is, the traffic cards' data provided by the Seoul Metropolitan Government Big Data Campus, the analyzed results can be used as an important referenced data when the Seoul government makes a plan about the metropolitan traffic policies.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2014년도 제49차 동계학술대회논문집 22권1호
• /
• pp.415-416
• /
• 2014

오늘날 인터넷상에서는 무수히 많은 디지털 데이터가 생성되고 있으며, 그 디지털 데이터는 기존의 소프트웨어로는 처리할 수 없을 정도로 그 양이 방대해지고 있다. 이러한 데이터들을 사용자의 검색의도에 따라 문장 분석, 키워드 추출, 요약문 생성 등의 방법을 통하여, 사용자에게 개인화된 정보를 제공하기 위한 빅 데이터의 분석을 이용한 정보 자동 요약 시스템을 제안한다.

• 한국빅데이터학회지
• /
• 제2권2호
• /
• pp.1-7
• /
• 2017

ITS(Intelligent Transport Systems)는 시민들의 교통이용 안전과 편의를 도모하고 교통 시스템의 효율적인 운영 및 관리를 위해 대도시를 중심으로 도입되었다. 우리나라의 경우 ITS가 전국적으로 확대되면서 도로소통상황, 교통량, 대중교통운영현황 및 관리상황, 대중교통이용현황 등 다양한 교통정보가 생성되고 있다. 본 논문에서는 ITS에서 수집되는 데이터 중 하나인 DSRC(Dedicated Short Range Communications) 빅데이터를 활용하여 도시 교통구조를 네트워크 분석 기법을 통해 규명한다. 이를 통해 도심에서의 복잡한 교통현상을 단순화시키고, 차량 흐름에 따른 도시 교통의 구조적 특징을 도출한다. 분석 결과는 도시의 교통을 좀 더 쉽게 이해할 수 있도록 도와주고, 향후에 도시교통의 혼잡 해소방안, 도로 확장 계획 등의 교통정책 수립시 기초연구 자료로 활용할 수 있다.

• Journal of Information Processing Systems
• /
• 제17권3호
• /
• pp.645-657
• /
• 2021

Along with the rapid development of the economy, the urban scale has extended rapidly, leading to the formation of different types of urban function districts (UFDs), such as central business, residential and industrial districts. Recognizing the spatial distributions of these districts is of great significance to manage the evolving role of urban planning and further help in developing reliable urban planning programs. In this paper, we propose an automatic UFD division method based on big data analysis of point of interest (POI) data. Considering that the distribution of POI data is unbalanced in a geographic space, a dichotomy-based data retrieval method was used to improve the efficiency of the data crawling process. Further, a POI spatial feature analysis method based on the mean shift algorithm is proposed, where data points with similar attributive characteristics are clustered to form the function districts. The proposed method was thoroughly tested in an actual urban case scenario and the results show its superior performance. Further, the suitability of fit to practical situations reaches 88.4%, demonstrating a reasonable UFD division result.