• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2008년도 한국컴퓨터종합학술대회논문집 Vol.35 No.1 (C)
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• pp.387-390
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• 2008

본 논문에서는 교구용 소형 로봇으로 구현한 군집로봇 시스템을 소개한다. 각 로봇에 내장된 블루투스 무선통신으로 군집로봇 네트워크를 구성하였다. 실험에 사용한 로봇은 $LEGO^{(R)}$ $MINDSTORMS^{(R)}$ NXT이다. 여러 로봇이 라인으로 표현한 대형 미로를 동시에 탐사하는 환경을 가정하였다. 이런 상황에서 각 로봇은 주어진 임무를 수행하면서 센서로 주변 환경 정보를 측정해서 대표 로봇에게 보낸다. 여기에 필요한 메시지 구조를 군집로봇에 적절하도록 설계하였다. 이렇게 군집로봇을 구현하고 실험한 결과, 그룹 대표로봇이 통신을 중계하는 방법으로 통신거리 제약을 해소할 수 있었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.827-831
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• 2002

MediaGIS is a concept of tightly integrated multimedia with spatial information. VideoGIS is an example of MediaGIS focused on the interaction or interaction of video and spatial information. Our suggested GeoVideo, a new concept of VideoGIS has its key feature in interactiveness. In GeoVideo, the geographic tasks such as browsing, searching, querying, spatial analysis can be performed based on video itself. GeoVideo can have the meaning of paradigm shift from artificial, static, abstracted and graphical paradigm to natural, dynamic, real, and image-based paradigm. We discuss about the integration of video and geography and also suggest the GeoVideo system design. Several considerations on expanding the functionalities of GeoVideo are explained for the future works.

• Journal of Information Processing Systems
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• 제14권4호
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• pp.989-1009
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• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (A)
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• pp.57-58
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• 2007

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2851-2873
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• 2016

Cloud storage as a service provides high scalability and availability as per need of user, without large investment on infrastructure. However, data security risks, such as confidentiality, privacy, and integrity of the outsourced data are associated with the cloud-computing model. Over the year's techniques such as, remote data checking (RDC), data integrity protection (DIP), provable data possession (PDP), proof of storage (POS), and proof of retrievability (POR) have been devised to frequently and securely check the integrity of outsourced data. In this paper, we improve the efficiency of PDP scheme, in terms of computation, storage, and communication cost for large data archives. By utilizing the capabilities of JAR and ZIP technology, the cost of searching the metadata in proof generation process is reduced from O(n) to O(1). Moreover, due to direct access to metadata, disk I/O cost is reduced and resulting in 50 to 60 time faster proof generation for large datasets. Furthermore, our proposed scheme achieved 50% reduction in storage size of data and respective metadata that result in providing storage and communication efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4126-4142
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• 2015

Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2004년도 제16회 한글.언어.인지 한술대회
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• pp.12-19
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• 2004

기존의 다중 문서요약은 전체 대상문서에 대해서 한번에 요약문을 생산하지만, 본 논문은 요약 대상문서 집합에서 핵심내용을 갖는 문서를 기본 문서로 선택, 임시 요약문장을 추출하고 대상문서 집합에서 순차적으로 문서를 입력받아 중요문장을 추출, 이전에 구축된 요약문장과 현재 추출된 문장을 비교하면서 요약에 필요한 문장을 선택하는 적응 기법을 제안한다. 제안한 방법으로 구현한 시스템은 NTCIR TSC 3에서 사용된 29개의 다중 문서집합을 통해서 성능을 평가하였다. 적응 기법 시스템은 TSC3의 baseline시스템인 Lead 방법보다는 높은 성능을 나타냈지만, TSC 3에 참가한 시스템들과의 비교에서는 월등한 성능 우위를 나타내지 못했다.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.121-126
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• 2022

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.107-112
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• 2023

World is moving towards digitization at a rapid pace, so the enterprises have developed information systems for management of their business. Empowering educational institutes with information systems are become very important and vital. Doing everything manually is very difficult for students, teachers and staff. Information system can enhance their efficiency and save a lot of time; this research proposed system will solve this issue by providing services like class room reservation, e-library facility, online submission etc. in a secured environment. Up till now limited attention has been paid to utilize robots and drones for automation inside educational institutes. Our proposed system incorporates robots and drones to fill this gap in automation being used in institutes. Through this research, the aim is to improve the efficiency of learning and services in educational institutions or universities.