• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.21-34
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• 2007

According to recent rapid price drop and capacity growth of main memory, the number of applications on main memory databases is dramatically increasing. Cache miss, which means a phenomenon that the data required by CPU is not resident in cache and is accessed from main memory, is one of the major causes of performance degradation of main memory databases. Several cache-conscious trees have been proposed for reducing cache miss and making the most use of cache in main memory databases. Since each cache-conscious tree has its own unique features, more than one cache-conscious tree can be used in a single application depending on the application's requirement. Moreover, if there is no existing cache-conscious tree that satisfies the application's requirement, we should implement a new cache-conscious tree only for the application's sake. In this paper, we propose the cache-conscious generalized search tree (CC-GiST). The CC-GiST is an extension of the disk-based generalized search tree (GiST) [HNP95] to be tache-conscious, and provides the entire common features and algorithms in the existing cache-conscious trees including pointer compression and key compression techniques. For implementing a cache-conscious tree based on the CC-GiST proposed in this paper, one should implement only a few functions specific to the cache-conscious tree. We show how to implement the most representative cache-conscious trees such as the CSB+-tree, the pkB-tree, and the CR-tree based on the CC-GiST. The CC-GiST eliminates the troublesomeness caused by managing mire than one cache-conscious tree in an application, and provides a framework for efficiently implementing arbitrary cache-conscious trees with new features.

• Journal of Information Management
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• v.40 no.4
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• pp.1-22
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• 2009

Large-scaled information extraction plays an important role in advanced information retrieval as well as question answering and summarization. Information extraction can be defined as a process of converting unstructured documents into formalized, tabular information, which consists of named-entity recognition, terminology extraction, coreference resolution and relation extraction. Since all the elementary technologies have been studied independently so far, it is not trivial to integrate all the necessary processes of information extraction due to the diversity of their input/output formation approaches and operating environments. As a result, it is difficult to handle scientific documents to extract both named-entities and technical terms at once. In this study, we define scientific as a set of 10 types of named entities and technical terminologies in a biomedical domain. in order to automatically extract these entities from scientific documents at once, we develop a framework for scientific core entity extraction which embraces all the pivotal language processors, named-entity recognizer, co-reference resolver and terminology extractor. Each module of the integrated system has been evaluated with various corpus as well as KEEC 2009. The system will be utilized for various information service areas such as information retrieval, question-answering(Q&A), document indexing, dictionary construction, and so on.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.97-105
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• 2010

This paper proposes an effective medium access mechanism which is based on IEEE 802.11 to transmit an urgent message, The main features of the proposed mechanism are as follows. First, when it comes to channel access to have high priority, it has a shorter interval between frames than that specified in standard document. Second, we use fixed window size instead of back-off window with an exponential increase. Performance evaluation of proposed mechanism is executed by simulation and compare with the node using the specified mechanism in standard document. Performance evaluation results show that according to increasing competition the nodes using proposed method have less accessing time than the conventional methods. Also, the proposed method can improve processing time because of the decreasing transmission delay.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.2035-2046
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• 2015

Recently, researches on resource allocation algorithms operating in a distributed way are widely conducted because of the increasing number of network nodes and the rapidly changing the network environment. In this paper, we propose Multi-Hop DESYNC(MH DESYNC), that is bio-inspired TDMA-based resource allocation scheme operating in a distributed manner in multi-hop networks. In this paper, we define a frame structure for the proposed MH DESYNC algorithm and firing message structure which is a reference for resource allocation and propose the related operating procedures. We show that MH DSYNC can resolve the hidden-node problem effectively and verify that each node shares resources fairly among its neighboring nodes. Through simulation evaluations, it is shown that MH DESYNC algorithm works well in a multi-hop networks. Furthermore, results show that MH DESYNC algorithm achieves better performance than CSMA/CA algorithm in terms of throughput.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.19-31
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• 2014

In this paper, a parallel backoff delay procedure on multiple IEEE 802.15.4 channels and a channel searching method considering the frequency spectrum of WiFi traffic are studied for IEEE 802.15.4 nodes to avoid the interference from WiFi traffic. In order to search the channels being occupied by WiFi traffic, we analyzed the methods measuring the powers of adjacent channels simultaneously, checking the duration of measured power levels greater than a threshold, and finding the same periodicity of sampled RSSI data as the beacon frame by signal processing. In an wireless channel overlapped with IEEE 802.11 network, the operation of CSMA-CA algorithm for IEEE 802.15.4 nodes is explained. A method to execute a parallel backoff procedure on multiples IEEE 802.15.4 channels by an IEEE 802.15.4 device is proposed with the description of its algorithm. When we analyze the data measured by the experimental system implemented with the proposed method, it is observed that medium access delay times increase at the same time in the associated IEEE 802.15.4 channels that are adjacent each other during the generation of WiFi traffic. A channel evaluation function to decide the interference from other traffic on an IEEE 802.15.4 channel is defined. A channel searching method considering the channel evaluations on the adjacent channels together is proposed in order to search the IEEE 802.15.4 channels interfered by WiFi, and the experimental results show that it correctly finds the channels interfered by WiFi traffic.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.109-111
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• 2006

본 논문은 애니메이터로 하여금 얼굴 표정들의 공간으로부터 일련의 표정을 선택하게 함으로써 3차원 아바타의 표정을 실시간적으로 제어할 수 있도록 하기 위한 표정공간의 생성방법에 관하여 기술한다. 본 시스템에서는 약 2400여개의 얼굴 표정 프레임을 이용하여 표정공간을 구성하였다. 본 기법에서는 한 표정을 표시하는 상태표현으로 얼굴특징 점들 간의 상호거리를 표시하는 거리행렬을 사용한다. 이 거리행렬의 집합을 표정공간으로 한다. 그러나 이 표정공간은 한 표정이 다른 표정까지 이동할 때 두 표정간의 직선경로를 통해 이동할 수 있는 그런 공간이 아니다. 본 기법에서는 한 표정에서 다른 표정까지 거쳐 갈 수 있는 경로를 캡쳐된 표정 데이터로부터 근사적으로 유추한다. 우선, 각 표정상태를 표현하는 거리행렬간의 거리가 일정 값 이하인 경우 두 표정을 인접해 있다고 간주한다. 임의의 두 표정 상태가 일련의 인접표정들의 집합으로 연결되어 있으면 두 표정간에 경로가 존재한다고 간주한다. 한 표정에서 다른 표정으로 변화할 때 두 표정간의 최단경로를 통해 이동한다고 가정한다. 두 표정간의 최단거리를 구하기 위해 다이내믹 프로그래밍 기법을 이용한다. 이 거리행렬의 집합인 표정공간은 다차원 공간이다. 3차원 아바타의 얼굴 표정 제어는 애니메이터들이 표정공간을 항해할 때 실시간적으로 수행된다. 이를 도와주기 위해 표정공간을 차원 스케일링 기법을 이용하여 2차원 공간으로 가시화하였고, 애니메이터들로 하여금 본 시스템을 사용하여 실시간 표정 제어를 수행하게 했는데, 본 논문은 그 결과를 평가한다.참여하는 빈들 간의 관계를 분석하여 워크플로우에 대한 성능 측정이 가능하도록 한다. 또한 제안된 메트릭을 통하여 EJB 어플리케이션의 성능 향상을 도모할 수 있도록 한다.로 표면 위로 자라났고, 부종은 창상 밑 조직까지 감소하였으며, 육아조직은 교원질 섬유로 대체되었다. 창상 유발 21일 후, 다른 창상에 비해, HG 처치창은 유의적으로 창상 표면이 거의 재생성 상피로 덮였으며, 육아조직은 창상 유발 14일 후와 비교해서 유의적으로 교원질 섬유로 대체되었다. 위의 모든 결과에서 보듯이, 개에서 전층피부 창상의 처치 시 HG의 사용은 HC와 생리 식염수에 비해 창상치유 복구기의 치유 속도를 촉진하는 것으로 사료된다.시범학교의 자녀를 들 부모들은 환경관련문제에 대한 의식 및 환경관련 제품에 대한 구매행동의 변화가 두드러지게 나타났다.EX>$d^{2+}$ + SA처리구는 두 화합물의 이중 효과에 의해 전반적인 생리 활성을 억제하여, 결국에는 식물의 고사를 유도하는 것으로 사료된다.목에 대한 보안'이 가장 중요한 것으로 나타났다. 본 연구에서는 솔루션 선정요인에 관한 중요도를 e-마켓플레이스의 유형과 산업 별로 평가해보았는데, 여기에서 밝혀진 중요도를 통해 e 마켓플레이스를 구축하고 자 하는 기업은 솔루션을 자체 개발하거나 구입할 때 올바른 의사결정을 할 수 있다. 그리고 솔루션을 제공하려는 기업측면에서는 e-마켓플레이스를 구축하고자 하는 기업에게 유형과 산업별로 적절한 솔루션을 제공할 수 있다.순환이 뇌대사 및 수술 후 신경학적 결과에 보다 유익한 효과를 제공해 줄 수 있음

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.