• Journal of Internet Computing and Services
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• v.3 no.6
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• pp.35-41
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• 2002

The Car Navigation System(CNS) requires lots of memory and calculating time because it works on the large and complex digital map. And the traffic circumstances vary time by time, so the traffic informations should be processed if we want to get mere realistic result. This paper proposes an effective path searching algorithm which uses less memories and calculating time by applying directional information between the starting place and destination place and by using realtime traffic informations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5547-5562
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• 2016

JPEG-LS is an international standard for lossless or near-lossless image-compression algorithms. In this paper, a simple method is proposed to improve the performance of the lossless JPEG-LS algorithm. With respect to JPEG-LS and its supplementary explanation, Golomb-Rice (GR) coding is mainly used for entropy coding, but it is not used for long codewords. The proposed method replaces a set of long codewords with a set of shorter location map information. This paper shows how efficiently the location map guarantees reversibility and enhances the compression rate in terms of performance. Experiments have also been conducted to verify the efficiency of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5287-5303
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• 2018

Handling vast amount of data found in large transactional datasets is an obvious challenge for the conventional data mining algorithms. Addressing this challenge, our paper proposes a parallel approach for proper decomposition of mining problem into sub-problems in order to find frequent patterns from these datasets. The proposed, Pattern Mining for Large Distributed Dataset (PMLDD) approach, ensures minimum dependencies as well as minimum communications among sub-problems. It establishes a linear aggregation of the intermediate results so that it can be adapted to large-scale programming models like MapReduce. In this context, an algorithmic structure for MapReduce programming model is presented. PMLDD guarantees an efficient load balancing among the sub-problems by a specific selection criterion. Further, it optimizes the number of required iterations over the dataset for mining frequent patterns as compared to the existing approaches. Finally, we believe that our approach is scalable enough to handle larger datasets in terms of performance evaluation, and the result analysis justifies all these mentioned concerns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.583-600
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• 2014

Social media is considered a valuable platform for gathering and analyzing the collective and subconscious opinions of people in Internet and mobile environments, where they express, explicitly and implicitly, their daily preferences for brands and products. Extracting and tracking the various attitudes and concerns that people express through social media could enable us to categorize brands and decipher individuals' cognitive decision-making structure in their choice of brands. We investigate the cognitive network structure of consumers by building an inter-category map through the mining of big data. In so doing, we create an improved online recommendation model. Building on economic sociology theory, we suggest a framework for revealing collective preference by analyzing the patterns of brand names that users frequently mention in the online public sphere. We expect that our study will be useful for those conducting theoretical research on digital marketing strategies and doing practical work on branding strategies.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.4
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• pp.49-58
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• 2007

Hierarchical Mobile IPv6 (HMIPv6) has been proposed by Internet Engineering Task Force (IETF) to compensate for such problems as handover latency and signaling overhead in employing Mobile IPv6 (MIPv6). HMIPv6 supports micro-mobility within a domain and introduces a new entity, namely mobility anchor point (MAP) as a local home agent. However, HMIPv6 causes load concentration at a particular MAP and longer handover latency when inter-domain handover occurs. In order to solve such problems, this paper establishes a virtual domain (VD) of a higher layer MAP and proposes a MAP changing algorithm in which the routing path changes between mobile node (MN) and correspondent node(CN) according to the mobile position and the direction of the MN before inter-domain handover occurs. The proposed algorithm not only enables complete handover binding-update of the on-link care of address (LCoA) only when inter-domain handover occurs, but concentrated load of a particular MAP is distributed as well. This is because the MNs registered with higher layer MAP and lower layer MAP coexist in the VD. We simulate the performance of the proposed algorithm and compare with HMIPv6.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.48-58
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• 2008

This study intends to figure out the image evaluation properties of internet shopping malls and suggest a strategic direction for positioning through establishing a perceptual map on how the actual customers remember them and which image expression would make the most effective marketing. According to the result of analysis, the images of internet shopping malls were drawn as elements such as product information service, customer service after purchase, atmosphere, convenience, safety, and fame. And according to the result of making a perceptual map, it showed that there was a meaningful difference in the customers' perception on competitive shopping malls. The most discriminative property among the images of shopping malls was product information service, and the least discriminative property was convenience. In addition, there showed a meaningful difference in the customers' preference and ideal point on internet shopping malls between the subdivided groups of customers. It was verified that in this internet shopping mall market where competition is getting severe, the result of this study can be a useful foundational data in establishing a marketing strategy of market segmentation.

• ETRI Journal
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• v.41 no.5
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• pp.574-584
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• 2019

A new Mirai variant found recently was equipped with a dynamic update ability, which increases the level of difficulty for DDoS mitigation. Continuous development of 5G technology and an increasing number of Internet of Things (IoT) devices connected to the network pose serious threats to cyber security. Therefore, researchers have tried to develop better DDoS mitigation systems. However, the majority of the existing models provide centralized solutions either by deploying the system with additional servers at the host site, on the cloud, or at third party locations, which may cause latency. Since Internet service providers (ISP) are links between the internet and users, deploying the defense system within the ISP domain is the panacea for delivering an efficient solution. To cope with the dynamic nature of the new DDoS attacks, we utilized an unsupervised artificial neural network to develop a hierarchical two-layered self-organizing map equipped with a twofold feature selection for DDoS mitigation within the ISP domain.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.83-91
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• 2011

Recently, many researchers in the world are concentrated to develop the robot platform which is to reduce the developing cost by reusing existing softwares. In this paper, we describe that the 3 dimension recognition object components for OPRoS (Open Platform for Robotic Services) which is developed in Korea. We present that the structure of the component, disparity map and depth map algorithm for recognizing 3 dimension space. We used stereo matching and block matching method to produce the disparity map. We test the component on the computer with OPRoS platform and show the results of accuracy and performance time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.728-741
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• 2015

Stereo vision has become an important technical issue in the field of 3D imaging, machine vision, robotics, image analysis, and so on. The depth map extraction from stereo video is a key technology of stereoscopic 3D video requiring stereo correspondence algorithms. This is the matching process of the similarity measure for each disparity value, followed by an aggregation and optimization step. Since it requires a lot of computational power, there are significant speed-performance advantages when exploiting parallel processing available on processors. In this situation, multi-core CPU may allow many parallel programming technologies to be realized in users computing devices. This paper proposes parallel implementations for calculating disparity map using a shared memory programming and exploiting the streaming SIMD extension technology. By doing so, we can take advantage both of the hardware and software features of multi-core processor. For the performance evaluation, we implemented a parallel SAD algorithm with OpenMP and SSE2. Their processing speeds are compared with non parallel version on stereoscopic streaming video. The experimental results show that both technologies have a significant effect on the performance and achieve great improvements on processing speed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.