• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.155-175
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• 2003

In context of e-commerce, negotiation is a procedure to help negotiate between buyer and seller by adjusting their negotiation issues such as price and in terms of payment. We used intelligent agent and mobile device to promote new framework of e-commerce. Moreover, this framework can help buyers and sellers to carry their commercial transactions effectively. In regard to that issue, we need to carry out the research of negotiation agent that can be used in e-commerce fields. In this paper, we modeled the negotiation using CSP for the performance of agent in m-commerce environment. Furthermore we implemented interface for mobile device to extract buyer's requirement and preference easily Besides that we used utility function to make a decision for various evaluation functions and suggestions that are used for evaluation of negotiation issues. A difficulty of generating offer is dependent on the number of negotiation issues and the range of the values. Therefore, if any offer has a number of negotiation issues and the range of values are wide, the search space will be exponentially expanded. There have been many studies fur solving this problem, we applied those techniques to improve the agent's ability of negotiation. For example, a contract can be accomplished by exchanging seller and buyer's offer that is generated by agent to adjust the requisite profit for each party. Finally, we show the improvement of satisfaction as the negotiation is processed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2806-2811
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• 2013

New technologies such as multi-antenna and small cell were proposed as key technology for the next generation mobile system to cope with the explosively increasing mobile data traffic. In particular, heterogeneous mobile communication network which can improve spatial reuse factor by exploiting macro and small cells simultaneously is attracting attention. However, the heterogeneous network has a problem that the utilization of small cells becomes low because the transmit power of macro base stations is much higher than that of small base stations and then the probability that mobile stations are attached to the macro base stations becomes high. This problem is dominant in uplink. The concept of cell range expansion bias to mitigate the problem was proposed by 3GPP and the corresponding standardization is in progress. In this paper, we analyze the downlink performance of the heterogeneous mobile communication network based on a system level simulator with the cell range expansion bias in terms of average cell spectral efficiency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.527-533
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• 2018

Recently, the device-to-device (D2D) communication has been conceived as the key technology for the next-generation mobile communication systems. The neighbor discovery in which the nearby users are found, is essential for the proper operation of the D2D communication. In this paper, we propose new neighbor discovery scheme based on deep learning technology which has gained a lot of attention recently. In the proposed scheme, the neighboring users can be found using the uplink pilot transmission of users only, unlike conventional neighbor discovery schemes in which direct pilot communication among users is required, such that the signaling overhead can be greatly reduced in our proposed scheme. Moreover, the neighbors with different proximity can also be classified accordingly which enables more accurate neighbor discovery compared to the conventional schemes. The performance of our proposed scheme is verified through the tensorflow-based computer simulations.

• Journal of KIISE:Databases
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• v.37 no.3
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• pp.146-164
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• 2010

Due to an increasing volume of XML data, it is considered prudent to store XML data on an industry-strength database system instead of relying on a domain specific application or a file system. For shredded XML data stored in relational tables, however, it may not be straightforward to apply existing algorithms for twig query processing, since most of the algorithms require XML data to be accessed in a form of streams of elements grouped by their tags and sorted in a particular order. In order to support XML query processing within the common framework of relational database systems, we first propose several bitmap indexes and their strategies for supporting holistic twig joining on XML data stored in relational tables. Since bitmap indexes are well supported in most of the commercial and open-source database systems, the proposed bitmapped indexes and twig query processing strategies can be incorporated into relational query processing framework with more ease. The proposed query processing strategies are efficient in terms of both time and space, because the compressed bitmap indexes stay compressed during data access. In addition, we propose a hybrid index which computes twig query solutions with only bit-vectors, without accessing labeled XML elements stored in the relational tables.

• The KIPS Transactions:PartA
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• v.14A no.3 s.107
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• pp.173-184
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• 2007

Garbage collection in the Java virtual machine is a core function that relieves application programmers of difficulties related to memory management. In this paper, we evaluate and analyze the performance of GenGC and GenRGC, garbage collectors for embedded Java virtual machines. For performance evaluation, we employ CVM, a real embedded Java virtual machine developed by Sun Microsystems, Inc., as a platform and also use a widely-used SpecJVM98 as a set of benchmark programs. To compare the performance of GenGC and GenRGC, we first evaluate the time of garbage collection and the delay time caused by garbage collection. Second, for more detailed performance analysis of GenRGC, we evaluate the time of garbage collection and the delay time caused by garbage collection while changing the sizes of a block and a frame. Third, we analyze the size of storage space required for performing GenRGC, and show GenRGC to be suitable for embedded environment with a limited mont of memory. Since CVM is the most representative one of embedded Java virtual machines, this performance study is quite meaningful in that we can predict the performance of garbage collectors in real application environments more accurately.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.127-132
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• 2006

K-NN (k-Nearest Neighbors), which is a well-known instance-based learning algorithm, simply stores entire training patterns in memory, and uses a distance function to classify a test pattern. K-NN is proven to show satisfactory performance, but it is notorious formemory usage and lengthy computation. Various studies have been found in the literature in order to minimize memory usage and computation time, and NGE (Nested Generalized Exemplar) theory is one of them. In this paper, we propose RPA (Recursive Partition Averaging) and IRPA (Incremental RPA) which is an incremental version of RPA. RPA partitions the entire pattern space recursively, and generates representatives from each partition. Also, due to the fact that RPA is prone to produce excessive number of partitions as the number of features in a pattern increases, we present IRPA which reduces the number of representative patterns by processing the training set in an incremental manner. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.307-317
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• 2003

This paper presents an algorithm that automatically extracts buildings among many different features on the earth surface by fusing LIDAR data with panchromatic aerial images. The proposed algorithm consists of three stages such as point level process, polygon level process, parameter space level process. At the first stage, we eliminate gross errors and apply a local maxima filter to detect building candidate points from the raw laser scanning data. After then, a grouping procedure is performed for segmenting raw LIDAR data and the segmented LIDAR data is polygonized by the encasing polygon algorithm developed in the research. At the second stage, we eliminate non-building polygons using several constraints such as area and circularity. At the last stage, all the polygons generated at the second stage are projected onto the aerial stereo images through collinearity condition equations. Finally, we fuse the projected encasing polygons with edges detected by image processing for refining the building segments. The experimental results showed that the RMSEs of building corners in X, Y and Z were 8.1cm, 24.7cm, 35.9cm, respectively.

• Journal of the Economic Geographical Society of Korea
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• v.21 no.4
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• pp.335-349
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• 2018

This study analyzes the effect of the changes in traffic environments such as transportation speeds on the time-distance accessibility for the public transportation passengers. To do this, we use passenger transaction databases of the Seoul metropolitan public transportation system: one week for each of the three years (2011, 2013, and 2015). These big data contain the information about time and space on the traffic trajectories of every passenger. In this study, the time-distances of links between subway stations and bus stops of the public transportation system at each time are calculated based on the actual travel time extracted from the traffic-card transaction database. The changes in the time-distance accessibility of the integrated transportation network from the experimental results can be summarized in two aspects. First, the accessibility tends to decline as the year goes by. This is because the transportation network becomes more complicated and then the average moving speed of the vehicles is lowered. Second, the accessibility tends to increase on the weekend in the analysis of accessibility changes by day. This tendency is because the bus speeds on bus routes on the weekend are faster than other days. In order to analyze the accessibility changes, we illustrate graphs of the vehicle speeds and the numbers of passengers by year and day.

• Journal of Korea Water Resources Association
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• v.52 no.4
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• pp.279-289
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• 2019

Unlike other natural disasters, drought is a reoccurring and region-wide phenomenon after being triggered by a prolonged precipitation deficiency. Considering that remote sensing products provide consistent temporal and spatial measurements of precipitation, this study developed a remote sensing data-based drought outlook model. The meteorological drought was defined by the Standardized Precipitation Index (SPI) achieved from PERSIANN_CDR, TRMM 3B42 and GPM IMERG images. Bayesian networks were employed in this study to combine the historical drought information and dynamical prediction products in advance of drought outlook. Drought outlook was determined through a decision-making model considering the current drought condition and forecasted condition from the Bayesian networks. Drought outlook condition was classified by four states such as no drought, drought occurrence, drought persistence, and drought removal. The receiver operating characteristics (ROC) curve analysis were employed to measure the relative outlook performance with the dynamical prediction production, Multi-Model Ensemble (MME). The ROC analysis indicated that the proposed outlook model showed better performance than the MME, especially for drought occurrence and persistence of 2- and 3-month outlook.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.175-182
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• 2020

The non-contact eye tracking is a nonintrusive human-computer interface providing hands-free communications for people with severe disabilities. Recently. it is expected to do an important role in non-contact systems due to the recent coronavirus COVID-19, etc. This paper proposes a novel approach for an eye mouse using an eye tracking method based on a context-aware based AdaBoost multi-region classifier and ASSL algorithm. The conventional AdaBoost algorithm, however, cannot provide sufficiently reliable performance in face tracking for eye cursor pointing estimation, because it cannot take advantage of the spatial context relations among facial features. Therefore, we propose the eye-region context based AdaBoost multiple classifier for the efficient non-contact gaze tracking and mouse implementation. The proposed method detects, tracks, and aggregates various eye features to evaluate the gaze and adjusts active and semi-supervised learning based on the on-screen cursor. The proposed system has been successfully employed in eye location, and it can also be used to detect and track eye features. This system controls the computer cursor along the user's gaze and it was postprocessing by applying Gaussian modeling to prevent shaking during the real-time tracking using Kalman filter. In this system, target objects were randomly generated and the eye tracking performance was analyzed according to the Fits law in real time. It is expected that the utilization of non-contact interfaces.