• Journal of IKEEE
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• v.28 no.1
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• pp.65-71
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• 2024

This paper proposes a neural network that can reduce the number of parameters while reducing matching errors in occluded regions to increase the accuracy of depth maps in stereo matching. Stereo matching-based object recognition is utilized in many fields to more accurately recognize situations using images. When there are many objects in a complex image, an occluded area is generated due to overlap between objects and occlusion by background, thereby lowering the accuracy of the depth map. To solve this problem, existing research methods that create context information and combine it with the cost volume or RoIselect in the occluded area increase the complexity of neural networks, making it difficult to learn and expensive to implement. In this paper, we create a depthwise seperable neural network that enhances regional feature extraction before cost volume generation, reducing the number of parameters and proposing a neural network that is robust to occlusion errors. Compared to PSMNet, the proposed neural network reduced the number of parameters by 30%, improving 5.3% in color error and 3.6% in test loss.

• The Journal of Information Systems
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• v.15 no.3
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• pp.159-174
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• 2006

Employment rate of graduating students has been one of the most important issues at universities. Recently interest on internship abroad has been increased significantly due to globalization of the society In particular, central and local governments have developed policies and encouraged university students to participate in internships abroad. However, activities and resources for internships abroad are very limited to a few organizations. This paper investigated the current status of internship in the U.S. and Korea. Then, this paper analyzed differences in demand and supply of the internship and matching mechanism of the internship between the U.S. and Korea. From the results of those analyses, this paper developed an international network model which can help effective and efficient increase in the demand and supply of the internship as well as the internship matching mechanism in Korea. This network model utilizes international NGOs in order to develop internationally cooperative environment. This model provides mechanism for (1) effectively identifying intern applicants who like to work abroad and evaluating thent (2) effectively identifying new internship positions and evaluating companies which plan to hire interns, (3) efficiently matching demand for and supply of internship by identifying appropriate candidates, (4) monitoring companies for their quality of working conditions and interns for their qualities of work This model for internship has been applied for a NGO which is International CBMC (Christian Businessman Committee International)

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2258-2276
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• 2019

Resource sharing is one of the main goals achieved by network virtualization technology to enhance network resource utilization and enable resource customization. Though resource sharing can improve network efficiency by accommodating various users in a network, limited infrastructure capacity is still a challenge to ultra-low latency service operators. In this paper, we propose an inter-slice resource borrowing schema based on the device-to-device (D2D) potentiality especially for ultra-low latency transmission in cellular networks. An extended and modified Kuhn-Munkres bipartite matching algorithm is developed to optimally achieve inter-slice resource borrowing. Simulation results show that, proper D2D user matching can be achieved, satisfying ultra-low latency (ULL) users' quality of service (QoS) requirements and resource utilization in various scenarios.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.801-808
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• 2022

Research on how to embed knowledge in large-scale Linked Data and apply neural network models for entity matching is relatively scarce. The most fundamental problem with this is that different labels lead to lexical heterogeneity. In this paper, we propose an extended GCN (Graph Convolutional Network) model that combines re-align structure to solve this lexical heterogeneity problem. The proposed model improved the performance by 53% and 40%, respectively, compared to the existing embedded-based MTransE and BootEA models, and improved the performance by 5.1% compared to the GCN-based RDGCN model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.1
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• pp.114-121
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• 2003

In this paper, low voltage and two stage CMOS Class E RF power amplifier for ISM(Industrial/Scientific/Medical) Open Band is presented. The power amplifier operates at 2.4GHz frequency, and is designed and simulated with a 0.35um CMOS technology and HSPICE simulator. The power amplifier is simple structure of two stage Class E power amplifier. The design procedure determing matching network was presented. The power amplifier is composed of input stage matching network, preamplifier, interstage matching network, power amplifier, and output stage matching network. The matching networks of input stage and interstage were constituted by pi($\pi$) type and L type respectively. At 2.4GHz operating frequency, and with a 2.5V supply voltage, the power amplifier delivers 23dBm output power to a 50${\Omega}$ load with 39% power added efficiency(PAE).

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.675-685
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• 2011

This paper proposes a job matching function that calculates the job matching probability of a job-seeker to an employer taking the working conditions of a job-seeker and an employer into account. In addition, this study analysis the degree of centrality that means interactions of a job-seeker and an employer utilizing social network analysis. The results are follows. First, a degree of centrality is found to be severely concentrated in certain job-seekers or certain employers; in addition, there are many job-seekers and employers who have no matching results. Second, according to decision tree analysis, characteristics of a job-seeker that influences the degree of centrality are gender, age and degree of education in order of importance. The characteristics of a employer that influences the degree of centrality are proposed salary, industry classification and firm size in order of importance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.12
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• pp.1337-1342
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• 2012

In this paper, we propose a novel reconfigurable antenna that can change the electrical shape of the matching network using RF switches of PIN diodes. The designed antenna operates at two different modes that are Mode 1 (HSDPA band, 2.1~2.2 GHz) and Mode 2(WiBro WiFi band, 2.3~2.5 GHz). The antenna is built on both sides of a polyarcylate substrate. The measured reflection coefficient shows a matching bandwidth of 547 MHz($S_{11}$ <-3 dB, 2.035~2.582 GHz) for Mode 1 and 600 MHz($S_{11}$ <-3 dB, 2.2~2.8 GHz) for Mode 2, and it shows average vertical gains of -4.4 dBi and -4.5 dBi in x-y plane, respectively.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.928-931
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• 2002

Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.1 no.1
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• pp.35-53
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• 1997

This paper analyzes the time complexity of Genetic Algorithm based Task Scheduling (GATS) which is designed for the scheduling of parallel programs with diverse embedded parallelism types in a heterogeneous network systems. The analysis of time complexity is performed based on two representation methods (REIA, REIS) which are proposed in this paper to encode the scheduling information. And the heterogeneous network systems consist of a set of loosely coupled parallel and vector machines connected via a high-speed network. The objective of heterogeneous network computing is to solve computationally intensive problems that have several types of parallelism, on a suite of high performance and parallel machines in a manner that best utilizes the capabilities of each machine. Therefore, when scheduling in heterogeneous network systems, the matching of the parallelism characteristics between tasks and parallel machines should be carefully handled in order to obtain more speedup. This paper shows how the parallelism type matching affects the time complexity of GATS.

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.796-804
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• 2021

In computer vision, visual tracking method addresses the problem of localizing an specific object in video sequence according to the bounding box. In this paper, we propose a tracking method by introducing the feature correlation comparison into the siamese network to increase its matching identification. We propose a way to compute location of object to improve matching performance by a correlation operation, which locates parts for solving the searching problem. The higher layer in the network can extract a lot of object information. The lower layer has many location information. To reduce error rate of the object center point, we built a siamese network that extracts the distribution and location information of target objects. As a result of the experiment, the average center error rate was less than 25%.