• Structural Engineering and Mechanics
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• v.8 no.2
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• pp.165-180
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• 1999

The cost effectiveness of using steel roof systems for residential buildings is becoming increasingly apparent with the decrease in manufacturing cost of steel components, reliability and efficiency in construction practices, and the economic and environmental concerns. While steel has been one of the primary materials for structural systems, it is only recently that its use for residential buildings is being explored. A comprehensive system for the design of residential steel roof truss systems is presented. In the first stage of the research the design curves obtained from the AISI-LRFD code for the manufactured cross-sections were verified experimentally. Components of the truss systems were tested in order to determine their member properties when subjected to axial force and bending moments. In addition, the experiments were simulated using finite element analysis to provide an additional source of verification. The second stage of the research involved the development of an integrated design approach that would automatically design a lowest cost roof truss given minimal input. A modified genetic algorithm was used to handle sizing, shape and topology variables in the design problem. The developed methodology was implemented in a software system for the purpose of designing the lowest cost truss that would meet the AISI code provisions and construction requirements given the input parameters. The third stage of the research involved full-scale testing of a typical residential steel roof designed using the developed software system. The full scale testing established the factor of safety while validating the analysis and design procedures. Evaluation of the test results indicates that designs using the present approach provide a structure with enough reserve strength to perform as predicted and are very economical.

• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.29-32
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• 2000

Layered video coding schemes allow the video information to be transmitted in multiple video bitstreams to achieve scalability. they are attractive in theory for two reasons. First, they naturally allow for heterogeneity in networks and receivers in terms of client processing capability and network bandwidth. Second, they correspond to optimal utilization of available bandwidth when several video qualify levels are desired. In this paper we propose a scalable video codec architectures with motion estimation, which is suitable for real-time audio and video communication over packet networks. The coding algorithm is compatible with ITU-T recommendation H.263+ and includes various techniques to reduce complexity. Fast motion estimation is Performed at the H.263-compatible base layer and used at higher layers, and perceptual macroblock skipping is performed at all layers before motion estimation. Error propagation from packet loss is avoided by Periodically rebuilding a valid Predictor in Intra mode at each layer.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.37-45
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• 2020

The development process of deep learning is an iterative task that requires a lot of manual work. Among the steps in the development process, pre-processing of learning data is a very costly task, and is a step that significantly affects the learning results. In the early days of AI's algorithm research, learning data in the form of public DB provided mainly by data scientists were used. The learning data collected in the real environment is mostly the operational data of the sensors and inevitably contains various noises. Accordingly, various data cleaning frameworks and methods for removing noises have been studied. In this paper, we proposed a method for detecting and removing noises from time-series data, such as sensor data, that can occur in the IoT environment. In this method, the linear regression method is used so that the system repeatedly finds noises and provides data that can replace them to clean the learning data. In order to verify the effectiveness of the proposed method, a simulation method was proposed, and a method of determining factors for obtaining optimal cleaning results was proposed.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.29-38
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• 2021

Purpose: This is a study to design an optimal Edutech teaching-learning platform model that can be linked with various types of LMS to activate e-learning. Methods: For this purpose, the contents of e-learning systems that can be used in the 4th industrial technology of cyber universities and general universities were cross-sectionally analyzed. Results: Cyber universities relied entirely on LMS, and general universities supplemented and utilized different Edutech methods for each professor such as Google Classroom, Zoom video communication, and YouTube in addition to LMS. It was considered that it would be meaningful to provide a minimal algorithm mapping to LMS to share metadata such as Google and YouTube for the Edutech teaching and learning platform model. Conclusion: Therefore, this study is expected to contribute to the improvement of teaching methods and academic achievement through the LMS-based Edutech teaching and learning platform model.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.269-276
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• 2021

Recently, smart sensors are used in various environments, and the implementation of ad-hoc sensor networks (ASNs) is a hot research topic. Unfortunately, traditional sensor network routing algorithms focus on specific control issues, and they can't be directly applied to the ASN operation. In this paper, we propose a new routing protocol by using the Q-learning technology, Main challenge of proposed approach is to extend the life of ASNs through efficient energy allocation while obtaining the balanced system performance. The proposed method enhances the Q-learning effect by considering various environmental factors. When a transmission fails, node penalty is accumulated to increase the successful communication probability. Especially, each node stores the Q value of the adjacent node in its own Q table. Every time a data transfer is executed, the Q values are updated and accumulated to learn to select the optimal routing route. Simulation results confirm that the proposed method can choose an energy-efficient routing path, and gets an excellent network performance compared with the existing ASN routing protocols.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.605-616
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• 2021

While malwares must be accurately identifiable from arbitrary programs, existing studies using classification techniques have limitations that they can only be applied to limited samples. In this work, we propose a method to utilize API call frequency to detect and classify malware families from arbitrary programs. Our proposed method defines a rule that checks whether the call frequency of a particular API exceeds the threshold, and identifies a specific family by utilizing the rate information on the corresponding rules. In this paper, decision tree algorithm is applied to define the optimal threshold that can accurately identify a particular family from the training set. The performance measurements using 4,443 samples showed 85.1% precision and 91.3% recall rate for family detection, 97.7% precision and 98.1% reproduction rate for classification, which confirms that our method works to distinguish malware families effectively.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.729-738
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• 2019

Recently, we aim to improve the quality of virtual reality contents based on quantitative analysis results of emotions through combination of emotional recognition field and virtual reality field. Emotions are analyzed based on the participant's vital signs. Much research has been done in terms of signal analysis, but the methodology for quantifying emotions has not been fully discussed. In this paper, we propose a normalization function design and expression method to quantify the emotion between various bio - signals. Use the Brute force algorithm to find the optimal parameters of the normalization function and improve the confidence score of the parameters found using the true and false scores defined in this paper. As a result, it is possible to automate the parameter determination of the bio-signal normalization function depending on the experience, and the emotion can be analyzed quantitatively based on this.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.483-497
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• 2018

Image mosaicking, which combines several images into one image, is effective for analyzing images and important in various fields of spatial information such as a continuous image map. The crucial processes of the image mosaicking are optimal seamline determination and color correction of mosaicked images. In this study, the overlap regions were determined by SURF (Speeded Up Robust Features) for image matching. Based on the characteristics of the edges extracted by Canny filter, seamline candidates were selected from classified edges with their characteristics, and the edges were connected by using Dijkstra algorithm. In particular, anisotropic filter and image pyramid were applied to extract reliable seamlines. In addition, it was possible to determine seamlines effectively and efficiently by utilizing building and road layers from digital maps. Finally, histogram matching and seamline feathering were performed to improve visual quality of the mosaicked images.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.211-223
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• 2018

Mobile relay systems have been adopted by $4^{th}$ generation mobile systems as an alternative method to extend cell coverage as well as to enhance the system throughput at cell-edges. In order to achieve such performance gains, the mobile relay systems require path selection and resource allocation schemes that are specifically designed for these systems which make use of additional radio resources not needed in single-hop systems. This paper proposes an integrated path selection and resource allocation scheme for LTE-Advanced relay systems using collaborative communication. We first define the problem of maximizing the downlink throughput of LTE-Advanced relay systems using collaborative communication and transform it into a multi-dimensional multi-choice backpacking problem. The proposed Lagrange multiplier-based heuristic algorithm is then applied to derive the approximate solution to the maximization problem. It is shown through simulations that the approximate solution obtained by the proposed scheme can achieve a near-optimal performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1511-1517
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• 2018

Mobile Device-to-Device (D2D) caching networks can transmit multimedia data to users directly without passing through any network infrastructure by storing popular multimedia contents in advance that are popular among many mobile users at caching server devices (CSDs) in distributed manners. Thus, mobile D2D caching networks can significantly reduce backhaul traffic in wired networks and service latency time of mobile users. In this paper, we propose an efficient transmission scheme that can enhance the transmission efficiency of mobile D2D caching networks by using multiple CSDs that are caching the contents that are popular among mobile users. By sorting the multiple CSDs that are caching a content that mobile users want to receive according to their channel gains, the proposed scheme can reduce the complexity of algorithm significantly, compared to an optimal scheme based on Brute-force searching, and can also obtain much higher network transmission efficiency than the existing Blanket and Opportunistic transmission schemes.