• Journal of Digital Convergence
• /
• v.19 no.10
• /
• pp.29-38
• /
• 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
• /
• v.10 no.10
• /
• pp.269-276
• /
• 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
• /
• v.31 no.4
• /
• pp.605-616
• /
• 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
• /
• v.9 no.6
• /
• pp.729-738
• /
• 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
• /
• v.36 no.6
• /
• pp.483-497
• /
• 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
• /
• v.17 no.6
• /
• pp.211-223
• /
• 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
• /
• v.22 no.11
• /
• pp.1511-1517
• /
• 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.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.21 no.5
• /
• pp.641-656
• /
• 2019

In this study, a machine learning method was proposed to use in predicting optimal replacement period of shield TBM (Tunnel Boring Machine) disc cutter. To do this, a large dataset of ground condition, disc cutter replacement records and TBM excavation-related data, collected from a shield TBM tunnel site in Korea, was built and they were used to construct a disc cutter replacement period prediction model using a machine learning algorithm, SVM (Support Vector Machine) and to assess the performance of the model. The results showed that the performance of RBF (Radial Basis Function) SVM is the best among a total of three SVM classification functions (80% accuracy and 10% error rate on average). When compared between ground types, the more disc cutter replacement data existed, the better prediction results were obtained. From this results, it is expected that machine learning methods become very popularly used in practice in near future as more data is accumulated and the machine learning models continue to be fine-tuned.

• Journal of the Korea Convergence Society
• /
• v.10 no.9
• /
• pp.31-36
• /
• 2019

Music therapy, a field that convergence music and treatment, which play a fundamental role in personality formation, possesses diverse and complex treatment methods. Music therapists in charge of music therapy may experience the same phenomenon as countertransference in consultation with clients. In addition, experiencing psychological burnout, there are many difficulties in reaching the final goal of music therapy. In this paper, we provide a collaborative filtering-based music therapy consultation data recommendation model for smooth music therapy consultation with clients who visited for music therapy. The proposed model grasps the similarity between the conventional consultation data and the new consultant data through the euclidean distance algorithm. This is to recommend similar consultation materials. Since music therapists can provide optimal consultation materials for consultants who need music therapy, smooth consultation is expected.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.3
• /
• pp.267-275
• /
• 2019

Currently, due to the widespread use of a smartphone, QR codes allow users to access a variety of added services. However, the QR codes posted on curved surfaces tend to be non-uniformly illuminated and bring about the decline of recognition rate. So, in this paper, the block-adaptive binarization policy is adopted to find an optimal threshold appropriate for bimodal image like QR codes. For a large block, its histogram distribution is found to get an initial threshold and then the block is partitioned to reflect the local characteristics of small blocks. Also, morphological operation is applied to their neighboring boundary at the discontinuous at the QR code junction. This paper proposes an authentication method based on the color code, uniquely painted within QR code. Through a variety of practical experiments, it is shown that the proposed algorithm outperforms the conventional method in detecting QR code and also maintains good recognition rate up to 40 degrees on curved surfaces.