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An Interference Reduction Scheme Using AP Aggregation and Transmit Power Control on OpenFlow-based WLAN
Mi-Rim Do, Sang-Hwa Chung, Chang-Woo Ahn
Recently, excessive installations of APs have caused WLAN interference, and many techniques have been suggested to solve this problem. The AP aggregation technique serves to reduce active APs by moving station connections to a certain AP. Since this technique forcibly moves station connections, the transmission performance of some stations may deteriorate. The AP transmit power control technique may cause station disconnection or deterioration of transmission performance when power is reduced under a certain level. The combination of these two techniques can reduce interference through AP aggregation and narrow the range of interferences further through detailed power adjustment. However, simply combining these techniques may decrease the probability of power adjustment after aggregation and increase station disconnections upon power control. As a result, improvement in performance may be insignificant. Hence, this study suggests a scheme to combine the AP aggregation and the AP transmit power control techniques in OpenFlow-based WLAN to ameliorate the disadvantages of each technique and to reduce interferences efficiently by performing aggregation for the purpose of increasing the probability of adjusting transmission power. Simulations reveal that the average transmission delay of the suggested scheme is reduced by as much as 12.8% compared to the aggregation scheme and by as much as 18.1% compared to the power control scheme. The packet loss rate due to interference is reduced by as much as 24.9% compared to the aggregation scheme and by as much as 46.7% compared to the power control scheme. In addition, the aggregation scheme and the power control scheme decrease the throughput of several stations as a side effect, but our scheme increases the total data throughput without decreasing the throughput of each station.
SSD Caching for Improving Performance of Virtualized IoT Gateway
It is important to improve the performance of storage in the home cloud environment within the virtualized IoT gateway since the performance of applications deeply depends on storage. Though SSD caching is applied in order to improve the storage, it is only used for read-cache due to the limitations of SSD such as poor write performance and small write endurance. However, it isimportant to improve performance of the write operation in the home cloud server, in order to improve the end-user experience. This paper propose a novel SSD caching which considers write-data as well as read-data. We validate the enhancement in the performance of random-write by transforming it to the sequential patterns.
A MapReduce based Algorithm for Spatial Aggregation of Microblog Data in Spatial Social Analytics
Hyun Gu Cho, Pyoung Woo Yang, Ki Hyun Yoo, Kwang Woo Nam
In recent times, microblogs have become popular owing to the development of the Internet and mobile environments. Among the various types of microblog data, those containing location data are referred to as spatial social Web objects. General aggregations of such microblog data include data aggregation per user for a single piece of information. This study proposes a spatial aggregation algorithm that combines a general aggregation with spatial data and uses the Geohash and MapReduce operations to perform spatial social analysis, by using microblog data with the characteristics of a spatial social Web object. The proposed algorithm provides the foundation for a meaningful spatial social analysis.
Similarity Analysis and API Mapping with HLA and DDS for L-V-C Realization
Kunryun Cho, Giseop No, Chongkwon Kim
The rapid growth of network technology makes the high-tech weapon. Thus, in the modern war, the ability to immediately use of the high-tech weapon is important. To realize this ability, continuous trainning is necessary but, this trainning spends many money. To improve the budget efficiency, Modeling and Simulation(M&S) are used. However, they seriously decrease the reality. Recently, the system which can support the combination of Live with Virtual simulation is on the rise. The typical example is L-V-C Environment and many kind of middleware which can support the L-V-C Envrionment are already proposed. Previous middleware can support the interoperability between different simulations but, it cannot completely interoperate three(Live, Virtual, Constructive) simulation environments. In this paper, to solve this problem, we propose the scheme which is combination between different middlewares. And we conduct the API mapping between HLA and DDS which are typical middleware and verify the scheme.
Automatic Construction of a Negative/positive Corpus and Emotional Classification using the Internet Emotional Sign
Kyoungae Jang, Sanghyun Park, Woo-Je Kim
Internet users purchase goods on the Internet and express their positive or negative emotions of the goods in product reviews. Analysis of the product reviews become critical data to both potential consumers and to the decision making of enterprises. Therefore, the importance of opinion mining techniques which derive opinions by analyzing meaningful data from large numbers of Internet reviews. Existing studies were mostly based on comments written in English, yet analysis in Korean has not actively been done. Unlike English, Korean has characteristics of complex adjectives and suffixes. Existing studies did not consider the characteristics of the Internet language. This study proposes an emotional classification method which increases the accuracy of emotional classification by analyzing the characteristics of the Internet language connoting feelings. We can classify positive and negative comments about products automatically using the Internet emoticon. Also we can check the validity of the proposed algorithm through the result of high precision, recall and coverage for the evaluation of this method.
A Method of Interoperating Heterogeneous Simulation Middleware for L-V-C Combined Environment
Kunryun Cho, Giseop No, Sihyun Jung, Nopphon Keerativoranan, Chongkwon Kim
Simulation is used these days to verify the hypothesis or the new technology. In particular, National Defense Modeling & Simulation (M&S) is used to predict wartime situation and conduct the military training. National Defense M&S can be divided into three parts, live simulation, virtual simulation, and constructive simulation. Live simulation is based on the real environment, which allows more realistic sumulation; however, it has decreased budget efficiency, but reduced depictions of reality. In contrast, virtual and constructive simulations which are based on the virtual environment, have increased budget efficiency, but reduced depictions of reality. Thus, if the three parts of the M&S are combined to make the L-V-C combined environment, the disadvantages of each simulation can be complemented to increases the quality of the simulation. In this paper, a method of interworking heterogeneous simulation middeware for L-V-C combined environment is proposed, and the test results of interworking between Data Distribution Service (DDS) and High Level Architecture (HLA) are shown.
A Mechanism to Provide Telepresence Service Information to Heterogeneous Services
Yunjin Lee, Younghan Kim, Sunwan Choi
This paper proposes a method for providing the information about multimedia streams for telepresence services to heterogeneous services such as IMS (IP Multimedia Subsystem) and RTCWeb (Real-Time Communication in WEB-browsers). First of all, we design an interworking gateway for each service and suggest a procedure for providing the information about multimedia streams, which is defined by CLUE, a working group for standardization, to the heterogeneous services. We also apply the method of the actual CLUE information exchange and implement it in our experiment environment. Finally, we show that the proposed method can exchange more information than previous methods even though the media session re-establishment time is similar to legacy systems in terms of performance analysis. With the proposed method, the heterogeneous services can collect a variety of information about the telepresence service and use it according to user preference. In this way it provides rich multimedia streaming services for many areas.
A Graph Neural Network Approach for Predicting the Lung Carcinogenicity of Single Molecular Compounds
http://doi.org/10.5626/JOK.2025.52.6.482
Cancer is one of the major diseases causing millions of deaths worldwide every year, and lung cancer has been recorded as the leading cause of cancer-related deaths in Korea in 2022. Therefore, research on lung cancer-causing compounds is essential, and this study proposes and evaluates a novel approach to predict lung cancer-causing potential using graph neural networks to overcome the limitations of existing machine learning and deep learning methods. Based on SMILES(Simplified Molecular Input Line Entry System) information from the compound carcinogenicity databases CPDB, CCRIS, IRIS and T3DB, the structure and chemical properties of molecules were converted into graph data for training, and the proposed model showed superior prediction performance compared to other models. This demonstrates the potential of graph neural networks as an effective tool for lung cancer prediction and suggests that they can make important contributions to future cancer research and treatment development.
Root Cause Analysis for Microservice Systems Using Anomaly Propagation by Resource Sharing
Junho Park, Joyce Jiyoung Whang
http://doi.org/10.5626/JOK.2025.52.4.341
Identifying root causes of failures in microservice systems remains a critical challenge due to intricate interactions among resources and propagation of errors. We propose AnoProp, a novel model for root cause analysis to address challenges by capturing inter-resource interactions and the resulting propagation of anomalies. AnoProp incorporates two core techniques: the anomaly score measurement for metrics using regression models and the root cause score evaluation for resources based on the propagation rate of these anomalies. Experimental results using an Online Boutique dataset demonstrated that AnoProp surpassed existing models across various evaluation metrics, validating its ability to provide balanced performance for different types of root causes. This study underscores the potential of AnoProp to enhance system stability and boost operational efficiency in microservice environments.
Improved Software Defect Prediction with Gated Tab Transformer
Saranya Manikandan, Duksan Ryu
http://doi.org/10.5626/JOK.2025.52.3.196
Software Defect Prediction (SDP) plays a crucial role in ensuring software quality and reliability. Although, traditional machine learning and deep learning models are widely used for SDP, recent advancements in the field of natural language processing have paved the way for applying transformer-based models in software engineering tasks. This paper investigated transformer-based model as a potential approach to improve SDP model quality, ultimately aiming to enhance software quality and optimize testing resource allocation. Inspired by the Gated Tab Transformer’s (GTT) ability to effectively model relationship within features, we evaluated its effectiveness in SDP. We conducted experiments using 15 software defect datasets and compared results with other state-of-the-art machine learning and deep learning models. Our experiments showed that GTT outperformed state-of-the-art machine learning models in terms of recall, balance, and AUC (increase by 42.1%, 10.93%, and 7.1%, respectively). Cohen's d confirmed this advantage with large and medium effect sizes for GTT on these metrics. Additionally, an ablation study assessed the impact of hyperparameter variations on performance. Thus, GTT's effectiveness address the challenges of SDP, potentially leading to more effective testing resource allocation and improved software quality.
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