• International Journal of Advanced Culture Technology
• /
• v.12 no.3
• /
• pp.471-479
• /
• 2024

Recent advancements in cloud computing have significantly increased its importance across various sectors. As sensors, devices, and customer demands have become more diverse, workloads have become increasingly variable and difficult to predict. Cloud providers, connected to multiple physical servers to support a range of applications, often over-provision resources to handle peak workloads. This approach results in inconsistent services, imbalanced energy usage, waste, and potential violations of service level agreements. In this paper, we propose a novel engine equipped with a scheduler based on the Hot-Threshold concept, aimed at optimizing resource usage and improving energy efficiency in cloud environments. We developed this engine to employ both proactive and reactive methods. The proactive method leverages workload estimate-based provisioning, while the reactive Hot-Cold Scheduler consists of a Predictor, Solver, and Processor, which together suggest an intelligent migration flow. We demonstrate that our approach effectively addresses existing challenges in terms of cost and energy consumption. By intelligently managing resources based on past user statistics, we provide significant improvements in both energy efficiency and service consistency.

• Science of Emotion and Sensibility
• /
• v.13 no.1
• /
• pp.225-234
• /
• 2010

To investigate the role of positive emotion in education, we have reviewed the previous studies on positive emotion, learning and motivation. In the present study, we examined the definition of positive emotion, and influences of positive emotion on cognition, creativity, social relationship, psychological resource such as life satisfaction, and interactive relationship among positive emotion, motivation and learning. To investigate the role of positive emotion on motivation and learning more scientifically, we examined the recent results of neuroscience. In other words, we have reviewed diverse research on positive emotion, learning and motivation based on brain-based learning. We also examined the research of autonomy-supportive environment as the specific example of improving positive emotion. As one of the most effective methods for emotional education, we discussed brain-based learning, the new research field. As the future prospects, we discussed the implications, possibilities and limitations of brain-based learning.

• Electronics and Telecommunications Trends
• /
• v.35 no.5
• /
• pp.14-29
• /
• 2020

The 6G mobile communication system will become a backbone infrastructure around 2030 for the future digital world by providing distinctive services such as five-sense holograms, ultra-high reliability/low-latency, ultra-high-precision positioning, ultra-massive connectivity, and gigabit-per-second data rate for aerial and maritime terminals. The recent remarkable advances in machine learning (ML) technology have recognized its efficiency in wireless networking fields such as resource management and cell-configuration optimization. Further innovation in ML is expected to play an important role in solving new problems arising from 6G network management and service delivery. In contrast, an approach to apply ML to a physical-layer (PHY) target tackles the basic problems in radio links, such as overcoming signal distortion and interference. This paper reviews the methodologies of ML-based PHY, relevant industrial trends, and candiate technologies, including future research directions and standardization impacts.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.1035-1048
• /
• 2023

The IEEE 802.11 WLAN adopts a random backoff algorithm for its collision avoidance mechanism, and it is well known that the contention-based algorithm may suffer from performance degradation especially in congested networks. In this paper, we design an efficient backoff algorithm that utilizes a reinforcement learning method to determine optimal values of backoffs. The mobile nodes share a common contention window (CW) in our scheme, and using a Q-learning algorithm, they can avoid collisions by finding and implicitly reserving their optimal time slot(s). In addition, we introduce Frame Size Control (FSC) algorithm to minimize the possible degradation of aggregate throughput when the number of nodes exceeds the CW size. Our simulation shows that the proposed backoff algorithm with FSC method outperforms the 802.11 protocol regardless of the traffic conditions, and an analytical modeling proves that our mechanism has a unique operating point that is fair and stable.

• International Journal of Computer Science & Network Security
• /
• v.24 no.4
• /
• pp.170-178
• /
• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.8
• /
• pp.157-164
• /
• 2024

In this paper, we propose a discussion that the feasibility of deploying a deep learning-based detector on the resource-limited board. Although many studies evaluate the detector on machines with high-performed GPUs, evaluation on the board with limited computation resources is still insufficient. Therefore, in this work, we implement the deep-learning detectors and deploy them on the compact board by parsing and optimizing a detector. To figure out the performance of deep learning based detectors on limited resources, we monitor the performance of several detectors with different H/W resource. On COCO detection datasets, we compare and analyze the evaluation results of detection model in On-Board and the detection model in On-GPU in terms of several metrics with mAP, power consumption, and execution speed (FPS). To demonstrate the effect of applying our detector for the military area, we evaluate them on our dataset consisting of thermal images considering the flight battle scenarios. As a results, we investigate the strength of deep learning-based on-board detector, and show that deep learning-based vision models can contribute in the flight battle scenarios.

• Educational Technology International
• /
• v.6 no.1
• /
• pp.41-63
• /
• 2005

E-Learning is widely introduced with cyber universities in Korea from 2001 whencyber universities were first authorized by the Ministry of Education and Human Resource Development. E-learning amplified by cyber university gave a big impact in the campus based university which became the cause for the educational paradigm shift. The changes of status of cyber university shows important trend in college education which was analyzed by enrollment rate, types of cyber university, demography, and study areas. The enrollment rate of cyber universities is ever since 2001 and variety of study areas gives popularity to students. The demography of students is as expected older than traditional students. Female students at the cyber university outnumbered that at campus based university in Korea. For analyzing the trend of e-learning in Korea, there were studies twice in 2001 May-June from 213 faculty members and staff, 630 students and in 2004 May-June with 401 students. Most of e-learning students tent to spend less time yet, students feel more burden with e-learning. Professors tend to load more materials for the e-learning in 2001but in 2004 study, the difference no longer exists. Professors and students feel the academic achievement through e-learning is not as good as from the traditional classes. Difficulties for e-learning in 2001 were the lack of administrative information but in 2004, boring contents and lack of instructional strategies for e-learning. Technical problems still do exist but less serious. Suggestions for e-learning are blended learning, online students prefer video streaming with their own lecturer, new definition of instructor is needed, professional development for content development and online instruction is needed, success story of online learning should be encouraged, guidance for online students needed. The cyber university experiencegave a positive impact on the traditional universities such as rethinking the roles of universities, the quality control of classes, professional development, student oriented educational service of e-learning pedagogy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.10
• /
• pp.4177-4190
• /
• 2015

Current research on Internet of Things (IoT) has primarily addressed the means to enhancing smart resource allocation, automatic network operation, and secure service provisioning. In particular, providing satisfactory security service in IoT systems is indispensable to its mission critical applications. However, limited resources prevent full security coverage at all times. Therefore, these limited resources must be deployed intelligently by considering differences in priorities of targets that require security coverage. In this study, we have developed a new application of Cognitive Radio (CR) technology for IoT systems and provide an appropriate security solution that will enable IoT to be more affordable and applicable than it is currently. To resolve the security-related resource allocation problem, game theory is a suitable and effective tool. Based on the Blotto game model, we propose a new strategic power allocation scheme to ensure secure CR communications. A simulation shows that our proposed scheme can effectively respond to current system conditions and perform more effectively than other existing schemes in dynamically changeable IoT environments.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.9
• /
• pp.3663-3679
• /
• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.3
• /
• pp.87-93
• /
• 2019

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.