• 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 Digital Convergence
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• v.17 no.9
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• pp.199-212
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• 2019

Digitalization is creating a variety of changes and innovations in our business environment. In manufacturing, robots have long been used for automation to innovate processing speed and quality. The RPA brings these innovations in manufacturing sites to the office space. The purpose of this study is to improve productivity for simple, repetitive tasks in these office space. For identify the potential of automation related to productivity improvement, I looked at the concept of business automation, and then simulated the five areas of business documentation works with agile methodology. In conclusion, I confirmed that productivity improvement of 97.3% in quality inspection and 31.7% in editorial design is possible, and examined the direction to apply to actual work. Based on these results, future study will explore the application of Intelligent Process Automation (IPA).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.479-481
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• 2019

In recent years, training systems in various industrial fields have been made using virtual reality (VR) technology and widely used in education. Virtual reality based training system is safe, there is no waste of material, and there are many advantages to be able to practice anytime and anywhere. For example, virtual reality welding training simulation system is widely used for field worker because it can perform actual joining of steel plate in immersive environment. At this time, realistic representation of the steel plate joint is important to maximize the effectiveness of the training, but existing techniques have limited the natural expression of the effect. In this study, we propose a method of visualizing joint effect based on shader in order to construct welding training system. The results of this study can be applied to the welding training system to improve the weld training effect to provide the user with high-quality visualization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1248-1253
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• 2019

Using current evacuation methods, a crew describes the physical location of an accident and guides evacuation using alarms and emergency guide lights. However, in case of an accident on a large and complex building, an intelligent and effective emergency evacuation system is required to ensure the safety of evacuees. Therefore, several studies have been performed on intelligent path finding and emergency evacuation algorithms which are centralized guidance methods using gathered data from distributed sensor nodes. However, another important aspect is effective rescue guidance in an emergency situation. So far, there has been no consideration on the efficient rescue guidance scheme. Therefore, this paper proposes the genetic algorithm based emergency rescue guidance method using distributed wireless sensor networks. Performance evaluation using a computer simulation shows that the proposed scheme guarantees efficient path finding. The fitness converges to the minimum value in reasonable time. The density of each exit node is remarkably decreased as well.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.157-166
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• 2022

In this study, The goal is to analyze the feasibility of introducing a Piggyback system that can reduce the time and cost incurred by transshipment work and improve the transportation speed when transporting complex cargo by rail. To this end, the feasibility analysis methodology is reviewed through domestic and international literature review. In order to quantitatively derive the feasibility analysis values, a transportation database was applied to develop a freight transport simulation model and a freight demand prediction model for major freight transport O-D routes with a transportation distance of 200 km or more. As a result of analyzing economic feasibility by setting the analysis period to 15 years on the premise that the Piggyback System will be introduced on major cargo transport O-D routes in 2025, the NPV value was positive and the B/C value was 1.18, indicating that the Piggyback system was economical. The proposed research method can be meaningful data for establishing transportation policies that can improve the competitiveness of railroad transportation.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1153-1160
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• 2021

This paper proposes a routing algorithm that determines the optimal path using deep reinforcement learning in software-defined networks. The deep reinforcement learning model for learning is based on DQN, the inputs are the current network state, source, and destination nodes, and the output returns a list of routes from source to destination. The routing task is defined as a discrete control problem, and the quality of service parameters for routing consider delay, bandwidth, and loss rate. The routing agent classifies the appropriate service class according to the user's quality of service profile, and converts the service class that can be provided for each link from the current network state collected from the SDN. Based on this converted information, it learns to select a route that satisfies the required service level from the source to the destination. The simulation results indicated that if the proposed algorithm proceeds with a certain episode, the correct path is selected and the learning is successfully performed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1551-1558
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• 2022

We propose a two-tier cooperative computing system comprised of a mobile device and an edge server for partial offloading of multi-branch structures in Fog/Edge Computing environments in this paper. The proposed system includes an algorithm for splitting up application service processing by using reconstructive linearization techniques for multi-branch structures, as well as an optimal collaboration algorithm based on partial offloading between mobile device and edge server. Furthermore, we formulate computation offloading and CNN layer scheduling as latency minimization problems and simulate the effectiveness of the proposed system. As a result of the experiment, the proposed algorithm is suitable for both DAG and chain topology, adapts well to different network conditions, and provides efficient task processing strategies and processing time when compared to local or edge-only executions. Furthermore, the proposed system can be used to conduct research on the optimization of the model for the optimal execution of application services on mobile devices and the efficient distribution of edge resource workloads.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.39-46
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• 2023

We present an efficient packet transmission strategy for massively multiplayer online first-person shooter (MMOFPS) games using movement-adaptive packet transmission interval. The player motion in FPS games shows a wide spectrum of movement variability both in speed and orientation, where there is room for reducing the number of packets to be transmitted to the server depending on the predictability of the character's movement. In this work, the degree of variability (nonlinearity) of the player movements is measured at every packet transmission to calculate the next transmission time, which implements the adaptive transmission frequency according to the amount of movement change. Server-side prediction with a few auxiliary heuristics is performed in concert with the incoming packets to ensure reliability for synchronizing the connected clients. The comparison of our method with the previous fixed-interval transmission scheme is presented by demonstrating them using a test game environment.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.101-110
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• 2023

This study was conducted to identify factors influencing weight loss efforts in hypertensive patients with obesity in Korea using data from the 2016-2019 National Health and Nutrition Examination Survey. Weight perception, weight loss efforts, and weight control methods were investigated for 1,910 subjects. Data were analyzed using descriptive analysis, cross-tabulation analysis and logistic regression. Among obese hypertensive patients, 12.6% perceived their weight as normal. Weight loss efforts were 2.03 times (95% CI: 1.48 to 2.78) higher in people with overweight perception than those with normal weight perception, and 1.74 times (95% CI: 1.33 to 2.26) higher in women than in men. In addition, those with class 1 obesity were 1.50 times (95% CI: 0.85 to 2.65) higher than those with class 3 obesity, and those with class 2 obesity were 2.16 times (95% CI: 1.16 to 4.00) higher than those with class 3 obesity. These results suggest that weight management approaches for hypertensive patients with obesity should be individually designed according to weight perception, gender, and obesity class.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.473-480
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• 2023

This study evaluated a novel method of defining the automatic flight path of unmanned aerial vehicles (UAVs) for search and rescue missions in a VR environment. The developed VR content reserves miniature digital twins of a building in the fire and a steep mountain terrain site. The users drow the UAV's scanning path using hand gestures on the surface of digital twins, and then the UAV make an automatic flight along the defined path. According to human-in-the-loop simulation tests comparing the novel method with a conventional manual flight task with 19 participants, the novel method did not improve the mission performance but participants felt a lower mental workload. The designer may need to consider the automation support on the vulnerable points of the SAR mission environment while maintaining experts' mapping capability.