• Journal of Drive and Control
• /
• v.17 no.4
• /
• pp.80-86
• /
• 2020

Autonomous driving is one of the most important new technologies of our time; it has benefits in terms of safety, the environment, and economic issues. Path following algorithms, such as automated lane keeping systems (ALKSs), are key level 3 or higher functions of autonomous driving. Pure-Pursuit and Stanley controllers are widely used because of their good path tracking performance and simplicity. However, with the Pure-Pursuit controller, corner cutting behavior occurs on curved roads, and the Stanley controller has a risk of divergence depending on the response of the steering system. In this study, we use the advantages of each controller to propose a hybrid control strategy that can be stably applied to complex driving environments. The weight of each controller is determined from the global and local curvature indexes calculated from HD map information and the current driving speed. Our experimental results demonstrate the ability of the hybrid controller, which had a cross-track error of under 0.1 m in a virtual environment that simulates K-City, with complex driving environments such as urban areas, community roads, and high-speed driving roads.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.261-273
• /
• 2022

Over the last decade, the scientific community has been actively developing technologies for automated software bug fixes called Automated Program Repair (APR). Several APR techniques have recently been proposed to effectively address multiple classroom programming errors. However, little attention has been paid to the advances in effective APR techniques for software bugs that are widely occurring during the software life cycle maintenance phase. To further enhance the concept of software testing and debugging, we recommend an optimized automated software repair approach based on hybrid technology (OAPR-HOML'1). The first contribution of the proposed OAPR-HOML'1 technique is to introduce an improved grasshopper optimization (IGO) algorithm for fault location identification in the given test projects. Then, we illustrate an opposition learning based artificial neural network (OL-ANN) technique to select AST node-level transformation schemas to create the sketches which provide automated program repair for those faulty projects. Finally, the OAPR-HOML'1 is evaluated using Defects4J benchmark and the performance is compared with the modern technologies number of bugs fixed, accuracy, precession, recall and F-measure.

• International Journal of Computer Science & Network Security
• /
• v.23 no.2
• /
• pp.126-132
• /
• 2023

Web technology is evolving with the passage of time, from a single node server to high availability and then in the form of Kubernetes. In recent years, the research community have been trying to provide high availability in the form of multi master cluster with a solid election algorithm. This is helpful in increasing the resources in the form of pods inside the worker node. There are new impact of known DDoS attack, which is utilizing the resources at its peak, known as Yoyo attack. It is kind of burst attack that can utilize CPU and memory to its limit and provide legit visitors with a bad experience. In this research, we tried to mitigate the Yoyo attack by introducing a firewall at load-balancer level to prevent the attack from going to the cluster network.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.145-145
• /
• 2021

Water distribution networks (WDNs) are designed to satisfy water requirement of an urban community. One of the central issues in human history is providing sufficient quality and quantity of water through WDNs. A WDN consists of a great number of pipelines with different ages, lengths, materials, and sizes in varying degrees of deterioration. The available annual budget for rehabilitation of these infrastructures only covers part of the network; thus it is important to manage the limited budget in the most cost-effective manner. In this study, a novel pipe replacement scheduling approach is proposed in order to smooth the annual investment time series based on a life cycle cost assessment. The proposed approach is applied to a real WDN currently operating in South Korea. The proposed scheduling plan considers both the annual budget limitation and the optimum investment on pipes' useful life. A non-dominated sorting genetic algorithm is used to solve a multi-objective optimization problem. Three decision-making objectives, including the minimum imposed LCC of the network, the minimum standard deviation of annual cost, and the minimum average age of the network, are considered to find optimal pipe replacement planning over long-term time period. The results indicate that the proposed scheduling structure provides efficient and cost-effective rehabilitation management of water network with consistent annual budget.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.97-99
• /
• 2021

As the aging society continues in recent years, interest in dementia is increasing. Among them, Alzheimer's disease is a degenerative brain disease that accounts for the largest percentage of all dementia patients, with the medical community currently not offering clear prevention and treatment for Alzheimer's disease, and the importance of early treatment and early prevention is emphasized. In this paper, we intend to find the most efficient activation function by combining various activation functions centering on convolutional neural networks using MRI datasets of normal people and patients with Alzheimer's disease. In addition, it is intended to be used as a dementia classification modeling suitable for the medical field in the future through Alzheimer's dementia classification modeling.

• International conference on construction engineering and project management
• /
• 2020.12a
• /
• pp.417-422
• /
• 2020

Vast quantities of environmental pollutants from construction projects are causing significant damage to nearby local communities and thus generate environmental complaints. The construction company, responsible for compensating and resolving environmental complaints, suffers economic damages due to additional expenditures and schedule delays in construction projects. Meanwhile, the construction industry can stagnate from a broader perspective. Therefore, this study aimed to propose a framework for developing an automated management system which consists of two models for environmental complaints in construction projects: (i) the prediction model: a model for predicting environmental complaints based on factors related to environmental complaints; and (ii) the prevention model: a model for providing construction companies with the optimal prevention measure to effectively prevent environmental complaints according to the results of the prediction model. In addition, the algorithm for integrating the developed models into the management system in construction projects was proposed. Eventually, the application of the management system to construction projects can ensure the profitability of construction companies and mitigate damage from environmental pollutants to the nearby local community.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.389-391
• /
• 2023

In the era of Big Data, humanity is facing a huge overflow of information. To overcome such an obstacle, many new cutting-edge technologies are being introduced. The movie recommendation system is also one such technology. To date, many theoretical and practical kinds of research have been conducted. Our research also focuses on the movie recommendation system by implementing methods from Social Network Analysis(SNA) and Parallel Programming. We applied the Girvan-Newman algorithm to detect communities of users, and a future package to perform the parallelization. This approach not only tries to improve the accuracy of the system but also accelerates the execution time. To do our experiment, we used the MovieLense Dataset.

• International Journal of Advanced Culture Technology
• /
• v.12 no.1
• /
• pp.242-248
• /
• 2024

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

• Geophysics and Geophysical Exploration
• /
• v.18 no.4
• /
• pp.223-231
• /
• 2015

Self-potential (SP) is sensitive to groundwater flow and there are many causes to generate SP. Among many mechanisms of SP, pore-fluid flow in porous media can generate potential without any external current source, which is referred to as electrokinetic potential or streaming potential. When calculating SP responses on the surface due to geothermal fluid within an engineered geothermal system (EGS) reservoir, SP anomaly is usually considered to be generated by fluid injection or production within the reservoir. However, SP anomaly can also result from geothermal water fluid within EGS reservoirs experiencing temperature changes between injection and production wells. For more precise simulation of SP responses, we developed an algorithm being able to take account of SP anomalies produced by not only water injection and production but also the fluid of geothermal water, based on three-dimensional finite-element-method employing tetrahedron elements; the developed algorithm can simulate electrical potential responses by both point source and volume source. After verifying the developed algorithm, we assumed a simple geothermal reservoir model and analyzed SP responses caused by geothermal water injection and production. We are going to further analyze SP responses for geothermal water in the presence of water production and injection, considering temperature distribution and geothermal water flow in the following research.

• Korean Journal of Community Nutrition
• /
• v.10 no.5
• /
• pp.592-602
• /
• 2005

This study was conducted to compare nutrient intakes regarding stages of change in dietary fiber increasing behavior. Subjects were consisted of healthy 383 college students (2S0 females and 133 males) in Kyunggi-Do. Stages of change classified by an algorithm based on 6 items were designed each subjects into one of the 5 stages: precontemplation (PC), contemplation (CO), preparation (PR), action (AC), maintenance (MA). Nutrient intakes were assessed by 24-hr recall method. Regarding the S stages of changes, PR stage comprised the largest group $(39.4\%)$, followed by AC $(33.7\%)$, MA$(14.6\%)$, PC$(7.6\%)$, CO$(34.7\%)$. Female were more belong to either AC or MA. The higher stage of change in dietary fiber increasing behavior, the higher self-efficacy. In all male and female, there were no differences in energy, protein, monounsaturated fatty acids, polyunsaturated fatty acids and cholesterol intakes across the 5 stages. But, fiber, postassuim (K), vitamin A and vitamin C intakes of AC or MA were higer than those of PC, CO and PR $Energy\%$ from fat of $PR(25.4\~26.5\%)$ was higher than $20\%$, and those of AC and MA was lower than the other groups. Dietary P/S and ${\varepsilon}6/{\varepsilon}$ 3 ratios of AC and MA were similar to the recommended ratio. Female of PR had the most total saturated fat and palmitic acid and those of MA had the least. Male of PR had the least $\alpha-LNA\;({\varepsilon}3)$ and total ${\varepsilon}3$ fatty acids and those of MA had the most. In male and female in AC or MA, fiber and K intakes from breakfast, dinner and snack and vitamin C intakes from all meals were higher than those of the other stages. These results of our study confirm differences in stages of change in fiber intake in terms of nutritional status. To have lower $energy\%$ from fat, higher intakes of K, fiber and vitamins, desirable ratio of dietary fatty acids, it needs consistent nutritional education leading to the AC or MA of fiber increasing behavior.