• Journal of Multimedia Information System
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• v.9 no.1
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• pp.33-42
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• 2022

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.58-63
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• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.27-32
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• 2022

As the need for eco-friendly transportation increases due to the deepening climate crisis, many local governments in Korea are introducing shared bicycles. Due to anxiety about public transportation after COVID-19, bicycles have firmly established themselves as the axis of daily transportation. The use of shared bicycles is spread, and the demand for bicycles is increasing by rental offices, but there are operational and management difficulties because the demand is managed under a limited budget. And unfortunately, user behavior results in a spatial imbalance of the bike inventory over time. So, in order to easily operate the maintenance of shared bicycles in Seoul, bicycles should be prepared in large quantities at a time of high demand and withdrawn at a low time. Therefore, in this study, by using machine learning, the linear regression algorithm and MS Azure ML are used to predict and analyze when demand is high. As a result of the analysis, the demand for bicycles in 2018 is on the rise compared to 2017, and the demand is lower in winter than in spring, summer, and fall. It can be judged that this linear regression-based prediction can reduce maintenance and management costs in a shared society and increase user convenience. In a further study, we will focus on shared bike routes by using GPS tracking systems. Through the data found, the route used by most people will be analyzed to derive the optimal route when installing a bicycle-only road.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.133-134
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• 2022

This study proposed vision artificial intelligence-based automated supervision technology for external insulation and finishing system, and basic research was conducted for it. The automated supervision technology proposed in this study consists of the object detection model (YOLOv5) and the part that derives necessary information based on the object detection result and then determines whether the external insulation-related adhesion regulations are complied with. As a result of a test, the judgement accuracy of the proposed model showed about 70%. The results of this study are expected to contribute to securing the external insulation quality and further contributing to the realization of energy-saving eco-friendly buildings. As further research, it is necessary to develop a technology that can improve the accuracy of the object detection model by supplementing the number of data for model training and determine additional related regulations such as the adhesive area ratio.

• Journal of Biosystems Engineering
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• v.18 no.2
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• pp.133-143
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• 1993

In developing an Expert System(ES), there are two ways. One is to develop an ES using AI(artificial Intelligence) languages and another using ES-development tools. CLIPS is an ES-development tool and has a powerful inference engine in it. Using the tool like CLIPS, knowledge engineer can concentrate on constructing a knowledge base without wasting time in developing an inference engine. However, CLIPS is lack of user-friendly interfaces for both knowledge enginners and users. Because CLIPS was developed in USA, it can not afford to use Korean language. Therefore, several user-friendly interfaces including hmenu, htille, hpcxdisplay were develpoed and added to CLIPS. CLIPS with the interfaces is called HCLIPS(Hangul CLIPS) in this paper. HCLIPS provides a new I/O device to be utilized for expert systems in Korean. HCLIPS can be efficiently used for developing expert systems in agriculture and consulting farmers interactively who are not familiar with computer programming and ES itself.

• Steel and Composite Structures
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• v.47 no.6
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.159-160
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• 2021

We are facing major changes such as an aging population along with the 4th industrial revolution based on ICT technologies such as artificial intelligence, the Internet of Things, autonomous driving, and factory automation. For the local governments that are indexed in high population extinction risk, it is essential to consider market expansion and re-education policies suitable for regional characteristics in order to respond to changes such as advanced industrial automation and population aging. For the reemployment of the elderly, we will analyze previous public strategies for elderly-friendly jobs, expand investment in age-friendly industries. In this study, we suggest to improvement direction of the elderly labor market in Cheonan-Si.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5904-5927
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• 2019

Many artificial intelligence (AI) techniques have been embedded into various engineering technologies to assist them in achieving different goals. The integration of modern technologies with energy consumption management system and occupant's comfort inside buildings results in the introduction of intelligent building concept. The major aim of this integration is to manage the energy consumption effectively and keeping the occupant satisfied with the internal environment of the building. The last few couple of years have seen many applications of AI techniques for optimizing the energy consumption with maximizing the user comfort in smart buildings but still there is much room for improvement in this area. In this paper, a hybrid of two AI algorithms called firefly algorithm (FA) and genetic algorithm (GA) has been used for user comfort maximization with minimum energy consumption inside smart building. A complete user friendly system with data from various sensors, user, processes, power control system and different actuators is developed in this work for reducing power consumption and increase the user comfort. The inputs of optimization algorithms are illumination, temperature and air quality sensors' data and the user set parameters whereas the outputs of the optimization algorithms are optimized parameters. These optimized parameters are the inputs of different fuzzy controllers which change the status of different actuators according to user satisfaction.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.16-24
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• 2021

This paper introduces a smart aging service that helps the elderly lead a happy old age by actively utilizing IoT and AI technologies for the elderly who are increasing rapidly as they enter the aging society. In particular, we propose a future-oriented, age-friendly well-being support system that breaks away from the existing welfare concept to solve the aging problem but leads to a paradigm shift toward building a vibrant aging society by protecting from emergency and satisfying emotions. By introducing IoT and AI, it judges the life situation and emotional state from the living information of the elderly can respond to emergencies and suggest meetings as a change of mood and give an emotional comfort. Since the proposed system uses artificial intelligence techniques to determine the degree of depression when inputting information such as pulse-rate, dangerous word usage, and external communication, I think it showed the feasibility of the new concept of wellbeing support system that is totally different from conventional wellbeing concept of health-care.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1059-1065
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• 2011

In line with the expansion of electric railway, reducing carbon emission and optimal train operation are required by economical, eco-friendly and efficient management. Most of the energy consumption in electric railway is consumed by train operation. So it is important that minimize the energy consumption in train operation. An analysis of the operation performance of the new model vehicle which in South Korea, Korail introduced shows that the energy consumption is different in line with the skill level of the engine driver. In this study, the know-how of train operation of a skilled engine driver is systematized by using artificial intelligence, and the technique which supports engine drivers with train operation was offered. As a result of applying in South Korea, the Gyeongbu line by using simulation, it confirmed that the maximum 20% can reduce the energy consumption in comparison with unskilled engine drivers in case of applying the Expert System.