• Journal of Korea Game Society
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• v.15 no.5
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• pp.89-98
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• 2015

The artificial intelligence (AI) of Non-Player Companions (NPC), especially opponents, is a key element to adjust the level of games in game design. Smart opponents can make games more challenging as well as allow players for diverse experiences, even in the same game environment. Since game users interact with more than one opponent in most of today's games, collaboration control of opponent characters becomes more important than ever before. In this paper, we introduce a cooperative strategy based on the A* algorithm for enemies' AI in the Pac-Man game. A survey from 17 human testers shows that the levels with our collaborative opponents are more difficult but interesting than those with either the original Pac-Man's personalities or the non-cooperative greedy opponents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3567-3582
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• 2020

Human activities are often affected by weather conditions. Automatic weather recognition is meaningful to traffic alerting, driving assistance, and intelligent traffic. With the boost of deep learning and AI, deep convolutional neural networks (CNN) are utilized to identify weather situations. In this paper, a three-channel convolutional neural network (3C-CNN) model is proposed on the basis of ResNet50.The model extracts global weather features from the whole image through the ResNet50 branch, and extracts the sky and ground features from the top and bottom regions by two CNN5 branches. Then the global features and the local features are merged by the Concat function. Finally, the weather image is classified by Softmax classifier and the identification result is output. In addition, a medium-scale dataset containing 6,185 outdoor weather images named WeatherDataset-6 is established. 3C-CNN is used to train and test both on the Two-class Weather Images and WeatherDataset-6. The experimental results show that 3C-CNN achieves best on both datasets, with the average recognition accuracy up to 94.35% and 95.81% respectively, which is superior to other classic convolutional neural networks such as AlexNet, VGG16, and ResNet50. It is prospected that our method can also work well for images taken at night with further improvement.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.215-221
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• 2020

The paradigm of agriculture is also changing to address the problem of food shortages due to the increase of the world population, climate conditions that are increasingly subtropical, and labor shortages in rural areas due to aging population. With the development of Information Communication Technology (ICT), our daily lives are changing rapidly and heralds a major change in agricultural management. In a hyper-connected society, the introduction of high-tech into traditional Agriculture of the past is absolutely necessary. In the development process of Agriculture, the first generation produced by hand, the second generation applied mechanization, and the third generation introduced automation. The fourth generation is the current ICT operation and the fifth generation is artificial intelligence. This paper investigated Smart Farm that increases productivity through convergence of Agriculture and ICT, such as smart greenhouse, smart orchard and smart Livestock. With the development of sustainable food production methods in full swing to meet growing food demand, Smart Farming is emerging as the solution. In overseas cases, the Netherlands Smart Farm, the world's second-largest exporter of agricultural products, was surveyed. Agricultural automation using Smart Farms allows producers to harvest agricultural products in an accurate and predictable manner. It is time for the development of technology in Agriculture, which benchmarked cases of excellence abroad. Because ICT requires an understanding of Internet of Things (IoT), big data and artificial intelligence as predicting the future, we want to address the status of theory and actual Agriculture and propose future development measures. We hope that the study of the paper will solve the growing food problem of the world population and help the high productivity of Agriculture and smart strategies of sustainable Agriculture.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.35-40
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• 2020

As we enter the society of the future, efforts to increase healthy living are a major area of concern for modern people. In particular, the development of technology for a healthy life that combines ICT technology with a competitive healthcare industry environment is becoming the next growth engine. Therefore, in this paper, artificial intelligence-based data analysis of the examination results was applied in the health examination process. Through this, a research was conducted to build a knowledge base modeling that can improve the reliability of the overall judgment. To this end, an algorithm was designed through deep learning analysis to calculate and verify the test result index. Then, the modeling that provides comprehensive examination information through judgment knowledge was studied. Through the application of the proposed modeling, it is possible to analyze and utilize big data on national health, so it can be expected to reduce medical expenses and increase health.

• Science of Emotion and Sensibility
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• v.23 no.1
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• pp.69-78
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• 2020

This study compares and analyzes user experiences of intelligent personal assistant services based on the evaluation criteria of human-computer interaction to explore positive elements of user experiences and factors that could be improved. The research was conducted on Apple's Siri, Google's Google Associate, and Samsung's Bixby, which is presently the smartest personal assistant service on the market. The research method was to compare and analyze the concepts and characteristics of the current services through a literature review and by interviewing seven UI/UX design experts for the second 2 weeks using contextual inquiry. The interview reorganized Peter Morville's user experience honeycomb, reducing his seven usability principles down to five, asking questions about usability, convenience, visual attractiveness, reliability, and satisfaction. On the basis of the reconfigured usability principle, the assessment was conducted on the basis of the assessment timing and the system usability scale. This study is meaningful in that it analyzed the user experience of artificial intelligence personal assistant services both quantitatively and qualitatively.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.23-29
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• 2018

Computational creativity is a field of artificial intelligence research to replicate creativity of human beings, creating works in various fields or helping human authors. The copyright of works produced by computational creativity has not been established in most countries yet, however, there will be the need for systems to protect the copyrights with the development of the technology in the future. In this paper, we propose a copyright protection system based on blockchain technology that protects the copyright of various contributors contributing to the creation of computer creative creativity, and transparently and safely records the contribution of copyrighted works. The proposed system records the contribution of all related works from the machine learning of computer creativity to the creation of the final work on the blockchain so that it is possible to establish quantitative evaluation criteria for the copyright when the future copyright law system is revised.

• Journal of Korea Game Society
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• v.16 no.6
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• pp.59-68
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• 2016

Many researchers have paid attention to contents generation within the area of game artificial intelligence these days with various reasons. Efforts on automatic contents generation without game level designers' help were continuously progressed in various game contents. This study suggests an automatic map generation without an isolated cave using cellular automation enhanced by binary space partitioning(BSP). In other words, BSP makes it possible to specify the number of desired area and cellular automation reduces the time to search a path. Based upon our preliminary simulation results, we show the usefulness of our automatic map generation by applying the contents generation using cell automation, which is enhanced by BSP to games.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.13-24
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• 2020

There are about nine million power line poles and 1.3 million kilometers of the power line for electric power distribution in Korea. Maintenance of such a large number of electric power facilities requires a lot of manpower and time. Recently, various fault diagnosis techniques using artificial intelligence have been studied. Therefore, in this paper, proposes a power line deflection detect system using artificial intelligence and computer vision technology in images taken by vision system. The proposed system proceeds as follows. (i) Detection of transmission tower using object detection system (ii) Histogram equalization technique to solve the degradation in image quality problem of video data (iii) In general, since the distance between two transmission towers is long, a panoramic video stitching process is performed to grasp the entire power line (iv) Detecting deflection using computer vision technology after applying power line detection algorithm This paper explain and experiment about each process.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1181-1187
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• 2010

This paper proposes a mobile AI (Artificial Intelligence) conducting decision-making in the game through education for intelligent character on the basis of Neural Network. Neural Network is learned through the input/output value of the algorithm which defines the game rule and the problem solving method. The learned character is able to perceive the circumstances and make proper action. In this paper, the mobile AI using Neural Network has been step-by-step designed, and a simple game has been materialized for its functional experiment. In this game, the goal, the character, and obstacles exist on regular 2D space, and the character, evading obstacles, has to move where the goal is. The mobile AI can achieve its goals in changing environment by learning the solution to several problems through the algorithm defined in each experiment. The defined algorithm and Neural Network are designed to make the input/output system the same. As the experimental results, the suggested mobile AI showed that it could perceive the circumstances to conduct action and to complete its mission. If mobile AI learns the defined algorithm even in the game of complex structure, its Neural Network will be able to show proper results even in the changing environment.