• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.180-182
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• 2022

As various crimes such as theft, assault, and sex crimes are increasing, each local government is installing CCTVs to prevent them, and operating and managing control centers for emergency response. When the control center detects a dangerous situation in the field, it responds immediately in connection with the police or 911. However, since it is managed by humans, the response speed is anomalous and the reality is that it is mainly used for post-processing. Therefore, through the artificial intelligence LED signage, it notifies the emergency situation at the site, and it serves as a warning function before getting help from passers-by or an accident occurs. In this paper, we design and research a warning system such as changing the lighting color of the LED signboard or making a sound by reflecting the artificial intelligence algorithm. We intend to contribute to public safety and social safety through this study.

• Journal of Korea Multimedia Society
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• v.10 no.5
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• pp.631-641
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• 2007

The domestic game market is developmental in the form which is strange produces only the MMORPG. But the level of the intelligence elder brother character is coming to a standstill as ever. It uses a gene algorithm and the neural network from the dissertation which it sees and embodies the character which has a more superior intelligence the plan which to sleep and it presents it does. When also currently it is used complaring different artificial intelligence technologies and this algorism from the MMORPG, the efficiency proves is not turned over and explains the concrete algorithm it will be able to apply in the MMORPG and an embodiment method.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.677-687
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• 2022

We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.106-117
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• 2022

In 2021, AI basics were introduced in the high school curriculum. There are many worries that the problem of utilization-oriented education will be repeated with the introduction of artificial intelligence education rather than the principles that occurred when ICT was applied to education in the past. Most of the existing AI education platforms focus only on the use of AI. For artificial intelligence education of middle school students, there are difficulties in learning about the process by which artificial intelligence derives results and learning the principles of artificial intelligence algorithms. Recently, as the educational application of metaverse has become a hot topic, research has been started to improve learning achievement by arousing students' immersion and interest. This research developed educational game contents about decision tree algorithm using metaverse as educational contents that can be used in middle school AI education. By applying games to education, it was intended to increase students' interest and immersion in artificial intelligence, and to increase educational effectiveness. In this paper, the educational effectiveness, difficulty, and level of interest were analyzed for pre-service teachers regarding the developed game content. Based on this, a future principle-oriented artificial intelligence education method was suggested.

• Journal of the Korean Society of Radiology
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• v.12 no.6
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• pp.793-799
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• 2018

As interest in artificial intelligence has increased, artificial intelligence has been actively studied in the medical field. In Korea, artificial intelligence has been applied to medical imaging devices such as X-ray imaging, Computer Tomography and Magnetic Resonance Imaging and artificial intelligence capable of acquiring radiation images of patients without radiologists in the future Medical devices are expected to be invented. This study was an initial study on the automation of patient positioning in X - ray imaging. We used x-ray equipment and human phantoms to evaluate the positioning. The program used Visual Studio 2010 MFC and the image was in the size $1450{\times}1814$. The pixel values were converted to contrasts with values of 0 to 255 that can be visually recognized and output to the monitor. We developed a procedure algorithm program that predicts the angle of the output image through three pixel coordinate values and induces the patient to perform correct positioning according to the voice guidance according to the angle. In the next study, we will study the artificial intelligence to grasp the structure itself and calculate the angle, rather than conveying the reference of coordinates to artificial intelligence. In the future, it is expected that it will be helpful in the study of artificial intelligence from shooting to positioning through the automation of positioning.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.43-51
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• 2017

As the game industry is blooming, the generation of contents is far behind the consumption of contents. With this reason, it is necessary to afford the game contents considering level of game player's skill. In order to effectively solve this problem, Procedural Content Generation(PCG) using Artificial Intelligence(AI) is one of the plausible options. This paper proposes the procedural method to generate various monsters considering level of player's skill using genetic algorithm. One gene consists of the properties of a monster and one genome consists of genes for various monsters. A generated monster is evaluated by battle simulation with a player and then goes through selection and crossover steps. Using our proposed scheme, players adaptive monsters are generated procedurally based on genetic algorithm and the variety of monsters which are generated with different number of genome is compared.

• The Journal of Bigdata
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• v.7 no.1
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• pp.1-14
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• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.4
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• pp.437-445
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• 2024

In future complex and diverse battlefield situations, the existing command system faces the challenge of delayed human judgement of strategy and low objectivity. This paper proposes an artificial intelligence model that takes situation information and course of action simulation results as input and automatically assigns scores to various evaluation elements and a comprehensive score. This tool is expected to assist the commander in making decisions, reduce the time required for making judgments, and promote impartial decision-making.

• Dementia and Neurocognitive Disorders
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• v.17 no.3
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• pp.83-89
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• 2018

Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data. An artificial neural network is a machine learning algorithm based on the concept of a human neuron. The purpose of this review is to explain the fundamental concepts of artificial neural networks.