• International journal of advanced smart convergence
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• v.12 no.4
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• pp.142-146
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• 2023

We present a method for generating 3D structures and rendering objects by combining VAE (Variational Autoencoder) and GAN (Generative Adversarial Network). This approach focuses on generating and rendering 3D models with improved quality using residual learning as the learning method for the encoder. We deep stack the encoder layers to accurately reflect the features of the image and apply residual blocks to solve the problems of deep layers to improve the encoder performance. This solves the problems of gradient vanishing and exploding, which are problems when constructing a deep neural network, and creates a 3D model of improved quality. To accurately extract image features, we construct deep layers of the encoder model and apply the residual function to learning to model with more detailed information. The generated model has more detailed voxels for more accurate representation, is rendered by adding materials and lighting, and is finally converted into a mesh model. 3D models have excellent visual quality and accuracy, making them useful in various fields such as virtual reality, game development, and metaverse.

• Journal of Korea Game Society
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• v.21 no.3
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• pp.13-24
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• 2021

This paper shows the face rotation applicable to game character facial illustration. Existing studies limited data to human face data, required a large amount of data, and the synthesized results were not good. In this paper, the following method was introduced to solve the existing problems of existing studies. First, a 3D model with features of the input image was rotated and then rendered as a 2D image to construct a data set. Second, by designing GAN that can learn features of various poses from the data built through the 3D model, the input image can be synthesized at a desired pose. This paper presents the results of synthesizing the game character face illustration. From the synthesized result, it can be confirmed that the proposed method works well.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• Cartoon and Animation Studies
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• s.50
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• pp.239-273
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• 2018

There have been many topics in gamer research on gamers' game addiction, education, and psychological interest. This paper investigates how to perceive the virtual environment of gamers based on James Gibson 's theories of cognitive science. Gibson's theory is not a stimulus input through individual sensory receptors, but rather a learning process such as establishing a cognitive relationship between perceptual systems, external invariant property separation, behavioral learning, invariant property separation of events, selectiveism development. Based on this analysis tool, I collected and verified gamers' perception of game environment of by FGI survey method. The results of the analysis showed that Gibson 's perceptual learning process was perceived as a virtual environment as in reality, and there was also perceptual difference found only in games. Patterned perception develops in the direction of classifying invariant properties appearing in the game based on the purpose of the game. In this study, it can be seen as a result of the research that FGI interview can be summarized as patterning (typification) perception process based on the goal consciousness of gamers. But,The results of the study suggest that the psychological analysis of the gamer can not be presented by the FGI results alone. In the future, we need a model study to confirm the causality and the verification through statistical analysis.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.593-600
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• 2021

In this paper, we present the FlappyBird Competition System (FCS) implementation, a competition-based automated assessment system used in an entry-level artificial intelligence (AI) course at a university. The proposed system provides an evaluation method suitable for AI courses while taking advantage of automated assessment methods. Students are to design a neural network structure, train the weights, and tune hyperparameters using the given reinforcement learning code to improve the overall performance of game AI. Students participate using the resulting trained model during the competition, and the system automatically calculates the final score based on the ranking. The user evaluation conducted after the semester ends shows that our competition-based automated assessment system promotes active participation and inspires students to be interested and motivated to learn AI. Using FCS, the instructor significantly reduces the amount of time required for assessment.

• ETRI Journal
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• v.42 no.1
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• pp.67-77
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• 2020

Image captioning has received significant interest in recent years, and notable results have been achieved. Most previous approaches have focused on generating visual descriptions from images, whereas a few approaches have exploited visual descriptions for image classification. This study demonstrates that a good performance can be achieved for both description generation and image classification through an end-to-end joint learning approach with a loss function, which encourages each task to reach a consensus. When given images and visual descriptions, the proposed model learns a multimodal intermediate embedding, which can represent both the textual and visual characteristics of an object. The performance can be improved for both tasks by sharing the multimodal embedding. Through a novel loss function based on class activation mapping, which localizes the discriminative image region of a model, we achieve a higher score when the captioning and classification model reaches a consensus on the key parts of the object. Using the proposed model, we established a substantially improved performance for each task on the UCSD Birds and Oxford Flowers datasets.

• Journal of KIISE
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• v.42 no.11
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• pp.1423-1429
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• 2015

The Internet of Things (IoT) is a new concept associated with the future Internet, and it has recently become a popular concept to build a dynamic, global network infrastructure. However, the deployment of IoT creates difficulties in satisfying different Quality of Service (QoS) requirements and achieving rapid service composition and deployment. In this paper, we propose a new QoS control scheme for IoT systems. The Markov game model is applied in our proposed scheme to effectively allocate IoT resources while maximizing system performance. The results of our study are validated by running a simulation to prove that the proposed scheme can promptly evaluate current IoT situations and select the best action. Thus, our scheme approximates the optimum system performance.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.3
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• pp.47-54
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• 2016

In this paper, we propose a new opportunistic routing scheme based on timed bargaining game. The proposed algorithm effectively formulates the opportunistic routing mechanism as an repeated bargaining model using timed learning method. Additionally, we formulate a new contention window adjusting scheme for reduce collision rate. Simulation results indicate that the proposed scheme has excellent performance than other existing schemes under widely diverse VANET environments.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.835-843
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• 2017

Humans have often been used gesture to communicate with each other. The communication between computer and person was also not different. To interact with a computer, we command with gesture, keyboard, mouse and extra devices. Especially, the gesture is very useful in many environments such as gaming and VR(Virtual Reality), which requires high specification and rendering time. In this paper, we propose a gesture recognition method based on CNN model to apply to gaming and real-time applications. Deep learning for gesture recognition is processed in a separated server and the preprocessing for data acquisition is done a client PC. The experimental results show that the proposed method is in accuracy higher than the conventional method in game environment.

• The Journal of Bigdata
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• v.5 no.2
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• pp.231-240
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• 2020

With the development in the IT industry and the growth in the game industry, user's game data is recorded in seconds according to various plays and options, and a vast amount of game data can be analyzed based on Bigdata. Combined with business, Bigdata is used to discover new values for profit creation in various fields, but it is utilized in the game industry in insufficient ways. In this study, considering the characteristics of the subdivided lines, we constructed a win-loss prediction model for each line using the game data of League of Legends, and derived the importance of variables. This study can contribute to planning of strategies for general game users to get information about team members in advance and increase the win rate by using the record search sites.