• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.730-732
• /
• 2017

While the short term effect of diet and exercise therapy has been proven, there has still been a problem of its long term effect. So, researchers has utilized behaviour modification therapy. It is expected to lead to natural weight loss by modifying wrong dietary life patterns and practices. However, this approach has turned out to be a more effective method for weight maintenance than loss of weight. In spite of its strength, as a matter of fact, persistent and continuous effort for weight management has not worked properly. This study proposes an artificial intelligence approach with the advantages of behaviour modification therapy, overcoming current approaches which is goal-driven and too uniform. For this, we plan to develop a health management program in which users get the messages that are customized for themselves according to different situations so that it can promotes persistent effort for exercise. Here, customized messages are handled by AI techniques, which eventually promotes soft persuasion, encouragement, and motivation.

• Journal of Convergence for Information Technology
• /
• v.10 no.8
• /
• pp.23-34
• /
• 2020

Recently, the artificial intelligence deep learning field has been hard to commercialize due to the high computing power and the price problem of computing resources. In this paper, we apply a double pruning techniques to evaluate the performance of the in-depth neural network and various datasets. Double pruning combines basic Network-slimming and Parameter-prunning. Our proposed technique has the advantage of reducing the parameters that are not important to the existing learning and improving the speed without compromising the learning accuracy. After training various datasets, the pruning ratio was increased to reduce the size of the model.We confirmed that MobileNet-V3 showed the highest performance as a result of NetScore performance analysis. We confirmed that the performance after pruning was the highest in MobileNet-V3 consisting of depthwise seperable convolution neural networks in the Cifar 10 dataset, and VGGNet and ResNet in traditional convolutional neural networks also increased significantly.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.1
• /
• pp.111-120
• /
• 2020

As an application of big data and artificial intelligence techniques, this study proposes an atypical language-based sentimental opinion poll methodology, unlike conventional opinion poll methodology. An alternative method for the sentimental classification model based on existing statistical analysis was to collect real-time Twitter data related to parliamentary elections and perform empirical analyses on the Polarity and Intensity of public opinion using attribute-based sensitivity analysis. In order to classify the polarity of words used on individual SNS, the polarity of the new Twitter data was estimated using the learned Lasso and Ridge regression models while extracting independent variables that greatly affect the polarity variables. A social network analysis of the relationships of people with friends on SNS suggested a way to identify peer group sensitivity. Based on what voters expressed on social media, political opinion sensitivity analysis was used to predict party approval rating and measure the accuracy of the predictive model polarity analysis, confirming the applicability of the sensitivity analysis methodology in the political field.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.2
• /
• pp.213-219
• /
• 2020

In this paper, we propose a classification analysis method for diagnosing and predicting problematic smartphone use in order to provide policy data on problematic smartphone use, which is getting worse year after year. Attempts have been made to identify key variables that affect the study. For this purpose, the classification rates of Decision Tree, Random Forest, and Support Vector Machine among machine learning analysis methods, which are artificial intelligence methods, were compared. The data were from 25,465 people who responded to the '2018 Problematic Smartphone Use Survey' provided by the Korea Information Society Agency and analyzed using the R statistical package (ver. 3.6.2). As a result, the three classification techniques showed similar classification rates, and there was no problem of overfitting the model. The classification rate of the Support Vector Machine was the highest among the three classification methods, followed by Decision Tree and Random Forest. The top three variables affecting the classification rate among smartphone use types were Life Service type, Information Seeking type, and Leisure Activity Seeking type.

• Journal of the Korea Society for Simulation
• /
• v.15 no.2
• /
• pp.1-12
• /
• 2006

As the needs of intelligent systems increase, there have been diverse approaches that combine artificial intelligence (AI) and simulation in the last decade. RG-DEVS, which is the basis for this paper, embedded AI planning techniques into the simulation modeling methodology of DEVS, in order to specify dynamically a simulation model. However, a hierarchy concept, which is used for various types of problem solving systems. is not included in the planning of RG-DEVS. The hierarchy concept reduces the computational cost of planning by reducing the search space, and also makes it easy to apply the hierarchical process flow of a target system to planning. This paper proposes Hierarchical RG-DEVS (HRG-DEVS) in an attempt to insert hierarchical planning capability into RG-DEVS. For the verification of the proposed modeling methodology, HRG-DEVS is applied to model the block's world problem of ABSTRIPS, which is a classical planning problem.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.10
• /
• pp.2477-2484
• /
• 1997

Best-first heuristic search algorithm, such as $A^{\ast}$ algorithm, are one of the most important techniques used to solve many problems in artificial intelligence. A common feature of heuristic search is its high computational complexity, which prevents the search from being applied to problems is practical domains such as route-finding in road map with significantly many nodes. In this paper, several heuristic search algorithms are concerned. A new dynamic weighting heuristic method called the pat-sensitive heuristic is proposed. It is based on a dynamic weighting heuristic, which is used to improve search effort in practical domain such as admissible heuristic is not available or heuristic accuracy is poor. It's distinctive feature compared with other dynamic weighting heuristic algorithms is path-sensitive, which means that ${\omega}$(weight) is adjusted dynamically during search process in state-space search domain. For finding an optimal path, randomly scattered road-map is used as an application area.

• The KIPS Transactions:PartB
• /
• v.10B no.1
• /
• pp.33-40
• /
• 2003

Data mining plays an important role in a knowledge discovery process and various algorithms of data mining can be selected for the specific purpose. Most of traditional hierachical clustering methode are suitable for processing small data sets, so they difficulties in handling large data sets because of limited resources and insufficient efficiency. In this study we propose a hybrid neural networks clustering technique, called PPC for Pre-Post Clustering that can be applied to large data sets and find unknown patterns. PPC combinds an artificial intelligence method, SOM and a statistical method, hierarchical clustering technique, and clusters data through two processes. In pre-clustering process, PPC digests large data sets using SOM. Then in post-clustering, PPC measures Similarity values according to cohesive distances which show inner features, and adjacent distances which show external distances between clusters. At last PPC clusters large data sets using the simularity values. Experiment with UCI repository data showed that PPC had better cohensive values than the other clustering techniques.

• Journal of the Korea Convergence Society
• /
• v.12 no.10
• /
• pp.9-17
• /
• 2021

With the advancement of big data analysis, artificial intelligence, machine learning, etc., data analytics technology has developed to help with optimal decision-making. However, in certain areas, the lack of data restricts the use of these techniques. For example, real estate related data often have a long release cycle because of its recent release or being a non-liquid asset. In order to overcome these limitations, we studied the scalability of the existing time series through the TimeGAN model. A total of 45 time series related to weekly real estate data were collected within the period of 2012 to 2021, and a total of 15 final time series were selected by considering the correlation between the time series. As a result of data expansion through the TimeGAN model for the 15 time series, it was found that the statistical distribution between the real data and the extended data was similar through the PCA and t-SNE visualization algorithms.

• Smart Media Journal
• /
• v.10 no.2
• /
• pp.30-37
• /
• 2021

Recently, 3D contents used in various fields have been attracting people's attention due to the development of virtual reality and augmented reality technology. In order to produce 3D contents, it is necessary to model the objects as vertices. However, high-quality modeling is time-consuming and costly. In order to convert a 2D character into a 3D model, it is necessary to express it as line drawings through feature line extraction. The extraction of consistent line drawings from 2D cartoon cartoons is difficult because the styles and techniques differ depending on the designer who produces them. Therefore, it is necessary to extract the line drawings that show the geometrical characteristics well in 2D cartoon shapes of various styles. This study proposes a method of automatically extracting line drawings. The 2D Cartoon shading image and line drawings are learned by using adversarial network model, which is artificial intelligence technology and outputs 2D cartoon artwork of various styles. Experimental results show the proposed method in this research can be obtained as a result of the line drawings representing the geometric characteristics when a 2D cartoon painting as input.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1127-1136
• /
• 2021

In the field of power analysis attacks, electrical noise and misalignment of the power consumption trace are the major factors that determine the success of the attack. Therefore, several studies have been conducted to overcome this problem, and one of them is a signal processing method based on wavelet transform. Up to now, discrete wavelet transform, which can compress the trace, has been mostly used for power side-channel power analysis because continuous wavelet transform techniques increase data size and analysis time, and there is no efficient scale selection method. In this paper, we propose an efficient scale selection method optimized for power analysis attacks. Furthermore, we show that the analysis performance can be greatly improved when using the proposed method. As a result of the CPA(Correlation Power Analysis) and DDLA(Differential Deep Learning Analysis) experiments, which are non-profiling attacks, we confirmed that the proposed method is effective for noise reduction and trace alignment.