• Smart Media Journal
• /
• v.10 no.1
• /
• pp.32-38
• /
• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.4
• /
• pp.510-518
• /
• 2022

The diagnostic evaluation conducted at the national level is very important to detect underachievers in school early. This study used an artificial intelligence method to find the characteristics of underachievers that affect learning development for middle school students. In this study an artificial intelligence model was constructed and analyzed to determine whether the Busan Education Longitudinal Data in 2020 by entering data from the first year of middle school in 2019. A predictive model was developed to predict basic middle school Korean, English, and mathematics education with machine learning algorithms, and it was confirmed that the accuracy was 78%, 82%, and 83%, respectively, in the prediction for the next school year. In addition, by drawing an achievement prediction decision tree for each middle school subject we are analyzing the process of prediction. Finally, we examined what characteristics affect achievement prediction.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.2
• /
• pp.60-75
• /
• 2024

The demand of users, who want to focus on the core functionality of their applications without having to manage complex virtual environments in the cloud environment, has created a new computing model called serverless computing. Within the serverless paradigm, resource provisioning and server administration tasks are delegated to cloud services, facilitating application development exclusively focused on program logic. Serverless computing has upgraded the utilization of cloud computing by reducing the burden on cloud service users, and it is expected to become the basic model of cloud computing in the future. A serverless platform is responsible for managing the cloud virtual environment on behalf of users, and it is also responsible for executing serverless functions that compose applications in the cloud environment. Considering the characteristics of serverless computing in which users are billed in proportion to the resources used, the efficiency of the serverless platform is a very important factor for both users and service providers. This paper aims to identify various factors that affect the performance of serverless computing and analyze the latest research trends related to it. Drawing upon the analysis, the future directions for serverless computing that address key challenges and opportunities in serverless computing are proposed.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.11-20
• /
• 2024

Among the generative models in Artificial Intelligence (AI), especially Generative Adversarial Network (GAN) has been successful in various applications such as image processing, density estimation, and style transfer. While the GAN models including Conditional GAN (CGAN), CycleGAN, BigGAN, have been extended and improved, researchers face challenges in real-world applications in specific domains such as disaster simulation, healthcare, and urban planning due to data scarcity and unstable learning causing Image distortion. This paper proposes a new progressive learning methodology called Progressive Step Training (PST) based on the Auxiliary Classifier GAN (ACGAN) that discriminates class labels, leveraging the progressive learning approach of the Progressive Growing of GAN (PGGAN). The PST model achieves 70.82% faster stabilization, 51.3% lower standard deviation, stable convergence of loss values in the later high resolution stages, and a 94.6% faster loss reduction compared to conventional methods.

• Journal of Korean Society of Forest Science
• /
• v.83 no.2
• /
• pp.175-190
• /
• 1994

In order to get fundamental information for prediction of landslide hazard, both forest and site factors affecting slope stability were investigated in many areas of active landslides. Twelve descriptors were identified and quantified to develop the prediction model by multivariate statistical analysis. The main results obtained could be summarized as follows : The main factors influencing a large scale of landslide were shown in order of precipitation, age group of forest trees, altitude, soil texture, slope gradient, position of slope, vegetation, stream order, vertical slope, bed rock, soil depth and aspect. According to partial correlation coefficient, it was shown in order of age group of forest trees, precipitation, soil texture, bed rock, slope gradient, position of slope, altitude, vertical slope, stream order, vegetation, soil depth and aspect. The main factors influencing a landslide occurrence were shown in order of age group of forest trees, altitude, soil texture, slope gradient, precipitation, vertical slope, stream order, bed rock and soil depth. Two prediction models were developed by magnitude and frequency of landslide. Particularly, a prediction method by magnitude of landslide was changed the score for the convenience of use. If the total store of the various factors mark over 9.1636, it is evaluated as a very dangerous area. The mean score of landslide and non-landslide group was 0.1977 and -0.1977, and variance was 0.1100 and 0.1250, respectively. The boundary value between the two groups related to slope stability was -0.02, and its predicted rate of discrimination was 73%. In the score range of the degree of landslide hazard based on the boundary value of discrimination, class A was 0.3132 over, class B was 0.3132 to -0.1050, class C was -0.1050 to -0.4196, class D was -0.4195 below. The rank of landslide hazard could be divided into classes A, B, C and D by the boundary value. In the number of slope, class A was 68, class B was 115, class C was 65, and class D was 52. The rate of landslide occurrence in class A and class B was shown at the hige prediction of 83%. Therefore, dangerous areas selected by the prediction method of landslide could be mapped for land-use planning and criterion of disaster district. And also, it could be applied to an administration index for disaster prevention.

• Korean Journal of Construction Engineering and Management
• /
• v.18 no.6
• /
• pp.78-88
• /
• 2017

With the growing use of BIM in the AEC industry, various new applications are being developed to meet these specific needs. Such developments have increased the importance of Industry Foundation Classes, which is the international standard for sharing BIM data and thus ensuring interoperability. However, mapping individual BIM objects to IFC entities is still a manual task, and is a main cause for errors or omissions during data transfers. This research focused on addressing this issue by applying novelty detection, which is a technique for detecting anomalies in data. By training the algorithm to learn the geometry of IFC entities, misclassifications (i.e., outliers) can be detected automatically. Two IFC classes (ifcWall, ifcDoor) were trained using objects from three BIM models. The results showed that the algorithm was able to correctly identify 141 of 160 outliers. Novelty detection is thus suggested as a competent solution to resolve the mapping issue, mainly due to its ability to create multiple inlier boundaries and ex ante training of element geometry.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.6
• /
• pp.212-220
• /
• 2013

Recently, there has been tremendous increase in human-computer/machine interaction system, where the goal is to provide with an appropriate service to the user at the right time with minimal human inputs for human augmented cognition system. To develop an efficient human augmented cognition system based on human computer/machine interaction, it is important to interpret the user's implicit intention, which is vague, in addition to the explicit intention. According to cognitive visual-motor theory, human eye movements and pupillary responses are rich sources of information about human intention and behavior. In this paper, we propose a novel approach for the identification of human implicit visual search intention based on eye movement pattern and pupillary analysis such as pupil size, gradient of pupil size variation, fixation length/count for the area of interest. The proposed model identifies the human's implicit intention into three types such as navigational intent generation, informational intent generation, and informational intent disappearance. Navigational intent refers to the search to find something interesting in an input scene with no specific instructions, while informational intent refers to the search to find a particular target object at a specific location in the input scene. In the present study, based on the human eye movement pattern and pupillary analysis, we used a hierarchical support vector machine which can detect the transitions between the different implicit intents - navigational intent generation to informational intent generation and informational intent disappearance.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1215-1225
• /
• 2021

Although Artificial Intelligence (AI) techniques have shown impressive performance in various fields, they are vulnerable to adversarial examples which induce misclassification by adding human-imperceptible perturbations to the input. Previous studies to defend the adversarial examples can be classified into three categories: (1) model retraining methods; (2) input transformation methods; and (3) adversarial examples detection methods. However, even though the defense methods against adversarial examples have constantly been proposed, there is no research to classify the type of adversarial attack. In this paper, we proposed an adversarial attack family classification method based on dimensionality reduction and clustering. Specifically, after extracting adversarial perturbation from adversarial example, we performed Linear Discriminant Analysis (LDA) to reduce the dimensionality of adversarial perturbation and performed K-means algorithm to classify the type of adversarial attack family. From the experimental results using MNIST dataset and CIFAR-10 dataset, we show that the proposed method can efficiently classify five tyeps of adversarial attack(FGSM, BIM, PGD, DeepFool, C&W). We also show that the proposed method provides good classification performance even in a situation where the legitimate input to the adversarial example is unknown.

• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.107-119
• /
• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of Gifted/Talented Education
• /
• v.11 no.1
• /
• pp.81-98
• /
• 2001

The purpose of this study was to indicate the ways to identify the gifted child in the sports, and the research direction in this area in the future. The most of researches related to the early development of the children who showed the gifted talents in the certain sports skills were published from the European area such as the old East Germany or Soviet Union. Those research papers or articles were main focus of this study to be analyzed. The analysis in this study will be assistant to provide the direction of the research in the future about the method to identify appropriately on the children with the gifted sports skills. -Related to the period of selection of the children with sports skill gifted, the divided selection procedure will be good such as the 1st selection and 2nd selection. In the 1st selection, the goal will be to test the potentials of the children with the sports talents in the public school with creating the special group, and the 2nd selection will be to examine the qualification of the admission to the special schools for the children with the sports talents. -Related to the method of selection of the children with sports talents, the special item on the developmental procedure should be considered on the each field as well as the potentials focused. And, the physical factors, the condition of growth and development, psychological factor, and environmental factor should be comprehensively considered. -The system of promotion in the period of the primary school will be appropriate in the special group for the children with the sports talents, and the special school will be good from the middle school. The system outlooks will be adequate if shaped as the hierarchical structure. -The curriculum for the children with the sports talents will be proper that the basic fitness for the earlier ages and the professional skills will be recommended if getting older. -The continuous research is strongly required for the judgement and promotion of the children with sports talents, therefore, the multiple intelligences approach will be helpful for the development of the bodily-kinesthetic intelligence, and in other words, the new era of the genetic method will be another prospective area such as the DNA analysis in the identification of the talented children for the sports filed.