• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.26 no.6
• /
• pp.1387-1399
• /
• 2016

In a hyper-connected society realized through IoT-enabled technology, a large amount of data is collected by various devices and is processed to provide new services to users. While communicating through a network, it is essential for devices to execute mutual authentication since users' privacy can be infringed by malicious attackers. ID-based signature enables authentication and key exchange with a unique ID of a device. However, most of the previous ID-based signature schemes based on RSA require an additional step to share parameters for key exchange so that they are not suitable for resource-constrained devices in terms of efficiency. In this paper, we design an efficient ID-based signature and thereby propose an efficient ID-based authentication and key exchange protocol in which sessions for both an authentication and a key exchange are executed simultaneously. In addition, we prove the security of our scheme under the RSA onewayness problem and analyze the efficiency by comparing with the previous schemes.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.4
• /
• pp.739-748
• /
• 2012

In this paper we propose a mixed effects least squares support vector machine (LS-SVM) for the censored data which are observed from different groups. We use weights by which the randomly right censoring is taken into account in the nonlinear regression. The weights are formed with Kaplan-Meier estimates of censoring distribution. In the proposed model a random effects term representing inter-group variation is included. Furthermore generalized cross validation function is proposed for the selection of the optimal values of hyper-parameters. Experimental results are then presented which indicate the performance of the proposed LS-SVM by comparing with a standard LS-SVM for the censored data.

• Journal of Environmental Science International
• /
• v.28 no.11
• /
• pp.1019-1025
• /
• 2019

The aim of this study was to analyze the light traits in a solar light-collector device and its effects on lettuce growth at an early growth stage. The three hyper parameters used were the reflector diameter (2 cm and 4 cm), coating inside the reflector (chrome-coated, non-coated) and distance from the light fiber (15 cm and 20 cm). The results showed that light efficiency, which is the ratio of light intensity inside the fiber to the solar intensity, improved by 41.1 % when using a 2 cm diameter chrome-coated reflector at a distance of 15 cm from the light fiber; whereas it only improved by 20.6% when a non-coated reflector was used. As the reflector size was increased to 4 cm, the light efficiency for the coated and non-coated reflectors increased by 28.5 % and 26.4 %, respectively, hence, no significant difference was observed. When the light fiber was placed at a distance of 20 cm, the increase in light efficiency with coating treatment was 8 % higher than without coating treatment. We also compared the efficiency of light-fiber treatment with that of LED treatment in our lettuce nursery, and observed that the plants exhibited better growth with light-fiber treatment. We observed an average increase of 1.7 cm in leaf height, $7cm^2/plant$ increase in leaf area, and 32 mm increase in root length upon light-fiber treatment as opposed to those observed with LED treatment. These findings indicate that the collector light-fiber is economically feasible and it improves lettuce growth compared with the LED treatment.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.9
• /
• pp.339-348
• /
• 2021

In recent software defect prediction research, defect prediction between cross projects and cross-version projects are actively studied. Cross-version defect prediction studies assume WP(Within-Project) so far. However, in the CV(Cross-Version) environment, the previous work does not consider the distribution difference between project versions is important. In this study, we propose an automated Bayesian optimization framework that considers distribution differences between different versions. Through this, it automatically selects whether to perform transfer learning according to the difference in distribution. This framework is a technique that optimizes the distribution difference between versions, transfer learning, and hyper-parameters of the classifier. We confirmed that the method of automatically selecting whether to perform transfer learning based on the distribution difference is effective through experiments. Moreover, we can see that using our optimization framework is effective in improving performance and, as a result, can reduce software inspection effort. This is expected to support practical quality assurance activities for new version projects in a cross-version project environment.

• Journal of the Korea Society for Simulation
• /
• v.30 no.4
• /
• pp.1-8
• /
• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.3
• /
• pp.106-118
• /
• 2019

In recent years, research and development on image object recognition using artificial intelligence have been actively carried out, and it is expected to be used for road maintenance. Among them, artificial intelligence models for object detection of road surface are continuously introduced. In order to develop such object recognition algorithms, a backbone network that extracts feature maps is essential. In this paper, we will discuss how to select the appropriate neural network. To accomplish it, we compared with 4 different deep neural networks using 6,000 road surface damage images. Based on three evaluation methods for analyzing characteristics of neural networks, we propose a method to determine optimal neural networks. In addition, we improved the performance through optimal tuning of hyper-parameters, and finally developed a light backbone network that can achieve 85.9% accuracy of road surface damage classification.

• Journal of the Society of Naval Architects of Korea
• /
• v.58 no.5
• /
• pp.281-293
• /
• 2021

Oil and steel prices, which are major pricescosts in the shipbuilding industry, were predicted. Firstly, the error of the moving average line (N=3-5) was examined, and in all three error analyses, the moving average line (N=3) was small. Secondly, in the linear prediction of data through existing theory, oil prices rise slightly, and steel prices rise sharply, but in reality, linear prediction using existing data was not satisfactory. Thirdly, we identified the limitations of linear prediction methods and confirmed that oil and steel price prediction was somewhat similar to actual moving average line prediction methods. Due to the high volatility of major price flows, large errors were inevitable in the forecast section. Through the time series analysis method at the end of this paper, we were able to achieve not bad results in all analysis items relative to artificial intelligence (Prophet). Predictive data through predictive analysis using eight predictive models are expected to serve as a good research foundation for developing unique tools or establishing evaluation systems in the future. This study compares the basic settings of artificial intelligence programs with the results of core price prediction in the shipbuilding industry through time series prediction theory, and further studies the various hyper-parameters and event effects of Prophet in the future, leaving room for improvement of predictability.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.5
• /
• pp.969-976
• /
• 2019

A lightweight artificial intelligence hardware has made great strides in many application areas. In general, a lightweight artificial intelligence system consist of lightweight artificial intelligence engine and preprocessor including feature selection, generation, extraction, and normalization. In order to achieve optimal performance in broad range of applications, lightweight artificial intelligence system needs to choose a good preprocessing function and set their respective hyper-parameters. This paper proposes a unified framework for a lightweight artificial intelligence system and utilization method for finding models with optimal performance to use on a given dataset. The proposed unified framework can easily generate a model combined with preprocessing functions and lightweight artificial intelligence engine. In performance evaluation using handwritten image dataset and fall detection dataset measured with inertial sensor, the proposed unified framework showed building optimal artificial intelligence models with over 90% test accuracy.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.4
• /
• pp.12-22
• /
• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.24-29
• /
• 2022

Semantic role labeling task used in various natural language processing fields, such as information extraction and question answering systems, is the task of identifying the arugments for a given sentence and predicate. Predicate used as semantic role labeling input are extracted using lexical analysis results such as POS-tagging, but the problem is that predicate can't extract all linguistic patterns because predicate in korean language has various patterns, depending on the meaning of sentence. In this paper, we propose a korean predicate recognition method using neural network model with pre-trained embedding models and lexical features. The experiments compare the performance on the hyper parameters of models and with or without the use of embedding models and lexical features. As a result, we confirm that the performance of the proposed neural network model was 92.63%.