• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.205-206
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• 2018

본 논문에서는 IoT 환경의 무선 센서 네트워크 시스템 상의 효율적인 패킷 전달을 위해 큐러닝(Q-learning)에 기반한 다중 대기열 동적 스케쥴링 기법을 제안한다. 이 정책은 다중 대기열(Multiple queue)의 각 큐가 요구하는 딜레이 조건에 맞춰 최대한 패킷 처리를 미룸으로써 효율적으로 CPU자원을 분배한다. 또한 각 노드들의 상태를 큐러닝(Q-learning)을 통해 지속적으로 상태를 파악하여 기아상태(Starvation)를 방지한다. 제안하는 기법은 무선 센서 네트워크 상의 가변적이고 예측 불가능한 환경에 대한 사전지식이 없이도 요구하는 서비스의 질(Quality of service)를 만족할 수 있도록 한다. 본 논문에서는 모의실험을 통해 기존의 학습 기반 패킷 스케쥴링 알고리즘과 비교하여 제안하는 스케쥴링 기법이 복잡한 요구조건에 따라 유연하고 공정한 서비스를 제공함에 있어 우수함을 증명하였다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.37-45
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• 2020

The development process of deep learning is an iterative task that requires a lot of manual work. Among the steps in the development process, pre-processing of learning data is a very costly task, and is a step that significantly affects the learning results. In the early days of AI's algorithm research, learning data in the form of public DB provided mainly by data scientists were used. The learning data collected in the real environment is mostly the operational data of the sensors and inevitably contains various noises. Accordingly, various data cleaning frameworks and methods for removing noises have been studied. In this paper, we proposed a method for detecting and removing noises from time-series data, such as sensor data, that can occur in the IoT environment. In this method, the linear regression method is used so that the system repeatedly finds noises and provides data that can replace them to clean the learning data. In order to verify the effectiveness of the proposed method, a simulation method was proposed, and a method of determining factors for obtaining optimal cleaning results was proposed.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.11-18
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• 2022

De novo drug design is the process of developing new drugs that can interact with biological targets such as protein receptors. Traditional process of de novo drug design consists of drug candidate discovery and drug development, but it requires a long time of more than 10 years to develop a new drug. Deep learning-based methods are being studied to shorten this period and efficiently find chemical compounds for new drug candidates. Many existing deep learning-based drug design models utilize recurrent neural networks to generate a chemical entity represented by SMILES strings, but due to the disadvantages of the recurrent networks, such as slow training speed and poor understanding of complex molecular formula rules, there is room for improvement. To overcome these shortcomings, we propose a deep learning model for SMILES string generation using variational autoencoders with self-attention mechanism. Our proposed model decreased the training time by 1/26 compared to the latest drug design model, as well as generated valid SMILES more effectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.983-989
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• 2006

Current emigration and immigration control inspects passports by the naked eye, registers them by manual input, and compares them with items of database. In this paper, we propose the method to recognize information codes of passports. The proposed passport recognition method extracts character-rows of information codes by applying sobel operator, horizontal smearing, and contour tracking algorithm. The extracted letter-row regions is binarized. After a CDM mask is applied to them in order to recover the individual codes, the individual codes are extracted by applying vertical smearing. The recognizing of individual codes is performed by the RBF network whose hidden layer is applied by ART 2 algorithm and whose learning between the hidden layer and the output layer is applied by a generalized delta learning method. After a photo region is extracted from the reference of the starting point of the extracted character-rows of information codes, that region is verified by the information of luminance, edge, and hue. The verified photo region is certified by the classified features by the ART 2 algorithm. The comparing experiment with real passport images confirmed the good performance of the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.21 no.5
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• pp.455-461
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• 2002

This paper presents a method that improves the performance of the personal voice dialling system in which speaker independent phoneme HMM's are used. Since the speaker independent phoneme HMM based voice dialing system uses only the phone transcription of the input sentence, the storage space could be reduced greatly. However, the performance of the system is worse than that of the system which uses the speaker dependent models due to the phone recognition errors generated when the speaker independent models are used. In order to solve this problem, a new method that jointly estimates transformation vectors for the speaker adaptation and transcriptions from training utterances is presented. The biases and transcriptions are estimated iteratively from the training data of each user with maximum likelihood approach to the stochastic matching using speaker-independent phone models. Experimental result shows that the proposed method is superior to the conventional method which used transcriptions only.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.696-700
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• 2007

A new method that improves the performance of the phonetic transcription based speech recognition system is presented with the speaker-independent phonetic recognizer. Since SI phoneme HMM based speech recognition system uses only the phoneme transcription of the input sentence, the storage space could be reduced greatly. However, the performance of the system is worse than that of the speaker dependent system due to the phoneme recognition errors generated from using SI models. A new training method that iteratively estimates the phonetic transcription and transformation vectors is presented to reduce the mismatch between the training utterances and a set of SI models using speaker adaptation techniques. For speaker adaptation the stochastic matching methods are used to estimate the transformation vectors. The experiments performed over actual telephone line shows that a reduction of about 45% in the error rates could be achieved as compared to the conventional method.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.17-23
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• 2023

In this study, the thermoluminescence kinetics of electron trap in Li₆Y_0.5Gd_0.5(BO₃)₃ (LY_0.5G_0.5BO) scintillator for neutron detection composed of Li, Gd, and B with a high neutron response cross-section were investigated. The thermoluminescence glow curve of the LY_0.5G_0.5BO scintillation single crystal was measured and analyzed using the peak shape method, the initial rise method, and the machine learning algorithm to evaluate the physical parameters of the electron trap. The glow curve of the LY_0.5G_0.5BO scintillation single crystal consisted of a single peak. As a result of analyzing this peak, the activation energy, emission order, and frequency factor of the electron trap were 0.61 eV, 1.1, and 1.7×10⁷ s^-1, respectively. In addition, the possibility of thermoluminescence analysis of scintillators using machine learning was confirmed.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.488-498
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• 2022

The purpose of this study is to examine the impact of the GRIT of the students who major in landscaping upon their will to continue online learning and the mediating effect of the academic engagement in this process. For this purpose, the researcher surveyed the landscape major students in the universities located in Seoul and Gyeonggi regions, from November 25, 2021, to December 9, 2021. The result of the analysis of the responses from 296 participants showed that the GRIT of the landscaping major students during online learning in their universities had a positive and significant impact on their academic engagement and learning persistence. Also, academic engagement had a positive and significant impact on learning persistence. In addition, in the relationship between GRIT of the landscaping major students in online learning and their learning persistence, academic engagement had a significant mediating effect. The findings of this study indicated that the GRIT and the academic engagement of a student were important in order to enhance the learning persistence during online learning for landscaping major students. Due to the nature of landscaping departments, where practical classes are important because the subject itself is a practice-oriented one and the subject covers a variety of areas, it is the implication of this study that the internal factors of students, as well as the external factors of the university, need positive improvement to reinforce the academic engagement during online learning.

• Korean Journal of Culture and Social Issue
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• v.14 no.1_spc
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• pp.319-348
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• 2008

Self-efficacy, achievement goals, task value, and attribution are some of the representative motivation constructs that explain adolescents' cognition, affect, and behavioral patterns in achievement settings. These constructs have won researchers' recognition by demonstrating explanatory and predictive utility that transcends various social and cultural milieus learners are exposed to. Korean adolescents' motivation is generally in line with this universal trend and can be described adequately with these constructs. Nonetheless, there also exist a host of indigenous factors that shape these motivation constructs to be uniquely Korean. The purpose of the present article was to explore some of the socio-cultural factors that appear to wield particularly determining effects on Korean adolescents' academic motivation. Review of the relevant literature identified interdependent self-construal, traditional morals of filial piety, familism, educational fervor, academic elitism, and the college entrance system as important cultural, social, and policy-related such factors. Also discussed in this article were the roles of these factors in creating more immediate psychological learning environments for Korean adolescents, such as parent-child relationships, teacher-student relationships, and classroom goal structures.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.319-330
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• 2019

Forecasting the electricity consumption through analyzing the past electricity consumption a advantageous for energy planing and policy. Machine learning is widely used as a method to predict electricity consumption. Among them, ensemble learning is a method to avoid the overfitting of models and reduce variance to improve prediction accuracy. However, ensemble learning applied to daily data shows the disadvantages of predicting a center value without showing a peak due to the characteristics of ensemble learning. In this study, we overcome the shortcomings of ensemble learning by considering the temperature trend. We compare nine models and propose a model using random forest with the linear trend of temperature.