• 한국과학교육학회지
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• 제33권4호
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• pp.863-879
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• 2013

The purpose of this study was to analyze task commitment types of science learning in high school students' biology classification. Thirty students were selected as the representatives of five task commitment types according to task commitment type inventory scores. They participated in think-aloud biology classification task. To analyze the procedural characteristics of task commitment, a coding scheme and think-aloud task were developed. Characteristics of respective task commitment types were identified from the result of the think-aloud protocol coding analysis. They are TGC(task goal commitment) type, LGC(low goal commitment) type, CC(conditional commitment) type, SC(suspended commitment) type, and DC(delayed commitment) type. Findings gained from this study are expected to serve as the foundation of task commitment enhancement strategies and as the information on the characteristics of each task commitment type. Also, future studies are required to investigate the commitment-related properties not only in biology classification but also in other science learning situations.

• 과학교육연구지
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• 제37권1호
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• pp.170-185
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• 2013

본 연구의 목적은 생물 분류 탐구에서 과제 집착의 인지적인 모형을 규명하는 것이다. 이를 위해 생명 과학 탐구에서 과제 집착에 대한 다양한 문헌들을 분석하여 과제 집착에 대한 가설적인 인지적 모형을 고안하였다. 이 후, 고안한 모형의 규명을 위해 과제 집착의 분석을 위한 과제를 개발하고 사고 발성법과 회상적 면접법을 이용하여 연구 참여자의 프로토콜을 수집, 분석함으로써 생물 분류 탐구에서 과제 집착의 인지적 모형을 규명하였다. 연구 결과, 문헌 기반의 모형을 고안하고 프로토콜 분석을 통하여 규명한 과제 집착의 인지적 모형을 크게 과제 집착 유발, 과제 집착 강화, 과제 집착 유지의 세 단계의 과정으로 구성하였다. 과제 집착 유발 단계에서는 과제에 대한 관찰, 과제 관련 경험 표상, 탐구 예비 수행, 목표 평가의 하위과정으로 구성하였다. 과제 집착 강화 단계는 경험 기반 탐구 계획 설정 또는 경험 미기반 탐구 계획 설정, 적극적인 수행 및 소극적인 수행, 탐구 수행중 자기 평가, 가설 검증까지 반복적인 수행의 하위 과정으로 구성하였다. 과제 집착 유지 단계에서는 완료 후 피드백 수행, 자발적인 후속 탐구 수행의 하위 과정으로 구성하였다. 각 단계마다 과제 집착 구성 요소인 자신감, 목표설정, 주의집중이 변화하는 것으로 구성하였다. 위 연구 결과에 의해 생물 분류 탐구에서 과제 집착의 인지적 모형을 통해 생물 분류 탐구에서 과제 집착 향상을 위한 구체적인 교수-학습 전략을 구성하기 위한 기초 정보를 제공할 수 있으며 탐구과정에서 과제 집착의 단계적인 평가와 피드백 제시에 도움이 될 것이다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 추계학술대회 논문집
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• pp.361-362
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• 2009

• 한국기록관리학회지
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• 제18권1호
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• pp.155-175
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• 2018

이 연구의 목적은 함평사건희생자유족회의 소장 기록물에 대한 분류체계를 마련하는 데에 있다. 이에 따라 기록물의 맥락을 기능적 출처주의를 통해 구현하며, 기록물을 효과적으로 활용할 수 있도록 유형별 특성과 생산시기별 특성을 반영한 분류표를 제시하였다. 기능분류체계 개발 방법론인 DIRKS를 사용하여 함평사건희생자유족회의 업무분석을 수행함으로써, 업무기능-업무활동-처리행위로 이어지는 업무분류표를 도출한다. 함평사건희생자유족회 소장 기록물을 유형과 생산시기별 특성을 고려하여 그 범주를 결정한다. 기록물 맵핑은 업무분류표에 해당하는 업무분류체계에 1차적으로 실행하고, 2차적으로는 업무분류에 유형분류와 시대분류를 접목한 다중분류체계에 맵핑한다. 업무주제-업무활동-처리행위-유형-시대의 형태로 이어지는 기록물 분류표를 도출한다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2002년도 가을 학술발표논문집 Vol.29 No.2 (2)
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• pp.232-234
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• 2002

EEG-based brain computer interface (BCI) provides a new communication channel between human brain and computer. The classification of EEG data is an important task in EEG-based BCI. In this paper we present methods which jointly employ principal component analysis (PCA) and linear dynamical system (LDS) modeling for the task of EEG classification. Experimental study for the classification of EEG data during imagination of a left or right hand movement confirms the validity of our proposed methods.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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• pp.671-674
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• 2004

We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.

• Journal of Multimedia Information System
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• 제3권3호
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• pp.53-61
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• 2016

This research proposes the index optimization as a classification task and application of the graph based KNN. We need the index optimization as an important task for maximizing the information retrieval performance. And we try to solve the problems in encoding words into numerical vectors, such as huge dimensionality and sparse distribution, by encoding them into graphs as the alternative representations to numerical vectors. In this research, the index optimization is viewed as a classification task, the similarity measure between graphs is defined, and the KNN is modified into the graph based version based on the similarity measure, and it is applied to the index optimization task. As the benefits from this research, by modifying the KNN so, we expect the improvement of classification performance, more graphical representations of words which is inherent in graphs, the ability to trace more easily results from classifying words. In this research, we will validate empirically the proposed version in optimizing index on the two text collections: NewsPage.com and 20NewsGroups.

• 지능정보연구
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• 제28권3호
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• pp.45-55
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• 2022

최근 자연어 처리 분야에서 대규모 사전학습 언어모델(Large-scale pretrained language model, LPLM)이 발전함에 따라 이를 미세조정(Fine-tuning)한 의도 분류 모델의 성능도 개선되었다. 하지만 실시간 응답을 요하는 대화 시스템에서 대규모 모델을 미세조정하는 방법은 많은 운영 비용을 필요로 한다. 이를 해결하기 위해 본 연구는 저성능 자원에서도 멀티에이전트 운영이 가능한 의도 분류 모델 경량화 방법을 제안한다. 제안 방법은 경량화된 문장 인코더를 학습하는 과제 독립적(Task-agnostic) 단계와 경량화된 문장 인코더에 어답터(Adapter)를 부착하여 의도 분류 모델을 학습하는 과제 특화적(Task-specific) 단계로 구성된다. 다양한 도메인의 의도 분류 데이터셋으로 진행한 실험을 통해 제안 방법의 효과성을 입증하였다.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• Journal of Information Processing Systems
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• 제19권6호
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• pp.830-841
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• 2023

Tokenization is the process of segmenting the input text into smaller units of text, and it is a preprocessing task that is mainly performed to improve the efficiency of the machine learning process. Various tokenization methods have been proposed for application in the field of natural language processing, but studies have primarily focused on efficiently segmenting text. Few studies have been conducted on the Korean language to explore what tokenization methods are suitable for document classification task. In this paper, an exploratory study was performed to find the most suitable tokenization method to improve the performance of a representative spatio-temporal document classifier in Korean. For the experiment, a convolutional neural network model was used, and for the final performance comparison, tasks were selected for document classification where performance largely depends on the tokenization method. As a tokenization method for comparative experiments, commonly used Jamo, Character, and Word units were adopted. As a result of the experiment, it was confirmed that the tokenization of word units showed excellent performance in the case of representative spatio-temporal document classification task where the semantic embedding ability of the token itself is important.