• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.373-376
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• 2018

Traditional paprika smart farm systems are often harmful to paprika growth because they are set to follow the values of several sensors to the reference value, so the system is often unable to make optimal judgement. Using decision tree techniques, the expert system for the paprika smart farm is designed to create a control system with a decision-making structure similar to that of farmers using data generated by factors that depend on their surroundings. With the current smart farm control system, it is essential for farmers to intervene in the surrounding environment because it is designed to follow sensor values to the reference values set by the farmer. To solve this problem even slightly, it is going to obtain environmental data and design controllers that apply decision tree method. The expert system is established for complex control by selecting the most influential environmental factors before controlling the paprika smart farm equipment, including criteria for selecting decisions by farmers. The study predicts that each environmental element will be a standard when creating smart farms for professionals because of the interrelationships of data, and more surrounding environmental factors affecting growth.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.229-231
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• 2003

일반적으로 음성은 시간함수로 표현되며 음성인식에서 표준모델을 모델링하는 것은 매우 중요한 문제이다. 음절 단어, 연속음성을 발성할 때 자음과 모음에 따라 발성시간에 차이가 있으며 이를 잘 모델링하는 것 또한 음성인식에서는 중요한 문제라고 할 수 있다. 따라서 본 연구에서는 강건한 음향모델을 학습하기 위해 시간의 변화와 상태분할과정에서의 모델의 변화를 고려하여 다양한 구조의 초기모델을 작성하였다. 각 초기모델에 의한 HM-Net 문맥의존 음향모델은 음소결정트리 기반 SSS 알고리즘(PDT-SSS)을 이용하였다. PDT-SSS 알고리즘은 미지의 문맥정보를 해결하기 위해 문맥방향과 시간방향으로 목표 상태수에 도달할 때까지 상태분할을 수행하여 모델을 작성하는 방법이다. 음성의 시간변이를 고려한 강건한 문맥의존 음향모델을 작성하기 위해 설정한 각 모델의 구조에 대한 유효성을 확인하기 위해 국어공학센터의 452 단어를 대상으로 음소와 단어인식 실험을 수행한 결과. 음소인식의 경우 상태수 2000개에서 2상태 구조의 모델에 비해 4상태 구조가 약 11.4% 향상된 인식성능과 39.2초의 인식시간을 단축할 수 있었다. 또한 단어인식의 경우 상태수 2000개에서 1상태 구조의 모델에 비해 4상태 구조가 약 5% 향상된 인식성능과 4상태 구조에서 한 단어를 인식하는데 평균 0.8초가 소요되었다. 따라서 강건한 문맥의존 음향모델을 작성하기 위해 수행한 초기모델의 구조에 관한 연구가 향후 음성인식 시스템을 구축하는데 유효함을 확인할 수 있었다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.605-616
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• 2021

While malwares must be accurately identifiable from arbitrary programs, existing studies using classification techniques have limitations that they can only be applied to limited samples. In this work, we propose a method to utilize API call frequency to detect and classify malware families from arbitrary programs. Our proposed method defines a rule that checks whether the call frequency of a particular API exceeds the threshold, and identifies a specific family by utilizing the rate information on the corresponding rules. In this paper, decision tree algorithm is applied to define the optimal threshold that can accurately identify a particular family from the training set. The performance measurements using 4,443 samples showed 85.1% precision and 91.3% recall rate for family detection, 97.7% precision and 98.1% reproduction rate for classification, which confirms that our method works to distinguish malware families effectively.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.83-90
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• 2020

Prototype selection offers the advantage of ensuring low learning time and storage space by selecting the minimum data representative of in-class partitions from the training data. This paper designs a new training data generation method using hyper-rectangles that can be applied to general classification algorithms. Hyper-rectangular regions do not contain different class data and divide the same class space. The median value of the data within a hyper-rectangle is selected as a prototype to form new training data, and the size of the hyper-rectangle is adjusted to reflect the data distribution in the class area. A set cover optimization algorithm is proposed to select the minimum prototype set that represents the whole training data. The proposed method reduces the time complexity that requires the polynomial time of the set cover optimization algorithm by using the greedy algorithm and the distance equation without multiplication. In experimented comparison with hyper-sphere prototype selections, the proposed method is superior in terms of prototype rate and generalization performance.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.91-97
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• 2021

This paper uses a data extension technique originally designed for the rule refinement problem to handling incomplete data. This technique is characterized in that each event can have a weight indicating importance, and each variable can be expressed as a probability value. Since the key problem in this paper is to find the probability that is closest to the missing value and replace the missing value with the probability, three different algorithms are used to find the probability for the missing value and then store it in this data structure format. And, after learning to classify each information area with the SVM classification algorithm for evaluation of each probability structure, it compares with the original information and measures how much they match each other. The three algorithms for the imputation probability of the missing value use the same data structure, but have different characteristics in the approach method, so it is expected that it can be used for various purposes depending on the application field.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.529-534
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• 2021

Computing thinking is regarded as one of the important skills required in the 21st century, and many countries have introduced and implemented computing thinking training courses. Among computational thinking education methods, educational game-based methods increase student participation and motivation, and increase access to computational thinking. Autothinking is an educational game developed for the purpose of providing computational thinking education to learners. It is an adaptive system that dynamically provides feedback to learners and automatically adjusts the difficulty according to the learner's computational thinking ability. However, because the game was designed based on rules, it cannot intelligently consider the computational thinking of learners or give feedback. In this study, game data collected through Autothikning is introduced, and game score prediction that reflects computational thinking is performed in order to increase the adaptability of the game by using it. To solve this problem, a comparative study was conducted on linear regression, decision tree, random forest, and support vector machine algorithms, which are most commonly used in regression problems. As a result of the study, the linear regression method showed the best performance in predicting game scores.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.30-39
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• 2003

In this paper, we carried out the performance evaluation of HM-Net(Hidden Markov Network) speech recognition system for Korean speech databases. We adopted to construct acoustic models using the HM-Nets modified by HMMs(Hidden Markov Models), which are widely used as the statistical modeling methods. HM-Nets are carried out the state splitting for contextual and temporal domain by PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) algorithm, which is modified the original SSS algorithm. Especially it adopted the phonetic decision tree to effectively express the context information not appear in training speech data on contextual domain state splitting. In case of temporal domain state splitting, to effectively represent information of each phoneme maintenance in the state splitting is carried out, and then the optimal model network of triphone types are constructed by in the parameter. Speech recognition was performed using the one-pass Viterbi beam search algorithm with phone-pair/word-pair grammar for phoneme/word recognition, respectively and using the multi-pass search algorithm with n-gram language models for sentence recognition. The tree-structured lexicon was used in order to decrease the number of nodes by sharing the same prefixes among words. In this paper, the performance evaluation of HM-Net speech recognition system is carried out for various recognition conditions. Through the experiments, we verified that it has very superior recognition performance compared with the previous introduced recognition system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1979-1986
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• 2010

Biological early warning system detects toxicity by looking at behavior of organisms in water. The system uses classifier for judgement about existence and amount of toxicity in water. Boosting algorithm is one of possible application method for improving performance in a classifier. Boosting repetitively change training example set by focusing on difficult examples in basic classifier. As a result, prediction performance is improved for the events which are difficult to classify, but the information contained in the events which can be easily classified are discarded. In this paper, an incremental learning method to overcome this shortcoming is proposed by using the extended data expression. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression by exploiting the necessary information not only from the well classified, but also from the weakly classified events. Experimental results show that the new algorithm outperforms the former Learn++ method without using the weight parameter.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.531-539
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• 2019

This paper proposes the training features for malware family analysis and analyzes the multi-classification performance of ensemble models. We construct training data by extracting API and DLL information from malware executables and use Random Forest and XGBoost algorithms which are based on decision tree. API, API-DLL, and DLL-CM features for malware detection and family classification are proposed by analyzing frequently used API and DLL information from malware and converting high-dimensional features to low-dimensional features. The proposed feature selection method provides the advantages of data dimension reduction and fast learning. In performance comparison, the malware detection rate is 93.0% for Random Forest, the accuracy of malware family dataset is 92.0% for XGBoost, and the false positive rate of malware family dataset including benign is about 3.5% for Random Forest and XGBoost.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.261-261
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• 2020

물관리의 기본이 되는 연속적인 유량 자료 확보를 위해서는 정확도 높은 수위-유량 관계 곡선식 개발이 필수적이다. 수위-유량 관계곡선식은 모든 수문시설 설계의 기초가 되며 홍수, 가뭄 등 물재해 대응을 위해서도 중요한 의미를 가지고 있다. 그러나 일반적으로 유량 측정은 많은 비용과 시간이 들고, 식생성장, 단면변화 등의 통제특성(control)이 변함에 따라 구간분리, 기간분리와 같은 비선형적인 양상이 나타나 자료 해석에 어려움이 존재한다. 특히, 국내 하천의 경우 자연적 및 인위적인 환경 변화가 다양하여 지점 및 기간에 따라 세밀한 분석이 요구된다. 머신러닝(Machine Learning)이란 데이터를 통해 컴퓨터가 스스로 학습하여 모델을 구축하고 성능을 향상시키는 일련의 과정을 뜻한다. 기존의 수위-유량 관계곡선식은 개발자의 판단에 의해 데이터의 종류와 기간 등을 설정하여 회귀식의 파라미터를 산출한다면, 머신러닝은 유효한 전체 데이터를 이용해 스스로 학습하여 자료 간 상관성을 찾아내 모델을 구축하고 성능을 지속적으로 향상 시킬 수 있다. 머신러닝은 충분한 수문자료가 확보되었다는 전제 하에 복잡하고 가변적인 수자원 환경을 반영하여 유량 추정의 정확도를 지속적으로 향상시킬 수 있다는 이점을 가지고 있다. 본 연구는 머신러닝의 대표적인 알고리즘들을 활용하여 유량을 추정하는 모델을 구축하고 성능을 비교·분석하였다. 대상지역은 안정적인 수량을 확보하고 있는 한강수계의 거운교 지점이며, 사용자료는 2010~2018년의 시간, 수위, 유량, 수면폭 등 이다. 프로그램은 파이썬을 기반으로 한 머신러닝 라이브러리인 사이킷런(sklearn)을 사용하였고 알고리즘은 랜덤포레스트 회귀, 의사결정트리, KNN(K-Nearest Neighbor), rgboost을 적용하였다. 학습(train) 데이터는 입력자료 종류별로 조합하여 6개의 세트로 구분하여 모델을 구축하였고, 이를 적용해 검증(test) 데이터를 RMSE(Roog Mean Square Error)로 평가하였다. 그 결과 모델 및 입력 자료의 조합에 따라 3.67~171.46로 다소 넓은 범위의 값이 도출되었다. 그 중 가장 우수한 유형은 수위, 연도, 수면폭 3개의 입력자료를 조합하여 랜덤포레스트 회귀 모델에 적용한 경우이다. 비교를 위해 동일한 검증 데이터를 한국수문조사연보(2018년) 내거운교 지점의 수위별 수위-유량 곡선식을 이용해 유량을 추정한 결과 RMSE가 3.76이 산출되어, 머신러닝이 세분화된 수위-유량 곡선식과 비슷한 수준까지 성능을 내는 것으로 확인되었다. 본 연구는 양질의 유량자료 생산을 위해 기 구축된 수문자료를 기반으로 머신러닝 기법의 적용 가능성을 검토한 기초 연구로써, 국내 효율적인 수문자료 측정 및 수위-유량 곡선 산출에 도움이 될 수 있을 것으로 판단된다. 향후 수자원 환경 및 통제특성에 영향을 미치는 다양한 영향변수를 파악하기 위해 기상자료, 취수량 등의 입력 자료를 적용할 필요가 있으며, 머신러닝 내 비지도학습인 딥러닝과 같은 보다 정교한 모델에 대한 추가적인 연구도 수행되어야 할 것이다.