• 사료
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• s.62
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• pp.54-57
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• 2013

차랑 등의 수송연료인 휘발유나 경유에 일정비율을 식물에서 뽑아낸 신재생연료 즉 바이오 연료를 혼합하여 공급하도록 의무화하여 온실가스 배출을 줄이기 위한 신재생연료 혼합의무제도(RFS) 국내 시행방안과 관련한 공청회가 지난 2월 15일 서울교육문화회관에서 지식경제부 주최와 한국석유관리원 주관으로 개최됐다. 정부는 2020년까지 경유나 휘발유에 바이오 디젤 에탄올을 4~5% 섞게 하여 동 기간까지 온실가스 감축목표량 중 8~10%까지 달성한다는 계획을 세워놓고 있다. 하지만 녹색연대 등 민간단체들은 온실가스 감축 효과는 매우 불확실하며 특히 어떤 원료를 쓰느냐에 따라 오히려 기후변화를 악화시킬 수 있다는 주장과 산림훼손, 세계 곡물가 상승, 국내 유가 상승 등 많은 문제점들이 있음을 우려해 강하게 반대를 표명하고 있는 입장이다. 우리 협회에서도 곡물을 이용한 에탄올의 혼합의무가 시행되는 경우 옥수수 등 사료원료가격의 상승으로 가뜩이나 어려운 축산업의 경영상황을 더욱 약화시키게 될 것이 예견되는 바, 동 혼합의무제도의 시행을 적극 반대하는 대 국회 및 정부활동을 전개한 바 있다. 그 결과 지난 4월 17일 국회 지식경제위원회 법률심사소위원회에서는 신재생연료 혼합의무제도의 시행 시기를 2년간 유보하고 혼합의무연료에서 에탄올을 제외시키는 방안을 논의한 바 있어 향후 입법과정에 관심이 모아지고 있다. 이에 따라 본지는 이번 RFS의 국내 시행과 관련하여 어떠한 문제점들이 있는지 관련 업계 종사자의 글을 통해 알아본다.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2000.11a
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• pp.169-175
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• 2000

우리나라는 IMF 체제하의 경제악화로 기업의 구조조정이 가속화되어 근로자 감축으로 인한 작업강도가 오히려 증대되으나, 안전관리자 의무고용 완화, 기업규제완화에 관한 특별 조치법 시행 등 안전보건의 약화요인이 발생되어 사업장 내에서 사고발생 가능성이 높아졌으며, 또한 노동시장의 유연화에 따른 안전지식이 부족한 비정규 근로자의 증가로 인해 대형사고 위험성이 증대되고 있다.(중략)

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.69-74
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• 2015

In this paper the patterns of information system is reduced so that control rules can guarantee fast response of queries in database. Generally an information system includes many kinds of necessary and unnecessary attribute. In particular, inconsistent information system is less likely to acquire the accuracy of response. Hence we are interested in the simple and understandable rules that can represent useful patterns by means of rough entropy and Bayesian posterior probability. We propose an algorithm which can reduce control rules to a minimum without inadequate patterns such that the implication between condition attributes and decision attributes is measured through the framework of rough entropy. Subsequently the validation of the proposed algorithm is showed through test information system of new employees appointment.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.881-886
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• 2007

Fuzzy inference has the advantage which can process the ambiguous knowledge. However the associated attributes of fuzzy rules are difficult to determine useful and important rules because the redundant attribute of rules is more than enough. In this paper, we propose a method to minimize the number of rules and preserve the accuracy of inference results by using fuzzy relative cardinality after removing unnecessary attributes from rough set. From the experimental results, we can see the fact that the proposed method provides better results (e.g the number of rules) than those of general rough set with the redundant attributes.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.343-349
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• 2013

Rough set theory comes to derive optimal rules through the effective selection of features from the redundancy of lots of information in data mining using the concept of equivalence relation and approximation space in rough set. The reduction of attributes is one of the most important parts in its applications of rough set. This paper purports to define a information-theoretic measure for determining the most important attribute within the association of attributes using rough entropy. The proposed method generates the effective reduct set and formulates the core of the attribute set through the elimination of the redundant attributes. Subsequently, the control rules are generated with a subset of feature which retain the accuracy of the original features through the reduction.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.27-36
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• 1998

This paper suggests an integrated method based on rough sets for discovering useful knowledge from a large databse. Our approach applies attribute-oriented concept hierarchy ascension technique to extract generalized data from actual data in database, induction of decision trees to measure the information gain, and knowledge reduction method of rough set theory to remove superfluous attributes and attribute values. The integrated algorithm first reduces the size of database through the concept generalization, reduces the number of attributes by means of eliminating condition attributes which have little influence on decision attribute, and finally induces simplified decision rules by removing the superfluous attribute values by analyzing the dependency relationships among the attributes.

• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.865-883
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• 2014

This study investigated the carbon offset project activities as the activities of producing commodities by a case study on the Korean Clean Development Mechanism(CDM) industry. This study draw the networks of Korean CDM industry by extracting major agents and surrounding agents and by analyzing the characteristics of the linkages. The project participants owning the CDM projects hires CDM consultancies and designated operational entities(DOEs). The technical knowledge for carbon emission reduction made links between project participants and the CDM project operational knowledge made links between project participants and CDM consultancies. Links between project participants and DOEs are affected by social and geographical proximities. The value of the knowledge for CDM industrial activities determined the role of agents and type of linkages. The agents with the irreplaceable knowledge could be a project conducting firms. The agents without it became outsourcing contractors.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.61-69
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• 2015

This study presents a probabilistic knowledge discovery method to interpret heart rate variability (HRV) based on time and frequency domain indexes, extracted using discrete wavelet transform. The knowledge induction algorithm was composed of two phases: rule generation and rule estimation. Firstly, a rule generation converts numerical attributes to intervals using ROC curve analysis and constructs a reduced ruleset by comparing consistency degree between attribute-value pairs with different decision values. Then, we estimated three measures such as rule support, confidence, and coverage to a probabilistic interpretation for each rule. To show the effectiveness of proposed model, we evaluated the statistical discriminant power of five rules (3 for atrial fibrillation, 1 for normal sinus rhythm, and 1 for both atrial fibrillation and normal sinus rhythm) generated using a data (n=58) collected from 1 channel wireless holter electrocardiogram (ECG), i.e., HeartCall$^{(R)}$, U-Heart Inc. The experimental result showed the performance of approximately 0.93 (93%) in terms of accuracy, sensitivity, specificity, and AUC measures, respectively.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.223-229
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• 2014

In an attempt to retrieve useful information for an efficient decision in the large knowledge system, it is generally necessary and important for a refined feature selection. Rough set has difficulty in generating optimal reducts and classifying boundary objects. In this paper, we propose quick reduction algorithm generating optimal features by rough entropy analysis for condition and decision attributes to improve these restrictions. We define a new conditional information entropy for efficient feature extraction and describe procedure of feature selection to classify the significance of features. Through the simulation of 5 datasets from UCI storage, we compare our feature selection approach based on rough set theory with the other selection theories. As the result, our modeling method is more efficient than the previous theories in classification accuracy for feature selection.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.232-241
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• 2000

This study suggests an integrated method based on concept hierarchies, information gain, and rough set theory for efficient discovery rules from a large amount of data, and implements an optimal rules discovery system. Our approach applies attribute-oriented concept ascension technique to extract generalized knowledge from a database, knowledge reduction technique to remove superfluous attributes and attribute values, and significance of attributes to induce optimal rules. The system first reduces the size of database by removing the duplicate tuples through the condition attributes which have no influences on the decision attributes, and finally induces simplified optimal rules by removing the superfluous attribute values by analyzing the dependency relationships among the attributes. And we induce some decision rules from actual data by using the system and test rules to new data, and evaluate that the rules are well suited to them.