• Journal of Information Processing Systems
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• 제20권3호
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• pp.296-306
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• 2024

Traditional ecological driving (Eco-Driving) evaluations often rely on mathematical models that predominantly offer subjective insights, which limits their application in real-world scenarios. This study develops a robust, data-driven Eco-Driving evaluation model by integrating dynamic and distributed multi-source data, including vehicle performance, road conditions, and the driving environment. The model employs a combination weighting method alongside K-means clustering to facilitate a nuanced comparative analysis of Eco-Driving behaviors across vehicles with identical energy consumption profiles. Extensive data validation confirms that the proposed model is capable of assessing Eco-Driving practices across diverse vehicles, roads, and environmental conditions, thereby ensuring more objective, comprehensive, and equitable results.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.1255-1258
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• 1998

Hidden Markov Model (HMM) is the most widely used method in speech recognition. In general, HMM parameters are trained to have maximum likelihood (ML) for training data. This method doesn't take account of discrimination to other words. To complement this problem, this paper proposes a word verification method by re-recognition of the recognized word and its similar word using the discriminative function between two words. The similar word is selected by calculating the probability of other words to each HMM. The recognizer haveing discrimination to each word is realized using the weighting to each state and the weighting is calculated by genetic algorithm.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문초록집
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• pp.334.2-334
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• 2002

In conventional model updating using single-objective optimization techniques, imcompatible physical data are compared with each other using weighting factors. There are no general rules fur selecting the weighting factors since they are not directly related with the dynamic behavior of an updated model. So one of the most difficult tasks, in mr)del updating study, is 'balancing among the correlations', i.e. 'trade-off'. (omitted)

• 한국조사연구학회지:조사연구
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• 제2권1호
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• pp.73-86
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• 2001

본 논문은 복합표본조사 분석에서 회귀모형 접근법으로 사용되는 모형 기반 접근법, 설계 기반 접근법과 일반화 추정 방정식 접근법을 설명하고, 이들을 실증적으로 비교한 것이다. 또한 설계 기반 접근법과 일반화 추정 방정식 접근법에 대해서 설계효과와 가중치 효과 분석을 통해서 표본 설계가 모수 추정에 미치는 영향을 살펴보았다.

• Animal cells and systems
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• 제1권2호
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• pp.305-311
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• 1997

Dermaptera was investigated, examined and reviewed in taxonomy and for distribution in Youngnam region. Based on the data, the local species groups were measured to choose priority-conservation-area by taxonomic root weighting and distribution analysis at 232 geographical conservation units. Eleven species belonging to 4 families and 8 genera were recorded mounting up to 68.75% of species diversity known in Korea. Found remarkably were the rare and endangered Challia fletcheri Burr at Sobaek Mountain National Park, and unusually Anisolabis maritima (Bonelli) in Taegu, Euborellia pallipes (Shiraki) at Island Geoje and E. plebeja (Dohrn) at Hwanho near Pohang. The highest species diversity was found at the temple Huibang area at Sobaek Mountain National Park with 8 species, which was measured also as the primary priority-conservation-area with 83.41 % of accumulated taxonomic root weighting indices in percentage. Geoje and Hwanho both measured as 12.18% of accumulated taxonomic root weighting index in percentage and complimentary to Sobaek Mountain National Park but supporting 5 and 3 species, respectively. The priority goes to the geographical conservation unit supporting higher species richness between two geographical conservation units in comparison. By the rule, the second priority-conservation-area should be Geoje and the third Hwanho. It is, thus, demonstrated how 11 species can be all conserved by choosing 3 priority-conservation-areas out of 232 geographical conservation units to maintain maximum species in minimum areas.

• 한국조사연구학회지:조사연구
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• 제5권1호
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• pp.1-26
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• 2004

The aim of this study lies in answering the question "How to form weighting cells to enhance sample representativeness in telephone, Internet and mobile surveys\ulcorner". For this, we explored 2% raw data of Year 2000 Population and Housing Census of Korea looking for meaningful patterns for ownership of telephones, the usage of Internet and/or mobile phones. We found that telephone coverage rates vary significantly by household size; 84.6% for one member households, contrasting 98.5% for two-or-more member households. Thus, telephone survey samples need to be weighted differently in sub-groups by household size for proportional representation of target population. Searching socio-demographic factors influencing the use of Internet by C5.0 tree models, we found that education levels and the occupation (or housing type, the automobile ownership) are two most important factors in addition to gender and age. Thus, surveyor might form weighting cells by such factors at the stage of post-stratification or set quotas, a priori, proportional to size of the cells by such factors. For mobile surveys, we approached similarly and found that education levels and the occupation (or the automobile ownership, marriage status) are two additional factors that may be used in forming weighing cells or in setting quotas for cells.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.77-80
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• 2000

In this paper, the speaker-recognition process based on both DTW and discrete HMM was performed using the method to evaluate state-dependent parameter weighting from training data so as the personal audio-characteristics are to be well reflected. In the suggested method below, we found the optimal state sequence using the Viterbi algorithm. The optimal path could be evaluated after comparing the sequence of base pattern which already have, with that of the other patterns. After that the frame of which the pattern was matched with the base pattern in the same state are to be found so that the reference pattern can be gained by weighting on the numbers of matched frames.

• 대한음성학회지:말소리
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• 제46호
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• pp.117-126
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• 2003

This paper presents our language model adaptation for Korean spontaneous speech recognition. Korean spontaneous speech is observed various characteristics of content and style such as filled pauses, word omission, and contraction as compared with the written text corpus. Our approaches focus on improving the estimation of domain-dependent n-gram models by relevance weighting out-of-domain text data, where style is represented by n-gram based tf/sup */idf similarity. In addition to relevance weighting, we use disfluencies as Predictor to the neighboring words. The best result reduces 9.7% word error rate relatively and shows that n-gram based relevance weighting reflects style difference greatly and disfluencies are good predictor also.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.295-300
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• 2002

In conventional model updating using single-objective optimization techniques, incompatible physical data are compared with each other using weighting factors. There are no general rules for selecting the weighting factors since they are not directly related with the dynamic behavior of an updated model. So one of the most difficult tasks, in model updating study, is 'balancing among the correlations' i.e. 'trade-off'. In this work, a multiobjecitive optimization technique called 'satisficing trade-off method' is introduced to extremize several correlations simultaneously. The absurd need for the weighting factors can be avoided using this technique. And the updated model with the most appropriate correlations is obtained easily in interactive way. Especially automatic trade-off is employed to increase the rate of convergence to the desired model. Its effectiveness is verified by application to a real engineering problem, HDD cover model updating.

• Communications for Statistical Applications and Methods
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• 제20권6호
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• pp.455-466
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• 2013

In nested case-control studies, the most common way to make inference under a proportional hazards model is the conditional logistic approach of Thomas (1977). Inclusion probability methods are more efficient than the conditional logistic approach of Thomas; however, the epidemiology research community has not accepted the methods as a replacement of the Thomas' method. This paper promotes the inverse probability weighting method originally proposed by Samuelsen (1997) in combination with an approximate jackknife standard error that can be easily computed using existing software. Simulation studies demonstrate that this approach yields valid type 1 errors and greater powers than the conditional logistic approach in nested case-control designs across various sample sizes and magnitudes of the hazard ratios. A generalization of the method is also made to incorporate additional matching and the stratified Cox model. The proposed method is illustrated with data from a cohort of children with Wilm's tumor to study the association between histological signatures and relapses.