• Journal of architectural history
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• v.22 no.3
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• pp.37-48
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• 2013

The purpose of this study is to evaluate Aldo Rossi's work as representation or non-representation, for some hold that the works of Aldo Rossi are representative and others say that they are non-representative. According to the three kinds of novelties appearing after Stephen Pepper's concept, "the breaking of reference" happens, and Aldo Rossi's concept, "the sense of deposition" Rossi's work is uncovered as non-representation. In order to clarify Rossi's work as non-representation, we are going to borrow Pepper's terms, intrusive novelty, emergent novelty, and naive novelty. The breaking of reference accompanies intrusive novelty to bring a sense of representation, emergent novelty to intuit a sense of non-representation, and naive novelty to a sense of newness of disorder. We hope to verify a hypothesis that Aldo Rossi's architectural thought and the architecture come from 'emergent novelty' on the basis of his two books, A Scientific Autobiography and Architecture of the City. Also this paper discusses qualitative aspects rather than visual aspects. The main concepts of emergent novelty are applied to Aldo Rossi's works and his thought. Finally this paper verifies 'the hypothesis' through revealing what Aldo Rossi means by the quality of suspension, the sense of deposition, and idea of the unfinished(repetition). Rossi's work is not textural reference of representation appearing after blocking, but qualitative reference of non-representation.

• Language and Information
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• v.10 no.1
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• pp.21-46
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• 2006

This study proposes a Word Sense Disambiguation (WSD) algorithm, based on concept learning with special emphasis on statistically meaningful lowest frequency words. Previous works on WSD typically make use of frequency of collocation and its probability. Such probability based WSD approaches tend to ignore the lowest frequency words which could be meaningful in the context. In this paper, we show an algorithm to extract and make use of the meaningful lowest frequency words in WSD. Learning method is adopted from the Find-Specific algorithm of Mitchell (1997), according to which the search proceeds from the specific predefined hypothetical spaces to the general ones. In our model, this algorithm is used to find contexts with the most specific classifiers and then moves to the more general ones. We build up small seed data and apply those data to the relatively large test data. Following the algorithm in Yarowsky (1995), the classified test data are exhaustively included in the seed data, thus expanding the seed data. However, this might result in lots of noise in the seed data. Thus we introduce the 'maximum a posterior hypothesis' based on the Bayes' assumption to validate the noise status of the new seed data. We use the Naive Bayes Classifier and prove that the application of Find-Specific algorithm enhances the correctness of WSD.

• Journal of Korean Elementary Science Education
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• v.32 no.2
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• pp.139-158
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• 2013

This study investigated elementary students' (grade 4~6) learning progressions for water cycling drawn from iterative assessments using ordered multiple-choice (OMC) items. An assessment system, which consisted of construct map, item design, outcome space, and measurement model, was employed in this study to examine children's learning progressions. At the first stage of the assessment system, a construct map was designed on which children's conceptual understandings from naive to most sophisticated were represented. At the item design stage, 8 OMC items were drawn from the construct map. Each item option of the OMC items was scored from 0 to 3 according to its level of understanding at the stage of outcome space. As a measurement model, Rasch model, a branch of item response theory, was applied to interpreting the outcomes of the OMC items. This cycle of assessment system was furtherly implemented iteratively in order to elaborate on the first version of water cycling learning progression. In conclusion, children's understanding of water cycling could be described in two aspects: water distribution and water movement. We identified children's conjectural developmental pathways about water cycling existed from superficial and naive accounts to more complex and abstract accounts.

• The Journal of Korean Medicine
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• v.30 no.6
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• pp.103-111
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• 2009

Objectives: To investigate the possibility of GamiSamgieum (SGMX) as a combination therapy with statins on hyperlipidemia using an animal model. Methods: Forty eight ICR mice (male) were divided into six groups of eight mice each: naive, induced, Lipitor 5 mg/kg, Lipitor 5 mg/kg plus SGMX 100 mg/kg, Lipitor 10 mg/kg, and Lipitor 10 mg/kg plus SGMX mg/kg treatment group. Hyperlipidemia was induced by feeding a purified high fat diet for all groups (except naive) along with treatment of drugs for 6 weeks, and then biological parameters were examined on the last experiential day. Results: Lipitor treatment lowered total cholesterol and increased HDL-cholesterol compared to the induced group with no statistical significance. However, co-treatment of SGMX with Lipitor revealed synergic effects on total cholesterol and HDL-cholesterol significantly (P < 0.05) in both. SGMX co-treatment also significantly protected liver tissues from the oxidative stress in liver tissues (P < 0.05) and augmented inhibitory effect of Lipitor against fat accumulation in the body. Conclusion: These results indicate the possibility of that SGMX can be used for patients having hyperlipidemia as a combination therapy with statin drugs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6009-6027
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• 2019

Support vector machine (SVM) classifiers have been widely used for object detection. These methods usually locate the object by finding the region with maximal score in an image. With bag-of-features representation, the SVM score of an image region can be written as the sum of its inside feature-weights. As a result, the searching process can be executed efficiently by using strategies such as branch-and-bound. However, the feature-weight derived by optimizing region classification cannot really reveal the category knowledge of a feature-point, which could cause bad localization. In this paper, we represent a region in an image by a collection of local feature-points and determine the object by the region with the maximum posterior probability of belonging to the object class. Based on the Bayes' theorem and Naive-Bayes assumptions, the posterior probability is reformulated as the sum of feature-scores. The feature-score is manifested in the form of the logarithm of a probability ratio. Instead of estimating the numerator and denominator probabilities separately, we readily employ the density ratio estimation techniques directly, and overcome the above limitation. Experiments on a car dataset and PASCAL VOC 2007 dataset validated the effectiveness of our method compared to the baselines. In addition, the performance can be further improved by taking advantage of the recently developed deep convolutional neural network features.

• Journal of The Korean Association For Science Education
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• v.17 no.4
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• pp.383-393
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• 1997

In order to study the effect of student's conceptions about force and motion into the graph construction in kinematics in college physics course, the tasks of constructing the qualitative graph in the similar problem context used in force conception was asked to the first 74 and third 97 student teacher in teachers' university. The frequencies analysis showed that student teachers had the naive conceptions that the throwing force was still acted to a upwarding ball. They also had the popular Aristotelian views about motion. These naive conceptions coexisted with the scientific conception about gravitational force. In a simple pendulum problem no one had the correct acceleration concepts which varies the direction in swing. This result suggest that student teacher had more difficulties in a acceleration problem than in a velocity problem In v-t and a-t graph construction tasks, the number of categories of a-t graphs were more than that of v-t graphs. There were many graph errors in a sign of velocity and acceleration. The acceleration conceptions without the relations of changes in velocity made the kinematics graphs more various shapes. The force and motion conceptions influenced the ability to construct the kinematics graphs.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.10
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• pp.901-906
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• 2013

Chaff is a kind of matter spreading atmosphere with the purpose of preventing aircraft from detecting by radar. The chaff is commonly composed of small aluminum pieces, metallized glass fiber, or other lightweight strips which consists of reflecting materials. The chaff usually appears on the radar images as narrow bands shape of highly reflective echoes. And the chaff echo has similar characteristics to precipitation echo, and it interrupts weather forecasting process and makes forecasting accuracy low. In this paper, the chaff echo recognizing and removing method is suggested using Bayesian network. After converting coordinates from spherical to Cartesian in UF (Universal Format) radar data file, the characteristics of echoes are extracted by spatial and temporal clustering. And using the data, as a result of spatial and temporal clustering, a classification process for analyzing is performed. Finally, the inference system using Bayesian network is applied. As a result of experiments with actual radar data in real chaff echo appearing case, it is confirmed that Bayesian network can distinguish between chaff echo and non-chaff echo.

• The Journal of Bigdata
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• v.1 no.1
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• pp.33-39
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• 2016

Recently, predicting personality with the help of smartphone usage becomes very interesting and attention grabbing topic in the field of research. At present there are some approaches towards detecting a user's personality which uses the smartphones usage data, such as call detail records (CDRs), the usage of short message services (SMSs) and the usage of social networking services application. In this paper, we focus on the assessing the correlation between MBTI based user personality and the smartphone usage data. We used $Na{\ddot{i}}ve$ Bayes and SVM classifier for classifying user personalities by extracting some features from smartphone usage data. From analysis it is observed that, among all extracted features facebook usage log working as the best feature for classification of introverts and extraverts; and SVM classifier works well as compared to $Na{\ddot{i}}ve$ Bayes.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.65-68
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• 2012

본 논문에서는 최근 활발히 연구가 진행되고 있는 행위인식 연구 분야 중에서 스마트폰 환경에서의 개인화된 행위 인식기 및 로거를 제안한다. 최근 스마트폰의 보급이 활발해지면서 행위 인식 연구 분야에서 스마트폰을 이용하는 연구가 활발히 진행되고 있다. 그러나 스마트폰에서는 센서를 이용하여 행위정보를 수집하고, 서버에서 는 분류 및 처리하는 방식으로 실시간 인식과 개발자에 의한 트레이닝으로 인해 개인화된 트레이닝이 불가능하다는 단점이 있다. 이러한 단점을 극복하고자 Naive Bayes Classifier를 사용하여 스마트폰 환경에서 실시간으로 사용자 행위 수집이 가능하고 행위정보의 분류 및 처리가 가능한 경량화 및 개인화된 행위 인식기 및 로거의 구현을 목적으로 한다. 제안하는 방법은 행위 인식기를 통해 행위 인식이 가능할 뿐만 아니라 로거를 통해 사용자의 라이프로그, 라이프패턴 등의 연구 분야에 이용이 가능하다.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.488-497
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• 2004

This paper describes the hybrid document summarization using the indicative summarization and the query-based summarization. The learning models are built from teaming documents in order to extract topic phrases. We use Naive Bayesian, Decision Tree and Supported Vector Machine as the machine learning algorithm. The system extracts topic phrases automatically from new document based on these models and outputs the summary of the document using query-based summarization which considers the extracted topic phrases as queries and calculates the locality-based similarity of each topic phrase. We examine how the topic phrases affect the summarization and how many phrases are proper to summarization. Then, we evaluate the extracted summary by comparing with manual summary, and we also compare our summarization system with summarization mettled from MS-Word.