• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.435-437
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• 2001

The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Besides, it is required the index that consolidate the price volatility experienced on the power exchange with gaming and strategic bidding by suppliers to increase profits. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads, the price-sensitive load model is needed. And the development of state-of-the-art technologies affects the electricity price, so the diffusion of high-efficient end-uses and these price affect load patterns. This paper shows the analysis on learning curves algorithms which is used to investigate the correlation of the end-uses' price and load patterns.

• The Mathematical Education
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• v.53 no.3
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• pp.435-447
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• 2014

Recently, the interdisciplinary integration and story-telling are often mentioned in mathematics education. It is probably because they might be helpful to students for positive attitude for mathematics. In this research, through brief discussion mathematics related with wallpaper patterns, we try to integrate mathematics and design, and eventually develop the teaching/learning materials for experience activities and story-telling.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.11
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• pp.56-65
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• 1993

This paper presents the design consideration of the MFNNs(Multilayer Feed forward Neural Networks) based on the distribution of the given teching patterns. By extracting the feature points from the given teaching patterns, the structure of a network including the netowrk size and interconnection weights of a network is initialized. This network is trained based on the modified version of the EBP(Error Back Propagation) algorithm. As a result, the proposed method has the advantage of learning speed compared to the conventional learning of the MFNNs with randomly chosen initial weights. To show the effectiveness of the suggested approach, the simulation result on the approximation of a two demensional continuous function is shown.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.192-198
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• 2021

Trustless, depression, happiness is a normal human emotion that everyone experiences at times. People face problems and hard circumstances every day due to an environment, social life, or traumatic developments in their lives. This study focused on a particular type of inconsistency patterns of behavior that experiences' students during the school time. Some students find depression interferes with their learning and test taking to such an extent that their grades are seriously affected. This study examined the awareness and readiness of a sample of Saudi Arabian high school teachers to recognize, understand, and respond to the ways in which students may respond to testing situations with depression. Findings suggest teachers learn from experience to use both direct and indirect ways to identify students with depression; employ test preparation and test taking strategies to help students reduce depression; and reach out to parents for additional assistance where teacher strategies are not sufficient.

• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.105-114
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• 2005

This paper describes a new measuring technique by analysing multiple time-series patterns. This paper's goal is that extracts a really measured value having a sample pattern which is the best matched with an inputted time-series, and calculates a difference ratio with the value. Therefore, the proposed technique is not a recognition but a measurement. and not a hardware but a software. The proposed technique is consisted of three stages, initialization, learning and measurement. In the initialization stage, it decides weights of all parameters using importance given by an operator. In the learning stage, it classifies sample patterns using LBG and DTW algorithm, and then creates code sequences for all the patterns. In the measurement stage, it creates a code sequence for an inputted time-series pattern, finds samples having the same code sequence by hashing, and then selects the best matched sample. Finally it outputs the really measured value with the sample and the difference ratio. For the purpose of performance evaluation, we tested on multiple time-series patterns obtained from etching machine which is a semiconductor manufacturing.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.432-439
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• 2020

This study aims at exploring features of Korean university students' written text, focusing on the written voice, rhetorical patterns, and writing practices through English letters. The data comprised examples of students' English job applications, and a 'purpose-will' model was adopted for the data analysis. The findings showed that the students used unique ways of strategies to convey their voice in a recontextualized setting. Their written voice in the job applications were various, and nobody applied the Korean convention of weather opening. Their rhetorical patterns were a transformation from convergence to divergence, showing integrated patterns of written voice. Students' writing practices revealed their internal values of writing for a task, and they do not directly learn from the teacher's syllabus. This supports the sociocultural framework that learning is a situated activity in a specific discourse community. The study concludes that writing teachers should understand that life-world and learning experience can impact on students' written voice and practices.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.521-525
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• 2009

In this paper, we introduce fire detection system and software learning algorithm that recognize fire patterns. Flame patterns means that periodical and consistent pattern about general conception of fire, and to process it with the definition. Learning algorithm for flame pattern recognition that we propose is the method which is faster and more exactly than existing algorithm. Also, we trying to elicit the method through experiment result and by applying it, we show the validity of an early fire warning system.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.4
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• pp.384-394
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• 2020

This paper proposes an outlier detection model based on machine learning that can diagnose the presence or absence of major engine parts through unsupervised learning analysis of main engine big data of a ship. Engine big data of the ship was collected for more than seven months, and expert knowledge and correlation analysis were performed to select features that are closely related to the operation of the main engine. For unsupervised learning analysis, ensemble model wherein many predictive models are strategically combined to increase the model performance, is used for anomaly detection. As a result, the proposed model successfully detected the anomalous engine status from the normal status. To validate our approach, clustering analysis was conducted to find out the different patterns of anomalies the anomalous point. By examining distribution of each cluster, we could successfully find the patterns of anomalies.

• Journal of Korean Elementary Science Education
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• v.27 no.3
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• pp.211-220
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• 2008

This study examined pre-service science teachers' perceptions of informal learning by adopting a discourse analysis method suggested by Mortimer and Scott(2003). The guiding research questions were: (1) What are some general patterns of the discourse occurring in science museums between a teacher and a student? (2) In what ways do the pre-service teachers perceive informal learning and teacher's role in informal settings? The 7 pre-service science teachers participated in this study. Each of them shepherd an elementary student around the museum and implemented their own instruction using a pre-planed lesson plan. Results indicated that even though the teachers had learned some characteristics of informal teaming in their college courses, they tended to implement their traditional view of science teaming into the instruction and the view affected them to set up their teaching purposes and contents, and to select communicative approach, patterns of discourse and ways of intervention.

• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.1-8
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• 2016

In the field of Radiology, the Computer Aided Diagnosis is the technology which gives valuable information for surgical purpose. For its importance, several computer vison methods are processed to obtain useful information of images acquired from the imaging devices such as X-ray, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). These methods, called pattern recognition, extract features from images and feed them to some machine learning algorithm to find out meaningful patterns. Then the learned machine is then used for exploring patterns from unseen images. The radiologist can therefore easily find the information used for surgical planning or diagnosis of a patient through the Computer Aided Diagnosis. In this paper, we present a review on three widely-used methods applied to Computer Aided Diagnosis. The first one is the image processing methods which enhance meaningful information such as edge and remove the noise. Based on the improved image quality, we explain the second method called segmentation which separates the image into a set of regions. The separated regions such as bone, tissue, organs are then delivered to machine learning algorithms to extract representative information. We expect that this paper gives readers basic knowledges of the Computer Aided Diagnosis and intuition about computer vision methods applied in this area.