• The Journal of the Acoustical Society of Korea
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• v.26 no.5
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• pp.194-198
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• 2007

In this paper, we apply a discriminative weight training to a statistical model-based voice activity detection(VAD). In our approach, the VAD decision rule is expressed as the geometric mean of optimally weighted likelihood ratios(LRs) based on a minimum classification error(MCE) method which is different from the previous works in that different weights are assigned to each frequency bin which is considered more realistic. According to the experimental results, the proposed approach is found to be effective for the statistical model-based VAD using the LR test.

• International Commerce and Information Review
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• v.6 no.3
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• pp.41-61
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• 2004

Today, most companies are needed to become interested on e-Biz and information management system. Especially, Data classification format system was very important for application to effective and efficiency management decision support. They should include main entry which consists of department, employee's name, title, publication date. Now, each company is using eleven different methods on data classification format system. In this paper finding result was as follows, in other words, general management document case using the nine date classification methods and special report management document ca se using the twodata classification methods. The aim of this study is to investigate problems that the present data classification format system has and some concerns that should be taken into account in case of the modification of the data classification system and change into a new one. This study is based on the survey in that the company managergave to 35 companies throughout the nation. As a result, the survey indicates that the crucial concerns of the participating managers are ineffective management information source and the duplication of data classification systems. This paper is the transcendental study the introduction of data classification format systems to business companies in Korea. This paper provided the fundamental data for the effective business process reengineering in business activity for management information.

• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.40-48
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• 2006

The purpose of this study was to investigate 6th grade pupil's thoughts during classification activities. Two suitable tools in classification activity achievement were developed to achieve this purpose. The first was an artificial stimulus card in which the attribute was prominent; and the other a natural stimulus card in which the attribute was less prominent. Participants of the study were 8 6th grade pupils from D elementary school in Yeongdeungpo-gu, Seoul. Data were collected from interviews with the pupils, the pupil's recordings of classification, the investigator's observation of pupil's actions, and video recordings of the pupil's subject classification process. Results found in this study were as following. First, when doing classification 6th grade pupils considered attribute observation, attribute estimation, preliminary inspection, criteria selection, and sample identification. Second, 6th grade pupil classification thought process was found to be repetitive, passing through the steps of attribute observation, attribute estimation, preliminary inspection, criteria selection, and lastly, sample identification. Third, 6th grade pupils took advantage of cognitive economic efficiency. Study findings also revealed guidance for the teaching and learning of scientific classification. First, once teachers understand the classification thought process of students, more effective classification guidance will be possible. Second, it is necessary that guidance fit each step of the classification thought process.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.39-45
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• 2022

For national industrial development in the Fourth Industrial Revolution, it is necessary to provide researchers with appropriate job information. This can be achieved by interconnecting the National Science and Technology Standard Classification System used for management of research activity with the Korean Employment Classification of Occupations used for job information management. In the present study, an automatic linkage model of classification systems is introduced based on a pre-trained language model for interconnecting science and technology information with job information. We propose for the first time an automatic model for linkage of classification systems. Our model effectively maps similar classes between the National Science & Technology Standard Classification System and Korean Employment Classification of Occupations. Moreover, the model increases interconnection performance by considering hierarchical features of classification systems. Experimental results show that precision and recall of the proposed model are about 0.82 and 0.84, respectively.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.127-135
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• 2020

The purpose of this study is to develop a human activity recognition (HAR) system, which distinguishes 13 activities, including five activities commonly dealt with in conventional HAR researches and eight activities from the Eastern-Asian culture. The eight special activities include floor-sitting/standing, chair-sitting/standing, floor-lying/up, and bed-lying/up. We used a 3-axis accelerometer sensor on the wrist for data collection and designed a machine learning model for the activity classification. Data clustering through preprocessing and feature extraction/reduction is performed. We then tested six machine learning algorithms for recognition accuracy comparison. As a result, we have achieved an average accuracy of 99.7% for the 13 activities. This result is far better than the average accuracy of current HAR researches based on a smartwatch (89.4%). The superiority of the HAR system developed in this study is proven because we have achieved 98.7% accuracy with publically available 'pamap2' dataset of 12 activities, whose conventionally met the best accuracy is 96.6%.

• Journal of Korea Game Society
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• v.16 no.6
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• pp.7-18
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• 2016

This study was conducted to construct a program combining play and laughing activities with personality education. Self-recorded questionnaire was administered to investigate popularity of play(playing rules area, traditional play area, board game area), the laughing activity, digital games preference among 5th and 6th grade students. Most popular activities was board game area, followed by rule play area, traditional play area, and laughing activity in order. Group play among the rule play area, and Yutnory among traditional play, and digital games among board games were most preferred. This study suggest primitive classification and characterization of play and activities among senior elementary students. Further study for define classification of other eligible play and activities may be encouraged to establis high quality play and activity programs among elementary school students.

• Journal of Korean Academy of Nursing Administration
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• v.14 no.2
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• pp.118-130
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• 2008

Purpose: The purpose of this study was to estimate of home-visiting nursing costs for low-income elderly with chronic disease in a metropolitan city using the severity classification and ABC(active-based costing). Methods: First, the HHC activity pool was established. The performance time of each nursing activity were estimated. Second, nursing resources(labor costs, operating costs, and traffic expenses) were analyzed and nursing cost per minute was calculated. And then the cost of each activity was estimated. Third, 202 visiting cases were classified into three group by their severity. And then nursing cost per visit according to their severity was estimated. Results: 59 nursing activities were included in HHC activity pool. The average working time of 59 nursing activity was 6.7minutes and nursing cost per minute was 489 won. According severity, nursing cost per visit were in class I, 54,296 (won), class II 83,124(won), and class III 93,455(won).

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.87-92
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• 2014

In this paper, we present a two-phase activity recognition system using smartphone's accelerometers. To consider the unique temporal pattern of accelerometer data for each activity, our system executes the decision-tree(DT) learning in the first phase, and then, in the second phase, executes the hidden Markov model(HMM) learning based on the sequences of classification results of the first phase classifier. Moreover, to build a robust recognizer for each activity, we trained our system using a large amount of data collected from different users, different positions and orientations of smartphone. Through experiments using 6720 examples collected for 6 different indoor activities, our system showed high performance based on its novel design.

• Journal of Biomedical Engineering Research
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• v.33 no.1
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• pp.15-24
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• 2012

Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (${\pm}9.64%$) was attained for the proposed method; whereas an average accuracy of 57% (${\pm}5.68%$) was attained for the conventional method (significance was assessed by the one-tail paired $t$-test, $p$ < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.

• Journal of Korean Clinical Nursing Research
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• v.24 no.1
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• pp.113-126
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• 2018

Purpose: The purpose of this study was to revise the KPCS-1 and to standardize the three patient classification systems for general ward, ICU and NICU. The actual utilization of the KPCS-1 score and each nursing activity was evaluated and the relationships between KPCS-1 score and nursing related variables were reviewed. Methods: The 47,711 KPCS-1 scores of 6,931 patients who discharged from $1^{st}$ to $30^{th}$ April 2017 were analyzed and the statistical significance between KPCS-1 score and nursing related variables was reviewed by Generalized Estimating Equation. The revision of the KPCS-1 was carried out by Partial Least Square model. The 3 patient classification systems (KPCS-1,KPCSC and KPCSN) were standardized by professional reviews. Results: KPCS-1 was a valid instrument to express nursing condition adequately and was revised as a new version which has 34 nursing activity items. The names and terminologies of pre-existing 3 patient classification systems developed by KHNA were standardized as KPCS-GW, KPCS-ICU, KPCS-NICU. Conclusion: KPCS-1 was a valid instrument to represent diverse nursing conditions precisely and was revised as a 34-item KPCS-GW. The terminologies of the other patient classification systems by KHNA were standardized as KPCS-ICU and KPCS-NICU.