• Genomics & Informatics
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• 제10권2호
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• pp.128-132
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• 2012

To learn the differences between the structure-activity relationship and molecular vibration-activity relationship in the ligand-receptor interaction of the histamine receptor, 47 ligands of the histamine receptor were analyzed by structural similarity and molecular vibrational frequency patterns. The radial tree that was produced by clustering analysis of molecular vibrational frequency patterns shows its potential for the functional classification of histamine receptor ligands.

• Biomaterials and Biomechanics in Bioengineering
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• 제2권3호
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• pp.173-183
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• 2015

In obstetrics, cardiotocography is a procedure to record the fetal heartbeat and the uterine contractions usually during the last trimester of pregnancy. It helps to monitor patterns associated with the fetal activity and to detect the pathologies. In this paper, random forest classifier is used to classify normal, suspicious and pathological patterns based on the features extracted from the cardiotocograms. The results showed that random forest classifier can detect these classes successfully with overall classification accuracy of 93.6%. Moreover, important features are identified to reduce the feature space. It is found that using seven important features, similar classification accuracy can be achieved by random forest classifier (93.3%).

• 간호행정학회지
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• 제3권2호
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• pp.123-136
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• 1997

The purpose of this study was to confirm the classification of nursing activity and to analyze the time of nursing workload in a new open hospital. The data were collected from 20 nurses working in 6 general nursing units by 4 trained observers. The tools used for this study were an observation recording sheet and a classification sheet of nursing activity. The classification sheet was constructed to be adaptable to each hospital system based on the instrument described in the literature. The results of the study are as follows : The direct nursing activities consisted of 6 sections, 33 subsections and the indirect nursing activities consisted of 14 sections, 53 subsections. The direct nursing activities included medication, measuring and observation, care of therapies, care of physical comfort, laboratory and treatment. The indirect nursing activities included preparation of medical utensils, collection of information and assessment, recording, phone communication, professional interaction related to patients, personal time, assigning work to staff, patient eaucation and training, interaction with lab, transfer of administration of utensils, checking physician's order, dietary service, management of pollution and contagion, guide direction. Nurses spent 127.6min for direct nursing activity during day duty. It was 24.5% of total nursing activity. Within that activity medication had the highest percentage of time(40.09%), followed by communication and education with patient(24.76%), measuring and observation (16.93%), laboratory and treatment (12.85%), care of therapies(3.21%) and care of physical comfort (2.16%). The time breakdown for indirect nursing activities is as follows ; the preparation of medical utensils 22.3%, collection of information and assessment 20.29%, recording 20.27%, phone communication 8.14%, professional interaction related to patients 7.33%, personal time 7.24%, with the remaining timeshared by staffing, patient education and training, interaction with lab, transfer of administration of utensils, checking physician's order, dietary service, management of pollution and contagion, guide direction. In the analysis of the relationships between the working time and the work allocation characters of the nurses(including nurse's experiences. nurse-patients ratio, nurse-rooms ratio, and character of nursing unit) ; There were no significant differences in direct-indirect nursing times between nurse's career years. There was significant difference in direct nursing time between assigned patient numbers. The nurses assigned larger number of patients spent significantly more time in direct nursing care than that of the smaller. On the other hand, there was no significant difference in indirect nursing workload between the assigned patient numbers. There were no significant differences in direct-indirect nursing time between an allocated patient's room numbers. There was significant difference in working time between working places. The nurse in the medical unit spent more time in direct nursing care than her counterpart in the surgical unit. However there was no difference in direct nursing time between two groups. The study results indicate that nurses spent less time in the direct nursing care than in the previous studies even though the hospital system has been modernized. On the other hand they spent much more time for the coordinating role within the interdisciplinary team and for the overlapping paperwork. Therefore it is recommended that patient oriented job description and more efficient usage of modernized utilities be made.

• 대한전자공학회논문지SP
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• 제45권4호
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• pp.100-106
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• 2008

본 논문에서는 다양한 잡음 환경에서 음성의 통계적 모델에 기반한 음성 검출기의 성능향상을 위해 PSFM (Power Spectral Flatness Measure)을 이용하여 실시간으로 변별적 가중치 학습 (Discriminative Weight Training) 기반의 최적화된 우도비 테스트 (Likelihood Ratio Test, LRT)를 제안한다. 먼저, 기존의 통계모델기반의 음성 검출기를 분석하고, 이를 기반으로 MCE (Minimum Classification Error)방법을 도입하여 도출한 각 주파수 채널별 가중치를 PSFM 값에 기반하여 실시간 매 프레임마다 다른 가중치를 적용한 우도비 기반의 음성 검출 결정법을 제시한다. 제안된 알고리즘은 다양한 잡음 환경에서 기존에 제시된 음성 검출기와 비교하였으며, 우수한 성능을 보인다.

• 대한원격탐사학회지
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• 제36권6_1호
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• pp.1303-1322
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• 2020

Indonesia is more prone to natural disasters due to its geological condition under the three main plates, making Indonesia experience frequent seismic activity, causing earthquakes, volcanic eruption, and tsunami. Those disasters could lead to other disasters such as landslides, floods, land subsidence, and coastal inundation. Monitoring those disasters could be essential to predict and prevent damage to the environment. We reviewed the application of remote sensing and Geographic Information System (GIS) for detecting natural disasters in the case of Indonesia, based on 43 articles. The remote sensing and GIS method will be focused on InSAR techniques, image classification, and susceptibility mapping. InSAR method has been used to monitor natural disasters affecting the deformation of the earth's surface in Indonesia, such as earthquakes, volcanic activity, and land subsidence. Monitoring landslides in Indonesia using InSAR techniques has not been found in many studies; hence it is crucial to monitor the unstable slope that leads to a landslide. Image classification techniques have been used to monitor pre-and post-natural disasters in Indonesia, such as earthquakes, tsunami, forest fires, and volcano eruptions. It has a lack of studies about the classification of flood damage in Indonesia. However, flood mapping was found in susceptibility maps, as many studies about the landslide susceptibility map in Indonesia have been conducted. However, a land subsidence susceptibility map was the one subject to be studied more to decrease land subsidence damage, considering many reported cases found about land subsidence frequently occur in several cities in Indonesia.

• 한국실내디자인학회논문집
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• 제15권2호
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• pp.138-149
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• 2006

A museum's collections is fundamental factors to construct important activity of museum performing a role as cultural facility for learning, education and research. Therefore, conservation of collections through appropriate environments is previously established in step of planing a museum. Hereby, the purpose of this study is to set up the classification of collections and suggest a useful guidance of the storage division in a domestic museum. The results of this study are as follows. First, the main factors of deterioration are temperature and relative humidity in a museum storage, so classification of collections should be set up according to the objective standards of these factors. Second, the classification of collections can be performed as follow: the group for nonorganic materials subdivide metal, chinaware, earthenware, and jade stone, the group for organic materials subdivide leather hair paper fabric, bone horn shell mound and wood herbage and the group for composed materials. Third, for storage division of a domestic museum, basically has to consider that it is reasonable to plan $4{\sim}5$ storages in metal, jade stone, chinaware earthenware, and organic materials of $1{\sim}2$ units in case of a serial of history like archaeological, antique museum. And in case of folk relics of modern and contemporary arts are collected, it is reasonable to plan over 5 storages add composed materials to foregoing classification.

• Journal of Electrical Engineering and Technology
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• 제9권3호
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.230-237
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• 2022

It is difficult to predict industrial accidents in the construction industry because many accident factors, such as human-related factors and environment-related factors, affect the accidents. Many studies have analyzed the severity of injuries and types of accidents; however, there were few studies on the prediction of injured body parts. This study aims to develop a classification model to predict the part of the injured body based on accident-related factors. Construction accident cases from June 2018 to July 2021 provided by the Korea Construction Safety Management Integrated Information were collected through web crawling and then preprocessed. A naïve Bayes classifier, one of the supervised learning algorithms, was employed to construct a classification model of the injured body part, which has four categories: 1) torso, 2) upper extremity, 3) head, and 4) lower extremity. The predictor variables are accident type, type of work, facility type, injury source, and activity type. As a result, the average accuracy for each injured body part was 50.4%. The accuracy of the upper extremity and lower extremity was relatively higher than the cases of the torso and head. Unlike the other classifications, such as spam mail filtering, a naïve Bayes classifier does not provide a good classification performance in construction accidents. The reasons are discussed in the study. Based on the results of this study, more detailed guidelines for construction safety management can be provided, which help establish safety measures at the construction site.

• 전기학회논문지
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• 제56권7호
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• pp.1324-1330
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• 2007

In this paper, we propose pattern recognition algorithm for activities of daily living by adopting extreme learning machine based on single layer feedforward networks(SLFNs) to the signal from bidirectional accelerometer. For activity classification, 20 persons are participated and we acquire 6, types of signals at standing, walking, running, sitting, lying, and falling. Then, we design input vector using reduced model for ELM input. In ELM classification results, we can find accuracy change by increasing the number of hidden neurons. As a result, we find the accuracy is increased by increasing the number of hidden neuron. ELM is able to classify more than 80 % accuracy for experimental data set when the number of hidden is more than 20.

• Bulletin of the Korean Chemical Society
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• 제30권11호
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• pp.2717-2722
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• 2009

The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-activity relationship (QSAR) context on a data set consisting of the binding affinities of 39 imidazobenzodiazepines for the α1 benzodiazepine receptor. The 3-D structures of these compounds were optimized using HyperChem software with semiempirical AM1 optimization method. After optimization a set of 1481 zero-to three-dimentional descriptors was calculated for each molecule in the data set. The response (dependent variable) in the tree model consisted of the binding affinities of drugs. Three descriptors (two topological and one 3D-Morse descriptors) were applied in the final tree structure to describe the binding affinities. The mean relative error percent for the data set is 3.20%, compared with a previous model with mean relative error percent of 6.63%. To evaluate the predictive power of CART cross validation method was also performed.