Interneuron diversity is one of the key factors to hinder understanding the mechanism of cortical neural network functions even with their important roles. We characterized inhibitory interneurons in layer II/III of the rat primary visual cortex, using patch-clamp recording and confocal reconstruction, and classified inhibitory interneurons into fast spiking (FS), late spiking (LS), burst spiking (BS), and regular spiking non-pyramidal (RSNP) neurons according to their electrophysiological characteristics. Global parameters to identify inhibitory interneurons were resting membrane potential (>-70 mV) and action potential (AP) width (<0.9 msec at half amplitude). FS could be differentiated from LS, based on smaller amplitude of the AP (<∼50 mV) and shorter peak-to-trough time (P-T time) of the afterhyperpolarization (<4 msec). In addition to the shorter AP width, RSNP had the higher input resistance (>200 $M{Omega}$) and the shorter P-T time (<20 msec) than those of regular spiking pyramidal neurons. Confocal reconstruction of recorded cells revealed characteristic morphology of each subtype of inhibitory interneurons. Thus, our results provide at least four subtypes of inhibitory interneurons in layer II/III of the rat primary visual cortex and a classification scheme of inhibitory interneurons.
Background: Nursing service is a nonroutine work with an excessive physical load and diverse tasks. This study derived representative common tasks based on the frequently occurring tasks with a high physical load in the nursing workers' daily work and developed indicators to evaluate the work risk by reflecting the characteristics of nonroutine work. Methods: Common tasks were classified through the following stages: literature review, first focus group interview (FGI) with experts, first classification of common tasks, second FGI with hospital health managers, a survey of nursing service workers, and the final classification of common tasks for each task type. To develop an objective risk index for physical load assessment, we investigated the frequency and duration of the derived common tasks via survey. Results: Nursing common tasks were categorized into six task types and 56 subtasks. To evaluate the risks of various tasks in nonroutine works, three frequencies and three working time levels were defined by examining the task frequency and working hours. Exposure time was defined to reflect the characteristics of a nonroutine job. The final risk assessment was the product of the exposure time level and job intensity level. From this, four risk action levels were derived. Conclusion: This study has the advantage of solving the problem of focusing on some tasks in evaluating the physical load. It was meaningful in that a new risk assessment index based on exposure time was proposed based on the development of an evaluation scale for frequency and time by reflecting the characteristics of nonroutine work.
This paper presents efficient models for bridge structures using CART-ANFIS (classification and regression tree-adaptive neuro fuzzy inference system). A fuzzy decision tree partitions the input space of a data set into mutually exclusive regions, each region is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it continuous and smooth everywhere. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.
Park, Jungchul;Baek, Jong-Bae;Lee, Jun-won;Lee, Jin-woo;Yang, Seung-hyuk
Journal of the Korean Society of Safety
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v.33
no.1
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pp.66-72
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2018
This study analyses the types, related operations, facilities, and causes of chemical accidents in Korea based on the RISCAD classification taxonomy. In addition, human error analysis was carried out employing different human error classification criteria. Explosion and fire were major accident types, and nearly half of the accidents occurred during maintenance operation. In terms of related facility, storage devices and separators were the two most frequently involved ones. Results of the human error-based analysis showed that latent human errors in management level are involved in many accidents as well as active errors in the field level. Action errors related to unsafe behavior leads to accidents more often compared with the checking behavior. In particular, actions missed and inappropriate actions were major problems among the unsafe behaviors, which implicates that the compliance with the work procedure should be emphasized through education/training for the workers and the establishment of safety culture. According to the analysis of the causes of the human error, the frequency of skill-based mistakes leading to accidents were significantly lower than that of rule-based and knowledge based mistakes. However, there was limitation in the analysis of the root causes due to limited information in the accident investigation report. To solve this, it is suggested to adopt advanced accident investigation system including the establishment of independent organization and improvement in regulation.
In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.
So far, the behaviors of Web users have been predicted or analyzed mostly by their demographic characteristics or by considering in which context they gain access to that. But now there is a question about whether those characteristics are the only factors to trigger their use of Web. If the answer is not affirmative, what types of additional factors could cause such an action and how they characterize it should be discussed. User profile information has been considered one of the crucial elements to define user characteristics in user-centered UI design sector, and in order to apply it to UI design, it's needed to meditate on the above-mentioned questions. In this study, it's first attempted to have a good understanding of the users of different media and to review existing user classification methods. Next, user classification variables and relevant scales were prepared to sort out users according to their type of using Web, and case study was conducted to identify the behavioral characteristics of users and classify them according to their behavioral features. Finally, the user profile features of individual user groups were figured out based on data that were gathered by making an experiment, and data mapping was fulfilled between the behavioral characteristics and user profile characteristics to find out what types of behaviors were caused by the characteristics of user profile. As a result, it's found that user characteristics could have an impact on not only their general information and relevant contexts but their attitude of using different media and personality type. There were some problems with the experimental design, but more accurate information on the relationship of user behaviors to user profile characteristics will be obtained if those problems are eliminated. As user behaviors could be predicted only by user profile characteristics, user classification is expected to make a contribution to enhancing the efficiency of UI design.
The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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v.10
no.1
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pp.209-246
/
1997
In the literatual study on the Ecthyma, the results were as follows. 1. The pathogenic factors of Ecthyma is divided three parts. One is exogenous pathogenic factors which including the wind evil, wind heat and toxic material produced by wetness evil. Another is endogenous pathogenic factors which including the declination of kidney-yang, impairment of the liver and kidney, the lower classes of yin-fire, accumulation of wetness-heat in the spleen asthenia, impairment of the liver and kidney, wetness-heat of three yang, asthenic heat-syndrome of three yin. The other is pathogenic factors neither endogenous nor exogenous which including the food and living, uncontrol sexual excess, anxiety and angry, injury of skin, injury of insects and animals. 2. Five viscera which was concerned with Ecthyma are liver, spleen and kidney. 3. Frequent region of Ecthyma are S-36(足三里) and C-7(陰交). External Ecthyma was rose to wetness-heat of three yang channel that cured easily. Internal Ecthyma was rose to asthenic heat-syndrome of three yin channel that cured hardly. 4. In the frequency of prescription, the most numerous prescription is Bojungikgitang(補中益氣湯) and the next are Kyukgigo(隔紙膏) and Yukmijihwanghwan(六味地滉丸). 5. In the frequency of medicine, the most medicine is Calomelas(輕粉) which included Hydrargyrum(水銀) and the next are Olibanum(乳香) and Resina Commiphorae Myrrhae(沒藥) which regulating vital energy and pain control medicine used that in order to destroy insects and remove polson. 6. In classification of the medical action, medicine of clearing away summer-heat and heat evil and activating blood circulation to dissipate blood stasis used to be very busy which in order to remove the disorder of vital energy for virulent heat-evil. 7. In classification of four characters, the most part is warm medicine, the next are cold and cool medicine and there is a few that is hot medicine. 8. In classification of five tastes, the most numerous tastes are bitter and acrid, the next are sweet, salty and sour tastes. 9. In classification of virulence of medicine, the most part is non-toxic, the next are weakly and deadly poison. 10. In classification of channel distribution, the most is the medicine that belongs to liver channel, the next are the lung, spleen, stomach and kidney channel.
This study is a trend analysis study that discusses the current status and directions of research methods of KAP research. The existing trend ana lysis studies dealing with research methods have problems in that the classification criteria of the studies used are rough and different from each other, rendering comparison between studies being difficult, and do not comprehensively cover research methods of diversified KAP research. Therefore, this study examined the research methods of KAP research from a critical point of view and suggested a set of classification criteria and an analysis framework that can be used consistently in classification and analysis of future KAP research methods. Based on the theoretical background of second language studies and applied linguistics, this study revised and supplemented Brown (2015)'s research method types and selected 289 journals and theses/dissertations from 2012 to 2016 and classified them into a new analysis framework. The primary and secondary studies, which are the major categories, were 219 and 70, respectively, so it was confirmed that there were much more primary studies. The primary studies then were subdivided into 128 qualitative research studies, 142 survey research studies, and 23 quantitative research studies, pointing to the trend that survey and qualitative research methods were preferred. In the qualitative research approaches, there were 21 action research studies, which were used the most. In addition, such qualitative research approaches as case studies and narrative inquiries which were difficult to find in the past, have gradually increased, confirming that the diversification of research methods is becoming common. However, there were still many studies that did not explicitly put forward research questions and there were many studies that did not report reliability and effect sizes in quantitative research. Of the 23 quantitative studies, only 50% reported reliability, and only three reported effect sizes. In order to enable systematic reviews (meta-analysis) of quantitative research and expect quality improvement of research in future KAP research, reporting of quantitative research should be done more systematically. This study is meaningful in that a systematic and detailed analysis framework was proposed to classify various research methods in the future and that the problems and directions for improvement of the KAP research methods were discussed through the analysis of the research trend of the KAP studies for the last 5 years.
In this paper, we propose an EEG-based mental state prediction method during a mental tasks. In the experimental task, a subject goes through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining selection time. EEG signals from four subjects were recorded while they performed three mental tasks. Feature vectors defined by these representations were classified with a standard, feed-forward neural network trained via the error back-propagation algorithm. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or cognitive decision discrimination methods.
Purpose : Primary writing tremor(PWT) can be classified as either type A or type B depending on whether tremor appeared during writing or whilst writing and also on adopting the hand postures normally used for writing. Through the clinical experience author has had an impression that PWT type B may not be purely dependant on specific writing postures. The objective of this study was to clarify whether PWT type B have writing posture-specificity or not. Results : The data indicated that type B PWT is not writing posture-specific. Various pronation and supination postures could evoke tremor as well as writing postures. Furthermore most of other pronation- and supination-related tasks could evoke tremors as well as action of writing. Conclusions : The present data suggest that PWT should be limited only on the pure form of task-spesific PWT type A.
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