• Genomics & Informatics
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• v.12 no.4
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• pp.151-155
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• 2014

In November 2013, the US Food and Drug Administration (FDA) sent a warning letter to 23andMe, Inc. and ordered the company to discontinue marketing of the 23andMe Personal Genome Service (PGS) until it receives FDA marketing authorization for the device. The FDA considers the PGS as an unclassified medical device, which requires premarket approval or de novo classification. Opponents of the FDA's action expressed their concerns, saying that the FDA is overcautious and paternalistic, which violates consumers' rights and might stifle the consumer genomics field itself, and insisted that the agency should not restrict direct-to-consumer (DTC) genomic testing without empirical evidence of harm. Proponents support the agency's action as protection of consumers from potentially invalid and almost useless information. This action was also significant, since it reflected the FDA's attitude towards medical application of next-generation sequencing techniques. In this review, we followed up on the FDA-23andMe incident and evaluated the problems and prospects for DTC genetic testing.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.9-20
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• 2014

Making good predictions about the outcome of one's actions would seem to be essential in the context of social interaction and decision-making. This paper proposes a computational model for learning articulated motion patterns for action recognition, which mimics biological-inspired visual perception processing of human brain. Developed model of cortical architecture for the unsupervised learning of motion sequence, builds upon neurophysiological knowledge about the cortical sites such as IT, MT, STS and specific neuronal representation which contribute to articulated motion perception. Experiments show how the model automatically selects significant motion patterns as well as meaningful static snapshot categories from continuous video input. Such key poses correspond to articulated postures which are utilized in probing the trained network to impose implied motion perception from static views. We also present how sequence selective representations are learned in STS by fusing snapshot and motion input and how learned feedback connections enable making predictions about future input sequence. Network simulations demonstrate the computational capacity of the proposed model for motion recognition.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 1998.06a
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• pp.50-69
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• 1998

Dimethomorph is a novel fungicide with a high level of activity against diseases induced by certain Oomycetes, including fungal populations that are resistant to other products. In several ways, this fungicide illustrates the opportunities and challenges presented by many modern pesticides. The specific mode of action, which affects cell wall formation, is associated with a very high level of performance and low dose rates under field conditions. These low dose rates, combined with a low level of toxicity to non-target organisms present an outstanding safety profile. This same highly-specific mode of action, however, limits the spectrum of activity and suggests the need for a resistance management plan, both of which must be addressed in new product development. In addition, the biological and physiochemical properties of this, and other new products are not adequately described by the traditional classification of fungicides into“protectant”and“systemic”types. These unique profiles provide novel and useful products for disease control.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.778-780
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• 1996

The predictive maximum demand controllers often bring about large number of control actions during the every integrating period and/or undesirable load-disconnecting operations during the begining stage if the integrating period. To solve these problems, a fuzzy predictive maximum demand control algorithm is proposed, which determines the sensitivity if control action by urgency if the load interrupting action along with the predicted demand reading to the target or the time arriving at the end stage if the integrating period. A prototype controller employing the proposed algorithm also is developed and its performances are tested by PROCOM SYSTEMS Corperation of Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.929-944
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• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• Journal of the Ergonomics Society of Korea
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• v.29 no.1
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• pp.129-138
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• 2010

Gori-I nuclear power plant has been examining the effectiveness and efficiency of the current emergency operation procedures from human factors viewpoint. Previous study showed that some mistakes that the procedures did not comply with the writers' guidelines. Reviewing the current writers' guidelines for emergency operating procedures revealed that they lack of some important human factors rules such as enumeration of switching conditions and detailed action requirements, definite expression for setup points, description for anticipated results, and recommendation for use of present tense, affirmative sentence and active voice. This study suggested a new classification system for the writers' guideline contents supplementing the deficiencies of the current emergency operation procedure text.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.99-102
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• 1997

A neural spike sorting technique has been developed that also has the capability of template learning. A system of software has been written that first obtains the templates by learning, and then performs the sorting of the spikes into single units. The spike sorting can be done in real time. The template learning consists of spike detection based on the discrete Haar transform (DHT), feature extraction by clustering of spike amplitude and duration, classification based on rms error, and fabrication of templates. The developed algorithms can be implemented into real time systems using digital signal processors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.477-479
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• 2018

This study was carried out to utilize LSTM(Long Short-Term Memory) technique which is one kind of artificial neural network. Among the 27 types of motion data released by the UTD(University of Texas at Dallas), 3-axis acceleration and angular velocity data were applied to the basic LSTM and Deep Residual Bidir-LSTM techniques to classify the behavior.

• Journal of the Ergonomics Society of Korea
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• v.15 no.1
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• pp.27-38
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• 1996

A total of 77 unanticipated trip cases induced by human errors in Korean nuclear power plants were collected from the nuclear power plant trip event reports and analyzed to investigate the areas of high priority for human error reduction. Prior to this analysis, a classification system was made on the four task-related categories including plant systems, work situations, task types, and error types. The erroneous actions affecting the unanticipated plant trips were indentified by reviewing carefully the description of trip events. Then, the events with erroneous action were analyzed by using the classification system. Based on the results for the individual cases, human error occurrences were counted for each of the four categories, also for the selected pairs of categories, to find out the relationships between the two categories in aspects of human errors. As a result, the plant systems, work situations, and task types, and error types which are dominant in human error occurrences were identified.

• Journal of the Chungcheong Mathematical Society
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• v.24 no.3
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• pp.569-581
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• 2011

Let a finite group R act smoothly on a closed manifold M. We assume that R acts freely on M except a union of closed submanifolds with codimension at least two. Then, we show that there exists an isomorphism between equivariant topological complex vector bundles over M and nonequivariant topological complex vector orbibundles over the orbifold M/R. By using this, we can classify nonequivariant vector orbibundles over the orbifold especially when the manifold is two-sphere because we have classified equivariant topological complex vector bundles over two sphere under a compact Lie group (not necessarily effective) action in [6]. This classification of orbibundles conversely explains for one of two exceptional cases of [6].