• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.603-614
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• 2021

Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.

• Journal of The Korean Association For Science Education
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• v.25 no.4
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• pp.503-513
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• 2005

The purpose of this study was to analyze the generating processes of scientific emotion, that appears during the generation of biological hypotheses. To perform the study, a tentative model was set up through pilot test, a think-aloud training procedure was planned and a standardized interview instrument was developed before getting protocols. In this study, 8 college students were selected to bring out protocol through the method of think-aloud, retrospective debriefing, focused interview and observing. As the result of analysis of the collected protocol through coding scheme, 4 types of process for scientific emotion-generating were sorted out. First type was a basic process which was a feeling process in prior to recognition. Second type was a retrospective process that explains the process of retrospect for emotional memory based on the past. Third type was a cognitive process and it explains emotion that occurs during thinking process to achieve cognitive goal. Fourth type was an attribution process and it explains that emotion is generated in the process of attribution for cognitive goal's achievement. These types of process of scientific emotion-generating can contribute the basis for developing cognitive model of EBL (Emotional Brain-based Learning) strategy.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.545-559
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• 2020

In this paper, we propose a content creation framework that enables users without programming experience to easily create interactive media content that responds to user gestures. In the proposed framework, users define the gestures they use and the media effects that respond to them by numbers, and link them in a text-based configuration file. In the proposed framework, the interactive media content that responds to the user's gesture is linked with the dynamic projection mapping module to track the user's location and project the media effects onto the user. To reduce the processing speed and memory burden of the gesture recognition, the user's movement is expressed as a gray scale motion history image. We designed a convolutional neural network model for gesture recognition using motion history images as input data. The number of network layers and hyperparameters of the convolutional neural network model were determined through experiments that recognize five gestures, and applied to the proposed framework. In the gesture recognition experiment, we obtained a recognition accuracy of 97.96% and a processing speed of 12.04 FPS. In the experiment connected with the three media effects, we confirmed that the intended media effect was appropriately displayed in real-time according to the user's gesture.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.13-25
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• 1998

It is necessary to develop methodologies for the application of artificial neural network into hydrologic rainfall-runoff process, although there is so much applicability by using the functions of associative memory based on recognition for the relationships between causes and effects and the excellent fitting capacity for the nonlinear phenomenon. In this study, some problems are presented in the application procedures of artificial neural networks and the simulation of runoff hydrograph experiences are reviewed with nonlinear functional approximator by artificial neural network for rainfall-runoff relationships in a watershed. which is regarded as hydrdologic black box model. The neural network models are constructed by organizing input and output patterns with the deserved rainfall and runoff data in Pyoungchang river basin under the assumption that the rainfall data is the input pattern and runoff hydrograph is the output patterns. Analyzed with the results. it is possible to simulate the runoff hydrograph with processing element of artificial neural network with any hydrologic concepts and the weight among processing elements are well-adapted as model parameters with the assumed model structure during learning process. Based upon these results. it is expected that neural network theory can be utilized as an efficient approach to simulate runoff hydrograph and identify the relationship between rainfall and runoff as hydrosystems which is necessary to develop and manage water resources.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.11-19
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• 2009

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

• Journal of the Korean Society of Food Science and Nutrition
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• v.31 no.6
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• pp.1084-1091
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• 2002

Mortierella alpina, a common soil fungus, is the most efficient organism for production of production acid presently known. Since arachidonic acid are important in human brain and retina development, it was undertaken the growing effect containing diet as a food ingredient. Arachidonic acid rich oil derived from Mortierella alpina, was subjected to a program of studies to establish for use in diet supplement. This study was compared the growth and learning effect of fungal oil rich in arachidonic acid by incorporated into diets ad libitum. Sprague-Dawley rats received experimental diets 5 groups (standard AIN 93 based control with beef tallow, extract oil 8%, and 4%, and Mortierella alpina in diet 10% and 20%) over all experiment duration (pre-mating, mating, gestation, lactation, and after weaning 4 weeks). Pups born during this period consumed same diets after wean for 4 weeks. There was no statistical significance of diet effects in reproductive performance and fertility from birth to weaning. But the groups of Mortierella alpine diet were lower of weight gain and diet intake after weaning. The serum lipids were significantly different with diet groups, higher TG in LO (oil 4%) group of dams, and higher total cholesterol in LF (M. alpina 10%) of pups, although serum albumin content was not significantly different in diet group. The spent-time and memory effect within 4 weeks of T-Morris water maze pass test in dam and 7-week- age pups did not differ in diet groups. On the count of backing error in weaning period of pups was lower in HO(extracted oil 8%) group. In the group of 10% and 20% Mortierella alpina diet, DNA content was lower in brain with lower body weight, but liver DNA relative to body weight was higher than control. Further correlation analyses would be needed DNA and arachidonic acid intakes, with Mortierella alpina diet digestion rate.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Life Science
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• v.25 no.9
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• pp.1043-1050
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• 2015

Glutamate is one of the principle transmitters in the CNS. Ionotropic receptors of glutamate, selectively activated by N-methyl-D-aspartate (NMDA), play an important role in the processes of cell development, learning, memory, and etc. On the other hand, many studies discovered that over-activation of glutamate receptors leads to neurodegeneration and are known to be implicated in major areas of brain pathology. Any sustained effect of a transient NMDA receptor activation is likely to involve signaling to the nucleus and to trigger coordinated changes in gene expression. Classically, a set of immediate-early genes are induced first; some of genes are by themselves transcription factors that control expression of other target genes. This study provides understanding of changes of inducible transcription factors mRNA levels with RT-PCR by inducing over-activation of NMDA receptor with intraperitoneal NMDA injection. The experimental conditions were varied by 1, 5, 25, and 125 g/ of body weight NMDA and measured transcription factors mRNA levels are Egr-1, c-Jun, JunB, and FosB. Based on result obtained, inducible transcription factors mRNA in NMDA injection to mice with 5 g/body weight showed the greatest change. And ITF mRNA showed greatest change 24 hr after injection. The expression level of JunB mRNA was markedly changed. Up to the present days, no study clearly understood how ITF mRNA affected the apoptosis of purkinje cells in the cerebellum. The current study improves the understanding of the mechanism of apoptosis of purkinje cells in the cerebellum.

• Biomolecules & Therapeutics
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• v.15 no.1
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• pp.16-26
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• 2007

Tissue plasminogen activator (tPA) is a serine protease catalyzing the proteolytic conversion of plasminogen into plasmin, which is involved in thrombolysis. During last two decades, the role of tPA in brain physiology and pathology has been extensively investigated. tPA is expressed in brain regions such as cortex, hippocampus, amygdala and cerebellum, and major neural cell types such as neuron, astrocyte, microglia and endothelial cells express tPA in basal status. After strong neural stimulation such as seizure, tPA behaves as an immediate early gene increasing the expression level within an hour. Neural activity and/or postsynaptic stimulation increased the release of tPA from axonal terminal and presumably from dendritic compartment. Neuronal tPA regulates plastic changes in neuronal function and structure mediating key neurologic processes such as visual cortex plasticity, seizure spreading, cerebellar motor learning, long term potentiation and addictive or withdrawal behavior after morphine discontinuance. In addition to these physiological roles, tPA mediates excitotoxicity leading to the neurodegeneration in several pathological conditions including ischemic stroke. Increasing amount of evidence also suggest the role of tPA in neurodegenerative diseases such as Alzheimer's disease and multiple sclerosis even though beneficial effects was also reported in case of Alzheimer's disease based on the observation of tPA-induced degradation of $A{\beta}$ aggregates. Target proteins of tPA action include extracellular matrix protein laminin, proteoglycans and NMDA receptor. In addition, several receptors (or binding partners) for tPA has been reported such as low-density lipoprotein receptor-related protein (LRP) and annexin II, even though intracellular signaling mechanism underlying tPA action is not clear yet. Interestingly, the action of tPA comprises both proteolytic and non-proteolytic mechanism. In case of microglial activation, tPA showed non-proteolytic cytokine-like function. The search for exact target proteins and receptor molecules for tPA along with the identification of the mechanism regulating tPA expression and release in the nervous system will enable us to better understand several key neurological processes like teaming and memory as well as to obtain therapeutic tools against neurodegenerative diseases.