• Spatial Information Research
• /
• v.8 no.1
• /
• pp.141-153
• /
• 2000

The purpose of this study is to develop and apply spatial landslide information system using Geographic information system (GIS) in concerned with spatial data. Landslide locations detected from interpretation of aerial photo and field survey, and topographic , soil , forest , and geological maps of the study area, Yongin were collected and constructed into spatial database using GIS. As landslide occurrence factors, slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective thickness of soil were extracted from the soil database, and type, age, diameter and density of wood were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM satellite image. In addition, landslide damageable objects such as building, road, rail and other facility were extracted from the topographic database. Landslide susceptibility was analyzed using the landslide occurrence factors by probability, logistic regression and neural network methods. The spatial landslide information system was developed to retrieve the constructed GIS database and landslide susceptibility . The system was developed using Arc View script language(Avenue), and consisted of pull-down and icon menus for easy use. Also, the constructed database can be retrieved through Internet World Wide Web (WWW) using Internet GIS technology.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.2
• /
• pp.8-14
• /
• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.

• Korean Journal of Construction Engineering and Management
• /
• v.16 no.1
• /
• pp.74-81
• /
• 2015

As one of urban infrastructure system, the electricity substation is critical for urban life and industrial activity as the electricity demands get higher than ever. However the substation is generally regarded as unpleasant or dangerous facility, which finally results in the continuous opposition movement by resident due to the belief of unidentified negative effect in apartment prices. Accordingly, as the scientifically objective and quantitative analysis is required to solve the social conflict, this study intends to examine the variation affected by urban infrastructure system, expecially for substation. After the independent variable defining the price of apartment and the dependent variable, which is apartment price, are identified and their spatial data has been filed, the forecasting model has been developed through the hedonic price function as well as artificial neural networks system. The research finding indicated that the spatial range affected by substation is not notable and the range of some case was applicable for less than 600m. It is expected that these research findings can be applied for establishing the one of solid cases for the analysis of economical effect to local housing market by the urban infrastructure system.

• Korean Journal of Biological Psychiatry
• /
• v.16 no.1
• /
• pp.5-14
• /
• 2009

Objectives : Most of the mechanisms reported for antidepressant drugs are the enhancement of neurite outgrowth and neuronal survival in the rat hippocampus. Neural cell adhesion molecule 140(NCAM140) has been implicated as having a role in cell-cell adhesion, neurite outgrowth, and synaptic plasticity. In this report, we have performed to elucidate a correlation among chronic antidepressant treatments, NCAM140 expression, and activation of phosphorylated cyclicAMP responsive element binding protein(pCREB) which is a downstream molecule of NCAM140-mediated intracellular signaling pathway in the rat hippocampus. Methods : Fluoxetine(10mg/kg) was injected acutely(daily injection for 5days) or chronically(daily injection for 14days) in adult rats. RNA and protein were extracted from the rat hippocampus, respectively. Real-time RT-PCR was performed to analyze the expression pattern of NCAM140 gene and western blot analyses for the activation of the phosphorylation ratio of CREB. Results : Chronic fluoxetine treatments increased NCAM140 expression 1.3 times higher than control in rat hippocampus. pCREB immunoreactivity in the rat hippocampus with chronic fluoxetine treatment was increased 4.0 times higher than that of control. Conclusion : Chronic fluoxetine treatment increased NCAM140 expression and pCREB activity in the rat hippocampus. Our data suggest that NCAM140 and pCREB may play a role in the clinical efficacy of antidepressants promoting the neurite outgrowth and neuronal survival.

• Journal of IKEEE
• /
• v.22 no.3
• /
• pp.829-835
• /
• 2018

Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smart grid, is implemented to improve the efficiency of energy use by controlling power of electric devices, and predicting trends of energy usage based on deep learning. We propose and develop an algorithm that controls the power of the electric devices by comparing the predicted power consumption with the real-time power consumption. To verify the performance of the proposed smart meter based on the deep running, we constructed the actual power consumption environment and obtained the power usage data in real time, and predicted the power consumption based on the deep learning model. We confirmed that the unnecessary power consumption can be reduced and the energy use efficiency increases through the proposed deep learning-based smart meter.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.6
• /
• pp.629-635
• /
• 2019

In Deep Learning models derivative is implemented by error back-propagation which enables the model to learn the error and update parameters. It can find the global (or local) optimal points of parameters even in the complex models taking advantage of a huge improvement in computing power. However, deliberately generated data points can 'fool' models and degrade the performance such as prediction accuracy. Not only these adversarial examples reduce the performance but also these examples are not easily detectable with human's eyes. In this work, we propose the method to detect adversarial datasets with random noise addition. We exploit the fact that when random noise is added, prediction accuracy of non-adversarial dataset remains almost unchanged, but that of adversarial dataset changes. We set attack methods (FGSM, Saliency Map) and noise level (0-19 with max pixel value 255) as independent variables and difference of prediction accuracy when noise was added as dependent variable in a simulation experiment. We have succeeded in extracting the threshold that separates non-adversarial and adversarial dataset. We detected the adversarial dataset using this threshold.

• Journal of KIISE
• /
• v.43 no.10
• /
• pp.1085-1093
• /
• 2016

Community-based Question Answering system is a system which provides answers for each question from the documents uploaded on web communities. In order to enhance the capacity of question analysis, former methods have developed specific rules suitable for a target region or have applied machine learning to partial processes. However, these methods incur an excessive cost for expanding fields or lead to cases in which system is overfitted for a specific field. This paper proposes a multiple machine learning method which automates the overall process by adapting appropriate machine learning in each procedure for efficient processing of community-based Question Answering system. This system can be divided into question analysis part and answer selection part. The question analysis part consists of the question focus extractor, which analyzes the focused phrases in questions and uses conditional random fields, and the question type classifier, which classifies topics of questions and uses support vector machine. In the answer selection part, the we trains weights that are used by the similarity estimation models through an artificial neural network. Also these are a number of cases in which the results of morphological analysis are not reliable for the data uploaded on web communities. Therefore, we suggest a method that minimizes the impact of morphological analysis by using character features in the stage of question analysis. The proposed system outperforms the former system by showing a Mean Average Precision criteria of 0.765 and R-Precision criteria of 0.872.

• Journal of the Institute of Convergence Signal Processing
• /
• v.4 no.1
• /
• pp.15-26
• /
• 2003

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an alternative approach which uses the least mean square (LMS) method and expectation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm In conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of Animal Science and Technology
• /
• v.44 no.6
• /
• pp.727-738
• /
• 2002

This study was conducted to investigate the effect of essential amino acid(EAA) deficient diets on short-term feeding response and the Fos expression in brain area when methionine deficiency diet fed, and thereby to know the mechanism of feed intake regulation. In all trials, experimental diets were formulated with pure amino acid mixture to level of 15% nitrogen. Rats were adapted to a 6-hr single-meal feeding per day(17:00${\sim}$21:00). Feed intake and body weight were monitored every hour after 7-day of feeding of individual EAA deficient diets in Exp. Ⅰ. In Exp. Ⅱ, Fos immuno- histochemistry was determined in various regions of brain to identify the regions that is related to suppressed feed intake following feeding methionine-deficient diet. Fos expression was examined to know the initial sensitive region in the brains of rats at 3h after feeding of the control and methionine deficient diet(-Met). Initial response to EAA deficiency diets was severely depressed in methionine deficiency diet, but the depression was low in threonine deficiency diet. However, the feed intake at 3rd day in rats was depressed in the order of His(71%), Leu(68%), Ile(66%), Thr(63%), Trp(61%), Val(55%), Phe(52%), Met(51%), Lys(44%) and Arg(24%). Fos immunoreaction in neural regions(PPC, amygdala and EPC) of pyrifrom cortex was increased in the -Met group more than in the control diet group, but those in LH, VMH and PVM were similar. Thus, based on these data, the PPC was identified as the initial response area in the -EAA diet.

• The Journal of Internal Korean Medicine
• /
• v.25 no.4
• /
• pp.324-336
• /
• 2004

Objectives : The purpose of this investigation is to evaluate the effects of Scutellaria baicalensis GEORGI on alteration in gene expression in a hypoxia model using cultured rat cortical cells. Methods : E18 rat cortical cells were grown in a Neurobasal medium containing B27 supplement. On 12 DIV, Scutellaria baicalensis GEORGI(20 ug/ml) was added to the culture media and left for 24 hrs. On 11 DIV, cells were given a hypoxic insult $(2%\;O_2/5%\;CO_2,\;37^{\circ}C,\;3\;hrs)$, returned to normoxia and cultured for another 24 hrs. Total RNA was prepared from Scutellaria baicalensis GEORGI-untreated (control) and -treated cultures and alteration in gene expression was analysed by microarray using rat 5K-TwinChips. Results : For most of the genes altered in expression, the Global M values were between -0.5 to +0.5. Among these, 1143 genes increased in their expression by more than Global M +0.1, while 1161 genes decreased by more than Global M -0.1. Effects on some of the genes whose functions are implicated in neural viability are as follows: 1) The expression of apoptosis-related genes such as Bad (Global M = 0.39), programmed cell death-2(Pdcd2) (Global M = 0.20) increased, while Purinergic receptor P2X(P2rxl) Global M = -0.22), Bc12-like1(Bc1211)(Global M = -0.19) decreased. 2) The expression of 'response to stress-related genes such as antioxidation-related AMP-activated protein kinase subunit gamma 1 gene (Prkag1) (Global M = 0.14), catalase gene (Global M = 0.14) and Heme Oxygenase(Hmoxl) increased. 3) The expression of Fos like antigen 2 (Fos12) expressed in neurons that survive ischemic insult increased (Global M = 0.97). Conclusions : these data suggest that Scutellaria baicalensis GEORGI increases the expression of antiapoptosis- and antioxidation- related genes in a way that can not yet be explained.