• Therapeutic Science for Rehabilitation
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• v.3 no.2
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• pp.5-48
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• 2014

Introduction : One of the important domains in OT is performance skills which include sensory perceptual skills, motor and praxis skills, emotional regulation skills, cognitive skills, and communication/social skills. All of these skills are support ed by integrated neurological processes. Body : Stereology robust tool when employed to investigate morphological changes in neurons, cortex area, and specific parts of brain involved in special brain function. Stereology is an interdisciplinary field focused or analyzing biological tissue with the three-dimensional interpretation of planer sections by using estimating method and mathematically unbiased sampling. With the unbiased stereological method based on probability theory, researchers can estimate morphological and anatomical changes in biological reference areas accurately and efficiently. Changes in anatomical and cytoarchitectural parameters, such as volume, number, and length, affect specific brain function related to the brain area. Occupational therapists provide treatment to improve functions for participation of occupation in neurological disorder. The functional improvements in neurological disorder reflect neurobiological changes because functional difficulties, such as motor cognitive disorder, are due to neurological disturbances. Thus, combination of two kinds of evidence, neurological changes and functional improvement, provide fundamental evidence for OT intervention in neurological disorder. Even though most of stereological studies are in animal model and in postmortem human because of practical and ethical issues, stereology provides fundamental knowledge to support OT theory and practice. Conclusion : Therefore, stereology informs translation from neuroscience to OT based on structure-function relationship in performance skills and experience-dependent neural plasticity.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.768-782
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• 2010

Because of the different shooting position between multi-view cameras and the imperfect camera calibration, Illumination mismatches of multi-view video can happen. This variation can bring about the performance decrease of multi-view video coding(MVC) algorithm. A histogram matching algorithm can be applied to recompensate these inconsistencies in a prefiltering step. Once all camera frames of a multi-view sequence are adjusted to a predefined reference through the histogram matching, the coding efficiency of MVC is improved. However the histogram distribution can be different not only between neighboring views but also between sequential views on account of movements of camera angle and some objects, especially human. Therefore the histogram matching algorithm which references all frames in chose view is not appropriate for compensating the illumination differences of these sequence. Thus we propose new algorithms both the image classification algorithm which is applied two criteria to improve the correlation between inter-view frames and the histogram matching which references and matches with a group of pictures(GOP) as a unit to advance the correlation between successive frames. Experimental results show that the compression ratio for the proposed algorithm is improved comparing with the conventional algorithms.

• Journal of Distribution Science
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• v.11 no.6
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• pp.59-65
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• 2013

Purpose - Studies of mechanism as a competitive strategy, a relatively new field in the study of strategic management research, has recently drawn the attention of the business management scholars. The literature has so far proposed the subjective-based view, environment-based view, and the resource-based view in its analyses of firm management. Hence, it is highly likely for the firm management to be reasonably thought of as a combination of and interaction among the three key elements of subject, environment, and resources this is the mechanism-based view (MBV). It is reasonable to consider firm management to be the combination of and interaction among the three key elements of subject, environment, and resources. The overall dynamic process that integrates these three elements and creates functional harmony is identified as the mechanism, the principle of firm management. Much of the extant literatures on MBV has mainly focused on case studies, a qualitative approach prone to subjectivity of the researcher, although the intuition from the study may lead to meaningful insights into a firm-specific mechanism. This study's focus is also on case analysis, but it still attempts a quantitative approach in order to reach a scientific and systematic understanding of the MBV. Research design, data, and methodology - I used both a qualitative and quantitative approach to a single model, given the complexity of the innovation processes. I conducted in-depth interviews with POSCO employees-20 from general management, two from human resources, eight from information technology, five from finance and accounting, and five from production and logistics management. Once the innovative events were selected, the interview results were double-checked by the interviewees themselves to ensure the accuracy of the answers recorded. Based on the interview, I then conducted statistical validation using the survey results as well. Results - This study analyzes the building process of innovation and the effect of the mechanism pattern on innovation by examining the case of POSCO, which has survived over the past 21 years. I apply a new analytical tool to study mechanism innovation types, perform a new classification, and describe the interrelationships among the mechanism factors. This process allows me to see how the "Subject"factor interacts with the other factors. I found that, in the innovation process of the adoption stage, Subject had a mediating effect but that the mediating effect of resource and performance was smaller than the effect of Subject on performance alone. During the implementation stage, the mediating effect of Subject increased. Conclusion - Therefore, I have confirmed that the subject utilizes resources reasonably and efficiently. I have also advanced mechanism studies: whereas the field's research methods have been largely confined to single case studies, I have used both qualitative and quantitative methods to examine the relationships among mechanisms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.122-126
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• 2017

The aim of this study was to develop a device for solving the heating problem of living space using heated air, utilizing a simple air heater type collector for solar energy. At the present time, this study assessed the possibility of a development system through theoretical calculations for the amount of available energy according to the size change of the air-heated solar energy collector. To produce and supply hot water using the heat energy of the sun, hot water at $100^{\circ}C$ or less was produced using a flat or vacuum tube type collector. The purpose of this study was to research the air heating type solar collector that utilizes heating energy with heating air above $75^{\circ}C$, by designing and manufacturing an air piping type solar collector that is a simpler type than a conventional solar collector system. The analysis results were obtained for the generated air temperature ($^{\circ}C$) and the production of air (kg/h) to determine the performance of air heating by an air-heated solar collector according to the heat transfer characteristics in the collector of the model when a specified amount of heat flux was dropped into a solar collector of a certain size using PHOENICS, which is a heat flow analysis program applying the Finite Volume Method. From the analysis result, the temperature of the air obtained was approximately $40.5^{\circ}C$, which could be heated using an air heating tube with an inner diameter of 0.1m made of aluminum in a collector with a size of $1.2m{\times}1.1m{\times}0.19m$. The production of air was approximately 161 m3/h. This device can be applied to maintain a suitable environment for human activity using the heat energy of the sun.

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

• Economic and Environmental Geology
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• v.41 no.6
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• pp.747-761
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• 2008

In order to experiment the acoustic remote classification of seabed sediment, we achieved ground-truth data(i.e. video and grab samples, etc.) and developed post-processing for automatic classification procedure on the basis of 300 kHz MultiBeam Echo Sounder(MBES) backscattering data, which was acquired using KONGBERG Simrad EM3000 at Sock-Cho Port, East Sea of South Korea. Sonar signal and its classification performance were identified with geo-referenced video imagery with the aid of GIS (Geographic Information System). The depth range of research site was from 5 m to 22.7 m, and the backscattering amplitude showed from -36dB to -15dB. The mean grain sizes of sediment from equi-distanced sampling site(50 m interval) varied from 2.86$(\phi)$ to 0.88(\phi). To acquire the main feature for the seabed classification from backscattering amplitude of MBES, we evaluated the correlation factors between the backscattering amplitude and properties of sediment samples. The performance of seabed remote classification proposed was evaluated with comparing the correlation of human expert segmentation to automatic algorithm results. The cross-model perception error ratio on automatic classification algorithm shows 8.95% at rocky bottoms, and 2.06% at the area representing low mean grain size.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.444-450
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• 2015

The accuracy of small and low cost CCD camera is insufficient to provide data for precisely tracking unmanned aerial vehicles(UAVs). This study shows how UAV can hover on a human targeted tracking object by using CCD camera rather than imprecise GPS data. To realize this, UAVs need to recognize their attitude and position in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for an UAV to estimate of his attitude by environment recognition for UAV hovering, as one of the best important problems. In this paper, we describe a method for the attitude of an UAV using image information of a maker on the floor. This method combines the observed position from GPS sensors and the estimated attitude from the images captured by a fixed camera to estimate an UAV. Using the a priori known path of an UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a marker on the floor and the estimated UAV's attitude. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the UAV. The Kalman filter scheme is applied for this method. its performance is verified by the image processing results and the experiment.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.58-63
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• 2017

Emergencies and disasters can happen any time without any warning, and things can change and escalate very quickly, and often it is swift and decisive actions that make all the difference. It is a responsibility of the building facility management to ensure that a proven evacuation plan in place to cover various worst scenario to handled automatically inside the facility. To mapping out optimal safe escape routes is a straightforward undertaking, but does not necessarily guarantee residents the highest level of protection. The emergency evacuation navigation approach is a state-of-the-art that designed to evacuate human livings during an emergencies based on real-time decisions using live sensory data with pre-defined optimum path finding algorithm. The poor decision on causalities and guidance may apparently end the evacuation process and cannot then be remedied. This paper propose a cloud connected emergency evacuation system model to react dynamically to changes in the environment in emergency for safest emergency evacuation using IoT based emergency exit sign system. In the previous researches shows that the performance of optimal routing algorithms for evacuation purposes are more sensitive to the initial distribution of evacuees, the occupancy levels, and the type and level of emergency situations. The heuristic-based evacuees routing algorithms have a problem with the choice of certain parameters which causes evacuation process in real-time. Therefore, this paper proposes an evacuee routing algorithm that optimizes evacuation by making using high computational power of cloud servers. The proposed algorithm is evaluated via a cloud-based simulator with different "simulated casualties" are then re-routed using a Dijkstra's algorithm to obtain new safe emergency evacuation paths against guiding evacuees with a predetermined routing algorithm for them to emergency exits. The performance of proposed approach can be iterated as long as corrective action is still possible and give safe evacuation paths and dynamically configure the emergency exit signs to react for real-time instantaneous safe evacuation guidance.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.11-19
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• 2021

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1215-1225
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• 2021

Although Artificial Intelligence (AI) techniques have shown impressive performance in various fields, they are vulnerable to adversarial examples which induce misclassification by adding human-imperceptible perturbations to the input. Previous studies to defend the adversarial examples can be classified into three categories: (1) model retraining methods; (2) input transformation methods; and (3) adversarial examples detection methods. However, even though the defense methods against adversarial examples have constantly been proposed, there is no research to classify the type of adversarial attack. In this paper, we proposed an adversarial attack family classification method based on dimensionality reduction and clustering. Specifically, after extracting adversarial perturbation from adversarial example, we performed Linear Discriminant Analysis (LDA) to reduce the dimensionality of adversarial perturbation and performed K-means algorithm to classify the type of adversarial attack family. From the experimental results using MNIST dataset and CIFAR-10 dataset, we show that the proposed method can efficiently classify five tyeps of adversarial attack(FGSM, BIM, PGD, DeepFool, C&W). We also show that the proposed method provides good classification performance even in a situation where the legitimate input to the adversarial example is unknown.