• Proceedings of the Korean Society of Medical Physics Conference
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• 2003.09a
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• pp.78-78
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• 2003

Purpose: This event related fMRI study was to further our understanding about how different brain regions could contribute to effective access of specific information stored in long term memory. This experiment has allowed us to determine the brain regions involved in recognition of familiar faces among non familiar faces. Materials and Methods: Twelve right handed normal, healthy volunteer adults participated in face recognition experiment. The paradigm consists of two 40 familiar faces, 40 unfamiliar faces and control base with scrambled faces in a randomized order, with null events. Volunteers were instructed to press on one of two possible buttons of a response box to indicate whether a face was familiar or not. Incorrect answers were ignored. A 1.5T MRI system(GMENS) was employed to evaluate brain activity by using blood oxygen level dependent (BOLD) contrast. Gradient Echo EPI sequence with TR/TE= 2250/40 msec was used for 17 contiguous axial slices of 7mm thickness, covering the whole brain volume (240mm Field of view, 64 ${\times}$ 64 in plane resolution). The acquired data were applied to SPM99 for the processing such as realignment, normalization, smoothing, statistical ANOVA and statistical preference. Results/Disscusion: The comparison of familiar faces vs unfamiliar faces yielded significant activations in the medial temporal regions, the occipito temporal regions and in frontal regions. These results suggest that when volunteers are asked to recognize familiar faces among unfamiliar faces they tend to activate several regions frequently involved in face perception. The medial temporal regions are also activated for familiar and unfamiliar faces. This interesting result suggests a contribution of this structure in the attempt to match perceived faces with pre existing semantic representations stored in long term memory.

• Journal of Physiology & Pathology in Korean Medicine
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• v.22 no.4
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• pp.930-942
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• 2008

Following conclusions were derived from researching various consumption caused after surgical operation. The meaning of surgery in HyungSang medical point of view is a big flaw of the original configuration. Therefore meaning, the loss of container to store something. Changes of the body after surgery can be formation of stagnated blood and abnormal cyst of the removed part and entangled undigested food and stagnant blood. After surgery when the symptoms become chronic or the patients develop side effects as they age, consumption and aging process begins. HyungSang medical treatments of consumption caused after surgery are the followings. We should prevent aging of the framework by using appropriate medication to make up for faults in Gall Bladder, Bladder type and six meridian type by considering its form, color, pulse and symptoms. Stability of emotion that can endure various mental stimulation is needed. Prevent stagnation, indigestion, obstruction and numbness by smoothing up the constructive energy and the defensive energy in the operated part. The Heart and Kidney should be under control and Stomach and Spleen should be reinforced so that food intake is accelerated and produce vital substances and Ki and blood. Eum blood and Yang Ki should circulate around the body well. Examine daily life and if small changes in skin complexion, appetite, sleep and excretion occur, also difference in adaptation of external stimulation and mental health occurs immediate treatment and care is necessary. Treatment of side-effects caused from both aging process and post-surgery in HyungSang's view point is considered better than other ways of treatment and will contribute in expanding the field of medical science.

• The Journal of Society for e-Business Studies
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• v.16 no.2
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• pp.1-18
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• 2011

Context-aware computing aims to enable the on-line applications and services to be executed in a timely and automated manner. Many of such applications and services involve group-level interactions. For more smoothing communication within a community, specific group-level issues such as group dynamics must be considered. To do so, obtaining group-level contexts such as the role, conflict resolution and norms, are key ingredients to improve group performance. Since group context is not the same as a simple summation of individual context, as group is not just a simple set of individuals, awaring individual context is not sufficient for group-level communication support. However, context-aware computing research still has stressed more on individual context. This leads us to the motivation of searching for group context aware method. Hence, the of this paper is to propose a novel methodology which automatically recognizes group context. Especially, we focus on group conhesiveness in this paper just because group cohosiveness is one of the important variables to control the performance of group interaction. To verify the applicability of the proposed method, an empirical test has been conducted to compare the performance of the proposed methodology with that of conventional methods.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.2
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• pp.47-56
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• 2015

In an energy saving server cluster environment, the power modes of servers are controlled according to load situation, that is, by making ON only minimum number of servers needed to handle current load while making the other servers OFF. This algorithm works well under normal circumstances, but does not guarantee QoS under abnormal circumstances such as sharply rising or falling loads. This is because the number of ON servers cannot be increased immediately due to the time delay for servers to turn ON from OFF. In this paper, we propose a new prediction algorithm of the power consumption for improving QoS under not only normal but also abnormal circumstances. The proposed prediction algorithm consists of two parts: prediction based on the conventional time series analysis and prediction adjustment based on trend analysis. We performed experiments using 15 PCs and compared performance for 4 types of conventional time series based prediction methods and their modified methods with our prediction algorithm. Experimental results show that Exponential Smoothing with Trend Adjusted (ESTA) and its modified ESTA (MESTA) proposed in this paper are outperforming among 4 types of prediction methods in terms of normalized QoS and number of good reponses per power consumed, and QoS of MESTA proposed in this paper is 7.5% and 3.3% better than that of conventional ESTA for artificial load pattern and real load pattern, respectively.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Journal of Life Science
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• v.30 no.5
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• pp.428-433
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• 2020

In this study, we created pupal stage extracts of Apis mellifera L. drones for use in cosmetic materials. The effect of the drone pupae extract (DPE) on HDF cells was assessed for analysis of anti-wrinkle activity by collagen or collagenase gene expression, and the skin-lightening effect was studied by in vitro tyrosinase inhibition and B16F10 melanoma assay; the two cells were found to be non-cellular when the concentration of DPE was 100 ㎍/ml. Albutin concentration (positive control) in the whitening test was set at a capacity of 100 ug/ml and m-melanocyte stimulating hormone (α-MSH). A melanin-producing induction material was set at a concentration of 100 nM, and the expression of collagen type I and MMP1 collagenase was measured using HDF cells. MMP1 expression was seen to reduce in a concentration-dependent manner in treatment with DPE. Inhibiting melanin generation with B16F12 cells indicated a tendency to decrease in the DPE treatment group. Both L-Tyrosine and L-DOPA as DPE were used in an in vitro tyrosinase induction test to demonstrate the effects of tyrosinase suppression on concentrations. The higher the concentration of DPE, the greater the wrinkle reduction and whitening effect. In conclusion, it was found that DPE is an effective smoothing and whitening material by increasing collagen generation and inhibiting collagenase expression and reducing melanin production.