• Journal of Gifted/Talented Education
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• v.23 no.5
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• pp.637-648
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• 2013

The relationship between the cerebral cortex and human intelligence has been studied using various methods, and related brain areas involved in intellectual manifestation have been discovered individually. Such studies have also shown the cerebellum is closely involved in various cognitive functions such as language, memory, and information processing. However, studies showing an activity difference between the cerebral cortex and cerebellum when performing specific tasks are hard to find. This study searched and analyzed the active regions of the cerebral cortex and cerebellum seen while performing the inference of geometrical figures. A WAIS intelligence test was conducted using 81 healthy boys (16.3 years of age on average), and five categories were classified. While performing the inference of shapes, their brain images were taken using functional magnetic resonance imaging (fMRI). As a result, the activity in 12 brain regions was observed, including in the cerebral cortex, the bilateral inferior parietal, the visual cortex, bilateral superior parietal, frontal-Inf-Tri-R, and bilateral caudate, while activities in 5 discrete areas were seen in the cerebellum. In particular, the higher the intelligence (IQ) of the subject, the stronger their activity. Among those with the most superior intelligence, subjects with an IQ of 140-147 showed significantly higher activity compared to the other groups. Such results seem to represent a very high utilization of intelligence in a highly gifted group, and we can expect to use this to determine the super gifted.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.201-209
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• 2012

The point-based complex event processing handled an instantaneous event by using one time stamp in each event. However, the activity period of the event plays the important role in the field which is the same as the finance, multimedia, medicine, and meteorology. The point-based event is insufficient for expressing the complex temporal relationship in this field. In the application field of the real-time world, the event has the period. The events more than two kinds can be temporally overlapped. In addition, one event can include the other event. The relation about the events of kind of these can not be successive like the point-based event. This thesis designs and implements the method detecting the patterns of the complex event by using the interval-based events. The interval-based events can express the overlapping relation between events. Furthermore, it can include the others. By using the end point of beginning and end point of the termination, the operator of interval-based events shows the interval-based events. It expresses the sequence of the interval-based events and can detect the complex event patterns. This thesis proposes the algorithm using the active instance stack in order to raise efficiency of detection of the complex event patterns. When comprising the event sequence, this thesis applies the window push down technique in order to reduce the number of intermediate results. It raises the utility factor of the running time and memory.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.7
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• pp.48-56
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• 2011

Recently, new storage device SSD(Solid State Disk) based on NAND flash memory is gradually replacing HDD(Hard Disk Drive) in mobile device and thus a variety of research efforts are going on to find the cost-effective ways of performance improvement. By increasing the NAND flash channels in order to enhance the bandwidth through parallel processing, DRAM buffer which acts as a buffer cache between host(PC) and NAND flash has become the bottleneck point. To resolve this problem, this paper proposes an efficient low-cost scheme to increase SSD performance by improving DRAM buffer bandwidth through scheduling techniques which utilize DRAM multi-banks. When both host and NAND flash multi-channels request access to DRAM buffer concurrently, the proposed technique checks their destination and then schedules appropriately considering properties of DRAMs. It can reduce overheads of bank active time and row latency significantly and thus optimizes DRAM buffer bandwidth utilization. The result reveals that the proposed technique improves the SSD performance by 47.4% in read and 47.7% in write operation respectively compared to conventional methods with negligible changes and increases in the hardware.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.5
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• pp.1-11
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• 2008

This work proposes a 15b 50MS/s CMOS pipeline ADC based on digital code-error calibration. The proposed ADC adopts a four-stage pipeline architecture to minimize power consumption and die area and employs a digital calibration technique in the front-end stage MDAC without any modification of critical analog circuits. The front-end MDAC code errors due to device mismatch are measured by un-calibrated back-end three stages and stored in memory. During normal conversion, the stored code errors are recalled for code-error calibration in the digital domain. The signal insensitive 3-D fully symmetric layout technique in three MDACs is employed to achieve a high matching accuracy and to measure the mismatch error of the front-end stage more exactly. The prototype ADC in a 0.18um CMOS process demonstrates a measured DNL and INL within 0.78LSB and 3.28LSB. The ADC, with an active die area of $4.2mm^2$, shows a maximum SNDR and SFDR of 67.2dB and 79.5dB, respectively, and a power consumption of 225mW at 2.5V and 50MS/s.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.4
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• pp.1-10
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• 2010

The scientific verification of the physiological and psychological effects that result from interaction with green plants would not only provide objective knowledge on the psychological effect of green but would also establish useful grounds for the creation of green spaces that consider human emotions. The present study measured the cerebral activity(cerebral blood flow) and the autonomic nervous system activity (blood pressure, pulse rate, amylase) of fifteen Korean male subjects as they inhaled the natural scent diffused by pine needles. Impression and mood state evaluations of the reactions to the pine scent were carried out using the SD method and POMS. Cerebral activity was observed to be significantly activated in the feeling, judgment, and motor areas of the frontal lobe, as well as the memory area in the temporal lobe. Verbal evaluations by the SD method and POMS indicated a pine scent left natural but stimulated and active impressions, provided vigor, and also reduced confusion. The autonomic nervous system activities, however, showed no significant differences. These findings verified scientifically that a pine scent vitalizes humans both physiologically and psychologically. These results could be useful as fundamental data for the design of green spaces that consider human emotional aspects.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.570-581
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• 2015

In recent years, many institutions predict that cloud services and big data will be popular IT trends in the near future. A number of leading IT vendors are focusing on practical solutions and services for cloud and big data. In addition, cloud has the advantage of unrestricted in selecting resources for business model based on a variety of internet-based technologies which is the reason that provisioning and virtualization technologies for active resource expansion has been attracting attention as a leading technology above all the other technologies. Big data took data prediction model to another level by providing the base for the analysis of unstructured data that could not have been analyzed in the past. Since what cloud services and big data have in common is the services and analysis based on mass amount of data, efficient operation and designing of mass data has become a critical issue from the early stage of development. Thus, in this paper, I would like to establish data processing architecture based on technological requirements of mass data for cloud and big data services. Particularly, I would like to introduce requirements that must be met in order for distributed file system to engage in cloud computing, and efficient compression technology requirements of mass data for big data and cloud computing in terms of cost-saving, as well as technological requirements of open-source-based system such as Hadoop eco system distributed file system and memory database that are available in cloud computing.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.8
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• pp.56-63
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• 2010

This work proposes a 12b 100MS/s 0.13um CMOS pipeline ADC for battery-powered mobile video applications such as DVB-Handheld (DVB-H), DVB-Terrestrial (DVB-T), Satellite DMB (SDMB), and Terrestrial DMB (TDMB) requiring high resolution, low power, and small size at high speed. The proposed ADC employs a three-step pipeline architecture to optimize power consumption and chip area at the target resolution and sampling rate. A single shared and switched op-amp for two MDACs removes a memory effect and a switching time delay, resulting in a fast signal settling. A two-step reference selection scheme for the last-stage 6b FLASH ADC reduces power consumption and chip area by 50%. The prototype ADC in a 0.13um 1P7M CMOS technology demonstrates a measured DNL and INL within 0.40LSB and 1.79LSB, respectively. The ADC shows a maximum SNDR of 60.0dB and a maximum SFDR of 72.4dB at 100MS/s, respectively. The ADC with an active die area of 0.92 $mm^2$ consumes 24mW at 1.0V and 100MS/s. The FOM, power/($f_s{\times}2^{ENOB}$), of 0.29pJ/conv. is the lowest of ever reported 12b 100MS/s ADCs.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.238-240
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• 2006

네트워크상에서 발생하는 다양한 형태의 대량의 데이터를 정확하고 효율적으로 분석하기 위해 설계되고 있는 마이닝 시스템들은 목표지향적으로 훈련데이터들을 어떻게 구축하여 다룰 것인지에 대한 문제보다는 대부분 얼마나 많은 데이터 마이닝 기법을 지원하고 이를 적용할 수 있는지 등의 기법에 초점을 두고 있다. 따라서, 점점 더 에이전트화, 분산화, 자동화 및 은닉화 되는 최근의 보안공격기법을 정확하게 탐지하기 위한 방법은 미흡한 실정이다. 본 연구에서는 유비쿼터스 환경 내에서 발생 가능한 문제 중 복잡하고 지능화된 침입패턴의 탐지를 위해 데이터 마이닝 기법과 결함허용방법을 이용하는 개선된 학습알고리즘과 후처리 방법에 의한 RTPID(Refinement Training and Post-processing for Intrusion Detection)시스템을 제안한다. 본 논문에서의 RTPID 시스템은 active learning과 post-processing을 이용하여, 네트워크 내에서 발생 가능한 침입형태들을 정확하고 효율적으로 다루어 분석하고 있다. 이는 기법에만 초점을 맞춘 기존의 데이터마이닝 분석을 개선하고 있으며, 특히 제안된 분석 프로세스를 진행하는 동안 능동학습방법의 장점을 수용하여 학습효과는 높이며 비용을 감소시킬 수 있는 자가학습방법(self learning)방법의 효과를 기대할 수 있다. 이는 관리자의 개입을 최소화하는 학습방법이면서 동시에 False Positive와 False Negative 의 오류를 매우 효율적으로 개선하는 방법으로 기대된다. 본 논문의 제안방법은 분석도구나 시스템에 의존하지 않기 때문에, 유사한 문제를 안고 있는 여러 분야의 네트웍 환경에 적용될 수 있다.더욱 높은성능을 가짐을 알 수 있다.의 각 노드의 전력이 위험할 때 에러 패킷을 발생하는 기법을 추가하였다. NS-2 시뮬레이터를 이용하여 실험을 한 결과, 제안한 기법이 AOMDV에 비해 경로 탐색 횟수가 최대 36.57% 까지 감소되었음을 알 수 있었다.의 작용보다 더 강력함을 시사하고 있다.TEX>로 최고값을 나타내었으며 그 후 감소하여 담금 10일에는 $1.61{\sim}2.34%$였다. 시험구간에는 KKR, SKR이 비교적 높은 값을 나타내었다. 무기질 함량은 발효기간이 경과할수록 증하였고 Ca는 $2.95{\sim}36.76$, Cu는 $0.01{\sim}0.14$, Fe는 $0.71{\sim}3.23$, K는 $110.89{\sim}517.33$, Mg는 $34.78{\sim}122.40$, Mn은 $0.56{\sim}5.98$, Na는 $0.19{\sim}14.36$, Zn은 $0.90{\sim}5.71ppm$을 나타내었으며, 시험구별로 보면 WNR, BNR구가 Na만 제외한 다른 무기성분 함량이 가장 높았다.O to reduce I/O cost by reusing data already present in the memory of other nodes. Finally, chunking and on-line compression mechanisms are included in both models. We demonstrate that we can obtain significantly high-performanc

• Korean Journal of Family Social Work
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• no.57
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• pp.159-182
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• 2017

With using the 4th and 5th wave of KLoSA(Korean Longitudinal Study of Aging), this study analyzed the effect of the factors of Rowe and Kahn's successful aging model on the life satisfaction of older adults. The target population of this study was 1,385 who is older than 65. As data analysis, hierarchical multiple regression was used. The variables such as demographics, avoiding diseases and disability, high cognitive and physical function and engagement with life, relationship with adult children were entered hierarchically for the regression analysis. Since there are differences of longevity by gender, the difference of factors of successful aging were compared by marital status. In this study (besides Rowe and Kahn's successful aging model such as avoiding disease and disability, high cognitive and physical function, and engagement with life), the relationships with adult children were analyzed additionally. Findings show that married older adults were older and reside with their adult children twice more than those who do not have a spouse. According to research results, regular exercise and memory skills were important factors of affecting life satisfaction. Among the categories of active engagement, meeting with friends, relatives and neighbors were significant. Contact with their adult children by mail or phone were the most important factors affecting the older adults' life satisfaction. The three parts of successful aging were various depending on the operationalization of variables.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.11-18
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• 2022

Recently, research to classify human activity using imagery has been actively conducted for the purpose of crime prevention and facility safety in public places and facilities. In order to improve the performance of human activity classification, most studies have applied deep learning based-transfer learning. However, despite the increase in the number of backbone network models that are the basis of deep learning as well as the diversification of architectures, research on finding a backbone network model suitable for the purpose of operation is insufficient due to the atmosphere of using a certain model. Thus, this study applies the transfer learning into recently developed deep learning backborn network models to build an intelligent system that classifies human activity using imagery. For this, 12 types of active and high-contact human activities based on sports, not basic human behaviors, were determined and 7,200 images were collected. After 20 epochs of transfer learning were equally applied to five backbone network models, we quantitatively analyzed them to find the best backbone network model for human activity classification in terms of learning process and resultant performance. As a result, XceptionNet model demonstrated 0.99 and 0.91 in training and validation accuracy, 0.96 and 0.91 in Top 2 accuracy and average precision, 1,566 sec in train process time and 260.4MB in model memory size. It was confirmed that the performance of XceptionNet was higher than that of other models.