• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.123-125
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• 2023

As deep learning has become an essential part of human lives, the requirement for Deep Learning as a Service (DLaaS) is growing. Since using remote cloud servers induces privacy concerns for users, a Fully Homomorphic Encryption (FHE) arises to protect users' sensitive data from a malicious attack in the cloud environment. However, the FHE cannot support several computations, including the most popular activation function, Rectified Linear Unit (ReLU). This paper analyzes several polynomial approximation methods for ReLU to utilize FHE in DLaaS.

• Transactions on Semiconductor Engineering
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• v.2 no.1
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• pp.9-16
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• 2024

This paper discusses the design of bio-implanted ultra-low-power MICS RF transceivers for wireless sensor networks. The 400 MHz MICS standard was considered for the implementation of the WBAN wireless sensor system, indirectly minimizing radio propagation losses in the human body and the inference with surrounding networks. This paper includes link budget, various transmission and reception architectures for a system design and ultra-low power transceiver circuit techniques for the implementation of RF transceivers that meet MICS standards.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.560-565
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• 2013

Population genetic structure and diversity of Ulmus davidiana var. japonica in South Korea were studied using ISSR markers. A total of 45 polymorphic ISSR amplicons were cropped from 7 ISSR primers and 171 individuals of 7 populations. The average of effective alleles and the proportion of polymorphic loci were 1.5 and 89% respectively. The Shannon's diversity index (I) was 0.435 and the expected heterozygosity from the frequentist's method ($H_e$) and the Bayesian inference (hs) were 0.289 and 0.323 respectively. From AMOVA, 4.2% of total genetic variation in the elm populations was explained with the difference among populations (${\Phi}_{ST}=0.042$) and the other 95.8% was distributed within populations. The ${\theta}^{II}$ value by Bayesian method which was comparable to the FST was 0.043. So the level of genetic diversity in the elm populations was similar to that in Genus Ulmus and the level of genetic differentiation was lower than that of others. No population showed a significant difference in the population-specific fixation indices (average of $PS-F_{IS}=0.822$) or the population-specific genetic differentiations (average of $PS-F_{ST}=0.101$). Seven populations were allocated into 3 groups in the UPGMA and the PCA, but the grouping patterns were different. Also, we could not confirm any geographic trend from Bayesian clustering.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.10
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• pp.3873-3879
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• 2010

Home service robot are not working in the fixed task such as industrial robot, because they are together with human in the same indoor space, but have to do in much more flexible and various environments. Most of them are developed on the base of the wheel-base mobile robot in the same method as a vehicle robot for factory automation. In these days, for holonomic system characteristics, omni-directional wheels are used in the mobile robot. A holonomicrobot, using omni-directional wheels, is capable of driving in any direction. But trajectory control for omni-directional mobile robot is not easy. Especially, azimuth control which sensor uncertainty problem is included is much more difficult. This paper develops trajectory controller of 3-wheels omni-directional mobile robot using fuzzy azimuth estimator. A trajectory controller for an omni-directional mobile robot, which each motor is controlled by an individual PID law to follow the speed command from inverse kinematics, needs a precise sensing data of its azimuth and exact estimation of reference azimuth value. It has imprecision and uncertainty inherent to perception sensors for azimuth. In this paper, they are solved by using fuzzy logic inference which can be used straightforward to perform the control of the mobile robot by means of the fuzzy behavior-based scheme already existent in literature. Finally, the good performance of the developed mobile robot is confirmed through live tests of path control task.

• Journal of Korean Society of Forest Science
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• v.103 no.2
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• pp.182-188
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• 2014

Genetic diversity and genetic differentiation in six natural populations of Abies holophylla Max were investigated using ISSR marker system. From 6 ISSR primers, the average percentage of polymorphic loci was 85.6%, and the average expected heterozygosity ($H_e$) was 0.288. From the result of AMOVA, 94.4% of total genetic variation came from the differences among individuals within populations, and 5.6% was caused by those of among-populations. On the basis of Bayesian inference, genetic differentiation (${\theta}^{II}$ and $G_{ST}$) and inbreeding coefficient for all populations were 0.045, 0.038, and 0.509, respectively. The correlation between genetic distance and geographical distance was highly significant at the Mental's test (r = 0.74, P < 0.05). Six populations divided into two groups according to the results of UPGMA and PCA. One group included Namwon, Cheongdo and Mungyeong population. The other was Inje, Hongcheon and Pyeongchang population. Also, in Bayesian clustering analysis, 6 populations were divided into two clusters. But Cheongdo population was assigned into the other cluster unlike those of UPGMA or PCA. Taking the regions based on the results of the cluster analysis into consideration of AMOVA, 3.9% of genetic variation came from the regional difference. The dendrogram from UPGMA could provide the most genetically reasonable explanation for the distribution of Abies holophylla populations in South Korea.

• Korean Journal of Psychosomatic Medicine
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• v.25 no.1
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• pp.63-72
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• 2017

Objectives : Attention-deficit hyperactivity disorder(ADHD) is characterized by significant impairments in executive functions, with a prevalence of approximately 3-5% of all children worldwide. The goal of this study was to examine the relationship between executive functions and electrophysiological activities in children and adolescents with ADHD. Methods : In 31 patients with ADHD, resting-state EEG was recorded, and Comprehensive Attention Test(CAT), Stroop Color-Word Inference Test(Stroop CWIT), Trail Making Test(TMT), and Wisconsin Card Sorting Test(CST) were administered. Korean version of the ADHD Rating Scale(K-ARS) was assessed. Results : Alpha and beta power positively correlated with the Attention Quotient(AQ), while delta power negatively correlated with AQ from CAT. In the Stroop CWIT, decreased delta power and increased beta power were related to higher performance. Power of the alpha band increased with higher TMT performance. Moreover, delta power negatively correlated with good performance on the CST, while alpha and high gamma band showed a positive correlation. Correlation with the parent-rating of ADHD symptoms showed a negative correlation between alpha power and higher scores on the K-ARS. Conclusions : These findings indicate that relative power in higher frequency bands of EEG is related to the higher executive function in children and adolescents with ADHD, while the association with the relative power in lower frequency bands of EEG seem to be vice versa. Furthermore, the findings suggest that QEEG may be a useful adjunctive tool in assessing patients with ADHD.

• Journal of KIISE:Information Networking
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• v.29 no.4
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• pp.412-427
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• 2002

This paper proposes a restoration scheme based on mobility learning and prediction in the presence of the failure of mobility databases in mobile communication systems. In mobile communication systems, mobility databases must maintain the current location information of users to provide a fast connection for them. However, the failure of mobility databases may cause some location information to be lost. As a result, without an explicit restoration procedure, incoming calls to users may be rejected. Therefore, an explicit restoration scheme against the failure of mobility databases is needed to guarantee continuous service availability to users. Introducing mobility learning and prediction into the restoration process allows systems to locate users after a failure of mobility databases. In failure-free operations, the movement patterns of users are learned by a Neuro-Fuzzy Inference System (NFIS). After a failure, an inference process of the NFIS is initiated and the users' future location is predicted. This is used to locate lost users after a failure. This proposal differs from previous approaches using checkpoint because it does not need a backup process nor additional storage space to store checkpoint information. In addition, simulations show that our proposal can reduce the cost needed to restore the location records of lost users after a failure when compared to the checkpointing scheme

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.1-16
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• 2007

According to the expansion of the Internet use and the utilization of e-business, there are an increasing number of studies of intelligent-based systems for the preparation of ubiquitous environment. In addition, expert systems have been developed from Stand Alone types to web-based Client-Server types, which are now used in various Internet environments. In this paper, we investigated the environment of development for web-based expert systems, we classified and analyzed them according to type, and suggested general typical models of web-based expert systems and their architectures. We classified the web-based expert systems with two perspectives. First, we classified them into the Server Oriented model and Client Oriented model based on the Load Balancing aspect between client and server. Second, based on the degree of knowledge and inference-sharing, we classified them into the No Sharing model, Server Sharing model, Client Sharing model and Client-Server Sharing model. By combining them we derived eight types of web-based expert systems. We also analyzed the location problems of Knowledge Bases, Fact Bases, and Inference Engines on the Internet, and analyzed the pros & cons, the technologies, the considerations, and the service types for each model. With the framework proposed from this study, we can develop more efficient expert systems in future environments.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.21-30
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• 2017

IoT(Internet of Things) can enable networking and computing using any devices is rapidly proliferated. In the existing IoT environment, bottlenecks and service delays can occur because it processes data and provides services to users using central processing based on Cloud. For this reason, Edge Computing processes data directly in IoT nodes and networks to provide the services to the users has attracted attention. Also, numerous researchers have been attracted to intelligent service efficiently based on Edge Computing. In this paper, expert system-based context awareness scheme for Edge Computing in IoT environment is proposed. The proposed scheme can provide customized services to the users using context awareness and process data in real-time using the expert system based on efficient cooperations of resource limited IoT nodes. The context awareness services can be modified by the users according to the usage purpose. The three service modes in the security system based on smart home are used to test the proposed scheme and the stability of the proposed scheme is proven by a comparison of the resource consumptions of the servers between the proposed scheme and the PC-based expert system.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.147-153
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• 2014

To create a personal menu, there are a number of considerations. Personal menus are different depending on the dietary therapy for disease, diet for weight control. In addition, the menu you choose, depending on the personal preference and the season, the weather, current personal feelings may differ. An individual should expect to recommend a balanced diet, taking nutritional status just for health care. In this paper, we propose a personalized menu recommendations System framework to meet such needs. To recommend menus the system receives data of the body's individual circumstances, ingredients situation, environmental conditions, psychological condition, emotional condition and provides a recommended menu by performing the inference using the ontology generated from external application systems. In order to provide such services, Internet of Things (IoT) environment should be the foundation. In this paper, we propose a personalized diet recommendation system framework in the IoT standardization environment that has oneM2M common service platform.