• Journal of Ecology and Environment
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• v.45 no.2
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• pp.78-87
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• 2021

Background: To understand shade and wind effects on seedling traits of common reed (Phragmites australis), we conducted a mesocosm experiment manipulating day length (10 h daytime a day as open canopy conditions or 6 h daytime a day as partially closed canopy conditions) and wind speed (0 m/s as windless conditions or 4 m/s as windy conditions). Results: Most values of functional traits of leaf blades, culms, and biomass production of P. australis were higher under long day length. In particular, we found sole positive effects of long day length in several functional traits such as internode and leaf blade lengths and the values of above-ground dry weight (DW), rhizome DW, and total DW. Wind-induced effects on functional traits were different depending on functional traits. Wind contributed to relatively low values of chlorophyll contents, angles between leaf blades, mean culm height, and maximum culm height. In contrast, wind contributed to relatively high values of culm density and below-ground DW. Conclusions: Although wind appeared to inhibit the vertical growth of P. australis through physiological and morphological changes in leaf blades, it seemed that P. australis might compensate the inhibited vertical growth with increased horizontal growth such as more numerous culms, indicating a highly adaptive characteristic of P. australis in terms of phenotypic plasticity under windy environments.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.133-141
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• 2001

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However is we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set. eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case, set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

• Smart Structures and Systems
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• v.32 no.6
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• pp.393-402
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• 2023

Recently, hybrid and electric vehicles have been actively developed to replace internal combustion engine (ICE) vehicles. However, their vibrations and noise with complex spectra cause discomfort to drivers. To reduce the vibrations transmitted through primary excitation sources such as powertrains, structural changes have been introduced. However, the interference among different parts is a limitation. Thus, active mounting systems based on smart materials have been actively investigated to overcome these limitations. This study focuses on diminishing the source movement when a structure with two active mounting systems is excited to a single sinusoidal and a multi-frequency signal, which were investigated for source movement reduction. The overall structure was modeled based on the lumped parameter method. Active vibration control was implemented based on the modeled structure, and a multi-normalization least mean square (NLMS) algorithm was used to obtain the control input for the active mounting system. Furthermore, the performance of the NLMS algorithm was compared with that of the quantification method to demonstrate the performance of active vibration control. The results demonstrate that the vibration attenuation performance of the source component was improved.

• IMMUNE NETWORK
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• v.23 no.1
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• pp.6.1-6.24
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• 2023

Intestinal microorganisms interact with various immune cells and are involved in gut homeostasis and immune regulation. Although many studies have discussed the roles of the microorganisms themselves, interest in the effector function of their metabolites is increasing. The metabolic processes of these molecules provide important clues to the existence and function of gut microbes. The interrelationship between metabolites and T lymphocytes in particular plays a significant role in adaptive immune functions. Our current review focuses on 3 groups of metabolites: short-chain fatty acids, bile acids metabolites, and polyamines. We collated the findings of several studies on the transformation and production of these metabolites by gut microbes and explained their immunological roles. Specifically, we summarized the reports on changes in mucosal immune homeostasis represented by the Tregs and Th17 cells balance. The relationship between specific metabolites and diseases was also analyzed through latest studies. Thus, this review highlights microbial metabolites as the hidden treasure having potential diagnostic markers and therapeutic targets through a comprehensive understanding of the gut-immune interaction.

• Annual Conference of KIPS
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• 2007.11a
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• pp.954-957
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• 2007

AODV 에서는 RREQ 메시지 전송을 통해 라우팅 경로를 설정한다. Ad-hoc 네트워크에서 노드가 자주 이동하거나 전송 지연 시간이 클 경우 RREQ 메시지 발생이 증가한다. 이러한 네트워크 변동에 따른 RREQ 메시지 발생 증가는 결과적으로 데이터 패킷 수신율을 저하시킬 뿐만 아니라, 노드의 에너지 소모율도 증가시킨다. 본 논문에서는 네트워크 토폴로지 변동 상황을 감지하여 AODV 에서의 RREQ 메시지 발생 빈도를 효과적으로 조절하는 알고리즘을 제안하였다. 제안된 알고리즘을 50개의 노드가 10m/s 이하의 속도로 무작위로 이동하는 Ad-hoc 네트워크에 적용한 결과, 기존 AODV 알고리즘에 비해 RREQ 메시지 발생 빈도가 25% 감소하였다. 뿐만 아니라 RREP 패킷과 RERR 패킷 역시 각각 26% 및 31%씩 감소하였다. 모든 종류의 메시지 발생 빈도 수가 감소함에 따라 데이터 패킷 수신율은 3% 증가했으며, 에너지 소모율 역시 13% 감소하였다.

• Steel and Composite Structures
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• v.52 no.5
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• pp.571-581
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• 2024

After a disaster like the catastrophic earthquake, the government have to use rapid assessment of the condition (or damage) of bridges, buildings and other infrastructures is mandatory for rapid feedbacks, rescue and post-event management. This work studies the tracking control problem of a class of strict-feedback nonlinear systems with input saturation nonlinearity. Under the framework of dynamic surface control design, RBF neural networks are introduced to approximate the unknown nonlinear dynamics. In order to address the impact of input saturation nonlinearity in the system, an auxiliary control system is constructed, and by introducing a class of first-order low-pass filters, the problems of large computation and computational explosion caused by repeated differentiation are effectively solved. In response to unknown parameters, corresponding adaptive updating control laws are designed. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Applied Microscopy
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• v.38 no.3
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• pp.213-219
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• 2008

The plasticity of nervous system is generated not only due to changes in neurons but also due to changes in neuroglial cells. Astrocyte is important for maintaining the normal brain function and controlling the neuronal functions. The amygdala receives an array of important sensory information of danger signals. This information is further transduced and integrated to produce the highly adaptive emotion, fear. In this study, morphometric changes in the cell bodies of astrocytes in the amygdala, induced by prenatal stress and restraint stress were examined. For this purpose. rats were classified into 4 groups; control group (CON), only restraint-stressed (starting on P90 for 3 days) group (CONR), prenatally-stressed group (PNS), and prenatally and restraint (on P90 for 3 days) stressed group (PNSR). Astrocytes were verified with anti-GFAP immunohistochemistry, counter stained with methylene blue/azure II and were examined using the Neurolucida. Results showed that astrocytes in the amygdala of PNS rats had significantly larger cell bodies than did CON rats and this was enhanced further by restraint stress. Thus this data showed that hypertrophy of the astrocytic cell bodies of amygdala complex is induced by prenatal and restraint stress.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.373-384
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• 2000

The goal of this paper is to develop a computational method to predict cancellous bone density distributions based upon continuum levels of volumetric strain. Volumetric strain is defined as the summation of normal strains, excluding shear strains, within an elastic range of loadings. Volumetric strain at a particular location in a cancellous structure changes with changes of the boundary conditions (prescribed displacements, tractions, and pressure). This change in the volumetric strain is postulated to predict the adaptive change in the bone apparent density. This bone remodeling theory based on volumetric strain is then used with the finite element method to compute the apparent density distribution for cancellous bone in both lumbar spine and proximal femur using an iterative algorithm, considering the dead zone of strain stimuli. The apparent density distribution of cancellous bone predicted by this method has the same pattern as experimental data reported in the literature (Wolff 1892, Keller et al. 1989, Cody et al. 1992). The resulting bone apparent density distributions predict Young's modulus and strength distributions throughout cancellous bone in agreement with the literature (Keller et al. 1989, Carter and Hayes 1977). The method was convergent and sensitive to changes in boundary conditions. Therefore, the computational algorithm of the present study appears to be a useful approach to predict the apparent density distribution of cancellous bone (i.e. a numerical approximation for Wolff's Law)

• Industry Promotion Research
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• v.9 no.3
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• pp.229-240
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• 2024

The purpose of this study was to develop a program to prevent recurrence, focusing on cognitive and behavioral factors to reduce gambling game behavior in children, and to verify the effectiveness to analyze basic data necessary for prevention education. Eight children in the 4th to 6th grades of male students were selected, an experiment and control group were formed, and the effectiveness was verified only after 3 months after the experimental group was conducted once a week. First, irrational gambling beliefs, the level of gambling problems, automatic thinking for children, and the level of gambling problems were reduced through cognitive behavior therapy programs to reduce gambling game behavior in children. Changes in maladaptive thinking that directly affect gambling game behavior instilled awareness of gambling game behavior. Second, self-control and impulsiveness, the behavioral variables, did not show any significant difference, but decreased in the overall average. Changes in cognitive variables influenced behavioral variables. Third, it was found to continue even 3 months after the end of the program. Changes in cognitive and behavioral variables later reduced children's gambling game behavior and helped school life and peer relationships through adaptive thinking.