• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.197-200
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• 1995

In this paper, we proposed the multi-step multi-layer artificial neural network(MMANN) to classify the chromosome, Which is used as a chromosome pattern classifier after learning. We extracted three chromosome morphological feature parameters such as centromeric index, relative length ratio, and relative area ratio by means of preprocessing method from ten chromosome images. The feature parameters of five chromosome images were used to learn neural network and the rest of them were used to classify the chromosome images. The experiment results show that the chromosome classification error is reduced much more, comparing with less feature parameters than that of the other researchers.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.565-568
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• 2003

This paper presents a method that can be used to reduce the decoding computational complexity in turbo codes. To reduce the decoding complexity we proposed an adaptive sliding window method which control the learning period of Viterbi sliding window method depending on channel signal to interference ratio (SIR). When received signal to interference ratio (SIR) is relatively high, we can reduce the decoding complexity without a noticeable degradation of BER performance at CDMA cellular system with power control error.

• Journal of the Korean Institute of Educational Facilities
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• v.2 no.3
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• pp.19-30
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• 1995

The primary purpose of this study is to suggest the policy for the effective utilization of empty classrooms in elementary school. The empty classroom in this study means the classroom that is not used now since the number of students decreases, but can be usable for other needs in future. The following results are obtained through this survey; 1) about forty percent of classrooms are empty classrooms, 2) the ratio of empty classrooms is higher in urban area than rural area, 3) the smaller the size of classroom is, the higher the ratio is, and 4) 56.5% of the schools have one to three empty classrooms and 30.0% have four to six empty classrooms. In conclusion it is suggested that 1) the reutilization plan of empty classrooms be established according to particular situation of each school, 2) the government develop the guidelines for reutilization and administrative procedures for renewal, 3) the reutilization plan be established from the view point of the quality improvement of schooling, and 4) the enlargement and rearranagemt of learning space be considerd when reutilization is planned.

• Journal of the Korean Institute of Educational Facilities
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• v.15 no.6
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• pp.30-38
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• 2008

The aim of this study is to investigate present situation about occurrence of surplus classrooms by the merger and abolition of rural elementary schools, and to deduce basic architectural data for efficient utilization. It was examined based on the analysis of using pattern and interview of teachers of modernized elementary model schools. The occurrence ratio of surplus classrooms was about 30 to 60 percentages among seven investigated schools, the ratio was gradually increasing. Most of them were nearly leaving without certain practical use because of inaccessibility and low degree of diversion. The characteristic about diversion of surplus classroom was shown to change from special or learning room to living-related room and management-related room in order, finally, it was used a room for child care with a lapse of time. Long-tenn and continuous data accumulation for diversion and utilization of surplus classroom should be required.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.165-170
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• 2022

Lying is nuisance to all, and all liars knows it is nuisance but still keep on lying. Sometime people are in confusion how to escape from or how to detect the liar when they lie. In this research we are aiming to establish a dynamic platform to identify liar by using video analysis especially by calculating the ratio of changes in their appearance when they lie. The platform will be developed using a machine learning algorithm along with the dynamic classifier to classify the liar. For the experimental analysis the dataset to be processed in two dimensions (people lying and people tell truth). Both parameter of facial appearance will be stored for future identification. Similarly, there will be standard parameter to be built for true speaker and liar. We hope this standard parameter will be able to diagnosed a liar without a pre-captured data.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.1
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• pp.1-7
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• 2023

In this paper, we implement a lossless compression technique for time-series data generated by IoT (Internet of Things) devices to reduce the disk spaces. The proposed compression technique reduces the size of the encoded data by selectively applying CNN (Convolutional Neural Networks) or Delta encoding depending on the situation in the Forecasting algorithm that performs prediction on time series data. In addition, the proposed technique sequentially performs zigzag encoding, splitting, and bit packing to increase the compression ratio. We showed that the proposed compression method has a compression ratio of up to 1.60 for the original data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.99-101
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• 2022

이상 행동을 탐지하는 딥러닝 기반 검지 시스템은 동영상 기반 데이터로부터 움직임을 보이는 객체를 추적하고 그 객체의 행동을 분석하여 정상적인 행동 범위를 벗어나는 패턴을 보이는 영역을 이상으로 탐지한다. 특히 생성적 적대 신경망(GAN)과 광학 흐름 추정(Optical flow estimation) 기법을 활용하여 움직임에 대한 특징 정보를 추출하고 이를 학습하여 행동 패턴에 대한 모델링을 수행한다. 모델 학습 및 테스트에 활용되는 데이터셋의 해상도가 낮거나 이상 행동을 표현하는 특징 정보가 부족할 경우 최종 모델 성능에 부정적 영향을 미치게 되며, 특히 광학 흐름이 표현하는 이동량 측면에서 차이가 크게 나지 않는 이상 객체의 경우 탐지가 정확하게 이뤄지지 않는다. 본 연구에서는 동영상 프레임에서 나타나는 객체의 평균 종횡비를 구하고 정상적인 비율을 벗어나는 객체에 대해서 이상 행동을 취하는 샘플로 처리하는 후처리단 모듈을 제안하여 최종적인 모델 성능을 향상시키는 방법을 고안한다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.32-43
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

• Journal of Nutrition and Health
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• v.29 no.3
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• pp.267-277
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• 1996

Effect of DHA-rich fish oil on brain development and learning ability has been studied in Sprague Dawley rats. Female rats were fed experimental diets containing either corn oil fish oil at 10%(w/w) level throughout the gestation and lactation. Corn oil was added in fish oil diet to supply essential fatty acid at 2.3% of the calories. All male pups were weaned to the same diets of dams at 21-days after birth. Plasma fatty acid composition was analyzed for dams and pups at 21-days, 28-days and 22-weeks after birth. The analysis of DNA and fatty acid profile in the brain were undertaken at birth, 3, 7, 14, 21, 28 days and 22 weeks after birth and learning ability was tested at 18-20 weeks of age. Regardless of dietary fats, arachidonic acid(AA) and docosahexaenoic acid(DHA) were the principal polyunsaturated fatty acids in the brain. Rats fed CO diet showed a continouus increase of AA content in the brain from 10.9%(at birth) to maximum 15.3% level (14-days old), while the rars fed FO diet showed 78-79% of CO group throughout the period. Rats fed FO diet showed higher incorparation of DHA from 15.2% at birth to a maximum level of 18.5% at 140days, while the rats fed CO diet showed only 7.0% incorporation of DHA at birth and a maximum level of 11.1% at 21-days. Compared to CO group, FO group showed lower ratio of chol/PL and higher content of DHA in brain microsomal membrane, resulting in better membrane fluidity. Total amount of DNA per gram of brain was reached maximum level at 21 days in both groups. This would be a period of the cell proliferation during brain development. Overall, the rats fed fish oil diet showed a higher incorporation of DHA and membrane fluidity in the brain and better learning performances (p<0.05).