• Korean Journal of Mathematics
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• v.28 no.4
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• pp.943-953
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• 2020

A university grades input system requires for professors to enter the normalized total scores for the letter grades and to input the scores from six fields such as Midterm, Final, Quiz which sum up to the total. All six fields have specified bounds which add up to 100. Professors should scale in the total scores to match up the letter grades and scale in every field of each student's original scores within the bounds to sum up to the scaled total score. We solve this problem by a novel design of simple shallow neural network.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.13
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• pp.5493-5498
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• 2015

Due to the lack of nationwide population-based cancer registration, the total cancer prevalence in Iran is unknown. Our previous work in which we used a basic network scale-up (NSU) method, failed to provide plausible estimates of total cancer prevalence in Kerman. The aim of the present study was to estimate total and partial prevalence of cancer in southeastern Iran using an adapted version of the generalized network scale-up method. A survey was conducted in 2014 using multi-stage cluster sampling. A total of 1995 face-to-face gender-matched interviews were performed based on an adapted version of the NSU questionnaire. Interviewees were asked about their family cancer history. Total and partial prevalence were estimated using a generalized NSU estimator. The Monte Carlo method was adopted for the estimation of upper/lower bounds of the uncertainty range of point estimates. One-yr, 2-3 yr, and 4-5 yr prevalence (per 100,000 people) was respectively estimated at 78 (95%CI, 66, 90), 128 (95%CI, 118, 147), and 59 (95%CI, 49, 70) for women, and 48 (95%CI, 38, 58), 78 (95%CI, 66, 91), and 42 (95%CI, 32, 52) for men. The 5-yr prevalence of all cancers was estimated at 0.18 percent for men, and 0.27 percent for women. This study showed that the generalized familial network scale-up method is capable of estimating cancer prevalence, with acceptable precision.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.101-110
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• 2021

In computer vision, single-image super resolution has been an area of research for a significant period. Traditional techniques involve interpolation-based methods such as Nearest-neighbor, Bilinear, and Bicubic for image restoration. Although implementations of convolutional neural networks have provided outstanding results in recent years, efficiency and single model multi-scalability have been its challenges. Furthermore, previous works haven't placed enough emphasis on real-number scalability. Interpolation-based techniques, however, have no limit in terms of scalability as they are able to upscale images to any desired size. In this paper, we propose a convolutional neural network possessing the advantages of the interpolation-based techniques, which is also efficient, deeming it suitable in practical implementations. It consists of convolutional layers applied on the low-resolution space, post-up-sampling along the end hidden layers, and additional layers on high-resolution space. Up-sampling is applied on a multiple channeled feature map via bicubic interpolation using a single model. Experiments on architectural structure, layer reduction, and real-number scale training are executed with results proving efficient amongst multi-scale learning (including scale multi-path-learning) based models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.620-636
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• 2015

Survivability is a necessary property of network system in disturbed environment. Recovery ability is a key actor of survivability. This paper concludes network survivability into a novel composite metric, i.e. Network Recovery Degree (NRD). In order to measure this metric in quantity, a concept of Source-Destination Pair (SD Pair), is created to abstract end-to-end activity based on end nodes in network, and the quality of SD Pair is also used to describe network performance, such as connectivity, quality of service, link degree, and so on. After that, a Survivability Test method in large scale Network based on SD pairs, called STNSD, is provided. How to select SD Pairs effectively in large scale network is also provided. We set up simulation environment to validate the test method in a severe destroy scenario and evaluate the method scalability in different large scale network scenarios. Experiment and analysis shows that the metric NRD correctly reflects the effort of different survivability strategy, and the proposed test method STNSD has good scalability and can be used to test and evaluate quantitative survivability in large scale network.

• Journal of computational fluids engineering
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• v.19 no.1
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• pp.47-56
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• 2014

Since a typical plate heat exchanger is made up of a huge number of unitary cells, it may be impossible to predict the aero-thermal performance of the full scale heat exchanger through three-dimensional numerical simulation due to the enormous amount of computing resources and time required. In the present study, a simple flow-network model using the friction factor correlation and a thermal-network model based on the effectiveness-number of transfer units (${\varepsilon}$-NTU) method has been developed. The complicated flow pattern inside the cross-corrugated heat exchanger has been modeled into flow and thermal networks. Using this model, the heat transfer between neighboring streams can be considered, and the pressure drop and the heat transfer rate of full-scale heat exchanger matrix are calculated. In the calculation, the aero-thermal performance of each unitary cell of the heat exchanger matrix was evaluated using correlations of the Fanning friction factor f and the Nusselt number Nu, which were calculated by unitary-cell CFD model.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1467-1472
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• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

• Human Ecology Research
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• v.51 no.5
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• pp.473-481
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• 2013

The purpose of this study was to investigate the moderating effects of a size of the friendship network and quality of friendship on the associations between mutual antipathy and maladjustment. The subjects were 678 fifth- and sixth-grade primary school children who were recruited from a public school in Bucheon City. The Peer Nomination Inventory was used to assess mutual antipathy, peer victimization, social withdrawal, aggression, and the friendship network. The children were given a classroom roster and asked to nominate up to three classmates who fit each description. Additionally, the children reported the quality of their friendships using the Friendship Quality Scale. Each child was asked to indicate his or her one best friend and rate how accurately a sentence describe done of their best friends on the scale. The results revealed that the friendship network and friendship quality significantly moderated the relationships between mutual antipathy and social withdrawal, and peer victimization. The magnitude of the association between mutual antipathy and social withdrawal was not significant for large friendship networks and high quality friendships. Although mutual antipathy was significantly associated with peer victimization, the association was stronger at lower levels than at higher levels of the friendship network and quality. However, there was no moderating effect of the friendship network and quality on the association between mutual antipathy and aggression. A large friendship network and high quality friendship could be protective factors among those who have mutual antipathy in peer groups.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.7
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• pp.19-30
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• 1992

In this paper, a new large scale ATM switch architecture for Broadband ISDN is presented and its performance is analyzed mathematically. The proposed two-stage ATM switch consists of a sorting network and several unit switches. The proposed switch is self-routing and nonlocking. Its maximum through put is 100% without speed up which other output buffered switch needs. The hardware complexity mainly depends on that of a sorting network, but sorting network is easy to be implemented to VLSI because of its regularity in the structure.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.97-106
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• 1999

This paper proposes the structure and the algorithm of the Fuzzy-Neural Controller(FNNC) which is able to adapt itself to unknown plant and the change of circumstances at the Fuzzy Logic Controller(FLC) with the Neural Network. This Learning Fuzzy Logic Controller is made up of Fuzzy Logic controller in charge of a main role and Neural Network of an adaptation in variable circumstances. This construct optimal fuzzy controller applied to the 2-area load frequency control of power system, and then it would examine fitness about parameter variation of plant or variation of circumstances. And it proposes the optimal Scale factor method wsint three preformance functions( E, , U) of system dynamics of load frequency control with error back-propagation learning algorithm. Applying the controller to the model of load frequency control, it is shown that the FNNC method has better rapidity for load disturbance, reduces load frequency maximum deviation and tie line power flow deviation and minimizes reaching and settling time compared to the Optimal Fuzzy Logic Controller(OFLC) and the Optimal Control for optimzation of performance index in past control techniques.

• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.126-131
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• 2000

Through many users employ TCP of which the performance has been proved in Internet, but many papers Proposed to improve TCP performance according to varying network architecture. In Particular, BWDP(bandwidth-delay Product) grew larger because of the increasing delay in satellite link and the network's speed-up. To consider these increased bandwidth-delay product, it is suggested that TCP options include Window Scale option. TimeStamp option, and PAWS. Because TCP window size should be commonly high in the network with these increased bandwidth-delay product, the multiple decrease and linear increase scheme of current TCP would cause underflow and instability within network. Then TCP performance is reduced as a result. Thus, to improve TCP congestion control algorithm in the network which has large sized window, this paper proposes the congestion control scheme that controls window size by using TimeStamp option.