• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.1-14
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• 2024

While text-to-image models have made remarkable progress in image synthesis, certain models, particularly generative diffusion models, have exhibited a noticeable bias to- wards generating images related to the culture of some developing countries. This paper introduces an empirical investigation aimed at mitigating the bias of image generative model. We achieve this by incorporating symbols representing Saudi culture into a stable diffusion model using the Dreambooth technique. CLIP score metric is used to assess the outcomes in this study. This paper also explores the impact of varying parameters for instance the quantity of training images and the learning rate. The findings reveal a substantial reduction in bias-related concerns and propose an innovative metric for evaluating cultural relevance.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• 한국실내디자인학회논문집
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• 제28호
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• pp.265-265
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• 2001

The perceptual presentation of interior design is to represent an interior space planned by a designer as if people see it in reality. The perceptual presentation methods that have developed are perspectives, full-scale models, small-scale models, photography of models, video taping of models, computer images, computer animation, and virtual reality. The purpose of this study is to investigate limits of those perceptual presentation methods according to their characteristics. The methods have characteristics that are either static or dynamic and either monoscopic or stereoscopic. In terms of representing interior spaces and perceiving interior spaces, the dynamic characteristic is more helpful than the static characteristic because the dynamic characteristic provides consecutively changing views of interior spaces when people walk around within the spaces. The stereoscopic characteristic is more helpful than the monoscopic characteristic because the stereoscopic characteristic provides the binocular depth perception. Full-scale models, small-scale models, virtual reality that have dynamic and stereoscopic characteristics, are most effective. The next effective methods are video taping of models and computer animation that have dynamic and monoscopic characteristics. The last effective methods are perspectives and photography of models that have static and monoscopic characteristics. But the most effective methods can not be said that those are perfect because each of them still has limits. Designers have to consider the limits of each perceptual presentation method to find a way that shows their designs most effectively. To develop the perceptual presentation methods of interior design, researchers should focus on the helpful characteristics that are dynamic and stereoscopic.

• 한국실내디자인학회논문집
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• 제29호
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• pp.265-272
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• 2001

The perceptual presentation of interior design is to represent an interior space planned by a designer as if people see it in reality. The perceptual presentation methods that have developed are perspectives, full-scathe models, small-scale models, photography of models, video taping of models, computer images, computer animation, and virtual reality. The purpose of this study is to investigate limits of those perceptual presentation methods according to their characteristics. The methods have characteristics that are either static or dynamic and either monoscopic or stereoscopic. In terms of representing interior spaces and perceiving interior spaces, the dynamic characteristic is more helpful than the static characteristic because the dynamic characteristic provides consecutively changing views of interior spaces when people walk around within the spaces. The stereoscopic characteristic is more helpful than the monoscopic characteristic because the stereoscopic characteristic provides the binocular depth perception. Full-scale models, small-scale models, virtual reality that have dynamic and stereoscopic characteristics, are most effective. The next effective methods are video taping of models and computer animation that have dynamic and monoscopic characteristics. The last effective methods are perspectives and photography each of models that haute static and monoscopic characteristics. But the most effective methods can nut be said that those are perfect because each of them still has limits. Designers have to consider the limits of each perceptual presentation method to find a way that shows their designs most effectively. To develop the perceptual presentation methods of interior design, researchers should focus on the helpful characteristics that are dynamic and stereoscopic.

• 한국컴퓨터정보학회논문지
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• 제21권4호
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• pp.73-80
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• 2016

Most fault prediction models have class imbalance problems because training data usually contains much more non-fault class modules than fault class ones. This imbalanced distribution makes it difficult for the models to learn the minor class module data. Data imbalance is much higher when severity-based fault prediction is used. This is because high severity fault modules is a smaller subset of the fault modules. In this paper, we propose severity-based models to solve these problems using the three sampling methods, Resample, SpreadSubSample and SMOTE. Empirical results show that Resample method has typical over-fit problems, and SpreadSubSample method cannot enhance the prediction performance of the models. Unlike two methods, SMOTE method shows good performance in terms of AUC and FNR values. Especially J48 decision tree model using SMOTE outperforms other prediction models.

• Ocean and Polar Research
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• 제41권1호
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• pp.19-34
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• 2019

The present study obtained universal mathematical models of all elements and characteristics regarding hook and line fishing systems. To describe the hook and line fishing systems on site we used three kinds of coordinate systems: the earth based coordinate system, natural coordinate system, and flow (velocity) coordinate system. Mathematical models presented in this article allow us to define the shape of the fishing gear, the tension of the rope at different points, hydrodynamic resistance, diameter of the hook's wire, immersion depth of the fishing hooks, distance from hooks to the ground and the required lifting force of the floats. These models allow for the performance of computer simulations regarding any kinds of hook and line gears in still water or water where flow occurs.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 하계학술대회
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• pp.63-66
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• 2018

Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

• ETRI Journal
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• 제44권4호
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• pp.624-640
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• 2022

Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1585-1590
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• 1991

This paper presents the principles for design of autonomous systems whose behavior is based on models that support the various tasks that must be performed. We propose a model-based architecture aimed at reducing the computational demands required to integrate high level symbolic models with low level dynamic models. Model construction methods are illustrated to outfit such an architecture with the models needed to meet given objectives.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.65-70
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• 2021

Waste accumulation is becoming a significant challenge in most urban areas and if it continues unchecked, is poised to have severe repercussions on our environment and health. The massive industrialisation in our cities has been followed by a commensurate waste creation that has become a bottleneck for even waste management systems. While recycling is a viable solution for waste management, it can be daunting to classify waste material for recycling accurately. In this study, transfer learning models were proposed to automatically classify wastes based on six materials (cardboard, glass, metal, paper, plastic, and trash). The tested pre-trained models were ResNet50, VGG16, InceptionV3, and Xception. Data augmentation was done using a Generative Adversarial Network (GAN) with various image generation percentages. It was found that models based on Xception and VGG16 were more robust. In contrast, models based on ResNet50 and InceptionV3 were sensitive to the added machine-generated images as the accuracy degrades significantly compared to training with no artificial data.