• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.49-49
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• 2010

The most commonly used numerical modelling techniques for acoustics and vibration are based on element based techniques, such as the nite element and boundary element method. Due to the huge computational eorts involved, the use of these deterministic techniques is practically restricted to low-frequency applications. For high-frequency modelling, probabilistic techniques such as SEA are well established. However, there is still a wide mid-frequency range, for which no adequate and mature prediction techniques are available. In this frequency range, the computational eorts of conventional element based techniques become prohibitively large, while the basic assumptions of the probabilistic techniques are not yet valid. In recent years, a vast amount of research has been initiated in a quest for an adequate solution for the current midfrequency problem. One family of research methods focuses on novel deterministic approaches with an enhanced convergence rate and computational eciency compared to the conventional element based methods in order to shift the practical frequency limitation towards the mid-frequency range. Amongst those techniques, a wave based prediction technique using an indirect Tretz approach is being developed at the K.U.Leuven - Noise and Vibration Research group. This paper starts with an outline of the major features of the mid-frequency modelling challenge and provides a short overview of the current research activities in response to this challenge. Next, the basic concepts of the wave based technique and its hybrid coupling with nite element schemes are described. Various validations on two- and threedimensional acoustic, elastic, poro-elastic and vibro-acoustic examples are given to illustrate the potential of the method and its benecial performance as compared to conventional element based methods. A closing part shares some views on the open issues and future research directions.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.452-472
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• 2005

We propose XIR-Branching, a novel method for processing partial match queries on heterogeneous XML documents using information retrieval(IR) techniques and novel instance join techniques. A partial match query is defined as the one having the descendent-or-self axis '//' in its path expression. In its general form, a partial match query has branch predicates forming branching paths. The objective of XIR-Branching is to efficiently support this type of queries for large-scale documents of heterogeneous schemas. XIR-Branching has its basis on the conventional schema-level methods using relational tables(e.g., XRel, XParent, XIR-Linear[21]) and significantly improves their efficiency and scalability using two techniques: an inverted index technique and a novel prefix match join. The former supports linear path expressions as the method used in XIR-Linear[21]. The latter supports branching path expressions, and allows for finding the result nodes more efficiently than containment joins used in the conventional methods. XIR-Linear shows the efficiency for linear path expressions, but does not handle branching path expressions. However, we have to handle branching path expressions for querying more in detail and general. The paper presents a novel method for handling branching path expressions. XIR-Branching reduces a candidate set for a query as a schema-level method and then, efficiently finds a final result set by using a novel prefix match join as an instance-level method. We compare the efficiency and scalability of XIR-Branching with those of XRel and XParent using XML documents crawled from the Internet. The results show that XIR-Branching is more efficient than both XRel and XParent by several orders of magnitude for linear path expressions, and by several factors for branching path expressions.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.315-327
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• 2007

The performance of database applications with large sets of multidimensional data depends on the performance of its access methods and the underlying disk system. In modeling the disk system, even though modem disks are manufactured with multiple physical zones, conventional access methods have been developed based on a traditional disk model with many simplifying assumptions. Thus, there is a marked lack of investigation on how to enhance the performance of access methods given a zoned disk model. The paper proposes novel zoning techniques that can be applied to any multidimensional access methods, both static and dynamic, enhancing the effective data transfer rate of underlying disk system by fully utilizing its zone characteristics. Our zoning techniques include data placement algorithms for multidimensional index structures and accompanying localized query processing algorithms for range queries. The experimental results show that our zoning techniques significantly improve the query performance.

• Journal of Communications and Networks
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• v.2 no.1
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• pp.76-84
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• 2000

Communication systems, including cell-based mobile communication systems, multiple satellite communication systems of multi-beam satellite systems, require reliable handoff methods between cell-to-cell, satellite-to-satellite of beam-to-team, respectively. Recent measurement of a CDMA cellular system indicates that the system is in handoff at about 35% to 70% of an average call period. Therefore, system reliability during handoff is one of the major system performance parameters and eventually becomes a factor in the overall system capacity. This paper presents novel and improved techniques for handoff in cellular communications, multi-beam and multi-satellite systems that require handoff during a session. this new handoff system combines the soft handoff mechanism currently implemented in the IS-95 CDMA with code and packet diversity combining techniques and an iterative decoding algorithm (Turbo Coding). the Turbo code introduced by Berrou et all. has been demonstrated its remarkable performance achieving the near Shannon channel capacity [1]. Recently. Turbo codes have been adapted as the coding scheme for the data transmission of the third generation international cellular communication standards : UTRA and CDMA 2000. Our proposed encoder and decoder schemes modified from the original Turbo code is suitable for the code and packet diversity combining techniques. this proposed system provides not only an unprecedented coding gain from the Turbo code and it iterative decoding, but also gain induced by the code and packet diversity combining technique which is similar to the hybrid Type II ARQ. We demonstrate performance improvements in AWGN channel and Rayleigh fading channel with perfect channel state information (CSI) through simulations for at low signal to noise ratio and analysis using exact upper bounding techniques for medium to high signal to noise ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5998-6016
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• 2017

In thin-film transistor liquid-crystal displays (TFT-LCDs), which are most commonly used in mobile devices, the backlight accounts for about 70% of the power consumption. Therefore, most low-power-related studies focus on realizing power savings through backlight dimming. Image compensation is performed to mitigate the visual distortion caused by the backlight dimming. Therefore, popular techniques include pixel compensation for brightness recovery and contrast enhancement, such as histogram equalization. However, existing pixel compensation techniques often have limitations with respect to blur owing to the pixel saturation phenomenon, or because contrast enhancement cannot adequately satisfy the human visual system (HVS). To overcome these, in this study, we propose a novel dimming technique to achieve both power saving and HVS-awareness by combining the pixel compensation and histogram specifications, which convert the original cumulative density function (CDF) by designing and using the desired CDF of an image. Because the process of obtaining the desired CDF is customized to consider image characteristics, histogram specification is found to achieve better HVS-awareness than histogram equalization. For the experiments, we employ the LIVE image database, and we use the structural similarity (SSIM) index to measure the degree of visual satisfaction. The experimental results show that the proposed technique achieves up to 15.9% increase in the SSIM index compared with existing dimming techniques that use pixel compensation and histogram equalization in the case of the same low-power ratio. Further, the results indicate that it achieves improved HVS-awareness and increased power saving concurrently compared with previous techniques.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.176-183
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• 2003

Various techniques including histograms, sampling and parametric techniques have been proposed to estimate query result sizes for the query optimization. Histogram-based techniques are the most widely used form for the selectivity estimation in relational database systems. However, in the spatio-temporal databases for the moving objects, the continual changes of the data distribution suffer the direct utilization of the state of the art histogram techniques. Specifically for the future queries, we need another methodology that considers the updated information and keeps the accuracy of the result. In this paper we propose a novel approach based upon the duality and the marginal distribution to construct a histogram with very little time since the spatio-temporal histogram requires the data distribution defined by query predicates. We use data synopsis method in the dual space to construct spatio-temporal histograms. Our method is robust to changing data distributions during a certain period of time while the objects keep the linear movements. An additional feature of our approach supports the dynamic update incrementally and maintains the accuracy of the estimated result.

• Journal of Applied Biological Chemistry
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• v.61 no.4
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• pp.305-314
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• 2018

'New Breeding Techniques (NBTs)' have been one of hot issues, since their future will be affected profoundly by national as well as international regulatory landscapes. In this review, we compare characteristics of NBTs with conventional and genetic modification, and analyze genetically modified organism (GMO) regulatory systems in the context of possible regulation of NBTs. NBTs are very heterogeneous in terms of principles, methodologies, and final products. As Living Modified Organisms (LMO) is defined in the Cartagena Protocol on Biosafety (CPB) as an organism containing novel combination of genetic materials obtained by the use of modern biotechnology, CPB as well as other national legislations locate itself somewhere in the middle between product-based and process-based regulations. It is also noted that jurisdictions with regulatory systems more oriented to product-based one tend to be more productive and decide or may decide to exempt site-directed nucleases-1 from GMO regulation. In this context, Korean legislations are reviewed to clarify the commons and differences in GMO definitions. Act on Transboundary Movement of LMO Act, Food Sanitation Act and Agricultural and Fishery Products Quality Control Act are three major acts to regulate GMOs. It is noted that there are differences in the definition of LMO or GM food/products especially between the LMO Act and the Food Sanitation Act. Such differences may cause conflicts between Acts when policy-decision regarding the regulation of NBTs is made. Therefore, it is necessary to reorganize legislations before policies regarding the regulation of any techniques from biotechnology are made.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.31-38
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• 2023

Style transfer is one of deep learning-based image processing techniques that has been actively researched recently. These research efforts have led to significant improvements in the quality of result images. Style transfer is a technology that takes a content image and a style image as inputs and generates a transformed result image by applying the characteristics of the style image to the content image. It is becoming increasingly important in exploiting the diversity of digital content. To improve the usability of style transfer technology, ensuring stable performance is crucial. Recently, in the field of natural language processing, the concept of Transformers has been actively utilized. Attention maps, which forms the basis of Transformers, is also being actively applied and researched in the development of style transfer techniques. In this paper, we analyze the representative techniques SANet and AdaAttN and propose a novel attention map-based structure which can generate improved style transfer results. The results demonstrate that the proposed technique effectively preserves the structure of the content image while applying the characteristics of the style image.