• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5723-5743
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• 2018

The evolving internet-based services demand high-speed data transmission in conjunction with scalability. The next generation optical network has to exploit artificial intelligence and cognitive techniques to cope with the emerging requirements. This work proposes a novel way to solve the dynamic provisioning problem in optical network. The provisioning in optical network involves the computation of routes and the reservation of wavelenghs (Routing and Wavelength assignment-RWA). This is an extensively studied multi-objective optimization problem and its complexity is known to be NP-Complete. As the exact algorithms incurs more running time, the heuristic based approaches have been widely preferred to solve this problem. Recently the software-defined networking has impacted the way the optical pipes are configured and monitored. This work proposes the dynamic selection of path computation algorithms in response to the changing service requirements and network scenarios. A software-defined controller mechanism with a novel packet matching feature was proposed to dynamically match the traffic demands with the appropriate algorithm. A software-defined controller with Path Computation Element-PCE was created in the ONOS tool. A simulation study was performed with the case study of dynamic path establishment in ONOS-Open Network Operating System based software defined controller environment. A java based NOX controller was configured with a parent path computation element. The child path computation elements were configured with different path computation algorithms under the control of the parent path computation element. The use case of dynamic bulk path creation was considered. The algorithm selection method is compared with the existing single algorithm based method and the results are analyzed.

• Korean Chemical Engineering Research
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• v.57 no.3
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• pp.425-431
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• 2019

As rechargeable metal-air batteries will be ideal energy storage devices in the future, an active cathode electrocatalyst is required with bi-functionality on both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) during discharge and charge, respectively. Here, a class of perovskite cathode catalyst with a micro-tubular structure has been developed by controlling bi-functionality from different Ru and Ni dopant ratios. A micro-tubular structure is achieved by the activated carbon fiber (ACF) templating method, which provides uniform size and shape. At the perovskite formula of $LaCrO_3$, the dual dopant system is successfully synthesized with a perfect incorporation into the single perovskite structure. The chemical oxidation states for each Ni and Ru also confirm the partial substitution to B-site of Cr without any changes in the major perovskite structure. From the electrochemical measurements, the micro-tubular feature reveals much more efficient catalytic activity on ORR and OER, comparing to the grain catalyst with same perovskite composition. By changing the Ru and Ni ratio, the $LaCr_{0.8}Ru_{0.1}Ni_{0.1}O_3$ micro-tubular catalyst exhibits great bi-functionality, especially on ORR, with low metal loading, which is comparable to the commercial catalyst of Pt and Ir. This advanced catalytic property on the micro-tubular structure and Ru/Ni synergy effect at the perovskite material may provide a new direction for the next-generation cathode catalyst in metal-air battery system.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.969-976
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• 2019

A lightweight artificial intelligence hardware has made great strides in many application areas. In general, a lightweight artificial intelligence system consist of lightweight artificial intelligence engine and preprocessor including feature selection, generation, extraction, and normalization. In order to achieve optimal performance in broad range of applications, lightweight artificial intelligence system needs to choose a good preprocessing function and set their respective hyper-parameters. This paper proposes a unified framework for a lightweight artificial intelligence system and utilization method for finding models with optimal performance to use on a given dataset. The proposed unified framework can easily generate a model combined with preprocessing functions and lightweight artificial intelligence engine. In performance evaluation using handwritten image dataset and fall detection dataset measured with inertial sensor, the proposed unified framework showed building optimal artificial intelligence models with over 90% test accuracy.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.45-52
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• 2021

In this paper, we propose a method of generating theme, background amd signal music usage monitoring information based on a blockchain, in which the music usage informations are recorded by the monitoring tool using feature-based filtering of monitoring organizations. Theme, background and signal music are music inserted into the broadcasting contents of broadcaster. Since they are recognized as created contents just like normal music, there are lyricists and composers who have the right for those music and all copyright holders of them have to receive the corresponding copyright fees, once the music was used in the broadcast. However, there are problems with inaccurate monitoring results for music usage, due to the omission of usage details and non-transparent settlement method. In order to solve these problems, If the information generation method proposed in this paper, accurate music usage history can be created, the details are stored in the blockchain without changes or omissions, and transparent settlement and distribution are possible by smart contract, avoiding the current non-transparent settlement method.

• Biomolecules & Therapeutics
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• v.29 no.2
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• pp.127-134
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• 2021

Neuroinflammation―a common pathological feature of neurodegenerative disorders such as Alzheimer's disease―is mediated by microglial activation. Thus, inhibiting microglial activation is vital for treating various neurological disorders. 7,3',4'-Trihydroxyisoflavone (THIF)―a secondary metabolite of the soybean compound daidzein―possesses antioxidant and anticancer properties. However, the effects of 7,3',4'-THIF on microglial activation have not been explored. In this study, antineuroinflammatory effects of 7,3',4'-THIF in lipopolysaccharide (LPS)-stimulated BV2 microglial cells were examined. 7,3',4'-THIF significantly suppressed the production of the proinflammatory mediators nitric oxide (NO), inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2) as well as of the proinflammatory cytokine interleukin-6 (IL-6) in LPS-stimulated BV2 microglial cells. Moreover, 7,3',4'-THIF markedly inhibited reactive oxygen species (ROS) generation. Western blotting revealed that 7,3',4'-THIF diminished LPS-induced phosphorylation of extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), glycogen synthase kinase-3β (GSK-3β), and nuclear factor kappa B (NF-κB). Overall, 7,3',4'-THIF exerts antineuroinflammatory effects against LPS-induced microglial activation by suppressing mitogen-activated protein kinase (MAPK) and NF-κB signaling, ultimately reducing proinflammatory responses. Therefore, these antineuroinflammatory effects of 7,3',4'-THIF suggest its potential as a therapeutic agent for neurodegenerative disorders.

• Journal of the Korea Computer Graphics Society
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• v.26 no.5
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• pp.15-24
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• 2020

We present a supervised learning method that estimates the simulation parameters required to simulate the fabric from the static drape shape of a given fabric sample. The static drape shape was inspired by Cusick's drape, which is used in the apparel industry to classify fabrics according to their mechanical properties. The input vector of the training model consists of the feature vector extracted from the static drape and the density value of a fabric specimen. The output vector consists of six simulation parameters that have a significant influence on deriving the corresponding drape result. To generate a plausible and unbiased training data set, we first collect simulation parameters for 400 knit fabrics and generate a Gaussian Mixed Model (GMM) generation model from them. Next, a large number of simulation parameters are randomly sampled from the GMM model, and cloth simulation is performed for each sampled simulation parameter to create a virtual static drape. The generated training data is fitted with a log-linear regression model. To evaluate our method, we check the accuracy of the training results with a test data set and compare the visual similarity of the simulated drapes.

• Progress in Medical Physics
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• v.31 no.3
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• pp.54-62
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• 2020

Dose calculation algorithms play an important role in radiation therapy and are even the basis for optimizing treatment plans, an important feature in the development of complex treatment technologies such as intensity-modulated radiation therapy. We reviewed the past and current status of dose calculation algorithms used in the treatment planning system for radiation therapy. The radiation-calculating dose calculation algorithm can be broadly classified into three main groups based on the mechanisms used: (1) factor-based, (2) model-based, and (3) principle-based. Factor-based algorithms are a type of empirical dose calculation that interpolates or extrapolates the dose in some basic measurements. Model-based algorithms, represented by the pencil beam convolution, analytical anisotropic, and collapse cone convolution algorithms, use a simplified physical process by using a convolution equation that convolutes the primary photon energy fluence with a kernel. Model-based algorithms allowing side scattering when beams are transmitted to the heterogeneous media provide more precise dose calculation results than correction-based algorithms. Principle-based algorithms, represented by Monte Carlo dose calculations, simulate all real physical processes involving beam particles during transportation; therefore, dose calculations are accurate but time consuming. For approximately 70 years, through the development of dose calculation algorithms and computing technology, the accuracy of dose calculation seems close to our clinical needs. Next-generation dose calculation algorithms are expected to include biologically equivalent doses or biologically effective doses, and doctors expect to be able to use them to improve the quality of treatment in the near future.

• Journal of English Language & Literature
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• v.58 no.2
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• pp.279-304
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• 2012

Since the publication of Foucault's Discipline and Punish, theatricality has become one of the key concepts in New Historicism. By defining theatricality as the most definitive feature of early modern society and culture, New Historicists have promoted the idea that theatrical practices in every day life were eventually replaced by textual practices as the western society started to undergo modernization with the advent of print culture and technologies. This paper questions this linear model of English literature, the shift of literary practices from theatricality to textuality in the event of modernization, by closely looking at the ways in which newsbooks and playlets during the English civil wars appealed to their target readers. The early print-based literary commodities during the English civil war (i.e. newsbooks and playlets) were able to win the attention of their audience not by breaking away from theatrical energy and creativity but instead by embracing and taking advantage of them through the use of dramatic conventions, dialogues, and many others. The newsbooks and the playlets during the time, however, did not simply replicate the dramatic forms and experiences of the previous generation. Instead, as the case study of Craftie Cromwell exemplifies, they went further to produce a different mode of theatricality by reshaping everyday lives into serialized drama, whose resolution is always already delayed and postponed into the ever-receding future. In conclusion, the study of the newsbook and playlets during the civil wars suggests that the textuality of modern times, materialized in print forms, have been co-evolved with the development of new theatricality, whose contents and forms are susceptible to the changes of everyday reality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1528-1534
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• 2019

Recently, due to the popularization of 3D depth cameras, new researches and opportunities have been made in research conducted on RGB images, but estimation of human hand pose is still classified as one of the difficult topics. In this paper, we propose a robust estimation method of human hand pose from various input 3D depth images using a learning algorithm. The proposed approach first generates a skeleton-based hand model and then aligns the generated hand model with three-dimensional point cloud data. Then, using a random forest-based learning algorithm, the hand pose is strongly estimated from the aligned hand model. Experimental results in this paper show that the proposed hierarchical approach makes robust and fast estimation of human hand posture from input depth images captured in various indoor and outdoor environments.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.