• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.91-97
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• 2018

This paper presents implementation possibilities of digital IoT doorlock systems via VLC(Visible Light Communication)'s color grid. The color grid-based VLC modulation scheme which are discussed in this paper utilize the straightness of light and abundant frequency resources which are the properties of the light. Performance results in this paper are compared to that of conventional modulations with Bit Error Rate (BER) and Signal to Noise Ratio (SNR) simulations. With respect to a channel model, the proposed modulation schemes select the nearest Line Of Sight (LOS) except Non Line Of Sight (NLOS). Experiments in this paper show error rates of received symbols by changing power dB at a distance of 3m between Tx and Rx in an indoor environment. Through performance results and experiments, this paper demonstrates superiority of the proposed color grid-based modulation schemes.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.357-369
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• 2020

This study tries to identify and clarify the concept of barriers to sexual health nursing care. A hybrid model was used to perform a concept of barriers to sexual health nursing care. To accomplish this, we analyzed data collected from 4 participants as well as from available literature regarding. We categorized barriers to sexual health nursing care were identified to have five dimensions and six attributes. The definition of barriers to sexual health nursing care recognizes the need and necessity of sexual health nursing care, but due to difficulty in accessing sex, prejudice, stereotypes, difficulty in communication, and lack of knowledge related to sex, the subjects are provided with professional sexual health nursing education and it is found that the service was not provided. This study is a basic data for evaluating barriers to sexual health nursing care as a prerequisite for reducing barriers to sexual health nursing care experienced by nurses to provide sexual health nursing care. Besides, the results may serve as a basis for proposing the development of a program to reduce barriers to sexual health nursing care.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.193-202
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• 2022

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.225-234
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• 2021

The purpose of this paper is to examine commercialization model of appropriate technology through the case of the Lao-Korea Science and Technology Center (LKSTC). LKSTC has developed washing, water treatment, and sterilization technology in the agrifood sector and three types of pico-hydro generator, Pico-solar hybrid system, and energy remote monitoring technology in the renewable energy sector. Commercialization of appropriate technology was successfully carried out through the establishment of Kaipan community business, school enterprises, and social enterprise. The policy implications are as follows. First, the commercialization of appropriate technology in developing countries should enhance the linkage with the regional development policies of the recipient countries. Second, in order to minimize market risk, innovative technology development and local startup networks should be properly established. Finally, the mid and long term efforts are needed to increase the sustainability of the business.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.37-44
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• 2022

Plug-in Hybrid electric vehicles (PHEV) show great potential to reduce gas emission, improve fuel efficiency and offer more driving range flexibility. Moreover, PHEV help to preserve the eco-system, climate changes and reduce the high demand for fossil fuels. To address this; some basic components and energy resources have been used, such as batteries and proton exchange membrane (PEM) fuel cells (FCs). However, the FC remains unsatisfactory in terms of power density and response. In light of the above, an electric storage system (ESS) seems to be a promising solution to resolve this issue, especially when it comes to the transient phase. In addition to the FC, a storage system made-up of an ultra-battery UB is proposed within this paper. The association of the FC and the UB lead to the so-called Fuel Cell Battery Electric Vehicle (FCBEV). The energy consumption model of a FCBEV has been built considering the power losses of the fuel cell, electric motor, the state of charge (SOC) of the battery, and brakes. To do so, the implementing a reinforcement-learning energy management strategy (EMS) has been carried out and the fuel cell efficiency has been optimized while minimizing the hydrogen fuel consummation per 100km. Within this paper the adopted approach over numerous driving cycles of the FCBEV has shown promising results.

• Mass Spectrometry Letters
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• v.13 no.3
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• pp.49-57
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• 2022

During electrospray ionization mass spectrometry (ESI-MS) analysis of proteins, the addition of supercharging agents allows for adjusting the maximal charge state, affecting the charge state distribution, and increases the number of ions reaching the detector thus, improving signal detection. We postulate that in di-substituted arene isomers, molecules with higher polarizability values should generate greater interactions and hence elicit higher signal intensities. Polarizability is an electronic parameter which has been demonstrated to predict many chemical interactions. Many properties can be predicted based on charge polarization. Molecular polarizability is a vital descriptor for explaining intermolecular interactions. We employed DFT (density functional/Hartree-Fock hybrid model, B3LYP)-derived descriptors and computed molecular polarizability for ten disubstituted arene reagents, each set made up of three (ortho, meta, para) isomers, with reported use as supercharging reagents during ESI experiments. The atomic electronic inputs were ionization potential (IP), electron affinity (EA), electronegativity (𝛘), hardness (η), chemical potential (µ), and dipole moment (D). We determined that the para isomers showed the highest polarizability values in nine of the ten sets. There was no difference between the ortho and meta isomers. Polarizability also increased with increasing complexity of the substituents on the benzene ring. Polarizability correlated positively with IP, EA, 𝛘, η, and D but correlated negatively with chemical potential. This DFT study predicts that the para isomers of di-substituted arene isomers should elicit the strongest ESI responses. An experimental comparison of the three isomers, especially of larger supercharging molecules, could be carried out to establish this premise.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.11-18
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• 2022

Linking Ukraine's education system with the trends of global digitalization is mandatory to ensure the sustainable, long-term development of the country, as well as to increase the sustainability of the education system and the economy as a whole during the crisis period. Now the main problems of the education system in Ukraine are manifested in a complex context caused by Russian armed aggression. In the context of war, problems include differences in adaptation to online learning among educational institutions, limited access to education for vulnerable groups in the zone of active hostilities, the lack of digital educational resources suitable for online learning, and the lack of basic digital skills and competencies among students and teachers necessary to properly conduct online classes. Some of the problems of online learning were solved in the pandemic, but in the context of war Ukrainian society needs a new vision of education and continuous efforts of all social structures in the public and private environment. In the context of war, concerted action is needed to keep education on track and restore it in active zones, adapting to the needs of a dynamic society and an increasingly digitized economy. Among the urgent needs of the education system are a change in the teaching-learning paradigm, which is based on content presentation, memorization, and reproduction, and the adoption of a new, hybrid educational model that will encourage the development of necessary skills and abilities for students and learners in a digitized society and enable citizens close to war zones to learn.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.100-108
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• 2023

In hybrid block-based video coding, transform coding converts spatial domain residual signals into frequency domain data and concentrates energy in a low frequency band to achieve a high compression efficiency in entropy coding. The state-of-the-art video coding standard, VVC(Versatile Video Coding), uses DCT-2(Discrete Cosine Transform type 2), DST-7(Discrete Sine Transform type 7), and DCT-8(Discrete Cosine Transform type 8) for primary transform. In this paper, considering that DCT-2, DST-7, and DCT-8 are all linear transformations, we propose an inverse transform that reduces the number of multiplications in the inverse transform by using the linearity of the linear transform. The proposed inverse transform method reduced encoding time and decoding time by an average 26%, 15% in AI and 4%, 10% in RA without the increase of bitrate compared to VTM-8.2.