• Journal of the Semiconductor & Display Technology
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• v.16 no.2
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• pp.44-48
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• 2017

We explain optical and electrical properties of top and bottom-emission structured alternating-current powder electroluminescent devices (ACPELDs) with Ga-doped ZnO(GZO) transparent electrode. The top-emission ACPELDs were layered as the metal electrode/dielectric layer/emission layer/top transparent electrode and the bottom-emission ACPELDs were structured as the bottom transparent electrode/emission layer/dielectric layer/metal electrode. The yellow-emitting ZnS:Mn, Cu phosphor and the barium titanate dielectric layers were layered through the screen printing method. The GZO transparent electrode was deposited by the sputtering, its sheet resistivity is $275{\Omega}/{\Box}$. The transparency at the yellow EL peak was 98 % for GZO. Regardless of EL structures, EL spectra of ACPELDs were exponentially increased with increasing voltages and they were linearly increased with increasing frequencies. It suggests that the EL mechanism was attributed to the impact ionization by charges injected from the interface between emitting phosphor layer and the transparent electrode. The top-emission structure obtained higher EL intensity than the bottom-structure. In addition, charge densities for sinusoidal applied voltages were measured through Sawyer-Tower method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.578-581
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• 2014

Blackhole attack, a kinds of attacks to routing function, can cause critical effects to network transmission function, Especially, on MANET(Mobile Ad-hoc Network) which it is not easy to prepare functions to respond malicious intrusion, transmission functions of entire networks could be degraded. In this paper, effects of blackhole attack to network transmission performance is analyzed on lattice structured MANET. Specially, performance is measured for various location of blackhole attack on lattice MANET, and compared with the performance of random structured MANET. This paper is done with computer simulation, VoIP(Voice over Internet Protocol) traffic is used in simulation. The results of this paper can be used for data to deal with blackhole attack.

• Asia Pacific Journal of Business Review
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• v.4 no.1
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• pp.61-77
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• 2019

This article explored the status of E-Government initiative in developing countries by taking the case of Ethiopia. The study had analyzed the practice, challenges and the future prospect of the E-Government initiative in the country. The paper had used both secondary and primary sources of data. In identifying the practice and future prospects of the E-Government imitative, related works in the area were reviewed and appraised. In ascertaining the major challenges, structured and semi-structured interviews were carried out with selected stakeholders from the government office and the private sector. The result of the study had shown that the country had registered a promising progress in E-Government index in the past four years ranking at 157th in the year 2014, which was standing at 172nd in the year 2012. Above all, high- level political commitment and the design of specific actions plans that are linked to the national sustainable development priorities were the key success factors. Nevertheless, there are still enormous challenges that have to be tackled for citizens to drive the benefits arising from the implementation of E-Government initiative. The study had identified limited cross-sectoral connectivity, lack of intra-organizational connectivity, low human resource capacity, language barrier, lack of awareness and absence of appropriate legal and regulatory framework as major challenges. Finally, the study forwarded constructive suggestion that can be adopted in the way forward of the E-Government initiative.

• Environmental Engineering Research
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• v.24 no.3
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• pp.376-381
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• 2019

In this study, a poly(vinyl) alcohol/sodium alginate (PVA/SA) mixture was used to fabricate core-shell structured gel beads for autotrophic single-stage nitrogen removal (ASNR) using aerobic and anaerobic ammonia-oxidizing bacteria (AAOB and AnAOB, respectively). For stable ASNR process, the mechanical strength and oxygen penetration depth of the shell layer entrapping the AAOB are critical properties. The shell layer was constructed by an interfacial gelling reaction yielding thickness in the range of 2.01-3.63 mm, and a high PVA concentration of 12.5% resulted in the best mechanical strength of the shell layer. It was found that oxygen penetrated the shell layer at different depths depending on the PVA concentration, oxygen concentration in the bulk phase, and free ammonia concentration. The oxygen penetration depth was around $1,000{\mu}m$ when 8.0 mg/L dissolved oxygen was supplied from the bulk phase. This study reveals that the shell layer effectively protects the AnAOB from oxygen inhibition under the aerobic conditions because of the respiratory activity of the AAOB.

• Journal of IKEEE
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• v.24 no.4
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• pp.1034-1038
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• 2020

In order to effectively process target recognition using radar, accurate signal information for the target is required. However, such a target signal is usually mixed with noise, and this part of the study is continuously carried out. Especially, image processing, target signal processing and target recognition for the target are examples. Since the field of target recognition is important from a military point of view, this paper carried out research on target recognition of aircraft using a tree-structured fuzzy neural networks. Fuzzy neural networks are learned by using reflected signal data for an aircraft to optimize the model, and then test data for the target are used for the optimized model to perform an experiment on target recognition. The effectiveness of the proposed method is verified by the simulation results.

• Journal of Industrial and Engineering Chemistry
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• v.67
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• pp.164-174
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• 2018

The purpose of this work designed hybrid-structured and plasma effect photocatalyst of $Ag/In_2O_3/TiO_2/HNTs$ via sol-gel and photo-reduction methods. The structures, morphologies, optical and photoelectric performances of as-prepared photocatalysts were characterized via XRD, TEM, XPS, BET, UV-vis DRS, PL and photocurrents. The photocatalytic activity was evaluated by degradation of TC. The results showed that the hybrid-structure and plasma effect can effectively cause the multi-transfer of electrons and increase the separation rate of electron and hole pairs which obtained high photocatalytic activity. The photocatalytic degradation processes reveal that $^{\bullet}O_2{^-}$ and $h^+$ are major active species.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4706-4724
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• 2020

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a word-level CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers' needs are very effective for predicting their behavior.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.374-388
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• 2022

Database systems usually have many parameters that must be configured by database administrators and users. RocksDB achieves fast data writing performance using a log-structured merged tree. This database has many parameters associated with write and space amplifications. Write amplification degrades the database performance, and space amplification leads to an increased storage space owing to the storage of unwanted data. Previously, it was proven that significant performance improvements can be achieved by tuning the database parameters. However, tuning the multiple parameters of a database is a laborious task owing to the large number of potential configuration combinations. To address this problem, we selected the important parameters that affect the performance of RocksDB using random forest. We then analyzed the effects of the selected parameters on write and space amplifications using analysis of variance. We used a genetic algorithm to obtain optimized values of the major parameters. The experimental results indicate an insignificant reduction (-5.64%) in the execution time when using these optimized values; however, write amplification, space amplification, and data processing rates improved considerably by 20.65%, 54.50%, and 89.68%, respectively, as compared to the performance when using the default settings.

• Journal of Society of Preventive Korean Medicine
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• v.25 no.3
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• pp.73-88
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• 2021

Objectives : The purpose of this study is to explore the experiences of traditional korean medical doctors (TKMD) who have treated victims of sexual violence by collecting their qualitative data that cannot be obtained with statistical data. Methods : In-depth interviews were conducted with semi-structured questionnaires for each of 5 TKMDs who had experiences in treating sexual violence victims. For analysis, the interviews were recorded along with their facial expressions and actions during the interview process, and all collected data were analyzed by colaizzi's method. In each process, a 'phenomenological reduction' was applied. Results : The identity of TKMD's experience of treating sexual violence victims was structured into 25 themes, 9 theme clusters, and 4 categories. The 4 categories included 'Professionalism and sense of efficacy', 'Personal life experiences and mental trauma treatment experiences that are mutually influencing each other', 'Factors needed to increase TKMD's participation in treatment of sexual violence victims', 'Deriving the future strategy of traditional korean medicine'. Conclusions : This study derived the essence and meaning of TKMDs' experiences in treating sexual violence victims, and presented future directions of traditional korean medicine in this field. It is expected that the results of this study would be used as the basis for establishing the traditional korean medical support system for sexual violence victims in the context of the korean medical system.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.677-687
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• 2022

We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms.