• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.47-54
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• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.

• Archives of Plastic Surgery
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• v.50 no.2
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• pp.200-209
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• 2023

Background As the coronavirus disease 2019 virus made its way throughout the world, there was a complete overhaul of our day-to-day personal and professional lives. All aspects of health care were affected including academics. During the pandemic, teaching opportunities for resident training were drastically reduced. Consequently, medical universities in many parts across the globe implemented online learning, in which students are taught remotely and via digital platforms. Given these developments, evaluating the existing mode of teaching via digital platforms as well as incorporation of new models is critical to improve and implement. Methods We reviewed different online learning platforms used to continue regular academic teaching of the plastic surgery residency curriculum. This study compares the four popular Web conferencing platforms used for online learning and evaluated their suitability for providing plastic surgery education. Results In this study with a response rate of 59.9%, we found a 64% agreement rate to online classes being more convenient than normal classroom teaching. Conclusion Zoom was the most user-friendly, with a simple and intuitive interface that was ideal for online instruction. With a better understanding of factors related to online teaching and learning, we will be able to deliver quality education in residency programs in the future.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.569-577
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• 2000

The position of tobacco leaves is affluence to the quality. To evaluate its quality, sample leaves was collected according to the position of attachment. In Korea, the position was divided into four classes such as high, middle, low and inside positioned leaves. Until now, the grade of standard sample was determined by human expert from korea ginseng and tobacco company. Many research were done by the chemical and spectrum analysis using NIR and computer vision. The grade of tobacco leaves mainly classified into 5 grades according to the attached position and its chemical composition. In high and low positioned leaves shows a low level grade under grade 3. Generally, inside and medium positioned leaf has a high level grade. This is the basic research to develop a real time tobacco leaves grading system combined with portable NIR spectrum analysis system. However, this research just deals with position recognition and grading using the color machine vision. The RGB color information was converted to HSI image format and the sample was all investigated using the bundle of tobacco leaves. Quality grade and position recognition was performed through well known general error back propagation neural network. Finally, the relationship about attached leaf position and its grade was analyzed.

• Earthquakes and Structures
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• v.24 no.5
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• pp.345-352
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• 2023

This paper studied the effect of soil-structure interaction (SSI) on the seismic response and retrofit of a reinforced concrete structure with a soft-first story for different soil types. A 5-story structure built on a 30m deep homogeneous soil mass was considered as a case study structure, and steel column jacketing and steel bracing were chosen as seismic retrofit methods. Seismic responses of a fixed-base and a flexible base structure subjected to seven scaled earthquake records were obtained using the software OpenSees to investigate the effect of soil on seismic response and retrofit. The nonlinearBeamColumn elements with the fiber sections were used to simulate the nonlinear behavior of the beams and columns. Soil properties were defined based on shear wave velocity according to categorized site classes defined in ASCE-7. The finite element model of the soil was made using isoparametric four-noded quadrilateral elements and the nonlinear dynamic responses of the combined system of soil and structure were calculated in the OpenSees. The analysis results indicate that the soil-structure interaction plays an important role in the seismic performance and retrofit of a structure with a soft-first story. It was observed that column steel jacketing was effective in the retrofit of the model structure on a fixed base, whereas stronger retrofit measures such as steel bracing were needed when soil-structure interaction was considered.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2034-2046
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• 2023

Industrial control systems in nuclear facilities are facing increasing cyber threats due to the widespread use of information and communication equipment. To implement cyber security programs effectively through the RG 5.71, it is necessary to quantitatively assess cyber risks. However, this can be challenging due to limited historical data on threats and customized Critical Digital Assets (CDAs) in nuclear facilities. Previous works have focused on identifying data flows, the assets where the data is stored and processed, which means that the methods are heavily biased towards information security concerns. Additionally, in nuclear facilities, cyber threats need to be analyzed from a safety perspective. In this study, we use the system theoretic process analysis to identify system-level threat scenarios that could violate safety constraints. Instead of quantifying the likelihood of exploiting vulnerabilities, we quantify Security Control Measures (SCMs) against the identified threat scenarios. We classify the system and CDAs into four consequence-based classes, as presented in NEI 13-10, to analyze the adversary impact on CDAs. This allows for the ranking of identified threat scenarios according to the quantified SCMs. The proposed framework enables stakeholders to more effectively and accurately rank cyber risks, as well as establish security and response strategies.

• Journal of Species Research
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• v.12 no.4
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• pp.267-276
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• 2023

To obtain unrecorded bacterial species in Korea, various soils of coastal areas were collected from the Republic of Korea in 2022. After plating the samples on marine agar and incubating aerobically and anaerobically, approximately 1,700 bacterial strains were isolated and identified using 16S rRNA gene sequences. A total of 20 strains showed ≥98.7% 16S rRNA gene sequence similarity with validly published bacterial species but not reported in Korea, indicating they are unrecorded bacterial species in Korea. The unrecorded bacterial strains belonged to four phyla, six classes, 15 orders, 16 families, and 19 genera which were assigned to Blastomonas and Sphingomonas of the class Alphaproteobacteria; Pseudidiomarina, Kushneria, Salinicola, and Salinisphaera of the class Gammaproteobacteria; Evansella, Virgibacillus, and Paenibacillus of the class Bacilli; Cyclobacterium of the class Cytophagia; Pedobacter of the class Sphingobacteriia; and Demequina, Ornithinimicrobium, Blastococcus, Jatrophihabitans, Kineococcus, Glaciihabitans, Aeromicrobium and Streptomyces of the class Actinomycetes. The details of the 20 unreported species, including Gram reaction, morphology, biochemical characteristics, and phylogenetic position are also provided in the description of the strains.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.945-952
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• 2023

In this paper, GRS chip driven JNI code application SW design based on Android platform was designed and fabricated as motion gesture frame module based on Android platform. The serial data reception module design proposed by the application-based network support API technology was designed with Android-based module design, Android-based module implementation, and Android-based function module implementation design. The data information of the sensor could be checked through Android applications such as classes of serial communication drivers, libraries, and frameworks for receiving data from wireless communication devices through Android OS applications. In addition, applications in Android implement application SW that can judge motion gestures in four directions using Java.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.304-310
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• 2023

With the development of information and communication technology, modern people are receiving various benefits, and knowledge and use of information and communication technology are becoming essential qualities in modern people's lives. There are people who do not enjoy the rich benefits of this information and communication society, and the so-called 'digital divide' acts as an obstacle that prevents the information and communication from enjoying a rich life. Currently, there are four major information underprivileged classes in Korea, the disabled, the elderly, low-income class and farmers and fishermen, respectively. The purpose of this study is to identify the current status of the digital divide for the low-income class and to analyze the causes of the digital divide. To this end, in this study, we analyze statistics on digital divide at the national level and analyzed the digital divide of low-income class from three perspectives: information access, information capability, and information use. As a result of the analysis, it was found that the lack of information capability was the biggest cause of the digital divide, and in particular, information management ability was the most insufficient among information capabilities.

• Structural Engineering and Mechanics
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• v.88 no.1
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• pp.13-23
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• 2023

Seismic risk assessment studies are one of the most crucial instruments for mitigating casualties and economic losses. This work utilizes fragility curves to evaluate the seismic risk of single-story precast buildings, which are generally favored in Marmara's organized industrial zones. First, the precast building stock in the region has been categorized into nine sub-classes. Then, seven locations in the Marmara region with a high concentration of industrial activities are considered. Probabilistic seismic hazard assessments were conducted for both the soil-dependent and soil-independent scenarios. Subsequently, damage analysis was performed based on the structural capacity and mean fragility curves. Considering four different consequence models, 630 sub-class-specific loss curves for buildings were obtained. In the current study, it has been determined that the consequence model has a significant impact on the loss curves, hence, average loss curves were computed for each case investigated. In light of the acquired results, it was found that the loss ratio values obtained at different locations within the same region show significant variation. In addition, it was observed that the structural damage states change from serviceable to repairable or repairable to unrepairable. Within the scope of the study, 126 average loss functions were presented that could be easily used by non-experts in earthquake engineering, regardless of structural analysis. These functions, which offer loss ratios for varying hazard levels, are valuable outputs that allow preliminary risk assessment in the region and yield sensible outcomes for insurance activities.

• Journal of Information Technology Applications and Management
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• v.30 no.3
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• pp.1-13
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• 2023

Demand forecasting is a crucial task for an online retail where has to manage daily fresh foods effectively. Failing in forecasting results loss of profitability because of incompetent inventory management. This study investigated the optimal performance of different forecasting models for a very short shelf-life product. Demand data of 13 perishable items with aging of 210 days were used for analysis. Our comparison results of four methods: Trivial Identity, Seasonal Naïve, Feed-Forward and Autoregressive Recurrent Neural Networks (DeepAR) reveals that DeepAR outperforms with the lowest MAPE. This study also suggests the managerial implications by employing coefficient of variation (CV) as demand variation indicators. Three classes: Low, Medium and High variation are introduced for classify 13 products into groups. Our analysis found that DeepAR is suitable for medium and high variations, while the low group can use any methods. With this approach, the case can gain benefit of better fill-rate performance.