• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.43-51
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• 2023

In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers. Dialect classification is one of the important techniques for speech analysis. For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition. According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach. We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences. In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers. Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features. The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2021.11a
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• pp.3-4
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• 2021

Korea government has developed Korean e-Navigation service to assist ship's collision avoidance, and is providing it since Jan. 2021 to korean vessels to reduce marine accidents caused by human error which is regarded as main reason of marine accidents. It is a huge achievement itself because it is a real-time maritime safety information service based on digital communication, but still has room for improvement to provide customized information for each vessel, such as considering ship's characteristics. This research analyzes current status and requirement of collision avoidance assistance service. Lastly, it suggests direction of improvement of service such as using data science, artificial intelligence(AI).

• Information Systems Review
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• v.25 no.4
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• pp.205-231
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• 2023

The Information Security Management System (ISMS) is a protection procedure and process that keeps information assets confidential, flawless, and available at any time. ISMS-P in Korea and ISO/IEC 27001 overseas are the most representative ISMS certification systems. In this paper, in order to understand the relationship between ISMS certification and organizational characteristics, data were collected from Korea Internet & Security Agency (KISA), Ministry of Science and ICT, Information Security Disclosure System (ISDS), Financial Supervisory Service, Data Analysis, Retrieval and Transfer System (DART), and probit regression analysis was performed. In the probit analysis, the relationship with four independent variables was confirmed for three cases: ISMS-P acquisition, ISO/IEC 27001 acquisition, and both ISMS-P and ISO/IEC 27001 acquisition. As a result of the analysis, it was found that companies that acquired both ISMS-P and ISO/IEC 27001 had a positive correlation with the total number of employees and a negative correlation with business history. In addition, the improvement direction of the ISMS-P certification system and information security disclosure system could also be confirmed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.15-20
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• 2023

Until now, Pollard's Rho algorithm has been known as the most efficient way for discrete algebraic problems to decrypt symmetric keys. However, the algorithm is being studied on how to further reduce the complexity of O(${\sqrt{p}}$) performance, along with the disadvantage of having to store the giant stride m=⌈${\sqrt{p}}$⌉ data. This paper proposes an array method for cycle detection in discrete logarithms. The proposed method reduces the number of updates of stack memory by at least 73%. This is done by only updating the array when (x_i<0.5x_i-1)∩(x_i<0.5(p-1)). The proposed array method undergoes the same number of modular calculation as stack method, but significantly reduces the number of updates and the execution time for array through the use of a binary search method.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1151-1156
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• 2023

In this paper, we propose a deep learning model that utilizes charge/discharge data from initial lithium-ion batteries to predict the remaining useful life of lithium-ion batteries. We build the DMP using the PNP model. To demonstrate the performance of DMP, we organize DML using the LSTM model and compare the remaining useful life prediction performance of lithium-ion batteries between DMP and DML. We utilize the RMSE and RMSPE error measurement methods to evaluate the performance of DMP and DML models using test data. The results reveal that the RMSE difference between DMP and DML is 144.62 [Cycle], and the RMSPE difference is 3.37 [%]. These results indicate that the DMP model has a lower error rate than DML. Based on the results of our analysis, we have showcased the superior performance of DMP over DML. This demonstrates that in the field of lithium-ion batteries, the PNP model outperforms the LSTM model.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.184-199
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• 2024

Residual crosstalk has been considered as a major drawback of conventional time-reversal processing in the case of simultaneous multiple focusing. In this paper, the Gram-Schmidt process is applied to time-reversal processing to mitigate crosstalk in ocean waveguides for multiple probe sources. Experimental data-based numerical simulations confirm that nulls can be placed at multiple locations, and it is shown that different signals can be simultaneously focused at different probe source locations, ensuring distortionless responses in terms of active time-reversal processing. This focusing property is also shown to be much less affected by a reduction in the number of receivers than the adaptive time-reversal mirror method. The proposed method is shown to be effective in eliminating crosstalk in passive multi-input multi-output communications using sea-going data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.95-101
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• 2024

The numberlink puzzle(NLP), which non-crossings with other numbers of connection in connecting lines through empty cells between a given pair of numbers, is an NP-complete problem with no known way to solve the puzzle in polynomial time. Until now, arbitrary numbers have been selected and puzzles have been solved using trial-and-error methods. This paper converts empty cells into vertices in lattice graphs connected by edge between adjacent cells for a given problem. Next, a straight line was drawn between the pairs of numbers and divided into groups of numbers where crossing occurred. A bypass route was established to avoid intersection in the cross-number group. Applying the proposed algorithm to 18 benchmarking data showed that the puzzle could be solved with a linear time complexity of O(n) for all data.

• Journal of the Health Care and Life Science
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• v.9 no.2
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• pp.231-235
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• 2021

This study collects and provides environmental data related to weather by measuring the concentration levels of fine dust at different altitudes, with the aim of forecasting fine dust concentration changes, particularly in the areas where the vulnerable reside. Institutions in the healthcare-related fields can use the real-time data on the changing fine dust concentration, which is collected through different combinations of various measuring devices and drone technologies, which have recently developed at a rapid pace. The study first collects data on the following: PM1 (fine dust particles <1 ㎛ in size), PM2.5 (fine dust particles <2.5 ㎛ in size), and PM10 (fine dust particles <10 ㎛ in size) and predicts respective changes and suggests data on various high levels. The device that was used in the study measured fine dust concentration, humidity, temperature, atmospheric pressure, carbon dioxide, total volatile organic compounds (TVoc), and formaldehyde.

• Journal of The Korean Association For Science Education
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• v.41 no.6
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• pp.471-481
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• 2021

The importance of science education for cultivating the competencies required by an intelligent information society is gradually being strengthened. The government's roles and responsibilities for science education are stipulated by laws and policies in Korea. In order to systematically support science education, continuous monitoring of related policies is essential. This study aims to develop indicators that can be used to systematically and continuously monitor the national policies on science education in Korea. To achieve this goal, we first derive the framework for the indicators that has two dimensions (learner and science education context) and three categories (input, process, and outcome) from literature reviews. In order to derive the components and subcomponents of the indicators, the contents of science education-related indicators developed in Korea or abroad were reviewed. In order to verify the suitability and validity of the framework and components of the initial indicators, a two-round Delphi method was conducted with 25 expert participants with five different professions in science education. Finally, three components of the 'input' category (student characteristics, teacher characteristics, and educational infrastructure), three components of the 'process' category (science curriculum implementation, science educational contents and programs implementation, and teacher professional development program implementation), and five components of the 'outcome' category (science competency, participation and action, affective achievement, cognitive achievement, and satisfaction) were derived. An instrument to collect data from students, teachers, and institutions was developed based on the components and subcomponents, and content validity and internal consistency of the instrument were analyzed. Korea's Science Education Indicators developed in this study can comprehensively measure the current status of science education and is expected to contribute to a more efficient and effective science education policy planning and implementation.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.24-35
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• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.