• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• Convergence Security Journal
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• v.23 no.2
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• pp.27-36
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• 2023

In recent years, ransomware attacks have become more organized and specialized, with the sophistication of attacks targeting specific individuals or organizations using tactics such as social engineering, spear phishing, and even machine learning, some operating as business models. In order to effectively respond to this, various researches and solutions are being developed and operated to detect and prevent attacks before they cause serious damage. In particular, honeypots can be used to minimize the risk of attack on IT systems and networks, as well as act as an early warning and advanced security monitoring tool, but in cases where ransomware does not have priority access to the decoy file, or bypasses it completely. has a disadvantage that effective ransomware response is limited. In this paper, this honeypot is optimized for the user environment to create a reliable real-time dynamic honeypot file, minimizing the possibility of an attacker bypassing the honeypot, and increasing the detection rate by preventing the attacker from recognizing that it is a honeypot file. To this end, four models, including a basic data collection model for dynamic honeypot generation, were designed (basic data collection model / user-defined model / sample statistical model / experience accumulation model), and their validity was verified.

• Journal of Naturopathy
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• v.7 no.2
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• pp.39-46
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• 2018

Purpose: The purpose of this study was to evaluate the effect of the subjects after visiting the Gyorae forest on the activity of the autonomic nervous system. Methods: Before and after the forest bath, it was measured using a ubiquitous machine. Results: After the bath there was no significant difference in the sympathetic nerve activity (LF) of the control group, but the difference was significant in the experimental group by increasing (p<.038), and in the variance analysis, there was a significant difference between the groups (p<.014), between pre-and post-bath (p<.026), and also between the groups and pre-and post-bath (p<.018). The changes in parasympathetic activity (HF) were not significant in both the control and experimental. In the LF/HF ratio, the experimental group was significantly increased, and in the analysis of variance, there was also significant difference between group and before and after bath (p<.04). Mean pulse rate in the experimental group was a significant increase after bath (p<.026). In the change of pulse standard deviation, the value of the control and the experimental groups by variance analysis was a significant difference between the groups (p<.014). There was no difference between the mean values of the control and the experimental groups in the change of mean heart rate deviation. Conclusions: The autonomic nervous systems were activated after Gyorae forest bathing, where may be useful place for healing.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.298-298
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• 2022

Milling is an important process that determines the quality of flour and is affected by milling machine type, scale, and tempering conditions. In addition, seed hardness is an important factor in determining the amount of tempering water and has been reported to affects flour yield and flour quality. There are reports that vitreousity is used as a measure to distinguish between soft and hard seeds, and the higher the vitreousity, the higher the protein contents. However, there is no established system for measuring viterousity of seeds and studies on the vitreousity and quality characteristics of flour are insufficient. Therefore, in this study, vitreousity, hardness, and milling characteristics were evaluated for 46 major domestic varieties, and their relationship was confirmed. After cutting the seeds using a seed cutter, vitreousity was measured, and seed hardness and flour particle size was measured using SKCS and PSI, respectively. As for the seed hardness index, 'Joa' was the lowest with 11.6, 'Yeonbaek' was the highest with 78.7. As for the milling yield, 'Saeol' had the lowest at 58.1%, and 'Hcjoong' had the highest at 88.6%. Seed hardness index and wheat flour production showed a high positively correlation, showing a similar to that of previous studies. Also, in flour particle size, 'Gobun' was the largest at 75.5 pm, and 'Joa' was the smallest at 43.1 um. Flour yield and flour particle size showed a high positively correlation. As a result of vitreousity, 'Hwangeumal' (55.2%), 'Saekeumkang' (59.1%), 'Baekkang' (52.3%), 'Goso' (44.6%), and 'Joa' (19.2%) were showed. Seed hardness and vitreousity showed a high positively correlation. Also as the vitreousity increased, the flour yield also showed a tendency to increase. In addition, as the seed hardness increased, particle size of the flour yield also showed a tendency to increase. It is thought that this result can be used as a measure to determine the quality of flour with vitreousity. However, further analysis of wheat varieties and methods of analyzing vitreousity are needed.

• Phonetics and Speech Sciences
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• v.15 no.3
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• pp.75-82
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• 2023

Automatic speech recognition (ASR) has been revolutionized with deep learning-based approaches, among which self-supervised learning methods have proven to be particularly effective. In this study, we aim to enhance the performance of OpenAI's Whisper model, a multilingual ASR system on the Korean language. Whisper was pretrained on a large corpus (around 680,000 hours) of web speech data and has demonstrated strong recognition performance for major languages. However, it faces challenges in recognizing languages such as Korean, which is not major language while training. We address this issue by fine-tuning the Whisper model with an additional dataset comprising about 1,000 hours of Korean speech. We also compare its performance against a Transformer model that was trained from scratch using the same dataset. Our results indicate that fine-tuning the Whisper model significantly improved its Korean speech recognition capabilities in terms of character error rate (CER). Specifically, the performance improved with increasing model size. However, the Whisper model's performance on English deteriorated post fine-tuning, emphasizing the need for further research to develop robust multilingual models. Our study demonstrates the potential of utilizing a fine-tuned Whisper model for Korean ASR applications. Future work will focus on multilingual recognition and optimization for real-time inference.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.358-366
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• 2023

This study aimed to provide a solution for improving ship collision alert of the 'accident vulnerable ship monitoring service' among the 'intelligent marine traffic information system' services of the Ministry of Oceans and Fisheries. The current ship collision alert uses a supervised learning (SL) model with survey labels based on large ship-oriented data and its operators. Consequently, the small ship data and the operator's opinion are not reflected in the current collision-supervised learning model, and the effect is insufficient because the alarm is provided from a longer distance than the small ship operator feels. In addition, the supervised learning (SL) method requires a large number of labeled data, and the labeling process requires a lot of resources and time. To overcome these limitations, in this paper, the classification model of collision alerts for small ships using unlabeled data with the semi-supervised learning (SSL) algorithms (Label Propagation and TabNet) was studied. Results of real-time experiments on small ship operators using the classification model of collision alerts showed that the satisfaction of operators increased.

• Journal of the Korean Geotechnical Society
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• v.40 no.1
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• pp.5-14
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• 2024

PHC piles demonstrate superior resistance to compression and bending moments, and their factory-based production enhances quality assurance and management processes. Despite these advantages that have resulted in widespread use in civil engineering and construction projects, the design process frequently relies on empirical formulas or N-values to estimate the soil-pile friction, which is crucial for bearing capacity, and this reliance underscores a significant lack of experimental validation. In addition, environmental factors, e.g., the pH levels in groundwater and the effects of seawater, are commonly not considered. Thus, this study investigates the influence of vibrating machine foundations on PHC pile models in consideration of the effects of varying pH conditions. Concrete model piles were subjected to a one-month conditioning period in different pH environments (acidic, neutral, and alkaline) and under the influence of seawater. Subsequent repeated direct shear tests were performed on the pile-soil interface, and the disturbed state concept was employed to derive parameters that effectively quantify the dynamic behavior of this interface. The results revealed a descending order of shear stress in neutral, acidic, and alkaline conditions, with the pH-influenced samples exhibiting a more pronounced reduction in shear stress than those affected by seawater.

• Journal of Korean Physical Therapy Science
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• v.31 no.1
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• pp.16-32
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• 2024

Background: Exercise equipments and assistive devices for the disabled are being developed, but improvements for usability are still needed. The purpose of this study was to improve and utilize the developed exercise equipment and assistance devices by conducting usability test for people with spinal cord injury. Design: Cross-sectional Study. Methods: Scenarios and usability indicators were derived by conducting a preliminary usability test, 5 non-disabled men and women aged 19 or older. In the scenario, a total of 9 tasks were sequentially performed, including 2 tasks of entry and exit, 5 tasks of assistance devices and weight stack adjustment, and 2 tasks of pre exercise and exercise. The usability indicators were task success (success or fail), execution time (sec), safety, and convenience. For safety, 7 questions (Likert scale, 1~5 point) related to safety, stability and hazard were derived, and for convenience, the system usability scale (SUS score) was used (range: 0~100, 50 percentile rank is 68 point). Results: As a result of the usability test of people with spinal cord injury, there was a large variation among subjects in the task of adjusting the position of the pulley and support in the execution time (11.64~25.44 seconds), and one person failed to adjust the pulley. The safety level showed a lower score (score = 3 points) than other items in the item of entrapment or skin pressure, and in the case of SUS, the average score was 64.5 points, which was close to the acceptable level. Conclusion: Through the usability test, it was confirmed that exercise equipment for the disabled needs improvement in operability, pinching, and pressure, and that it is necessary to develop an assistive device that provides unrestrained posture information (biofeedback) to maintain correct posture during exercise.