• International journal of advanced smart convergence
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• v.12 no.4
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• pp.208-216
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• 2023

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1345-1352
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• 2023

With the development of virtual reality (VR) and blockchain technology, Metaverse is being used in various fields such as games, education, and social networking. At the same time, shipments of wearable devices such as smartwatches are growing every year, becoming more and more integrated into people's daily lives. This study presents a new possibility of reflecting the user's body signals measured through the combination of the two phenomena in the metaverse character. Various biometric information such as the user's heart rate and amount of exercise collected through the smartwatch are reflected on the character in the metaverse, allowing the user's physical condition to be reflected in the virtual world. Through this, Metaverse is expected to provide a new experience that can be called 'extended reality' beyond simple virtual reality, improve user's satisfaction with Metaverse, and suggest a direction for the development of smartwatches.

• Journal of Drive and Control
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• v.20 no.4
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• pp.71-79
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• 2023

In the field of agricultural machinery, various on-field tests are conducted to measure design load for optimal design of agricultural equipment. However, field test procedures are costly and time-consuming, and there are many constraints on field soil conditions due to weather, so research on utilizing simulation to overcome these shortcomings is needed. Therefore, this study aimed to model agricultural soils using discrete element method (DEM) software. To simulate draft force, predictions are made according to travel speed and compared to field test results to validate the prediction accuracy. The measured soil properties are used for DEM modeling. In this study, the soil property measurement procedure was designed to measure the physical and mechanical properties. DEM soil model calibration was performed using a virtual vane shear test instead of the repose angle test. The DEM simulation results showed that the prediction accuracy of the draft force was within 4.8% (2.16~6.71%) when compared to the draft force measured by the field test. In addition, it was confirmed that the result was up to 72.51% more accurate than those obtained through theoretical methods for predicting draft force. This study provides useful information for the DEM soil modeling process that considers the working speed from the perspective of agricultural machinery research and it is expected to be utilized in agricultural machinery design research.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.53-58
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• 2024

This paper presents a novel miniaturized 3-D cubic antenna for use in wireless sensor network (WSN) application. The geometry of this antenna is designed as a cube including a meander dipole antenna. A truly omnidirectional pattern is produced by this antenna in both E-plane and H-plane, which allows for non-intermittent communication that is orientation independent. The operating frequency lies in the ISM band (centered in 2.45 GHz). The dimensions of this ultra-compact cubic antenna are 1.25*1.12*1cm3 which features a length dimension λ/11. The coefficient which presents the overall antenna structure is Ka=0.44. The cubic shape of the antenna is allowing for smart packaging, as sensor equipment may be easily integrated into the cube hallow interior. The major constraint of WSN is the energy consumption. The power consumption of radio communication unit is relatively high. So it is necessary to design an antenna which improves the energy efficiency. The parameters considered in this work are the resonant frequency, return loss, efficiency, bandwidth, radiation pattern, gain and the electromagnetic field of the proposed antenna. The specificity of this geometry is that its size is relatively small with an excellent gain and efficiency compared to previously structures (reported in the literature). All results of the simulations were performed by CST Microwave Studio simulation software and validated with HFSS. We used Advanced Design System (ADS) to validate the equivalent scheme of our conception. Input here the part of summary.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.41-49
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• 2024

Speech recognition technology is continuously advancing and widely used in various fields. In this study, we aimed to investigate the impact of speech data quality on speech recognition models by dividing the dataset into the entire dataset and the top 70% based on Signal-to-Noise Ratio (SNR). Utilizing Seamless M4T and Google Cloud Speech-to-Text, we examined the text transformation results for each model and evaluated them using the Levenshtein Distance. Experimental results revealed that Seamless M4T scored 13.6 in models using data with high SNR, which is lower than the score of 16.6 for the entire dataset. However, Google Cloud Speech-to-Text scored 8.3 on the entire dataset, indicating lower performance than data with high SNR. This suggests that using data with high SNR during the training of a new speech recognition model can have an impact, and Levenshtein Distance can serve as a metric for evaluating speech recognition models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.133-139
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• 2024

In response to the challenges posed by a super-aged society, local authorities are conducting educational programs on smartphone usage tailored for the elderly. However, obstacles such as the limitations of one-to-many education and suboptimal learning outcomes for the elderly have hindered the efficacy of smartphone education. This study suggests an educational service intended for direct application in offline settings, considering the identified problems. Through the utilization of generative AI, the proposed app identifies specific challenges encountered by users during actual smartphone use, offering personalized exercises to facilitate customized and repetitive learning experiences for individual users. When integrated with existing local government education initiatives, this app is anticipated to enhance the efficiency of smartphone education by providing personalized, one-on-one training that is efficient in terms of time and content.

• Information Systems Review
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• v.18 no.3
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• pp.137-153
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• 2016

Organizations depend on smart working environments, such as mobile networks. This development motivates companies to focus on information security. Information leakage negatively affects companies. To address this issue, management and information security researchers focus on compliance of employees with information security policies. Countermeasures in information security are known antecedents of intention to comply information security policies. Despite the importance of this topic, research on the antecedents of information security countermeasures is scarce. The present study proposes information security intelligence as an antecedent of information security countermeasures. Information security intelligence adapted the concept of safety intelligence provided by Kirwan (2008). Information security intelligence consists of problem solving skills, social skills, and information security knowledge related to information security. Results show that problem solving skills and information security knowledge have positive effects on the awareness of employees of information security countermeasures.

• The Korean Journal of Franchise Management
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• v.15 no.2
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• pp.57-75
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• 2024

Purpose: Nowadays, many restaurants use serving robots. Initially, many people thought that Covid-19 caused the spread of serving robots. However, even as the endemic, many restaurants still use serving robots. Therefore, this study examines why many customers choose restaurants with serving robots, using the UTAUT2 framework. Additionally, this study explores whether shyness has a moderating effect on these factors. Research design, data and methodology: Data were collected from 307 consumers who had visited a restaurant using a serving robot and analyzed using SmartPLS 4.0 software. A total of 286 datasets were analyzed. Result: We found that the precedence factors of UTAUT2 (Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Condition, Hedonic Motivation) had a positive effect on attitude. Furthermore, attitude had a significant positive effect on Behavioral Intention. However, shyness did not appear to have a moderating effect among these factors. This is likely due to customers using serving robots for very short time, as identified in the literature review. Conclusions: As a result of this study, it was explained that Hedonic Motivation had the most significant positive effect on shaping attitudes toward restaurants using serving robots through the UTAUT2 model.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.471-476
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• 2024

With the spread of various smart devices and computing devices, big data generation is occurring widely. Machine learning is an algorithm that performs reasoning by learning data patterns. Among the various machine learning algorithms, the algorithm that attracts attention is deep learning based on neural networks. Deep learning is achieving rapid performance improvement with the release of various applications. Recently, among deep learning algorithms, attempts to analyze data using graph structures are increasing. In this study, we present a graph generation method for transferring to a deep learning network. This paper proposes a method of generalizing node properties and edge weights in the graph generation process and converting them into a structure for deep learning input by presenting a matricization We present a method of applying a linear transformation matrix that can preserve attribute and weight information in the graph generation process. Finally, we present a deep learning input structure of a general graph and present an approach for performance analysis.

• Safety and Health at Work
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• v.14 no.4
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• pp.415-424
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• 2023

Background: Occupational safety & health management (OSH) has garnered greater attention for its significance in promoting corporate sustainability for organizations in recent decades. The construction industry, in particular, is a major contributor to Malaysia's thirst for corporate sustainability in order to provide long-term support for the country. Thus, the main tenet of this study is to examine the mediating effect of employee affective commitment on the relationship between OSH and corporate sustainability. Methods: A questionnaire was administered to 273 full-time employees of listed construction companies in Malaysia. Smart PLS software version 3 was used to test the proposed model and hypotheses. Both the measurement model and the structural model were evaluated. Results: According to the findings, OSH and its dimensions are positively related to employee affective commitment. Employee affective commitment, on the other hand, has been found to be significantly related to corporate sustainability and its dimensions: economic, social, and environmental sustainability. Apart from this, the prominent results reveal that employee affective commitment partially mediates the relationship between OSH and corporate sustainability and its dimensions: economic, social, and environmental sustainability. Conclusion: This empirical finding adds to the existing literature in explaining how OSH and affective commitment led to corporate sustainability. Several implications are offered to various stakeholders, such as construction companies, policymakers, and relevant regulators.