• Atmosphere
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• v.33 no.2
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• pp.125-154
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• 2023

Since the Korean Meteorological Society was organized in 1963, the climate dynamics fields have been made remarkable progress. Here, we documented the academic developments in the area of climate dynamics performed by members of Korean Meteorological Society, based on studies that have been published mainly in the Journal of Korean Meteorological Society, Atmosphere, and Asia-Pacific Journal of Atmospheric Sciences. In these journals, the fundamental principles of typical ocean-atmosphere climatic phenomena such as El Niño, Madden-Julian Oscillation, Pacific Decadal Oscillation, and Atlantic Multi-decadal Oscillation, their modeling, prediction, and its impact, are being conducted by members of Korean Meteorological Society. Recently, research has been expanded to almost all climatic factors including cryosphere and biosphere, as well as areas from a global perspective, not limited to one region. In addition, research using an artificial intelligence (AI), which can be called a cutting-edge field, has been actively conducted. In this paper, topics including intra-seasonal and Madden-Julian Oscillations, East Asian summer monsoon, El Niño-Southern Oscillation, mid-latitude and polar climate variations and some paleo climate and ecosystem studies, of which driving mechanism, modeling, prediction, and global impact, are particularly documented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.13-21
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• 2023

In the recent era of the fourth industrial revolution, many industries aim to maximize the efficiency of products and services by introducing cutting-edge technologies such as artificial intelligence and big data. In this situation, organizational culture is changing a lot due to the influx of the MZ generation with strong individualistic tendencies and the decreased face-to-face communication between members. However, active communication with colleagues is still essential to maximize performance, and the margins created by simplifying work processes and automating processes must be used for creating work performance. This requires cooperation and commitment through the job immersion of members who have an active attitude. This study analyzed how the organization's autonomous work environment and trust among members, which are creative work performance conditions, affect job immersion using raw data from the Occupational Safety and Health Research Institute. As a result, it was found that both the organization's autonomous working environment and trust among members significantly effected the members' job immersion. in order to achieve productivity and value improvement in companies, efforts are needed to increase workers' job immersion by building an autonomous working environment and trust among members. The results of this study are expected to contribute significantly to the search for ways to increase workers' job commitment to improve organizational productivity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.213-215
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• 2021

With the development of IoT technology, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. In particular, a system based on a digital image may cause a malfunction due to noise in the image due to a sensor defect or a communication environment problem. Therefore, research on image processing has been continued as a pre-processing process, and an effective noise reduction technique is required depending on the type of noise and the characteristics of the image. In this paper, we propose a modified spatial weight filter to protect edge components in the impulse noise reduction process. The proposed algorithm divides the filtering mask into four regions and calculates the standard deviation of each region. The final output was filtered by applying a spatial weight to the region with the lowest standard deviation value. Simulation was conducted to evaluate the performance of the proposed algorithm, and it showed superior impulse noise reduction performance compared to the existing method.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.510-515
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• 2022

In this paper, a microstrip patch sensor antenna (MPSA) for detecting the concentration of an ethanol-water solution in a microliter volume is proposed. A rectangular slot was added at the radiating edge of the patch to increase the sensitivity to the relative permittivity change. To improve a low input resistance caused by placing an ethanol-water solution, which is a polar liquid with high dielectric constant and high loss tangent, on the patch, a quarter-wave impedance transformer was added between the 50-ohm feedline and the patch, and the MPSA was fabricated on a 0.76 mm-thick RF-35 substrate. A cylindrical container was made of acryl, and 15 microliters of the ethanol-water solution was tested from 0% to 100% of ethanol concentration at 20% intervals. Experiment results show that the resonant frequency increased from 1.947 GHz to 2.509 GHz when the ethanol concentration of the ethanol-water solution was increased from 0% to 100%, demonstrating the performance as a concentration detecting sensor.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.104-110
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• 2023

In the user-centered design paradigm, information systems are created entirely tailored to the users who will use them. When the functions of a complex system meet a simple user interface, users can use the system conveniently. While web personalization services are emerging as a major trend in portal services, portal companies are competing for a second service, such as introducing 'integrated communication platforms'. Until now, the role of the portal has been content and search, but this time, the goal is to create and provide the personalized services that users want through a single platform. Personalization service is a login-based cloud computing service. It has the characteristic of being able to enjoy the same experience at any time in any space with internet access. Personalized web services like this have the advantage of attracting highly loyal users, making them a new service trend that portal companies are paying attention to. Researchers spend a lot of time collecting research-related information by accessing multiple information sources. There is a need to automatically build interest information profiles for each researcher based on personal presentation materials (papers, research projects, patents). There is a need to provide an advanced customized information service that regularly provides the latest information matched with various information sources. Continuous modification and supplementation of each researcher's information profile of interest is the most important factor in increasing suitability when searching for information. As researchers' interest in unstructured information such as technology markets and research trends is gradually increasing from standardized academic information such as patents, it is necessary to expand information sources such as cutting-edge technology markets and research trends. Through this, it is possible to shorten the time required to search and obtain the latest information for research purposes. The interest information profile for each researcher that has already been established can be used in the future to determine the degree of relationship between researchers and to build a database. If this customized information service continues to be provided, it will be useful for research activities.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.17-23
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• 2024

Losses in domestic water supply due to leaks are very large, such as fractures and defects in pipelines. Therefore, preventive measures to prevent water leakage are necessary. We propose the development of a leakage detection sensor utilizing vibration sensors and present an optimal leakage detection algorithm leveraging artificial intelligence. Vibrational sound data acquired from water pipelines undergo a preprocessing stage using FFT (Fast Fourier Transform), followed by leakage classification using an optimized tree-based boosting algorithm. Applying this method to approximately 260,000 experimental data points from various real-world scenarios resulted in a 97% accuracy, a 4% improvement over existing SVM(Support Vector Machine) methods. The processing speed also increased approximately 80 times, confirming its suitability for edge device applications.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.93-100
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• 2024

With the advent of the Fourth Industrial Revolution, cutting-edge technologies such as artificial intelligence, big data, the Internet of Things, and cloud computing are driving innovation across industries. These technologies are generating massive amounts of data that many companies are leveraging. However, there is a notable reluctance among users to share sensitive information due to the privacy risks associated with collecting personal data. This is particularly evident in the healthcare sector, where the collection of sensitive information such as patients' medical conditions poses significant challenges, with privacy concerns hindering data collection and analysis. This research presents a novel technique for collecting and analyzing medical data that not only preserves privacy, but also effectively extracts statistical information. This method goes beyond basic data collection by incorporating a strategy to efficiently mine statistical data while maintaining privacy. Performance evaluations using real-world data have shown that the propose technique outperforms existing methods in extracting meaningful statistical insights.

• Journal of Korean Clinical Health Science
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• v.12 no.1
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• pp.1668-1677
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• 2024

Objective: This study investigated the awareness of occupational therapy in the community in occupational therapy majors through a survey. This purpose is to investigate the awareness of occupational therapy in the community among occupational therapy majors and establish a theoretical foundation. Methods: The research subjects were surveyed among occupational therapy majors at M University living in Gyeongsangnam-do, and analyzed 141 questionnaires from September 2023 to December 2023. Results: The results of this study that education and awareness improvement are needed to increase awareness of occupational therapy in the community, and it was found that continuing education and case sharing are effective. Activation of home rehabilitation and continuous health management. This institutional development can induce employment activity through community rehabilitation, and activate programs in connection with adult day care centers. For the development of community occupational therapy, participation in education and development of customized treatment are necessary, and patient It should be developed to help with movement and movement, and it has been shown that it can also affect the quality of life of patients. In addition, cutting-edge technologies such as artificial intelligence are expected to be applied to remote support, telemedicine, etc., and are applied to dementia, cognitive patients, and central nervous system patients. In order to institutionalize occupational therapy in the community, it is helpful in daily life, nursing, and management. It was said that this would be helpful for community participation. Conclusion: This study investigated the awareness of occupational therapy in the community among occupational therapy majors. Education and awareness improvement are needed to increase awareness of occupational therapy in the community. Education to improve the professionalism of occupational therapists, strengthening connectivity with other majors, and local organizations. It is believed that collaboration with the local community and institutional supplementation tailored to the needs of the local community were necessary.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4717-4737
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• 2017

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.