• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.163-168
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• 2022

As cloud computing and the Internet of things are getting popular, the number of devices in the Internet of things computing environments is increasing. In addition, there exist various Internet-based applications, such as home automation and healthcare. In turn, existing studies explored the quality of service, such as downtime and reliability of tasks for Internet of things applications. To enhance the quality of service of Internet of things applications, cloud-fog computing (combining cloud computing and edge computing) can be used for offloading burdens from the central cloud server to edge servers. However, when devices inherit the mobility property, continuity and the quality of service of Internet of things applications can be reduced. In this paper, we propose a resource management scheme based on live migrations for mobility support in edge-based fog computing environments. The proposed resource management algorithm is based on the mobility direction and pace to predict the expected position, and migrates tasks to the target edge server. The performance results show that our proposed resource management algorithm improves the reliability of tasks and reduces downtime of services.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.4
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• pp.130-137
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• 2022

Due to the coronavirus disease 2019 (COVID-19) pandemic, appropriate management of sequelae and treatment of infectious symptoms became increasingly important healthcare issues. Although the practice guidelines and treatment cases based on the East Asian traditional medicine have been reported, there are rare studies on the use of Korean medicine in Korea. Therefore, this study aimed to present the progress of non-contact Korean medical treatment for infected patients at a local public health center using retrospective chart review. A total of 18 patients were prescribed with 5 days of herbal decoction and medicine extract covered by the national health insurance. With the questionnaire form, the progression and improvement of symptoms before and after treatment were evaluated using the numerical rating scale (NRS), and the treatment satisfaction and opinions were obtained. The symptoms such as cough (5.56±2.23 to 2.89±2.14), sputum (6.11±1,75 to 3.28±2.47), sore throat (6.06±2.70 to 1.47±1.62), anorexia (5.56±2.63 to 1.94±2.21), nausea (3.75±1.71 to 1.17±1.11), diarrhea (3.40±2.63 to 1.50±1.51), chest tightness (4.93±2.46 to 2.29±2.30) and fatigue (6.44±1.79 to 2.67±1.88) all improved according to the NRS, and the satisfaction with herbal medicine treatment on a 5-point Likert scale was 4.24±0.90. No side effects and adverse reactions were reported. Thereupon non-contact Korean medical treatment can be concluded that it effectively reduces the COVID-19 infection mild symptoms in restrictive extent. Since the retrospective data does not include a control group, the more confirmative data is needed by multicenter and large-scale controlled clinical study afterwards.