• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.1-7
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• 2023

The advent of deep learning technologies has led to the development of various medical applications, making healthcare services more convenient and effective. Among these applications, heart rate estimation is considered a vital method for assessing an individual's health. Traditional methods, such as photoplethysmography through smart watches, have been widely used but are invasive and require additional hardware. Recent advancements allow for contactless heart rate estimation through facial image analysis, providing a more hygienic and convenient approach. In this paper, we propose a lightweight methodology capable of accurately estimating heart rate in mobile environments, using a specialized 2-channel network structure based on 2D convolution. Our method considers both subtle facial movements and color changes resulting from blood flow and muscle contractions. The approach comprises two major components: an Encoder for analyzing image features and a regression layer for evaluating Blood Volume Pulse. By incorporating both features simultaneously our methodology delivers more accurate results even in computing environments with limited resources. The proposed approach is expected to offer a more efficient way to monitor heart rate without invasive technology, particularly well-suited for mobile devices.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.105-114
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• 2024

Recently, with an emphasis on software proficiency, universities are providing software education to all students regardless of their majors. However, non-majors often lack motivation for software education and perceive the unfamiliar learning content as more challenging. To address this issue, tailored software education according to the learners' characteristics is essential. Art students, for instance, with their strong visual comprehension and expressive abilities, can benefit from utilizing visual literacy to enhance the effectiveness of programming education. In this study, we propose decomposing everyday problems into flowcharts and pseudocode to construct procedural and visual images. Using the educational programming language PlayBot, we aim to analyze the effectiveness of teaching by coding to solve problems. Through this approach, students are expected to grasp programming concepts, understand problem-solving processes through computational thinking, and acquire skills to apply programming in their respective fields.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.888-896
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• 2023

When an aircraft is landing, it is expected that the aircraft will follow a specified approach procedure and then land at the airport. However, depending on the airport situation, neighbouring aircraft or the instructions of the air traffic controller, there can be a deviation from the specified approach. Detecting aircraft approach patterns is necessary for traffic flow and flight safety, and this paper suggests clustering techniques to identify aircraft patterns in the approach segment. The Gaussian Mixture Model (GMM), one of the machine learning techniques, is used to cluster the trajectories of aircraft, and ADS-B data from aircraft landing at the Gimhae airport in 2019 are used. The aircraft trajectories are clustered on the plane, and a total of 86 approach trajectory patterns are extracted using the centroid value of each cluster. Considering the correlation between the approach procedure pattern and overshoots, the distribution of overshoots is calculated.

• Archives of Plastic Surgery
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• v.50 no.6
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• pp.627-634
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• 2023

In recent decades, a number of simulation models for microsurgical training have been published. The human placenta has received extensive validation in microneurosurgery and is a useful instrument to facilitate learning in microvascular repair techniques as an alternative to using live animals. This study uses a straightforward, step-by-step procedure for instructing the creation of simulators with dynamic flow to characterize the placental vascular tree and assess its relevance for plastic surgery departments. Measurements of the placental vasculature and morphological characterization of 18 placentas were made. After the model was used in a basic microsurgery training laboratory session, a survey was given to nine plastic surgery residents, two microsurgeons, and one hand surgeon. In all divisions, venous diameters were larger than arterial diameters, with minimum diameters of 0.8 and 0.6 mm, respectively. The majority of the participants considered that the model faithfully reproduces a real microsurgical scenario; the consistency of the vessels and their dissection are similar in in vivo tissue. Furthermore, all the participants considered that this model could improve their surgical technique and would propose it for microsurgical training. As some of the model's disadvantages, an abundantly thick adventitia, a thin tunica media, and higher adherence to the underlying tissue were identified. The color-perfused placenta is an excellent tool for microsurgical training in plastic surgery. It can faithfully reproduce a microsurgical scenario, offering an abundance of vasculature with varying sizes similar to tissue in vivo, enhancing technical proficiency, and lowering patient error.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.415-424
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• 2023

The task of sorting and excluding blemished apples and others that lack commercial appeal is currently performed manually by human eye sorting, which not only causes musculoskeletal disorders in workers but also requires a significant amount of time and labor. In this study, an automated apple-sorting machine was developed to prevent musculoskeletal disorders in apple production workers and to streamline the process of sorting blemished and non-marketable apples from the better quality fruit. The apple-sorting machine is composed of an arm-rest, a main body, and a height-adjustable part, and uses object detection through a machine learning technology called 'You Only Look Once (YOLO)' to sort the apples. The machine was initially trained using apple image data, RoboFlow, and Google Colab, and the resulting images were analyzed using Jetson Nano. An algorithm was developed to link the Jetson Nano outputs and the conveyor belt to classify the analyzed apple images. This apple-sorting machine can immediately sort and exclude apples with surface defects, thereby reducing the time needed to sort the fruit and, accordingly, achieving cuts in labor costs. Furthermore, the apple-sorting machine can produce uniform quality sorting with a high level of accuracy compared with the subjective judgment of manual sorting by eye. This is expected to improve the productivity of apple growing operations and increase profitability.

• Journal of Distribution Science
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• v.10 no.6
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• pp.39-53
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• 2012

It is widely known that knowledge management plays a facilitating role that contributes to upgrading organizational performance. Knowledge management systems (KMS), especially, support the knowledge management process including the sharing, creating, and using of knowledge within a company, and maximize the value of knowledge resources within an organization. Despite this widely held belief, there are few studies that describe how companies actually develop, share, and practice their knowledge. Companies in the domestic small franchise sector, which are in the early stages in terms of knowledge management, need to improve their KMS to manage their franchisees effectively. From this perspective, this study uses a qualitative approach to explore the actual process of knowledge management implementation. This article presents a case study of PB (Paris Baguette) company, which is the first to build a KMS in the franchise industry. The study was able to confirm the following facts through the analysis of target companies. First, the chief executive's support is a critical success factor and this support can increase the participation of organization members. Second, it is important to build a process and culture that actively creates and leverages information in knowledge management activities. The organizational learning culture should be one where the creation, learning, and sharing of new knowledge is developed continuously. Third, a horizontal network organization is needed in order to make relationships within the organization more close-knit. Fourth, in order to connect the diverse processes such as knowledge acquisition, storage, and utilization of knowledge management activities, information technology (IT) capabilities are essential. Indeed, IT can be a powerful tool for improving the quality of work and maximizing the spread and use of knowledge. However, during the construction of an intranet based KMS, research is required to ensure that the most efficient system is implemented. Finally, proper evaluation and compensation are important success factors. In order to develop knowledge workers, an appropriate program of promotion and compensation should be established. Also, building members' confidence in the benefits of knowledge management should be an ongoing activity. The company developed its original KMS to achieve a flexible and proactive organization, and a new KMS to improve organizational and personal capabilities. The PB case shows that there are differences between participants perceptions and actual performance in managing knowledge; that knowledge management is not a matter of formality but a paradigm that assures the sharing of knowledge; and that IT boosts communication skills, thus creating a mutual relationship to enhance the flow of knowledge and information between people. Knowledge management for building organizational capabilities can be successful when considering its focus and ways to increase its acceptance. This study suggests guidelines for major factors that corporate executives of domestic franchises should consider to improve knowledge management and the higher operating activities that can be used.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• The Korean Journal of Physiology and Pharmacology
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• v.24 no.1
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• pp.11-18
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• 2020

The present study was aimed to explore the neuroprotective role of imatinib in global ischemia-reperfusion-induced cerebral injury along with possible mechanisms. Global ischemia was induced in mice by bilateral carotid artery occlusion for 20 min, which was followed by reperfusion for 24 h by restoring the blood flow to the brain. The extent of cerebral injury was assessed after 24 h of global ischemia by measuring the locomotor activity (actophotometer test), motor coordination (inclined beam walking test), neurological severity score, learning and memory (object recognition test) and cerebral infarction (triphenyl tetrazolium chloride stain). Ischemia-reperfusion injury produced significant cerebral infarction, impaired the behavioral parameters and decreased the expression of connexin 43 and phosphorylated signal transducer and activator of transcription 3 (p-STAT3) in the brain. A single dose administration of imatinib (20 and 40 mg/kg) attenuated ischemia-reperfusion-induced behavioral deficits and the extent of cerebral infarction along with the restoration of connexin 43 and p-STAT3 levels. However, administration of AG490, a selective Janus-activated kinase 2 (JAK2)/STAT3 inhibitor, abolished the neuroprotective actions of imatinib and decreased the expression of connexin 43 and p-STAT3. It is concluded that imatinib has the potential of attenuating global ischemia-reperfusion-induced cerebral injury, which may be possibly attributed to activation of JAK2/STAT3 signaling pathway along with the increase in the expression of connexin 43.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.9
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• pp.76-83
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• 2017

Fish respond sensitively to light, so it is possible to develop fish management technology using this feature. In this study, we developed a light-based fish barrier and analyzed itsblocking effect using the difference in illuminance for the major fish species in Korea, bass and bluegill. The light was generated by a light emitting diode and the facility was installed vertically from the bottom. Considering the fish's ability to travel upstream, the flow rate was divided into three stages (0.2, 0.1, and 0.05 m/s). To prevent the learning effect, an experiment was carried out with fish that had rested for more than one day in a rearing tank. The experiment was carried out in such a way as tocompare the number of fish which travelled upstream after the introductionof the fish barrier and that of the fish which travelled upstream after itsremoval. It was also carried out after sunset to increase the effectiveness of the barrier. According to the results of the experiment, the fish blocking effect depending on the difference in illuminance was high and, overall, the blocking rate for bass was lower than that for bluegill. Based on the total size of the experimental population, the blocking rates for bass and bluegill were 96.33% and 99.00%, respectively. Based on the number of fish that travelled upstream, the blocking rates for bass and bluegill were 91.73% and 98.73%, respectively.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.39-44
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• 2016

Due to the dazzling development of IT in this IT-oriented era, information delivering technology among objects, between objects and humans, and among humans has been actively performed. As information delivery technology has been actively performed, IoT became closely related to our daily lives and ubiquitous at any time and place. Therefore, IoT has become a part of our daily lives. CoAp, a web-based protocol, is mostly used in IoT environment. CoAp protocol is mostly used in the network where transmission speed is low along with the huge loss. Therefore, it is mostly used in IoT environment. However, there is a weakness on IoT that it is weak in security. If security issue occurs in IoT environment, there is a possibility for secret information of individuals or companies to be disclosed. If attackers infect the targeted device, and infected device accesses to the wireless frequently used in public areas, the relevant device sends arp spoofing to other devices in the network. Afterward, infected devices receive the packet sent by other devices in the network after occupying the packet flow in the internal network and send them to the designated hacker's server. This study suggests counter-attacks on this issues and a method of coping with them.