• Archives of Plastic Surgery
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• v.48 no.6
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• pp.685-690
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• 2021

Background Human immunodeficiency virus (HIV)-associated lipodystrophy is a known consequence of long-term highly active antiretroviral therapy (HAART). However, a significant number of patients on HAART therapy were left with the stigmata of complications, including fat redistribution. Few studies have described the successful removal of focal areas of lipohypertrophy with successful outcomes. This manuscript reviews the outcomes of excisional lipectomy versus liposuction for HIV-associated cervicodorsal lipodystrophy. Methods We performed a 15-year retrospective review of HIV-positive patients with lipodystrophy. Patients were identified by query of secure operative logs. Data collected included demographics, medications, comorbidities, duration of HIV, surgical intervention type, pertinent laboratory values, and the amount of tissue removed. Results Nine male patients with HIV-associated lipodystrophy underwent a total of 17 procedures. Of the patients who underwent liposuction initially (n=5), 60% (n=3) experienced a recurrence. There were a total of three cases of primary liposuction followed by excisional lipectomy. One hundred percent of these cases were noted to have a recurrence postoperatively, and there was one case of seroma formation. Of the subjects who underwent excisional lipectomy (n=4), there were no documented recurrences; however, one patient's postoperative course was complicated by seroma formation. Conclusions HIV-associated lipodystrophy is a disfiguring complication of HAART therapy with significant morbidity. Given the limitations of liposuction alone as the primary intervention, excisional lipectomy is recommended as the primary treatment. Liposuction may be used for better contouring and for subsequent procedures. While there is a slightly higher risk for complications, adjunctive techniques such as quilting sutures and placement of drains may be used in conjunction with excisional lipectomy.

• KNOM Review
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• v.24 no.1
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• pp.1-12
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• 2021

VM (Virtual Machine) live migration is a server virtualization technique for deploying a running VM to another server node while minimizing downtime of a service the VM provides. Currently, in cloud data centers, VM live migration is widely used to apply load balancing on CPU workload and network traffic, to reduce electricity consumption by consolidating active VMs into specific location groups of servers, and to provide uninterrupted service during the maintenance of hardware and software update on servers. It is critical to use VMlive migration as a prevention or mitigation measure for possible failure when its indications are detected or predicted. In this paper, we propose two VNF live migration methods; one for predictive load balancing and the other for a proactive measure in failure. Both need machine learning models that learn periodic monitoring data of resource usage and logs from servers and VMs/VNFs. We apply the second method to a vEPC (Virtual Evolved Pakcet Core) failure scenario to provide a detailed case study.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.147-156
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• 2023

Efforts to employ smart home sensors to monitor the indoor activities of elderly single residents have been made to assess the feasibility of a safe and healthy lifestyle. However, the bathroom remains an area of blind spot. In this study, we have developed and evaluated a new edge computer device that can automatically detect water usage activities in the bathroom and record the activity log on a cloud server. Three kinds of sound as flushing, showering, and washing using wash basin generated during water usage were recorded and cut into 1-second scenes. These sound clips were then converted into a 2-dimensional image using MEL-spectrogram. Sound data augmentation techniques were adopted to obtain better learning effect from smaller number of data sets. These techniques, some of which are applied in time domain and others in frequency domain, increased the number of training data set by 30 times. A deep learning model, called CRNN, combining Convolutional Neural Network and Recurrent Neural Network was employed. The edge device was implemented using Raspberry Pi 4 and was equipped with a condenser microphone and amplifier to run the pre-trained model in real-time. The detected activities were recorded as text-based activity logs on a Firebase server. Performance was evaluated in two bathrooms for the three water usage activities, resulting in an accuracy of 96.1% and 88.2%, and F1 Score of 96.1% and 87.8%, respectively. Most of the classification errors were observed in the water sound from washing. In conclusion, this system demonstrates the potential for use in recording the activities as a lifelog of elderly single residents to a cloud server over the long-term.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.184-186
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• 2022

Recently, as interest in personal information protection has increased, various apps for personal information protection have emerged. These apps protect data in various formats, such as photos, videos, and documents containing personal information, using encryption and hide functions. These apps can have a positive effect on personal information protection, but in digital forensics, they act as anti-forensic because they can be difficult to analyze data during the investigation process. In this paper, finds out PIN, an access control function, through reverse engineering on Calculator - photo vault, one of the personal information protection apps, and files such as photos and documents to which encryption and hide were applied. In addition, the vulnerability to this app was analyzed by research decryption for database files where logs for encrypted and hide files are stored.

• Journal of Practical Agriculture & Fisheries Research
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• v.10 no.1
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• pp.153-167
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• 2008

High temperature and natural sun light are considered as the core conditions for high quality and large quantity of Phellinus baumii production. However still now on there has been a mistake of excessively cutting off the natural light by spreading the closing nets on the mushroom cultivating house. For an example there are many houses where the closing nets under the roofs be extended to cover the sides of the houses, which way prevents the mushrooms in the houses from receiving sufficient natural sun light and getting the temperature sufficiently to grow so that the quantity and quality of the produced mushrooms are lowered even though the mushrooms can grow in those conditions. In order to avoid this mistake, the closing nets must be placed on the roofs of the houses only without dropping them to cover the sides. Further more when the closing nets are placed triply at the beginning stage of Phellinus baumii's growth in the house, the nets restrain the internal temperature of the house going up and intercept the natural bright light flowing into the house so that the growing tardiness occur to the Phellinus baumii. Therefore the roof only must have been covered by the closing net for 65% cutting off the light until May, and then covered by double folded the net for June, triple folded the net for July and August, double folded the net for September, and the single net for October. When the ventilation in the house has been maintained until the house tightly balloon out through controling lifting force of internal air, the Phellinus baumii can grow well while the bed logs themselves aren't dried out. Marketing is also very much important as well as increasing quality and quantity of Phellinus baumii production.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.162-172
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• 2022

Using content analysis, this study explored design differences between Korean and the U.S. corporate characters. Top 100 corporate logs are collected from Korea and the United States. The results showed that Korean characters appear a group of or friends than the ones of the U.S. This result stems from the collective nature of Korea. Korea used more motif of things than the U.S., and Korean personalized characters more often that those of the U.S. Uses of human and animals did not showed statistical differences. In addition, it is found that Korea used blue as main color, and the U.S. used red as main color more often. The number of colors used in character design is not statistically different. These findings could provide academic implications that cultural differences could be adapted to corporate character marketing, and also provide managerial implications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.186-188
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• 2021

The architecture named IDPS is a design concept of web-based integrated platform for heterogeneous naval combat system, which accomplishes efficiency(decreasing complexity) of diagnosis process and reduces time to diagnose system. Each type of surface vessel has its own diagnostic processes and applications, and that means it also requires its own diagnostic engineer(inefficiency in human resource management). In addition, man-based diagnostic causes quality issues such as difference approach of log analysis in accordance with engineer skills. Thus In this paper, we designed integrated diagnostic platform named IDPS with simplified common process regardless of type of surface vessel and we reinforced IDPS with status decision algorithm(SDA) that judges current software status of vessel based on gathered lots of logs. It will enable engineers to diagnose system more efficiently and to use more resources in utilizing SDA-analyzed diagnostic results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.255-257
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• 2021

During the winter season, when electricity usage increases rapidly every year, fires are frequent due to short circuits in aging electrical facilities in multi-use facilities such as traditional markets and jjimjilbangs, apartments, and multi-family houses. Most of the causes of such fires are caused by excessive loads applied to aging wires, causing the wire covering to melt and being transferred to surrounding ignition materials. In this study, we implement a system that measures the overload and overheating of the wire through a composite sensor, detects the toxic gas generated there, and logs it to the server through the gateway. Based on this, we will develop a platform that can predict, alarm and block electric fires in real time through big data analysis, and a simulator that can simulate fire occurrence experiments.

• Convergence Security Journal
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• v.22 no.2
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• pp.3-8
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• 2022

Security threats occur from the outside, but more often from the inside. In particular, since the internal user knows about the information service, the security threat damage caused by the internal user is greater. In this environment, the actions of all users accessing information services should be monitored and recorded in real-time. However, the current operating system records only the logs of system and application execution, so there is a limit to monitoring user behavior in real-time. In such a security environment, damage may occur due to user's unauthorized actions. To solve this problem, this study proposes an architecture that monitors user behavior in real-time in a Linux environment. As a result of verifying the function to confirm the effectiveness of the proposed architecture, the console input values and output angles of all users who have access to the operating system are monitored in real-time and stored. Although the performance of the proposed architecture is somewhat slower than the identification and authentication functions provided by the operating system, it was confirmed that the performance was not at a level that users would recognize, and thus it was judged to be sufficiently effective. However, since this study focuses on monitoring the console behavior, it is impossible to monitor the behavior of user applications running in the background, so additional research is needed.