• Journal of Chest Surgery
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• v.52 no.4
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• pp.205-220
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• 2019

Near-infrared (NIR) fluorescence imaging provides a safe and cost-efficient method for immediate data acquisition and visualization of tissues, with technical advantages including minimal autofluorescence, reduced photon absorption, and low scattering in tissue. In this review, we introduce recent advances in NIR fluorescence imaging systems for thoracic surgery that improve the identification of vital tissues and facilitate the resection of tumorous tissues. When coupled with appropriate NIR fluorophores, NIR fluorescence imaging may transform current intraoperative thoracic surgery methods by enhancing the precision of surgical procedures and augmenting postoperative outcomes through improvements in diagnostic accuracy and reductions in the remission rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.195-196
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• 2016

In the maritime mobile radiocommunication service, AIS(Automatic Identification System) devices are most widely using for the exchange of ship's navigational information. The AIS time slot usage increases due to increasing number of ships installed with AIS, and thus the reception rate of AIS data decrease. In order to mitigate this problem, international organizations recommend a sectorised receiving technique using directional antenna. This paper analyzed the sectorised receiving effectiveness of AIS data using Yaga antenna.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.159-168
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• 2019

Accurate forest fires detection algorithms remain a challenging issue, because, some of the objects have the same features with fire, which may result in high false alarms rate. This paper presents a new video-based, image processing forest fires detection method, which consists of four stages. First, a background-subtraction algorithm is applied to detect moving regions. Secondly, candidate fire regions are determined using CIE $L{\ast}a{\ast}b{\ast}$ color space. Thirdly, special wavelet analysis is used to differentiate between actual fire and fire-like objects, because candidate regions may contain moving fire-like objects. Finally, support vector machine is used to classify the region of interest to either real fire or non-fire. The final experimental results verify that the proposed method effectively identifies the forest fires.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1412-1420
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• 2018

An experiment was performed using the large-scale test facility (LSTF), which simulated a 1% vessel upper head small-break loss-of-coolant accident with an accident management (AM) measure under an assumption of total-failure of high-pressure injection (HPI) system in a pressurized water reactor (PWR). In the LSTF test, liquid level in the upper head affected break flow rate. Coolant was manually injected from the HPI system into cold legs as the AM measure when the maximum core exit temperature reached 623 K. The cladding surface temperature largely increased due to late and slow response of the core exit thermocouples. The AM measure was confirmed to be effective for the core cooling. The RELAP5/MOD3.3 code indicated insufficient prediction of primary coolant distribution. The author conducted uncertainty analysis for the LSTF test employing created phenomena identification and ranking table for each component. The author clarified that peak cladding temperature was largely dependent on the combination of multiple uncertain parameters within the defined uncertain ranges.

• ETRI Journal
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• v.43 no.1
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• pp.31-39
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• 2021

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.294-301
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• 2022

COVID-19 is a crisis with numerous casualties. The World Health Organization (WHO) has declared the use of masks as an essential safety measure during the COVID-19 pandemic. Therefore, whether or not to wear a mask is an important issue when entering and exiting public places and institutions. However, this makes face recognition a very difficult task because certain parts of the face are hidden. As a result, face identification and identity verification in the access system became difficult. In this paper, we propose a system that can detect masked face using transfer learning of Yolov5s and recognize the user using transfer learning of Facenet. Transfer learning preforms by changing the learning rate, epoch, and batch size, their results are evaluated, and the best model is selected as representative model. It has been confirmed that the proposed model is good at detecting masked face and masked face recognition.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.311-318
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• 2022

Tobacco sales enterprises often need to summarize and verify the daily sales bills, which may consume substantial manpower, and manual verification is prone to occasional errors. The use of artificial intelligence technology to realize the automatic identification and verification of such bills offers important practical significance. This study presents a novel multi-branch residual network for tobacco sales bills to improve the efficiency and accuracy of tobacco sales. First, geometric correction and edge alignment were performed on the input sales bill image. Second, the multi-branch residual network recognition model is established and trained using the preprocessed data. The comparative experimental results demonstrated that the correct recognition rate of the proposed method reached 98.84% on the China Tobacco Bill Image dataset, which is superior to that of most existing recognition methods.

• Advances in Energy Research
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• v.8 no.3
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• pp.165-174
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• 2022

Environmental sustainability is critical; else, the whole planet would face climatic disasters in the near future. A transportation system based on electric vehicles is assumed to be capable of providing long-term mobility. However, despite several attempts by national and international authorities, a great aim could not be met in India or the rest of the globe. Existing electric cars have a number of limits and obstacles. This report highlighted significant environmental and safety-related constraints that contribute to the low adoption rate of BEVs in India. A SWOT analysis was also carried out to identify the important elements influencing the future of BEV penetration in India.

• Annual Conference of KIPS
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• 2021.05a
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• pp.278-281
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• 2021

For a long time, CAPTCHA Recognition has been a major challenge in the field of artificial intelligence. Although there are many related technologies that can solve this identification problem, further breakthroughs are still needed. Based on the existing SSD network, this paper adds a non-block module. Compared with the original SSD network, the recognition rate of SSD + Non-local is improved from 86.12% to 88.47%.In addition, it is worth noting that the recognized character verification code consists of Arabic numerals, uppercase and lowercase English letters.

• The Journal of Internal Korean Medicine
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• v.43 no.6
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• pp.1105-1121
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• 2022

Objectives: The purpose of this study is to explore the effectiveness and safety of frequently used clinical herbal medicines (Yijung-tang [Lizhong-tang, LJT], Pyeongwi-san [Pingwei-san, PWS], and Shihosogan-tang [Chaihu Shugan-tang, SST]) in patients with functional dyspepsia (FD) when administered according to herbal medicine and Korean medicine pattern identification. The results of this study will be used to standardize the diagnostic instrument used in Korean medicine and to investigate biomarkers of Korean medicine pattern identification. Methods: This study will be a randomized, assessor-blind, 3-arm, parallel, open-label, multi-center clinical trial. A total of 300 FD participants will be recruited from 3 Korean medical hospitals and assigned to the LJT (n=100), PWS (n=100), and SST (n=100) groups according to FD pattern identification. The patients will take the medication for 8 weeks, 3 times a day, before or between meals. The primary outcome will be total dyspepsia symptom (TDS) and the secondary outcomes will be adequate relief (AR) for dyspepsia, overall treatment effect (OTE), visual analogue scale (VAS), functional dyspepsia-related quality of life (FD-QoL), gastrointestinal symptom score (GIS), and pattern identification questionnaires. For the exploratory outcomes, we will analyze blood and fecal metabolome profiles, microbiota from fecal and saliva samples, single nucleotide polymorphism (SNP), and results of Korean medicine diagnosis device measurements (heart rate variability, and tongue, pulse, and abdominal diagnosis). Conclusions: The results of this study will prove objectivity for Korean medicine pattern identifications, and the effectiveness and safety of herbal medicines for the population with FD.