• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.441-446
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• 2023

Electrical and optical characteristics of the GaN-based light-emitting diode (LED) with the improved multi-quantum well (MQW) structure have been studied for light source in bio-sensing systems. Novel GaN/In_0.1GaN/In_0.2GaN/In_0.1GaN/GaN and Al_0.1GaN/GaN/In_0.2GaN/GaN/Al_0.1GaN (MQW) structures were suggested, and their radiative recombination rate, light output power, electroluminescence, and external quantum efficiency were compared with those of the conventional GaN/In_0.2GaN/GaN MQW structure using device simulation. The LED with the GaN/In_0.1GaN/In_0.2GaN/In_0.1GaN/GaN MQW structure showed an excellent recombination rate of 5.57 × 10²⁸ cm^-3·s^-1 that was more than one order improvement over that of the conventional LED. In addition, the efficiency droop was relieved by the suggested stepped MQW structure.

• Korean Circulation Journal
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• v.53 no.11
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• pp.758-771
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• 2023

Background and Objectives: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. Methods: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. Results: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. Conclusions: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.5 s.57
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• pp.169-179
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• 2009

The purpose of this paper is to find an efficient heat source to amplify the surface temperature of damaged concrete structures for infrared thermography. we compare two different heat sources of far-infrared lamp and halogen lamp each other for their efficiency. The two heat sources were applied to the concrete specimens. Two different concrete specimens were used: one was the concrete containing internal void and the other was wrapped with partially unbonded fiber reinforced polymer sheet. it was found that the far-infrared lamp was more efficient than the halogen lamp. In addition, we propose a new algorithm to make the damage zone displayed clear in the image obtained from the thermographic operation. The algorithm is a combination of Gauss filtering process and the Prewitt mask operation.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Progress in Medical Physics
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• v.24 no.4
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• pp.253-258
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• 2013

The purpose of this study is to evaluate and analyze the relationship between the external radiation dose reconstruction which is transmitted from the patient who receives radiation treatment through electronic portal imaging device (EPID) and the internal dose derived from the Monte Carlo simulation. As a comparative analysis of the two cases, it is performed to provide a basic indicator for similar studies. The geometric information of the experiment and that of the radiation source were entered into Monte Carlo n-particle (MCNPX) which is the computer simulation tool and to derive the EPID images, a tally card in MCNPX was used for visualizing and the imaging of the dose information. We set to source to surface distance (SSD) 100 cm for internal measurement and EPID. And the water phantom was set to be 100 cm of the source to surface distance (SSD) for the internal measurement and EPID was set to 90 cm of SSD which is 10 cm below. The internal dose was collected from the water phantom by using mesh tally function in MCNPX, accumulated dose data was acquired by four-portal beam exposures. At the same time, after getting the dose which had been passed through water phantom, dose reconstruction was performed using back-projection method. In order to analyze about two cases, we compared the penetrated dose by calibration of itself with the absorbed one. We also evaluated the reconstructed dose using EPID and partially accumulated (overlapped) dose in water phantom by four-portal beam exposures. The sum dose data of two cases were calculated as each 3.4580 MeV/g (absorbed dose in water) and 3.4354 MeV/g (EPID reconstruction). The result of sum dose match from two cases shows good agreement with 0.6536% dose error.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.679-682
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• 2005

Negative-differential conductance (NDC) characteristics as well as negative-differential trans-conductance (NDT) characteristics have been observed in the room temperature I-V characteristics of Field-induced Inter-band Tunneling Effect Transistors (FITETs). These characteristics have been explained with inter-band tunneling physics, from which, inter-band tunneling current flows when the energy bands of degenerately doped regions align, and it does not flow when they don't. FITET is an SOI device and the body region is not directly connected to the external terminal. Therefore, Fermi energy in the body region is determined by electrical coupling among four regions - gate, source, drain and substrate. So, a quasi Fermi energy of the majority carriers in the floating body region can be changed by external voltages, and this causes the energy band movements in the body region, which determine whether the energy bands between degenerately doped junctions aligns or not. This is a key point for an explanation of NDT and NDC characteristics. In this paper, a quasi Fermi energy movement in the floating body region of FITET was investigated by a device simulation. This result was applied for the description of relation between quasi Fermi energy in the body region and external gate bias voltage.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.7
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• pp.143-148
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• 2020

Recently, web services have been expanded to various areas with the evolution of cloud environment. Whenever a user accesses a web service, the user's log information is stored in the web server. This log information is used as data to analyze the user's web service tendencies and is also used as important data to track the user's system access when a security problem in the system occurs. Currently, most web servers manage user log information in a centralized manner. When user log information is managed in a centralized manner, it is simple in the side of operation, but has a disadvantage of being very vulnerable to external malicious attacks. In the case of centralized management, user log information stored in the web server can be arbitrarily manipulated by external attacks, and in severe cases, the manipulated information can be leaked. In this case, it not only decreases the trust of the web service, but also makes it difficult to trace the source and cause of the attack on the web server. In order to solve these problems, this paper proposes a new method of managing user log information in a cloud environment by applying blockchain technology as an alternative to the existing centralized log management method. The proposed method can manage log information safely from external attacks because user log information is distributed and stored in blockchain on a private network with cloud environment.

• Journal of the Korea Organic Resources Recycling Association
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• v.28 no.2
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• pp.41-48
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• 2020

In this study, organic carbon was extracted from the condensed water of food waste drying process to estimate the applicability as external organic carbon sources. The COD, TN, TP and TS of condensed water were 21,374 mg/L, 148 mg/L, 4.19 mg/L, and 455.7 mg/L, respectively. In addition, the content of biodegradable organics in condensed water was 47%. The fractional distillation and the vacuum evaporation were employed for extracting organic carbon. There were 8 extraction conditions, but 4 conditions were available for extraction. They were 1) 0mmHg, 110℃ 2) -600mmHg, 70℃ 3) -500mmHg, 80℃ 4) -600mmHg, 80℃. All 4 conditions showed the highest organic concentration and the highest quantity of organics when extracted 10% of initial volume. It was estimated that optimum conditions were 80℃, -600mmHg and 10% extraction. Then, extraction concentration, extraction quantity, extraction efficiency, extraction time, BOD/TCOD ratio, TVFAs/TCOD ratio and NH₃-N were 174,200 mg/L, 8,710 mg, 46%, 10 min, 0.97, 0.74 and 75.5 mg/L respectively. Therefore, the extracted organic carbon can be utilized as external organic carbon sources.

• Radiation Oncology Journal
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• v.2 no.2
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• pp.261-270
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• 1984

The intrauterine irradiation is essential to achieve adequate tumor dose to central tumor mass of uterine malignancy in radiotherapy. The complications of pelvic organ are known to be directly related to radiation dose and physical parameters. The simulation radiation and medical records of 203 patients who were treated with intrauterine irradiation from Feb. 1983 to Oct. 1983, were critically analized. The physical parameters to include distances between lateral walls of vaginal fornices, longitudinal and lateral angles of tandem applicator to the body axis, the distance from the external os of uterine cervix to the central axis of ovoids were measured for low dose rate irradiation system and high dose rate remote control afterloading system. The radiation doses and dose distributions within cervical area including interesting points and bladder, rectum, according to sources arrangement and location of applicator, were estimated with personal computer. Followings were summary of study results ; 1. In distances between lateral walls of vaginal fornices, the low dose rate system showed as $4\~7cm$ width and high dose rate system showed as $5\~6cm$. 2. In horizontal angulation of tandem to body axis, the low dose rate system revealed mid position$64.6\%$, left deviation $19.2\%$and right deviation $16.2\%$. 3. In longitudinal angulation of tandem to body axis, the mid position was $11.8\%$ and anterior angulation $88.2\%$ in low dose rate system but in high dose rate system, anterior angulation was $98.5\%$. 4. Down ward displacement of ovoids below external os was only $3\%$ in low dose rate system and $66.7\%$ in high dose rate system. 5. In radiation source arrangement, the most activities of tandem and ovoid were 35 by 30 in low dose rate system but 50 by 40 in high dose rate system. 6. In low and high dose rate system, the total doses an4 TDF were 50, 70 Gy and 141, 123, including 40 Gy external irradiation. 7. The doses and TDF in interesting points Co, B, were 93, 47 Gy and 230, 73 in high dose rate system but in low doss rate system, 123, 52 Gy and 262, 75 respectively. 8. Doses and TDF in bladder and rectum were 70, 68 Gy and 124, 120 in low dose rate system, but in high dose rate system, 58, 64 Gy 98, 110 respectively, and then grades of injuries in bladder and rectum were 25, $30\%$ and 18, $23\%$ respectively.

• Journal of Aquaculture
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• v.10 no.3
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• pp.335-346
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• 1997

Denitrification is one of the important processes of removing nitrate from in recirculating aquaculture systems. And this process is affected by many factors such as external organic carbon sources, hydraulic retention time (HRT), COD/NO3--N (C:N) ratio, etc. However, not many studies were done for the optimum conditions of denitrification in the recirculation system for aquaculture. Therefore, this study was conducted to find out the optimum removal condition of NO3--N using submerged denitrification biofilter. The combinations of two external organic carbon sources (glucose and methanol), two HRT (4 and 8-hour) and four differnent C : N ratios (3, 4, 5, 6) were tested. The removal efficiencies of NO3--N and total inorganic nitrogen (TIM) at 8-hour HRT were better than those at 4-hour's (P<0.05). The maximum removal efficiency of NO3--N by methanol (97.8%) was achieved at HRT and C : N ratio were 8-hour and 4.0 respectively. The efficiencies of methanol for the removal of NO3--N and TIN were always better than those of glucose (P<0.05). The maximum removal efficiencies of total inorgainc nitrogen (TIN) were gained at C : N ration of 5.0. The maximum removel efficiencies of TIN using methanol and glucose were 96.9% and 71.5% respectively. Anaerobic condition which is necessary for denitrification process was not made until the 8-hour HRT and higher C : N ratio (5.0). Removal of NO3--N at 4-hour HRT and C : N ration lower than 5.0 were inhibited by oxygen and/or low quantity of external organic carbon. Removal efficiencies of NO3--N were also inhibited by high C : N (6.0) ratio when HRT was 8-hour.