• New & Renewable Energy
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• v.8 no.3
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• pp.14-22
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• 2012

Performance of photovoltaic (PV) generator systems relies on its operating conditions. Maximum power extracted from PV generators depends strongly on solar irradiation, load impedance, and ambient temperature. A most maximum power point tracking (MPPT) algorithm is based on a perturb and observe method and an incremental conductance method. It is well known the latter is better in terms of dynamics and tracking characteristics under condition of rapidly changing solar irradiation. However, in case of digital implementation, the latter has some error for determining a maximum power point. This paper presents a PID based MPPT algorithm for such PV systems. We use neural network technique for determining PID parameters by online learning approach. And we construct a boost converter to regulate the output voltage from PV generator system. Computer simulation is carried out to evaluate the proposed MPPT method and we accomplish comparative study with a perturb and observe based MPPT method to prove its superiority.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.674-677
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• 2009

The one of the big issues in fixed markets is the investment of next generation networks. The nation-wide incumbent has provided the copper cable based access networks in so far. However in the future multiple providers participant in investing network in first stage of the network investment like mobile networks. Each NRA makes efforts on the resolving construction cost of civil engineering costs through the opening conduits. To smooth operation, information opening service is needed then NRAs must determined the level of opened information and cost of usage information and so on. This paper proposed the alternation of the issues to boost the investment of the next generation networks.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.252-255
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• 2020

Unsupervised deep learning methods have shown impressive results for the challenging monocular depth estimation task, a field of study that has gained attention in recent years. A common approach for this task is to train a deep convolutional neural network (DCNN) via an image synthesis sub-task, where additional views are utilized during training to minimize a photometric reconstruction error. Previous unsupervised depth estimation networks are trained within a fixed depth estimation range, irrespective of its possible range for a given image, leading to suboptimal estimates. To overcome this suboptimal limitation, we first propose an unsupervised adaptive depth estimation method guided by minimum and maximum (min-max) depth priors for a given input image. The incorporation of min-max depth priors can drastically reduce the depth estimation complexity and produce depth estimates with higher accuracy. Moreover, we propose a novel network architecture for adaptive depth estimation, called the AdaMM-DepthNet, which adopts the min-max depth estimation in its front side. Intensive experimental results demonstrate that the adaptive depth estimation can significantly boost up the accuracy with a fewer number of parameters over the conventional approaches with a fixed minimum and maximum depth range.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.63-71
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• 2022

To investigate the short-run effects of social capital on migration decisions of individuals in the rural areas of Vietnam, we conducted dataset mining and performed regression model analysis in the form of panel data. As control variables, we employed the variable of social capital, which is measured by an individual's network, as well as demographic characteristics of individuals and households. We discovered that when a household is in financial distress, social networks such as linkages or asking for aid from others often enhance individual capacity. Individuals with a large social network outside of their immediate area are more inclined to relocate to the location where their connectors live. Individual participation and degree of participation in the organizational community, on the other hand, have little bearing on the likelihood of migration. In addition, this research examines theories and empirical research on the relationship between social capital and migration. Based on our research findings, we have recommended some measures to boost the efficiency of social capital and migration in rural areas of Vietnam through local government solutions.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.1-11
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• 2024

Payment systems are evolving, and this study examines how blockchain and AI improve online transactional security and service quality. The study examines micro and macro payment systems, compares online, and offline methods all over the world. The study also examines how blockchain and AI affect payment system security, privacy, and efficiency globally and rapidly digitizing economy. Digital payment methods are growing all over the world with high literacy and digital engagement, but they face challenges. The research highlights cybersecurity threats and the need to balance user convenience and security. It suggests blockchain and AI improve online payment services, supporting the policies for different countries. In this extensive research survey, we compare and evaluate the strengths and weaknesses of various payment systems, their practicality, and their robustness. This study also examines how technological innovations and payment systems interact to reveal how blockchain and AI could transform the financial sector. It seeks to understand how technology-enhancing service quality can boost customer satisfaction and financial stability in the digital age. The findings should help policymakers, financial institutions, and technology developers optimize online payment systems for a more secure and efficient digital economy.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.450-450
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• 2021

기후변화 보고서에 따르면 집중 호우의 강도 및 빈도 증가가 향후 몇 년동안 지속될 것이라 제시하였다. 이러한 집중호우가 빈번히 발생하게 된다면 강우 침식성이 증가하여 표토 침식에 더 취약하게 발생된다. Universal Soil Loss Equation (USLE) 입력 매개 변수 중 하나인 강우침식능인자는 토양 유실을 예측할때 강우 강도의 미치는 영향을 제시하는 인자이다. 선행 연구에서 USLE 방법을 사용하여 강우침식능인자를 산정하였지만, 60분 단위 강우자료를 이용하였기 때문에 정확한 30분 최대 강우강도 산정을 고려하지 못하는 한계점이 있다. 본 연구의 목적은 강우침식능인자를 이전의 진행된 방법보다 더 빠르고 정확하게 예측하는 머신러닝 모델을 개발하며, 총 월별 강우량, 최대 일 강우량 및 최대 시간별 강우량 데이터만 있어도 산정이 가능하도록 하였다. 이를 위해 본 연구에서는 강우침식능인자의 산정 값의 정확도를 높이기 위해 1분 간격 강우 데이터를 사용하며, 최근 강우 패턴을 반영하기 위해서 2013-2019년 자료로 이용했다. 우선, 월별 특성을 파악하기 위해 USLE 계산 방법을 사용하여 월별 강우침식능인자를 산정하였고, 국내 50개 지점을 대상으로 계산된 월별 강우침식능인자를 실측 값으로 정하여, 머신러닝 모델을 통하여 강우침식능인자 예측하도록 학습시켜 분석하였다. 이 연구에 사용된 머신러닝 모델들은 Decision Tree, Random Forest, K-Nearest Neighbors, Gradient Boosting, eXtreme Gradient Boost 및 Deep Neural Network을 이용하였다. 또한, 교차 검증을 통해서 모델 중 Deep Neural Network이 강우침식능인자 예측 정확도가 가장 높게 산정하였다. Deep Neural Network은 Nash-Sutcliffe Efficiency (NSE) 와 Coefficient of determination (R²)의 결과값이 0.87로서 모델의 예측성을 입증하였으며, 검증 모델을 테스트 하기 위해 국내 6개 지점을 무작위로 선별하여 강우침식능인자를 분석하였다. 본 연구 결과에서 나온 Deep Neural Network을 이용하면, 훨씬 적은 노력과 시간으로 원하는 지점에서 월별 강우침식능인자를 예측할 수 있으며, 한국 강우 패턴을 효율적으로 분석 할 수 있을 것이라 판단된다. 이를 통해 향후 토양 침식 위험을 지표화하는 것뿐만 아니라 토양 보전 계획을 수립할 수 있으며, 위험 지역을 우선적으로 선별하고 제시하는데 유용하게 사용 될 것이라 사료된다.

• IMMUNE NETWORK
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• v.23 no.2
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• pp.16.1-16.19
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• 2023

Bacillus Calmette-Guerin (BCG) vaccine is the only licensed vaccine for tuberculosis (TB) prevention. Previously, our group demonstrated the vaccine potential of Rv0351 and Rv3628 against Mycobacterium tuberculosis (Mtb) infection by directing Th1-biased CD4⁺ T cells co-expressing IFN-γ, TNF-α, and IL-2 in the lungs. Here, we assessed immunogenicity and vaccine potential of the combined Ags (Rv0351/Rv3628) formulated in different adjuvants as subunit booster in BCG-primed mice against hypervirulent clinical Mtb strain K (Mtb K). Compared to BCG-only or subunit-only vaccine, BCG prime and subunit boost regimen exhibited significantly enhanced Th1 response. Next, we evaluated the immunogenicity to the combined Ags when formulated with four different types of monophosphoryl lipid A (MPL)-based adjuvants: 1) dimethyldioctadecylammonium bromide (DDA), MPL, and trehalose dicorynomycolate (TDM) in liposome form (DMT), 2) MPL and Poly I:C in liposome form (MP), 3) MPL, Poly I:C, and QS21 in liposome form (MPQ), and 4) MPL and Poly I:C in squalene emulsion form (MPS). MPQ and MPS displayed greater adjuvancity in Th1 induction than DMT or MP did. Especially, BCG prime and subunit-MPS boost regimen significantly reduced the bacterial loads and pulmonary inflammation against Mtb K infection when compared to BCG-only vaccine at a chronic stage of TB disease. Collectively, our findings highlighted the importance of adjuvant components and formulation to induce the enhanced protection with an optimal Th1 response.

• IMMUNE NETWORK
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• v.23 no.2
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• pp.18.1-18.15
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• 2023

It has been reported that some exercise could enhance the anti-viral antibody titers after vaccination including influenza and coronavirus disease 2019 vaccines. We developed SAT-008, a novel digital device, consists of physical activities and activities related to the autonomic nervous system. We assessed the feasibility of SAT-008 to boost host immunity after an influenza vaccination by a randomized, open-label, and controlled study on adults administered influenza vaccines in the previous year. Among 32 participants, the SAT-008 showed a significant increase in the anti-influenza antibody titers assessed by hemagglutination-inhibition test against antigen subtype B Yamagata lineage after 4 wk of vaccination and subtype B Victoria lineage after 12 wk (p<0.05). There was no difference in the antibody titers against subtype "A." The SAT-008 also showed significant increase in the plasma cytokine levels of IL-10, IL-1β, and IL-6 at weeks 4 and 12 after the vaccination (p<0.05). A new approach using the digital device may boost host immunity against virus via vaccine adjuvant-like effects.