• 시스템엔지니어링학술지
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• 제15권2호
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• pp.66-71
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• 2019

In digital pathology, an electronic system in the biomedical domain storage of the files is a big constrain and because all the analysis and annotation takes place at every user-end manually, it becomes even harder to manage the data that is being shared inside an enterprise. Therefore, we need such a storage system which is not only big enough to store all the data but also manage it and making communication of that data much easier without losing its true from. A virtual server setup is one of those techniques which can solve this issue. We set a main server which is the main storage for all the virtual machines(that are being used at user-end) and that main server is controlled through a hypervisor so that if we want to make changes in storage overall or the main server in itself, it could be reached remotely from anywhere by just using the server's IP address. The server in our case includes XML-RPC based API which are transmitted between computers using HTTP protocol. JAVA API connects to HTTP/HTTPS protocol through JAVA Runtime Environment and exists on top of other SDK web services for the productivity boost of the running application. To manage the server easily, we use Tkinter library to develop the GUI and pmw magawidgets library which is also utilized through Tkinter. For managing, monitoring and performing operations on virtual machines, we use Python binding to XML-RPC based API. After all these settings, we approach to make the system user friendly by making GUI of the main server. Using that GUI, user can perform administrative functions like restart, suspend or resume a virtual machine. They can also logon to the slave host of the pool in case of emergency and if needed, they can also filter virtual machine by the host. Network monitoring can be performed on multiple virtual machines at same time in order to detect any loss of network connectivity.

• 전자공학회논문지
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• 제51권6호
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• pp.110-116
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• 2014

패턴 매칭 알고리즘은 컴퓨터 네트워크, 유비쿼터스 네트워크, 그리고 센서 네트워크 등을 위한 보안 프로그램에 주로 사용 된다. IT 기술의 발전과 함께 정보의 디지털화가 가속화되면서 네트워크를 통해 전달되는 데이터양이 급증하고 있다. 이에 따라 패턴 매칭 연산의 복잡도도 폭발적으로 증가하고 있다. 따라서 더 많은 패턴을 보다 빠르게 검색할 수 있는 고성능 알고리즘의 개발이 끊임없이 요구되고 있다. 본 논문은 서픽스 트리 기반 패턴 매칭 알고리즘을 새롭게 제안하여 대용량 패턴 매칭 연산의 성능을 높였다. 서픽스 트리는 사전에 정의된 복수 패턴들의 서픽스를 기반으로 생성된다. 이 트리에 쉬프트 노드 개념을 추가하여 기존 패턴 매칭 연산들 중 불필요한 연산의 수행 횟수를 줄였다. 결과적으로 제안하는 구조를 통해 기존 알고리즘 대비 24% 이상의 성능 향상을 이루었다.

• 방송공학회논문지
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• 제25권5호
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• pp.672-684
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• 2020

시차 기반 영상처리에 대한 연구들이 증가함에 따라 저해상도 및 모션 블러된 라이트필드 영상을 복원하는 연구는 필수적이 되었다. 이러한 기법들은 라이트필드 영상 향상 과정으로 알려져 있으나 두 개 이상의 문제를 동시에 해결하는 기존의 연구는 거의 존재하지 않는다. 본 논문에서는 라이트필드 공간 영역 초해상도 복원과 모션 블러 제거를 동시 수행하는 프레임워크를 제안한다. 특히, 저해상도 및 6-DOF 모션 블러된 라이트필드 데이터셋으로 훈련하는 간단한 네트워크를 생성한다. 또한 성능을 향상하기 위해 생성적 적대 신경망의 지역 영역 최적화 기법을 제안하였다. 제안한 프레임워크는 정량적, 정성적 측정을 통해 평가하고 기존의 state-of-the-art 기법들과 비교하여 우수한 성능을 나타냄을 보인다.

• Journal of Korea Trade
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• 제25권6호
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• pp.78-104
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• 2021

Purpose - Semiconductors are a significant export item for Korea that is expected to continue to contribute significantly to the Korean economy in the future. Thus, the semiconductor industry is a critical component in the 4th Industrial Revolution and is expected to continue growing as the non-face-to-face economy expands as a result of the COVID-19 pandemic. In this context, this paper aims to empirically investigate how semiconductors are imported and exported in Korea from a global supply chain perspective by analysing import and export data at the micro-level. Design/methodology - This study conducts a multifaceted analysis of the global supply chain for semiconductors and related equipment in Korea by examining semiconductor imports and exports by semiconductor type, year, target country, mode of transportation, airport/port, and domestic region, using import/export micro-data. The visualisation, flow analysis, and Bayesian Network methodologies were used to compensate for the limitations of each method. Findings - Korea is a major exporter of semiconductor memory and has the world's highest competitiveness but is relatively weak in the field of system semiconductors. The trade deficit in 'semiconductor equipment and parts' is clearly growing. As a result, continued investment in 'system semiconductors' and 'semiconductor equipment and parts' technology development is necessary to boost exports and ensure a stable supply chain. Originality/value - Few papers on semiconductor trade in Korea have been published from the perspective of the global supply chain or value chain. This study contributes to the literature in this area by focusing on import and export data for the global supply chain of the Korean semiconductor industry using a variety of approaches. It is our hope that the insights gained from this study will aid in the advancement of SCM research.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• 한국산학기술학회논문지
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• 제20권8호
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• pp.164-173
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• 2019

본 연구에서는 전 세계 3대 에어쇼 중의 하나인 "FIA(Farnborough International Airshow, 판보로 국제 에어쇼)"의 참여기관/업체를 대상으로 네트워크 분석을 활용하여 항공우주분야 기술동향을 분석하였다. 판보로 국제 에어쇼는 전 세계 주요 항공관련 민간 및 방산 업체와 각국 정부 및 군 관계자가 참여하여 항공우주산업분야에 대한 최신 기술을 선보이는 중요한 행사로서, 2018 FIA에서는 총 112개 국가에서 1,500여 업체(기관)가 참여하였다. 본 연구에서는, 항공우주분야 기술관련 45개 국가, 1,108개 업체를 대상으로 223개의 기술 분류 카테고리를 통해 네트워크 분석 중 하나인 키워드 기반의 중심성 분석을 수행하였다. 분석결과, 전 세계적 우주항공 분야의 핵심기술은 "Machining"으로 조사되었다. 하지만 지역(국가) 별로 분류되는 핵심기술은 다소 다른 경향을 보여주고 있었는데, 유럽(EU)과 영국의 경우 "Machining", 아시아의 경우 "Aircraft Components", 미국의 경우 "Engine Components/controls"가 식별되었다. 우리나라의 경우에는 관련 기관/업체 수의 부족으로 뚜렷한 중심 기술이 식별되지 않았다. 본 연구의 결과가 우주항공분야 기술기획 및 연구 방향성 제시를 위한 참고자료로서 활용 될 수 있으며, 또한 국내 관련 업체의 수출 진흥을 위한 국외 주요 기술 분야를 제시하는데 유용하게 활용되리라 기대한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.