• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• 한국정보통신학회논문지
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• 제12권10호
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• pp.1801-1807
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• 2008

일상생활을 편하고 재미있고 안전하게 만들기 위해 홈 네트워크가 수집과 전기적인 많은 장치를 연결한다. 이더넷과 거주 게이트웨이의 단 하나의 함께 나눠 진 광대역 커넥션에 대한 무선 기술의 수렴은 집 네트워크의 주요 특징이다. 이런 종류의 다종다양한 네트워크는 다른 QoS 장치를 실행할 필요성을 인식했다. 이 논문에서는 기본적으로 IP QoS을 통합하고 홈 네트워크에서 QoS단언을 위한 QoS 장치의 타전을 한다. 이 논문에는 PCF와의 결합 지 연 시간과 사용자 요청의 알고리즘을 결합 비교하고 사용자 조합과 PCF의 결합 실행이 가장 좋다는 것을 결론을 짓는다.

• Journal of Positioning, Navigation, and Timing
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• 제4권1호
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• pp.33-41
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• 2015

Compact Network Real-Time Kinematic (RTK) is a method that combines compact RTK and network RTK, and it can effectively reduce the time and spatial de-correlation errors. A network RTK user receives multiple correction information generated from reference stations that constitute a network, calculates correction information that is appropriate for one's own position through a proper combination method, and uses the information for the estimation of the position. This combination method is classified depending on the method for modeling the GPS error elements included in correction information, and the user position accuracy is affected by the accuracy of this modeling. Among the GPS error elements included in correction information, tropospheric delay is generally eliminated using a tropospheric model, and a combination method is then applied. In the case of a tropospheric model, the estimation accuracy varies depending on the meteorological condition, and thus eliminating the tropospheric delay of correction information using a tropospheric model is limited to a certain extent. In this study, correction information modeling accuracy performances were compared focusing on the Low-Order Surface Model (LSM), which models the GPS error elements included in correction information using a low-order surface, and a modified LSM method that considers tropospheric delay characteristics depending on altitude. Both of the two methods model GPS error elements in relation to altitude, but the second method reflects the characteristics of actual tropospheric delay depending on altitude. In this study, the final residual errors of user measurements were compared and analyzed using the correction information generated by the various methods mentioned above. For the performance comparison and analysis, various GPS actual measurement data were collected. The results indicated that the modified LSM method that considers actual tropospheric characteristics showed improved performance in terms of user measurement residual error and position domain residual error.

• 대한한방내과학회지
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• 제44권1호
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• pp.35-52
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• 2023

Objective: Internal injuries and consumptive disease have different causes, yet they can affect each other. The relationship and combination of prescription drugs in the clinical practice of internal injuries and consumptive disease were analyzed for various diseases of "Donguibogam" through network analysis. Methods: The prescriptions used in consumptive disease and internal injury were established by conducting a full survey on the papers extracted from Donguibogam. The R version 4.0.3 (2020-10-10) and the igraph and arules package were used to perform network analysis and association rule relationship mining analysis in the first and second prescription compositions. Results: The herb frequently used for internal injury was Glycyrrhizae Radix, while the herb combination frequently used was Citri Pericarpium-Glycyrrhizae Radix. For centrality, the main factor was generally Glycyrrhizae Radix. In the case of consumptive disease, the herb most frequently used was Angelicae Gigantis Radix, and the combination most frequently used was Rehmanniae Radix Preparata-Angelicae Gigantis Radix. In terms of centrality, it was Angelicae Gigantis Radix. As a result of the network analysis of herbal prescription frequency, each group was divided into three. Conclusion: The interrelationship between internal injury and consumptive disease prescription drugs may reveal the differences and similarities between internal injury and consumptive disease and may serve as a basis for the development of new drugs or materials that can enhance mutual effectiveness in the treatment of internal injury and consumptive diseases.

• 한국멀티미디어학회논문지
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• 제22권6호
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• pp.665-675
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• 2019

In this paper, we compare and analyze the classification performance of deep learning algorithm Convolutional Neural Network(CNN) ac cording to ensemble generation and combining techniques. We used several CNN models(VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, GoogLeNet) to create 10 ensemble generation combinations and applied 6 combine techniques(average, weighted average, maximum, minimum, median, product) to the optimal combination. Experimental results, DenseNet169-VGG16-GoogLeNet combination in ensemble generation, and the product rule in ensemble combination showed the best performance. Based on this, it was concluded that ensemble in different models of high benchmarking scores is another way to get good results.

• 한국정보과학회논문지:정보통신
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• 제30권1호
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• pp.82-96
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• 2003

인터넷은 개방 프로토콜의 영향으로 빠르게 성장하여 글로벌 네트워크 환경으로 진화하였지만, 많은 위협들로부터 자산을 보호해야하는 문제를 초래하게 되었다. 정보보호에 있어서, 조직 내 전체 보안시스템들을 완전 가동하여 사고 발생 이전에 침입을 차단하는 것은 최선책이지만, 사고 발생 이전 또는 새롭게 개발된 공격들을 차단하기는 대단히 어렵다. 보안연합은 신뢰할 수 있는 보안영역들간의 신속하고 정확한 보안 정보 교환과 긴밀한 강호 협력을 통해 잠재적인 공격들을 사전에 준비하여 대응할 수 있으며 새로운 보호 기능들을 능동적으로 갱신하여 보다 강력한 보안 기능과 신속하게 대응한 수 있는 구조이다.

• 한국지능시스템학회논문지
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• 제13권2호
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• pp.186-191
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• 2003

이동로봇(Mobile Robot)의 자율주행 기능에는 추종, 접근, 충돌회피, 경고 등의 여러 기능이 있다. 이 기능들을 하나의 Neural Network로 구성하고 학습하는 것은 쉬운 일이 아니다. 이동로봇의 자율주행 기능들을 각각의 Module로 구성하고 상황에 맞게 학습된 Module의 출력 값으로 이동로봇을 제어하면 단일 신경망의 단점을 보안할 수 있을 것이다. 이동로봇은 인간의 감각을 대신할 수 있는 다중 초음파 센서와 USB 카메라를 장착하고 있으며, 이곳에서 측정된 환경정보 데이터들은 Modular Neural Network(MNN)을 통해 학습을 한다. Expert Network(EN)의 활성화 함수를 최적결합으로 MNN을 구성하였고, 그 구조는 학습시간과 오차를 개선할 수 있을 것으로 본다. Gating Network(GN)는 MNN의 출력값인 이동로봇의 진행 방향과 속도를 스위칭 함으로써 제어하는 역할을 한다. 본 논문에서는 Modular Neural Network(MNN) 내의 Expert Network(EN)을 최적설계 하였고, 제안한 MNN의 검증을 위해 실시간으로 반복하여 이동로봇에 구현하였다. 그 실험의 결과값은 로봇을 상황에 맞게 운행, 제어하였고, 만족할 만한 성과를 얻을 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.20-20
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• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.3944-3951
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• 2021

Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.

• IMMUNE NETWORK
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• 제14권2호
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• pp.73-80
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• 2014

Because autoimmune diseases (AIDs) result from a complex combination of genetic and epigenetic factors, as well as an altered immune response to endogenous or exogenous antigens, systems biology approaches have been widely applied. The use of multi-omics approaches, including blood transcriptomics, genomics, epigenetics, proteomics, and metabolomics, not only allow for the discovery of a number of biomarkers but also will provide new directions for further translational AIDs applications. Systems biology approaches rely on high-throughput techniques with data analysis platforms that leverage the assessment of genes, proteins, metabolites, and network analysis of complex biologic or pathways implicated in specific AID conditions. To facilitate the discovery of validated and qualified biomarkers, better-coordinated multi-omics approaches and standardized translational research, in combination with the skills of biologists, clinicians, engineers, and bioinformaticians, are required.