• Journal of information and communication convergence engineering
• /
• 제14권3호
• /
• pp.177-183
• /
• 2016

A lot of Internet of Things devices has resource-restricted environment, so it is difficult to implement the existing block ciphers such as AES, PRESENT. By this reason, there are lightweight block ciphers, such as SIMON, SPECK, and Simeck, support various block/key sizes. These lightweight block ciphers can support the security on the IoT devices. In this paper, we propose efficient implementation methods and performance results for the Simeck family block cipher proposed in CHES 2015 on an 8-bit ATmega128-based STK600 board. The proposed methods can be adapted in the 8-bit microprocessor environment such as Arduino series which are one of famous devices for IoT application. The optimized on-the-fly (OTF) speed is on average 14.42 times faster and the optimized OTF memory is 1.53 times smaller than those obtained in the previous research. The speed-optimized encryption and the memory-optimized encryption are on average 12.98 times faster and 1.3 times smaller than those obtained in the previous studies, respectively.

• 대한방사선기술학회지:방사선기술과학
• /
• 제41권2호
• /
• pp.149-155
• /
• 2018

Quantitative analysis was performed to confirm that moire artifact was removed without loss of image when using grid suppression software in the diagnosis of micro lesions. we showed that grid suppression images can be morphologically different from original images as they are significantly lower than those of the optimized grid in the similarity analysis with reference images in mammographic phantom images. We were confirmed that images of microcalcification with smaller signal than noise were lost because the pixel values of all lesions increased significantly after the grid suppression, The change in contrast using the NORMI 13 X-ray test phantom was reduced to 30% of the reference image, This result was significantly lower than the 90% when using the optimized grid. In conclusion, the use of grid suppression software in clinical images should be carefully considered because of the possibility of misdiagnosis due to micro lesion loss and morphological changes.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.1505-1509
• /
• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

• 정보처리학회논문지A
• /
• 제8A권3호
• /
• pp.209-216
• /
• 2001

복잡한 언어 처리에 점진적 속성 문법을 적용하기 위해서는 최적화 알고리즘을 사용하는 것이 효과적이다. 점진적 속성문법의 최적화 알고리즘에서는 새로운 입력 속성 트리가 기존 입력 속성 트리와 정확히 비교되어서 새로운 속성 트리를 구성할 대 기존 속성 트리의 어떤 서브트리를 사용해야 하는가를 결정한다. 본 논문에서는 Carle과 Pollock에 의해 제안된 알고리즘을 분석하여 효과적인 최적화 알고리즘으로 재구성하고, 새로은 속성 트리 d'copy의 생성 과정과, 속성트리 d'copy의 새로운 최적화 알고리즘을 추가하였다. 이 논문에서 제안한 매칭 알고리즘의 성능평가를 통하여 기존의 알고리즘 보다 제안한 최적화 알고리즘의 실행 시간을 약 19.5% 향상 시킬 수 있었다.

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

• 한국도로학회논문집
• /
• 제12권2호
• /
• pp.123-127
• /
• 2010

본 논문에서는 최소단면 보수가 완료된 아스팔트 콘크리트 포장의 평탄성 평가를 위하여 경량 프로파일러를 이용하여 측정하였으며 평탄성 분석을 실시하였다. 측정 프로파일러의 정확도를 검토하기 위하여 2회 측정한 프로파일을 이용하여 반복성(repeatability) 검증을 수행하였다. Cross-Corelation 함수를 이용하여 측정치간의 일치를 검증하였다. 최소단면 보수가 이루어진 아스팔트 콘크리트 포장 부분과 기존 포장의 평탄성을 국제평탄성지수와 PrI 지수를 비교 분석하였으며, 최소단면 보수가 이루어진 차선의 평탄성이 대체적으로 좋지 않았다. 향후 최소단면 보수 지역의 평탄성 평가에 대한 기초자료로 사용할 수 있다고 판단된다.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제53권8호
• /
• pp.551-560
• /
• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• 한국정보통신학회논문지
• /
• 제10권2호
• /
• pp.406-412
• /
• 2006

본 논문에서는 차선 추출을 위해서 OHT(Optimized Hough Transform) 알고리듬을 제안한다. 입력 영상을 그레이 영상으로 변환하고 변환된 그레이 영상은 수평 투영을 통해 주변 배경 영역과 도로 영역으로 분리된다. 분리된 도로 영역에서 OHT(Optimized Hough Transform) 알고리듬을 적용한다. OHT(Optimized Hough Transform) 알고리듬은 다음과 같이 특징지어진다. 첫째, 윤곽선 방향각을 이용해서 차선후보 픽셀을 최소화하였으며, 둘째, 좌우 차선의 범위는 제한된 ${\theta}$값으로서 정의하였다. 실험 결과, 제안한 알고리듬이 기존의 Hough Transform보다 훨씬 효율적임을 알 수 있었다.

• 제어로봇시스템학회논문지
• /
• 제11권2호
• /
• pp.127-136
• /
• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• 대기
• /
• 제30권4호
• /
• pp.335-346
• /
• 2020

Three free parameters included in a cumulus parameterization are optimized by using micro-genetic algorithm for three precipitation cases occurred in the Korea Peninsula during the summer season in order to reduce biases in a regional model associated with the uncertainties of the parameters and thus to improve the predictability of precipitation. The first parameter is the one that determines the threshold in convective trigger condition. The second parameter is the one that determines boundary layer forcing in convective closure. Finally, the third parameter is the one used in calculating conversion parameter determining the fraction of condensate converted to convective precipitation. Optimized parameters reduce the occurrence of convections by suppressing the trigger of convection. The reduced convection occurrence decreases light precipitation but increases heavy precipitation. The sensitivity experiments are conducted to examine the effects of the optimized parameters on the predictability of precipitation. The predictability of precipitation is the best when the three optimized parameters are applied to the parameterization at the same time. The first parameter most dominantly affects the predictability of precipitation. Short-range forecasts for July 2018 are also conducted to statistically assess the precipitation predictability. It is found that the predictability of precipitation is consistently improved with the optimized parameters.