• Title/Summary/Keyword: optimized

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Efficient Implementation of Simeck Family Block Cipher on 8-Bit Processor

  • Park, Taehwan;Seo, Hwajeong;Bae, Bongjin;Kim, Howon
    • Journal of information and communication convergence engineering
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    • v.14 no.3
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    • pp.177-183
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    • 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.

Comparison of Images Using Optimized Grid and Images Using Grid Supperession Software in the Diagnosis of Micro Lesions (미세병변 진단에서 Optimized Grid을 사용한 영상과 Grid Supperession Software를 사용한 영상의 비교분석)

  • Lee, Sang-Ho
    • Journal of radiological science and technology
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    • v.41 no.2
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    • pp.149-155
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    • 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.

A New Architecture of Genetically Optimized Self-Organizing Fuzzy Polynomial Neural Networks by Means of Information Granulation

  • Park, Ho-Sung;Oh, Sung-Kwun;Ahn, Tae-Chon
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1505-1509
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    • 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.

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A study on the effectively optimized algorithm for an incremental attribute grammar (점진적 속성문법을 위한 효과적인 최적화 알고리즘에 관한 연구)

  • Jang, Jae-Chun;Ahn, Heui-Hak
    • The KIPS Transactions:PartA
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    • v.8A no.3
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    • pp.209-216
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    • 2001
  • The effective way to apply incremental attribute grammar to a complex language process is the use of optimized algorithm. In optimized algorithm for incremental attribute grammar, the new input attribute tree should be exactly compared with the previous input attribute tree, in order to determine which subtrees from the old should be used in constructing the new one. In this paper the new optimized algorithm was reconstructed by analyzing the algorithm suggested by Carle and Pollock, and a generation process of new attribute tree d’copy was added. Through the performance evaluation for the suggested matching algorithm, the run time is approximately improved by 19.5%, compared to the result of existing algorithm.

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An improved plasma model by optimizing neuron activation gradient (뉴런 활성화 경사 최적화를 이용한 개선된 플라즈마 모델)

  • 김병환;박성진
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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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.

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Evaluation of Pavement Smoothness on Optimized Rehabilitated Section (최소단면 보수지역의 평탄성 평가)

  • Park, Dae-Wook;Jin, Jung-Hoon
    • International Journal of Highway Engineering
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    • v.12 no.2
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    • pp.123-127
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    • 2010
  • In this study, the profiles of optimized rehabilitated section was measured by a lightweight inertial profiler, and pavement smoothness was evaluated. To analyze the repeatability of the used lightweight profiler, two repeatable measurements were conducted. The agreement between two repeatable measurements were evaluated by Cross-correlation function. Pavement smoothness of the optimized rehabilitated pavement section and existing area was compared in terms of International Roughness Index and Profilograh Index. In general, the pavement smoothness of the rehabilitated sections was not good compared to the existing pavement sections. The analysis results could be used for the evaluation of pavement smoothness of the optimized rehabilitated pavement sections.

Genetically Opimized Self-Organizing Fuzzy Polynomial Neural Networks Based on Fuzzy Polynomial Neurons (퍼지다항식 뉴론 기반의 유전론적 최적 자기구성 퍼지 다항식 뉴럴네트워크)

  • 박호성;이동윤;오성권
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.53 no.8
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    • pp.551-560
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    • 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.

An Efficient Lane Detection Based on the Optimized Hough Transform (최적화된 Hough 변환에 근거한 효율적인 차선 인식)

  • Park Jae-Hyeon;Lee Hack-Man;Cho Jae-Hyun;Cha Eui-Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.2
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    • pp.406-412
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    • 2006
  • In this paper, we propose OHT(optimized nough Transform) algorithm for the lane extraction. Input image is changed into 256 gray revel image. Gray level image is separated into background region and road region by using limited horizontal projection value. In separated road area, we apply OHT algorithm. OHT algorithm is characterized as follows. First, the number of candidate pixels is reduced using the outline orientation of the lane. Second, each range of the left and right lane is defined by limited ${\theta}$ Experimental results show that the proposed method is better than Hough Transform.

Genetically Optimized Rule-based Fuzzy Polynomial Neural Networks (진화론적 최적 규칙베이스 퍼지다항식 뉴럴네트워크)

  • Park Byoung-Jun;Kim Hyun-Ki;Oh Sung-Kwun
    • Journal of Institute of Control, Robotics and Systems
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    • v.11 no.2
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    • pp.127-136
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    • 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.

The Improvement of Summer Season Precipitation Predictability by Optimizing the Parameters in Cumulus Parameterization Using Micro-Genetic Algorithm (마이크로 유전알고리즘을 이용한 적운물리과정 모수 최적화에 따른 여름철 강수예측성능 개선)

  • Jang, Ji-Yeon;Lee, Yong Hee;Choi, Hyun-Joo
    • Atmosphere
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    • v.30 no.4
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    • pp.335-346
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    • 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.