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101.
Mohamed Abdellatif 《Color research and application》2015,40(6):564-576
The spectral overlap of color‐sampling filters increases errors when using a diagonal matrix transform, for color correction and reduces color distinction. Spectral sharpening is a transformation of colors that was introduced to reduce color‐constancy errors when the colors are collected through spectrally overlapping filters. The earlier color‐constancy methods improved color precision when the illuminant color is changed, but they overlooked the color distinction. In this article, we introduce a new spectral sharpening technique that has a good compromise of color precision and distinction, based on real physical constraints. The spectral overlap is measured through observing a gray reference chart with a set of real and spectrally disjoint filters selected by the user. The new sharpening method enables to sharpen colors obtained by a sensor without knowing the camera response functions. Experiments with real images showed that the colors sharpened by the new method have good levels of color precision and distinction as well. The color‐constancy performance is compared with the data‐based sharpening method in terms of both precision and distinction. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 564–576, 2015 相似文献
102.
This paper presents application of parametric techniques (Estimation of signal parameter by rotational invariance techniques (ESPRIT), Root multiple signal classification (Root MUSIC)) for the estimation of harmonics/inter harmonics produced by wind generator under constant, variable rotor speed, load imbalance. Accuracy of estimation by parametric techniques is checked on synthetic signal as well as signal extracted from DFIG based standalone system with constant rotor speed and balanced load condition. Further sliding window concept is applied to parametric techniques to estimate the harmonics/inter harmonics under variable rotor speed and load unbalance. Series of simulation results demonstrate the advantages of the sliding window ESPRIT over sliding window Root MUSIC in the estimation of harmonics and inter harmonics under variable rotor speed as well as load unbalance condition. 相似文献
103.
104.
Ching‐Ter Chang 《International Transactions in Operational Research》2021,28(1):493-511
Interval goal programming (IGP) with a marginal penalty function (PF) was first proposed by Charnes and Collomb in 1972, and further improved by Kvanli and other researchers. Recently, Lu and Chen proposed an efficient logarithmic method to formulate IGP with an S‐shaped PF. However, their method requires adding many binary variables when the problem size becomes large, which increases the computational burden in the solution process. This study proposes an efficient approach for the S‐shaped PF. The arbitrary PF frequently appears in the fields of business and industry. However, none of the previous approaches have addressed arbitrary PFs without adding binary variables. The proposed approach can be easily extended to formulate an arbitrary PF in which binary variables are no longer required, regardless of the number of break points. The proposed method can improve the efficiency of IGP for solving large size management and decision problems in considering PFs. In order to demonstrate the correctness, usefulness of the proposed model, illustrative examples are provided. 相似文献
105.
Cristian Podesta Natalie Coleman Amir Esmalian Faxi Yuan Ali Mostafavi 《Journal of the Royal Society Interface》2021,18(177)
This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population''s activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process. 相似文献
106.
Chin‐wei Huang 《International Transactions in Operational Research》2021,28(1):470-492
The purpose of this study is to develop a modification of the model developed by Chen and Zhu in 2004. Calculating stage and overall efficiencies precisely and consistently has become a major challenge of the two‐stage DEA model. However, most other models do not calculate the optimality of intermediates. Although the model developed by Chen and Zhu measures the optimality of intermediates, the calculated efficiency scores still have some shortfalls. The modified model, named the hybrid two‐stage DEA model, fills the gap between calculating the optimality of intermediates and the consistency of overall efficiency scores. In addition to obtaining an accurate measurement for the optimality of intermediates, the model confines efficiency scores to a range from zero to one (a ratio efficiency score). In an empirical evaluation, we use data from 64 medical manufacturing firms to test the performance of the hybrid model and offer recommendations for the industry. 相似文献
107.
The solder paste printing (SPP) is a critical procedure in a surface mount technology (SMT) based assembly line, which is one of the major attributes to the defect of the printed circuit boards (PCBs). The quality of SPP is influenced by multiple factors, such as the squeegee speed, pressure, the stencil separation speed, cleaning frequency, and cleaning profile. During printing, the printer environment is dynamically varying due to the physical change of solder paste, which can result in a dynamic variation of the relationships between the printing results and the influential factors. To reduce the printing defects, it is critical to understand such dynamic relationships. This research focuses on determining the printing performance during printing by implementing a wavelet filtering-based temporal recurrent neural network. To reduce the noise factor in the solder paste inspection (SPI) data, this research applies a three-dimensional dual-tree complex wavelet transformation for low-pass noise filtering and signal reconstruction. A recurrent neural network is utilized to model the performance prediction with low noise interference. Both printing sequence and process setting information are considered in the proposed recurrent network model. The proposed approach is validated using practical dataset and compared with other commonly used data mining approaches. The results show that the proposed wavelet-based multi-dimensional temporal recurrent neural network can effectively predict the printing process performance and can be a high potential approach in reducing the defects and controlling cleaning frequency. The proposed model is expected to advance the current research in the application of smart manufacturing in surface mount technology. 相似文献
108.
Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration. 相似文献
109.
Shaojiang Zhong 《计算机、材料和连续体(英文)》2019,60(2):465-479
Based on the three-dimensional classic Chua circuit, a nonlinear circuit containing two flux-control memristors is designed. Due to the difference in the design of the characteristic equation of the two magnetron memristors, their position form a symmetrical structure with respect to the capacitor. The existence of chaotic properties is proved by analyzing the stability of the system, including Lyapunov exponent, equilibrium point, eigenvalue, Poincare map, power spectrum, bifurcation diagram et al. Theoretical analysis and numerical calculation show that this heterogeneous memristive model is a hyperchaotic five-dimensional nonlinear dynamical system and has a strong chaotic behavior. Then, the memristive system is applied to digital image and speech signal processing. The analysis of the key space, sensitivity of key parameters, and statistical character of encrypted scheme imply that this model can applied widely in multimedia information security. 相似文献
110.
专利规避设计需从现有专利中遴选具有核心竞争力的目标专利,以提升技术起点。针对设计领域的海量专利信息,提出了专利质量多指标主客观综合评价模型。分析专利书目信息与专利质量存在的正相关关系,甄选存活期、权利项数、同族专利数、引证数和被引证数等书目信息指标,构建了专利质量多指标综合评价指标体系,提升了评价方法的科学性和可操作性;提出将德尔菲法、层次分析法和均方差决策法相结合的主客观赋权法,确定专利质量评价指标权重,既保证了评价的权威性,又减小了个人偏见与从众妥协等因素的影响;建立了基于质量评价模型遴选目标专利的过程模型,并通过应用实例验证了该模型对遴选具有核心竞争力专利的有效性。 相似文献