Journal articles: 'Component robustness'

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Blanchard, A., and Y. Zolnierowski. "Dark Energy: Nature and Robustness." International Astronomical Union Colloquium 192 (2005): 573–78. http://dx.doi.org/10.1017/s0252921100009684.

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SummarySupernovae have provided the evidence for the existence of a dominant dark energy component of the Universe. The commonly accepted form of such a component is the quintessence. Here, we show that the possible nature of this component is rather well constrained by combining the various existing observational cosmological data. However, relaxing some of the various hypothesis can lead to somewhat different results.

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Wang, Wenshan, Qixin Cao, Xiaoxiao Zhu, and Masaru Adachi. "An automatic switching approach of robotic components for improving robot localization reliability in complicated environment." Industrial Robot: An International Journal 41, no. 2 (March 11, 2014): 135–44. http://dx.doi.org/10.1108/ir-04-2013-338.

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Purpose – Robot localization technology has been widely studied for decades and a lot of remarkable approaches have been developed. However, in practice, this technology has hardly been applied to common day-to-day deployment scenarios. The purpose of this paper is to present a novel approach that focuses on improving the localization robustness in complicated environment. Design/methodology/approach – The localization robustness is improved by dynamically switching the localization components (such as the environmental camera, the laser range finder and the depth camera). As the components are highly heterogeneous, they are developed under the robotic technology component (RTC) framework. This simplifies the developing process by increasing the potential for reusability and future expansion. To realize this switching, the localization reliability for each component is modeled, and a configuration method for dynamically selecting dependable components at run-time is presented. Findings – The experimental results show that this approach significantly decreases robot lost situation in the complicated environment. The robustness is further enhanced through the cooperation of heterogeneous localization components. Originality/value – A multi-component automatic switching approach for robot localization system is developed and described in this paper. The reliability of this system is proved to be a substantial improvement over single-component localization techniques.

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Herrendörfer, G., A. Tuchscherer, G. Dietl, and M. Tuchscherer. "Die Robustheit von BLUP und EBLUP." Archives Animal Breeding 42, no. 3 (October 10, 1999): 303–18. http://dx.doi.org/10.5194/aab-42-303-1999.

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Abstract. Title of the paper: The Robustness of BLUP and EBLUP The expectations of BLP and BLUP used as estimator for fixed effects and as predictor for random effects were investigated concerning the property "unbiasedness". Different aspects of robustness research on prediction procedures are discussed. The robustness of BLUP and EBLUP was investigated with respect to the distribution ofthe random components of the model, the degree of balancedness of the experimental design, influence of the variance components ratio and different variance component estimators used in EBLUP(2) on the bases of a Computer Simulation in the random one-way model. Criteria used for the evaluation were the mean squared error (MSE) and the selection gain. Besides, an idea of the overestimation of the accuracy of EBLUP by the naive MSE approximation based on the MSE formulas of BLUP with variance component estimations instead of unknown parameters is given.

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Devitt, S. J., A. G. Fowler, and L. C. L. Hollenberg. "Robustness of Shor's algorithm." Quantum Information and Computation 6, no. 7 (November 2006): 616–29. http://dx.doi.org/10.26421/qic6.7-5.

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Shor's factorisation algorithm is a combination of classical pre- and post-processing and a quantum period finding (QPF) subroutine which allows an exponential speed up over classical factoring algorithms. We consider the stability of this subroutine when exposed to a discrete error model that acts to perturb the computational trajectory of a quantum computer. Through detailed state vector simulations of an appropriate quantum circuit, we show that the error locations within the circuit itself heavily influences the probability of success of the QPF subroutine. The results also indicate that the naive estimate of required component precision is too conservative.

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Sjödahl, Mikael, and Erik Olsson. "Robustness of Laser Speckles as Unique Traceable Markers of Metal Components." Digital 1, no. 1 (February 18, 2021): 54–63. http://dx.doi.org/10.3390/digital1010004.

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The traceability of manufactured components is growing in importance with the greater use of digital service solutions offered and with an increased digitalization of manufacturing logistics. In this paper, we investigate the use of image-plane laser speckles as a tool to acquire a unique code from the surface of the component and the ability to use this pattern as a secure component-specific digital fingerprint. Intensity correlation is used as a numerical identifier. Metal sheets of different materials and steel pipes are considered. It is found that laser speckles are robust against surface alterations caused by surface compression and scratching and that the correct pattern reappears from a surface contaminated by oil after cleaning. In this investigation, the detectability is close to 100% for all surfaces considered, with zero false positives. The exception is a heavily oxidized surface wiped by a cotton cloth between recordings. It is further found that the main source for lost detectability is caused by misalignment between the registration and detection geometries where a positive match is lost by a change in angle in the order of 60 mrad. Therefore, as long as the registration and detection systems, respectively, use the same optical arrangement, laser speckles have the ability to serve as unique component identifiers without having to add extra markings or a dedicated sensor to the component.

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Hien, Thai Duy, Zensho Nakao, and Yen-Wei Chen. "Intelligent Logo Watermarking Based on Independent Component Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 390–96. http://dx.doi.org/10.20965/jaciii.2004.p0390.

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We present new intelligent logo watermarking based on independent component analysis (ICA) in which a binary logo watermark is embedded in a host image in a wavelet domain. To improve robustness, an image adaptive watermarking algorithm is applied by a stochastic approach based on a noise visibility function (NVF). The algorithm design, evaluation, and experimentation are described. Experimental results show that the logo watermark is perfectly extracted by ICA with excellent invisibility and with robustness against various image and digital processing operators and almost all compression algorithms such as Jpeg, jpeg 2000, SPIHT, EZW, and principal component analysis (PCA) based compression.

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Chakrabarty, Nilanjana, and Dibyojyoti Bhattacharjee. "Assessing Robustness of Asian Countries Ranking." International Journal of Innovation in the Digital Economy 7, no. 1 (January 2016): 24–37. http://dx.doi.org/10.4018/ijide.2016010103.

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The term ‘digital divide' refers to the gap between individuals, households, businesses and geographic areas with regard to both their opportunities to access Information and Communication Technologies (ICT) and to their use of Internet. Composite indicators are regularly used for measuring the divide and in benchmarking the country's performance. But often it creates controversies regarding the subjectivity that is connected with their construction methodology more specifically the weighting and aggregation issues. The paper attempts to assess the robustness of the ranks generated by the composite digital divide index using different weighting and aggregation schemes in case of Asian countries. Here four weighting techniques Iyengar-Sudarshan Method, Benefit of Doubt Method, Principal Component Analysis and Unobserved Component Model and three techniques of aggregation viz. Linear Aggregation, Geometric Aggregation and Weighted Displaced Ideal Method are used for mutual comparison.

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Illarramendi, M., L. Etxeberria, X. Elkorobarrutia, and G. Sagardui. "Runtime Contracts Checker: Increasing Robustness of Component-Based Software Systems." IOP Conference Series: Materials Science and Engineering 575 (August 13, 2019): 012006. http://dx.doi.org/10.1088/1757-899x/575/1/012006.

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Dong, Shangjia, Haizhong Wang, Ali Mostafavi, and Jianxi Gao. "Robust component: a robustness measure that incorporates access to critical facilities under disruptions." Journal of The Royal Society Interface 16, no. 157 (August 2019): 20190149. http://dx.doi.org/10.1098/rsif.2019.0149.

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The objective of this paper is to integrate the post-disaster network access to critical facilities into the network robustness assessment, considering the geographical exposure of infrastructure to natural hazards. Conventional percolation modelling that uses generating function to measure network robustness fails to characterize spatial networks due to the degree correlation. In addition, the giant component alone is not sufficient to represent the performance of transportation networks in the post-disaster setting, especially in terms of the access to critical facilities (i.e. emergency services). Furthermore, the failure probability of various links in the face of different hazards needs to be encapsulated in simulation. To bridge this gap, this paper proposed the metric robust component and a probabilistic link-removal strategy to assess network robustness through a percolation-based simulation framework. A case study has been conducted on the Portland Metro road network during an M9.0 earthquake scenario. The results revealed how the number of critical facilities severely impacts network robustness. Besides, earthquake-induced failures led to a two-phase percolation transition in robustness performance. The proposed robust component metric and simulation scheme can be generalized into a wide range of scenarios, thus enabling engineers to pinpoint the impact of disastrous disruption on network robustness. This research can also be generalized to identify critical facilities and sites for future development.

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Novacek, Jan, Alexander Viehl, Oliver Bringmann, and Wolfgang Rosenstiel. "Reasoning-Supported Robustness Validation of Automotive E/E Components." International Journal of Semantic Computing 11, no. 04 (December 2017): 473–96. http://dx.doi.org/10.1142/s1793351x17400190.

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This article presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components. The approach uses formalized knowledge from the RV process and stress, operating, and load profiles, so-called Mission Profiles (MPs). In contrast to the error-prone industrially established manual procedure, we show how component characteristics are formalized in OWL in order to form the foundation of an efficient automated analysis selection and decision support during the RV process. Additionally, a rule-based transformation of component characteristics upon propagation via SWRL is described. The proposed approach is based on the idea of mapping MPs to an OWL representation in order to allow to execute semantic queries against MP data to improve their integration into the RV process. The resulting ontology-supported application framework has been applied to an industrial use-case from automotive power electronics. A generalization of the approach is described and demonstrated by applying it to stress test selection within the AEC Q100 standard. We present experimental results showing that the RV process can be significantly improved in terms of reduced design time and increased exhaustiveness by automating the analyses selection step and the provisioning of all the relevant data to be used.

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Diop, Issa Moussa, Cherif Diallo, Chantal Cherifi, and Hocine Cherifi. "Robustness Analysis of the Regional and Interregional Components of the Weighted World Air Transportation Network." Complexity 2022 (November 3, 2022): 1–17. http://dx.doi.org/10.1155/2022/6595314.

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The robustness of a system indicates its ability to withstand disturbances while maintaining its properties, performance, and efficiency. There are plenty of studies on the robustness of air transport networks in the literature. However, few works consider its mesoscopic organization. Building on the recently introduced component structure, we explore the impact of targeted attacks on the weighted world air transportation network on its components. Indeed, it contains five local components covering different regions (North America-Caribbean, Europe-Russia, East and Southeast Asia-Oceania, Africa-Middle East-Southern Asia, and South America) and one global component linking these regions. We investigate targeted attacks based on influential weighted centrality measures (strength, betweenness, and PageRank). Results show that the local components gradually separate from the world air transportation network as the fraction of removed airports grows. The weighted betweenness attack removes fewer top airports to isolate the regions compared to its alternatives. Furthermore, it is still convenient to travel locally in the separate areas. In contrast, strength and PageRank attacks need to target more airports to split the network. However, they are more disruptive. Indeed, the size of the isolated local components reduces drastically, so it becomes more challenging to travel locally. Looking at the world air transportation network through its component structure reveals a new viewpoint on its resilence. It opens new perspectives to design more efficient attacks.

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Zhao, Nan, and Hong Yu Shao. "Robust Principal Component Analysis Algorithm Based on Nonparametric Correlation Coefficient Matrix." Advanced Materials Research 989-994 (July 2014): 2613–16. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2613.

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Principal component analysis algorithm is widely applied in solving a large number of dimension reduction problems for high-dimensional data in fields such as image identification and comprehensive evaluation due to its clear conception and simple application. The PCA based on covariance matrix and correlation coefficient matrix is very sensitive to abnormal values so that the data structure is distorted. As a result, there exist great deviations in the analysis results and it is hard to explain. The traditional PCA is remade in this paper based on non-parametric statistical ideas, nonparametric correlation coefficient and gray relational coefficient, in order to define three kinds of robustness evaluation criteria and objectively evaluate the robustness of the algorithm through numerical experiments. The results of the numerical experiments show that the new algorithm is of good robustness to noise pollution and the algorithm is of good applicability.

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Shi, Xiaoshuang, Zhenhua Guo, Feiping Nie, Lin Yang, Jane You, and Dacheng Tao. "Two-Dimensional Whitening Reconstruction for Enhancing Robustness of Principal Component Analysis." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 10 (October 1, 2016): 2130–36. http://dx.doi.org/10.1109/tpami.2015.2501810.

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Huang, Su-Yun, Yi-Ren Yeh, and Shinto Eguchi. "Robust Kernel Principal Component Analysis." Neural Computation 21, no. 11 (November 2009): 3179–213. http://dx.doi.org/10.1162/neco.2009.02-08-706.

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This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions are derived, and numerical examples are presented as well. Both theoretical and numerical results indicate that the proposed robust method outperforms the conventional approach in the sense of being less sensitive to outliers. Our robust method and results also apply to functional principal component analysis.

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Knuth, Kevin H., Ankoor S. Shah, Wilson A. Truccolo, Mingzhou Ding, Steven L. Bressler, and Charles E. Schroeder. "Differentially Variable Component Analysis: Identifying Multiple Evoked Components Using Trial-to-Trial Variability." Journal of Neurophysiology 95, no. 5 (May 2006): 3257–76. http://dx.doi.org/10.1152/jn.00663.2005.

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Electric potentials and magnetic fields generated by ensembles of synchronously active neurons, either spontaneously or in response to external stimuli, provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult because detectors record signals simultaneously generated by various regions throughout the brain. We introduce a novel approach to this problem, the differentially variable component analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we demonstrate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. We then compare the source-separation capabilities of dVCA with those of principal component analysis and independent component analysis. Finally, we apply dVCA to neural ensemble activity recorded from an awake, behaving macaque—demonstrating that dVCA is an important tool for identifying and characterizing multiple components in the single trial.

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Higuchi, Isao, and Shinto Eguchi. "The Influence Function of Principal Component Analysis by Self-Organizing Rule." Neural Computation 10, no. 6 (August 1, 1998): 1435–44. http://dx.doi.org/10.1162/089976698300017241.

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This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

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Burgess, Ian. "Connection behaviour and the robustness of steel-framed structures in fire." MATEC Web of Conferences 149 (2018): 01008. http://dx.doi.org/10.1051/matecconf/201814901008.

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The full-scale fire tests at Cardington in the 1990s, and the collapse of at least one of the WTC buildings in 2001, illustrated that connections are potentially the most vulnerable parts of a structure in fire. Fracture of connections causes structural discontinuities and reduces the robustness provided by alternative load paths. An understanding of connection performance is essential to the assessment of structural robustness, and so to structural design against progressive collapse. The forces and deformations to which connectionscan be subjected during a fire differ significantly from those assumed in general design. The internal forces i generally start with moment and shear at ambient temperature, then superposing compression in the initial stages of a fire, which finally changes to catenary tension at high temperatures. If a connection does not have sufficient resistance or ductility to accommodate simultaneous large rotations and normal forces, then connections may fracture, leading to extensive damage or progressive collapse of the structure. Practical assessment of the robustness of steel connections in fire will inevitably rely largely on numerical modelling, but this is unlikely to include general-purpose finite element modelling, because of the complexity of such models. The most promising alternative is the component method, a practical approach which can be included within global three-dimensional frame analysis. The connection is represented by an assembly of individual components with known mechanical properties. Component characterization must include high-deflection elevated-temperature behaviour, and represent it up to fracture.In reality a connection may either be able to regain its stability after the initial fracture of one (or a few) components, or the first failure may trigger a cascade of failures of other components, leading to complete detachment of the supported member. Numerical modelling must be capable of predicting the sequence of failures of components, rather than considering the first loss of stability as signifying building failure. It is necessary to use a dynamic analysis, so that loss of stability and re-stabilization can be tracked, includingthe movements of disengaging members and the loadsharing mechanisms which maintain integrity and stability within the remaining structure, until total collapse occurs.

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Chen, Rui, Yu Lin Lan, and Reza Asharif Mohammad. "Digital Audio Watermarking Scheme Based on Independent Component Analysis in DWT Domain." Advanced Materials Research 219-220 (March 2011): 1121–25. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1121.

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This paper proposed a digital audio watermarking scheme based on independent component analysis (ICA) in DWT domain. The embedding process make full use of the multi-resolution characteristic of discrete wavelet transform (DWT), performing 3-level DWT. Selecting the low frequency coefficient appropriately as the embed location to make sure of the balance between the transparency and robustness. Then constructing the ICA model to embed the watermarking. The extraction process is similar with ICA’s goal, it’s used in extraction makes the scheme simple for implementation. The experiment results show that the proposed scheme has good robustness against common attacks, as well as transparency.

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Xu, Biao, Minyan Lu, Hong Zhang, and Cong Pan. "A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks." Sensors 21, no. 14 (July 17, 2021): 4873. http://dx.doi.org/10.3390/s21144873.

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A wireless sensor network (WSN) is a group of sensors connected with a wireless communications infrastructure designed to monitor and send collected data to the primary server. The WSN is the cornerstone of the Internet of Things (IoT) and Industry 4.0. Robustness is an essential characteristic of WSN that enables reliable functionalities to end customers. However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. This paper proposes a cross-domain agent-based model to analyze the connectivity robustness of a system in the malware propagation process. The agent characteristics and transition rules are also described in detail. To verify the practicality of the model, three scenarios based on different network topologies are proposed. Finally, the robustness of the scenarios and the topologies are discussed.

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Hossein Akbari, Schlumberger. "Wire-Bonding Reliability Evaluationa." International Symposium on Microelectronics 2020, no. 1 (September 1, 2020): 000242–45. http://dx.doi.org/10.4071/2380-4505-2020.1.000242.

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Abstract In microelectronic devices, wire bonding is the most common first-level interconnection method between die and lead. Failure of wire bonding causes component failure. Component failure may lead to system or sub-system failures, which often have very expensive consequences. Such failures are even more severe in the harsh operating conditions of the Oil and Gas industry, where services such as rig charge are extremely expensive. We have developed a robustness-evaluation method for microelectronic components using construction analysis.

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Sando, Keishi, and Hideitsu Hino. "Modal Principal Component Analysis." Neural Computation 32, no. 10 (October 2020): 1901–35. http://dx.doi.org/10.1162/neco_a_01308.

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Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and various robust PCA methods have been proposed. It has been shown that the robustness of many statistical methods can be improved using mode estimation instead of mean estimation, because mode estimation is not significantly affected by the presence of outliers. Thus, this study proposes a modal principal component analysis (MPCA), which is a robust PCA method based on mode estimation. The proposed method finds the minor component by estimating the mode of the projected data points. As a theoretical contribution, probabilistic convergence property, influence function, finite-sample breakdown point, and its lower bound for the proposed MPCA are derived. The experimental results show that the proposed method has advantages over conventional methods.

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Laranjeiro, Nuno, João Agnelo, and Jorge Bernardino. "A Systematic Review on Software Robustness Assessment." ACM Computing Surveys 54, no. 4 (May 2021): 1–65. http://dx.doi.org/10.1145/3448977.

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Robustness is the degree to which a certain system or component can operate correctly in the presence of invalid inputs or stressful environmental conditions. With the increasing complexity and widespread use of computer systems, obtaining assurances regarding their robustness has become of vital importance. This survey discusses the state of the art on software robustness assessment, with emphasis on key aspects like types of systems being evaluated, assessment techniques used, the target of the techniques, the types of faults used, and how system behavior is classified. The survey concludes with the identification of gaps and open challenges related with robustness assessment.

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Tan, Zuojun. "Improved robustness of Hartmann wavefront sensor using a principal component analysis algorithm." Optical Engineering 48, no. 10 (October 1, 2009): 103602. http://dx.doi.org/10.1117/1.3250291.

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Pervaiz, M., Q. Khan, A. I. Bhatti, and S. A. Malik. "Output Tracking via Adaptive Backstepping Higher Order Integral Sliding Mode for Uncertain Nonlinear Systems." Discrete Dynamics in Nature and Society 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/619010.

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The authors propose a new tracking control design strategy for uncertain non-linear systems which are convertible to Semi-Strict Feedback Form (SSFF). The system in SSFF is first converted into new variables via existing adaptive backstepping control techniques. The control law is obtained by combining adaptive backstepping procedure and higher order integral sliding mode. The component of control law designed via backstepping is continuous which shows robustness against parametric uncertainties where as the discontinuous control component provides robustness against unmodeled dynamics and external disturbances. Since, this strategy relies on an integral manifold of the adaptively developed variables, therefore, the reaching phase is eliminated in this approach, which is an advantage in term of robustness. Furthermore, the parameters update law correctly provides the estimation of parameters which is again results in enhanced robustness of the strategy. The stability of proposed method is analysed theoretically and validated through a numerical example.

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Chen, Rui, Yu Lin Lan, and Mohammad Reza Asharif. "Digital Audio Watermarking Scheme Based on Independent Component Analysis in Stereo Sound." Advanced Materials Research 459 (January 2012): 469–73. http://dx.doi.org/10.4028/www.scientific.net/amr.459.469.

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This paper proposed a digital audio watermarking scheme based on independent component analysis (ICA) in stereo sound. In order to make full use of the multi-channel characteristic of stereo sound, The watermarking embedded into the two channel, half respectively. Also using the multi-resolution characteristic of discrete wavelet transform, performing 3-level DWT in each channel. Selecting the low frequency coefficient appropriately as the embed location to make sure of the balance between the transparency and robustness. The extraction process is similar with ICA’ s goal, it’s used in extraction makes the scheme simple for implementation. The experiment results show that the proposed scheme has good robustness against common attacks, as well as transparency

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Zhao, Shuli, Weihua Yang, Shurong Zhang, and Liqiong Xu. "Component Edge Connectivity of Hypercubes." International Journal of Foundations of Computer Science 29, no. 06 (September 2018): 995–1001. http://dx.doi.org/10.1142/s012905411850017x.

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Fault tolerance is an important issue in interconnection networks, and the traditional edge connectivity is an important measure to evaluate the robustness of an interconnection network. The component edge connectivity is a generalization of the traditional edge connectivity. The [Formula: see text]-component edge connectivity [Formula: see text] of a non-complete graph [Formula: see text] is the minimum number of edges whose deletion results in a graph with at least [Formula: see text] components. Let [Formula: see text] be an integer and [Formula: see text] be the decomposition of [Formula: see text] such that [Formula: see text] and [Formula: see text] for [Formula: see text]. In this note, we determine the [Formula: see text]-component edge connectivity of the hypercube [Formula: see text], [Formula: see text] for [Formula: see text]. Moreover, we classify the corresponding optimal solutions.

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CAPOBIANCO, ENRICO. "ROBUSTNESS VERSUS REDUNDANCY IN BIOLOGICAL SYSTEMS." Fluctuation and Noise Letters 05, no. 03 (September 2005): L375—L385. http://dx.doi.org/10.1142/s0219477505002811.

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Genetic networks offer a wealth of data; this is mainly due to the genomic dimensionality rather than the samples, as the latter usually come from measurements obtained under a few experimental conditions or time points. It is therefore a challenging task to design suitable statistical models and to develop effective reverse engineering algorithms. The signature of noise is pervasive in genetic networks. For instance, in perturbation experiments only a few genes change expression value, while most genes are either noisy or constant. Consequently, a genetic regulatory network is a redundant system, due to the high-dimensionality and the dependence between genes, and also a sparse system through the gene-gene interaction matrix only partially active. In order to explore these two aspects, redundancy and sparsity, independent component analysis (ICA) is proposed as a flexible approximation model targeted to dimensionality reduction and gene feature selection.

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Chen, Yudong, Zhihui Lai, Jiajun Wen, and Can Gao. "Nuclear norm based two-dimensional sparse principal component analysis." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (March 2018): 1840002. http://dx.doi.org/10.1142/s0219691318400027.

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Two-Dimensional Principal Component Analysis (2D-PCA) is one of the most simple and effective feature extraction methods in the field of pattern recognition. However, the traditional 2D-PCA lacks robustness and the function of sparse feature extraction. In this paper, we propose a new feature extraction approach based on the traditional 2D-PCA, which is called Nuclear Norm Based Two-Dimensional Sparse Principal Component Analysis (N-2D-SPCA). To improve the robustness of 2D-PCA, we utilize nuclear norm to measure the reconstruction error of loss function. At the same time, we obtain sparse feature extraction by adding [Formula: see text]-norm and [Formula: see text]-norm regularization terms to the model. By designing an alternatively iterative algorithm, we can solve the optimization problem and learn a projection matrix for use with feature extraction. Besides, we present a bilateral projections model (BN-2D-SPCA) to further compress the dimensions of the feature matrix. We verify the effectiveness of our method on four benchmark face databases including AR, ORL, FERET and Yale databases. Experimental results show that the proposed method is more robust than some state-of-the-art methods and the traditional 2D-PCA.

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Sudha, M. S., and T. C. Thanuja. "Effect of different attacks on image watermarking using dual tree complex wavelet transform (DTCWT) and principle component analysis (PCA)." International Journal of Engineering & Technology 7, no. 2.9 (March 19, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i2-9.9249.

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Perceptibility and robustness are two incongruous requirements demanded by digital image watermarking for digital right management and other applications. A realistic way to concurrently satisfy the two contradictory requirements is to use robust watermark algorithm. The developed algorithm uses DTCWT and PCA techniques to embed watermark signal in host signal. To prove the algorithm robustness without much affecting perceptibility several attacks like noises, cropping, blurring, rotation are applied and tested by varying attack parameters. Parameters like Peak signal noise ratio and Correlation Coefficient are calculated for each attack. Attack percentage is varied and performance parameters are calculated to prove the robustness of the developed algorithm.

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KASPAR, JAROMIR, PETR BERNARDIN, and VACLAVA LASOVA. "INCREASING THE ROBUSTNESS OF AN INVERSE STAMPING ALGORITHM." MM Science Journal 2022, no. 2 (June 1, 2022): 5684–88. http://dx.doi.org/10.17973/mmsj.2022_06_2022081.

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Inverse stamping is a useful tool in engineering. It can be used to find the initial blank shape of stamped parts. It is also useful for quick and easy analysis of a forming process and its impact on the design of a component. Algorithms for inverse stamping are commonly available, but in some situations these algorithms can collapse or may provide confusing results. This article describes improvements to a newly designed inverse stamping algorithm which lead to an increase in its robustness.

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Guo, Hui Xin. "Design Optimization for the Robustness of Dimensional Tolerance by Using Evidence Theory." Applied Mechanics and Materials 483 (December 2013): 434–37. http://dx.doi.org/10.4028/www.scientific.net/amm.483.434.

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The design optimization of tolerance is researched to improve the robustness of plane dimension chain. The nonlinear functional relationship between the closed loop and the component loops of a dimension chain is formulated. The tolerances of component loops are regarded as design variables. The total manufacturing costs of the component loops and the success rate of assembly of the closed loop are treated as constraint conditions. Then, a robust design optimization model is established to minimize the variance of the closed loop and to make the mean value of the closed loop approach to target value. The evidence theory is used to deal with the constraint condition on the success rate of assembly. The dimension chain of a gear box is taken as an example to illustrate the proposed method and the example shows that the proposed method is effective.

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Kendon, Elizabeth J., David P. Rowell, Richard G. Jones, and Erasmo Buonomo. "Robustness of Future Changes in Local Precipitation Extremes." Journal of Climate 21, no. 17 (September 1, 2008): 4280–97. http://dx.doi.org/10.1175/2008jcli2082.1.

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Abstract Reliable projections of future changes in local precipitation extremes are essential for informing policy decisions regarding mitigation and adaptation to climate change. In this paper, the extent to which the natural variability of the climate affects one’s ability to project the anthropogenically forced component of change in daily precipitation extremes across Europe is examined. A three-member ensemble of the Hadley Centre Regional Climate Model (HadRM3H) is used and a statistical framework is applied to estimate the uncertainty due to the full spectrum of climate variability. In particular, the results and understanding presented here suggest that annual to multidecadal natural variability may contribute significant uncertainty. For this ensemble projection, extreme precipitation changes at the grid-box level are found to be discernible above climate noise over much of northern and central Europe in winter, and parts of northern and southern Europe in summer. The ability to quantify the change to a reasonable level of accuracy is largely limited to regions in northern Europe. In general, where climate noise has a significant component varying on decadal time scales, single 30-yr climate change projections are insufficient to infer changes in the extreme tail of the underlying precipitation distribution. In this context, the need for ensembles of integrations is demonstrated and the relative effectiveness of spatial pooling and averaging for generating robust signals of extreme precipitation change is also explored. The key conclusions are expected to apply more generally to other models and forcing scenarios.

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Mithun, Thudiyangal, Aleksandra Maluckov, Kenichi Kasamatsu, Boris A. Malomed, and Avinash Khare. "Modulational Instability, Inter-Component Asymmetry, and Formation of Quantum Droplets in One-Dimensional Binary Bose Gases." Symmetry 12, no. 1 (January 18, 2020): 174. http://dx.doi.org/10.3390/sym12010174.

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Quantum droplets are ultradilute liquid states that emerge from the competitive interplay of two Hamiltonian terms, the mean-field energy and beyond-mean-field correction, in a weakly interacting binary Bose gas. We relate the formation of droplets in symmetric and asymmetric two-component one-dimensional boson systems to the modulational instability of a spatially uniform state driven by the beyond-mean-field term. Asymmetry between the components may be caused by their unequal populations or unequal intra-component interaction strengths. Stability of both symmetric and asymmetric droplets is investigated. Robustness of the symmetric solutions against symmetry-breaking perturbations is confirmed.

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Kalabiska, Irina, Annamaria Zsakai, Dorina Annar, Robert M. Malina, and Tamas Szabo. "Sport Activity Load and Skeletomuscular Robustness in Elite Youth Athletes." International Journal of Environmental Research and Public Health 19, no. 9 (April 21, 2022): 5083. http://dx.doi.org/10.3390/ijerph19095083.

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In an earlier report, bone mineral reference values for young athletes were developed. This study addressed variations in bone mineral parameters of young athletes participating in sports with different mechanical loads. The bone mineral status of 1793 male and female athletes, 11 to 20 years of age, in several sports was measured with DEXA. Specific bone mineral parameters were converted to z-scores relative to age- and sex-specific reference values specified by the DEXA software. Z-score profiles and principal components analyses were used to identify body structural components in the young athletes and to evaluate the associations between the identified component and type of sport defined by mechanical load. A unique skeletomuscular robusticity of male wrestlers, pentathletes, and cyclists was noted: wrestlers had significantly more developed skeletomuscular robusticity and bone mineral density compared to the age-group average among elite athletes, while pentathletes and cyclists had lower bone mineral parameters than the age-group references among elite athletes. Among female athletes, bone mineral parameters of both the trunk and extremities of rhythmic gymnasts and pentathletes were significantly lower compared to the age-group means for elite athletes. The bone mineral development of elite young athletes varies with the impact forces associated with their respective sports. The skeletal development of cyclists, pentathletes, and rhythmic gymnasts should be monitored regularly as their bone development lags behind that of their athlete peers and the reference for the general population.

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Mondal, S. C. "Process capability – a surrogate measure of process robustness: a case study." International Journal of Quality & Reliability Management 33, no. 1 (December 31, 2015): 90–106. http://dx.doi.org/10.1108/ijqrm-12-2013-0202.

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Purpose – The purpose of this paper is to obtain a better understanding on robust performance of a hardening and tempering process producing component worm shaft used in the steam power plant. This research is capable to explaining the variation of process capability in terms of robustness. Design/methodology/approach – This paper proposed a methodology (a combination of simulation, regression modelling and robust design technique) to study robustness of a hardening and tempering process producing component worm shaft used in the steam power plant and process capability acts as a surrogate measure of robustness. In each experimental run, the values of responses and the corresponding multivariate process capability indices across the outer array are determined. The variation of process performance (process capability values) due to random noise variation is studied using a general purpose process control chart (R-chart). Findings – The results provide useful information in term of insensitiveness of the process against the noise (raw material and process noise) variation where the process capability acts as a surrogate measure of process robustness and explains the variation of process capability in term of robustness. Practical implications – This paper adds to the body of knowledge on robustness of a manufacturing process. This paper may be of particular interest to practicing engineers as it suggests what factors should be more emphasis to achieve robust (consistent) performance from the process. Originality/value – The originality of this paper lies within the context in which this study is to address key relationships between process robustness and process capability in a manufacturing industry.

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Spyridis, Panagiotis, and Alfred Strauss. "Robustness Assessment of Redundant Structural Systems Based on Design Provisions and Probabilistic Damage Analyses." Buildings 10, no. 12 (November 24, 2020): 213. http://dx.doi.org/10.3390/buildings10120213.

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Typically in structural design, foreseeable loads are assumed in a structural design and dimensioning exercise and design material properties may be handled in a semi-probabilistic approach. Structures can, however, be exposed to largely unforeseeable events such as intense environmental phenomena, accidents, malicious acts, and planning or execution errors, in addition to degradation with time. Recent significant collapses have highlighted the fact that robustness is an indispensable integral part of the structural design and provisions in upcoming codes are currently expanding in this respect. The paper examines the practical significance of quantitative robustness indicators included in recent research and upcoming standards and it assesses their efficiency based on case studies. Moreover, it proposes a probabilistic numerical methodology for robustness assessment under uncertainty, and it demonstrates its practical applicability based on computations with indicative structural truss systems, i.e., multi-component systems. The proposed method allows for quantifiable and comparable robustness measures, which can be integrated in reliability-based design and structural health monitoring of engineering systems. The redundancy aspect of robustness is pronounced as a plausible quantitative performance indicator for multi-component systems. In particular, the robustness index combining reliability and redundancy of the elements is proven to be the most useful one out of the examined approaches. This probabilistic elaboration does not only account for the reasonable treatment of variability and randomness, but it allows for an inverse identification of the critical failure paths and the characterization of weak links in the systems.

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Li, Chen Ming, Zhe Chen, Jie Shen, Xin Wang, and Hui Bin Wang. "Principal Component Analysis Based Underwater Object Recognition." Advanced Materials Research 850-851 (December 2013): 817–20. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.817.

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In this paper, the principal component analysis method is applied in the underwater image data for detecting the image objects. The system is designed to assist the underwater monitor system survey operations, specialized to the task of object identification. Firstly, the nature of the underwater is analyzed according to the image formation model and the appearance. Then, the discipline of the principal component analysis is theoretically analysis. Third, the principal component analysis method is applied in the underwater image for dimension reduction, extracting the image feather for recognition. Experimental results, which have been performed on a set of real underwater images, demonstrate the robustness and the accuracy of the principal component analysis in the task of underwater object recognition.

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Moussa, Richard Kouamé. "Heteroskedasticity in One-Way Error Component Probit Models." Econometrics 7, no. 3 (August 11, 2019): 35. http://dx.doi.org/10.3390/econometrics7030035.

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This paper introduces an estimation procedure for a random effects probit model in presence of heteroskedasticity and a likelihood ratio test for homoskedasticity. The cases where the heteroskedasticity is due to individual effects or idiosyncratic errors or both are analyzed. Monte Carlo simulations show that the test performs well in the case of high degree of heteroskedasticity. Furthermore, the power of the test increases with larger individual and time dimensions. The robustness analysis shows that applying the wrong approach may generate misleading results except for the case where both individual effects and idiosyncratic errors are modelled as heteroskedastic.

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Sarsri, D., and L. Azrar. "Convex Models and Component Mode Synthesis Methods for Robustness Function of Structures with Uncertain Parameters." Advanced Materials Research 682 (April 2013): 97–104. http://dx.doi.org/10.4028/www.scientific.net/amr.682.97.

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This paper presents a numerical procedure to compute the robustness function of large finite element models with parameter uncertainties based on convex models and component mode synthesis methods (CMS). For an optimal reduction of the size of the model the CMS with reduction of interface dof is used. This coupling methodological approach is used on one hand to reduce the size of the model and on the other hand to analyze the structures where the data on the uncertainties are limited enough. Robustness function is determined and the dynamic analysis of large and composed structures is investigated. Numerical results illustrating the efficiency of the proposed coupled procedures for large FE models with random parameters are presented.

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Ju, Zhichao, Jinlong Ma, Jianjun Xie, and Zhaohui Qi. "Cascading failure model for improving the robustness of scale-free networks." International Journal of Modern Physics C 29, no. 06 (June 2018): 1850044. http://dx.doi.org/10.1142/s0129183118500444.

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To control the spread of cascading failure on scale-free networks, we propose a new model with the betweenness centrality and the degrees of the nodes which are combined. The effects of the parameters of the edge weight on cascading dynamics are investigated. Five metrics to evaluate the robustness of the network are given: the threshold parameter ([Formula: see text]), the proportion of collapsed edges ([Formula: see text]), the proportion of collapsed nodes ([Formula: see text]), the number of nodes in the largest connected component ([Formula: see text]) and the number of the connected component ([Formula: see text]). Compared with the degrees of nodes’ model and the betweenness of the nodes’ model, the new model could control the spread of cascading failure more significantly. This work might be helpful for preventing and mitigating cascading failure in real life, especially for small load networks.

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Fornasari, Luca, Simone d'Agostino, and Dario Braga. "Supramolecular zwitterions based on a novel boronic acid–squarate dianion synthon." CrystEngComm 21, no. 20 (2019): 3186–91. http://dx.doi.org/10.1039/c9ce00367c.

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Hou, Yan Yan. "Video Copy Detection Based on Principal Component Analysis." Applied Mechanics and Materials 548-549 (April 2014): 693–97. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.693.

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Content-based video hashing was proposed for the purpose of video copy detection. Conventional video copy detection algorithms apply image hashing algorithm to either every frame or key frame which is sensitive to video variation. In our proposed algorithm, key frames including temporal and spatial information are used to video copy detection, Discrete cosine transform (DCT) is done for video key frame and feature vector is extracted by principal component analysis ( PCA ). An average true positive rate of 99.31% and false positive rate of 0.37% demonstrate the robustness and uniqueness of the proposed algorithm. Experiments indicate that it is easy to implement and more efficient than other video copy detection algorithms.

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Jones, Chris, and Karoline Wiesner. "Clarifying How Degree Entropies and Degree-Degree Correlations Relate to Network Robustness." Entropy 24, no. 9 (August 24, 2022): 1182. http://dx.doi.org/10.3390/e24091182.

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It is often claimed that the entropy of a network’s degree distribution is a proxy for its robustness. Here, we clarify the link between degree distribution entropy and giant component robustness to node removal by showing that the former merely sets a lower bound to the latter for randomly configured networks when no other network characteristics are specified. Furthermore, we show that, for networks of fixed expected degree that follow degree distributions of the same form, the degree distribution entropy is not indicative of robustness. By contrast, we show that the remaining degree entropy and robustness have a positive monotonic relationship and give an analytic expression for the remaining degree entropy of the log-normal distribution. We also show that degree-degree correlations are not by themselves indicative of a network’s robustness for real networks. We propose an adjustment to how mutual information is measured which better encapsulates structural properties related to robustness.

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Wan, Minghua, Xichen Wang, Hai Tan, and Guowei Yang. "Manifold Regularized Principal Component Analysis Method Using L2,p-Norm." Mathematics 10, no. 23 (December 5, 2022): 4603. http://dx.doi.org/10.3390/math10234603.

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The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples. However, the accuracy of the traditional principal component analysis method in dimension reduction is not very high, and it is very sensitive to outliers. In order to improve the robustness of image recognition to noise and the importance of geometric information in a given data space, this paper proposes a new unsupervised feature extraction model based on l2,p-norm PCA and manifold learning method. To improve robustness, the model method adopts l2,p-norm to reconstruct the distance measure between the error and the original input data. When the image is occluded, the projection direction will not significantly deviate from the expected solution of the model, which can minimize the reconstruction error of the data and improve the recognition accuracy. To verify whether the algorithm proposed by the method is robust, the data sets used in this experiment include ORL database, Yale database, FERET database, and PolyU palmprint database. In the experiments of these four databases, the recognition rate of the proposed method is higher than that of other methods when p=0.5. Finally, the experimental results show that the method proposed in this paper is robust and effective.

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Durrleman, Valdo. "Convergence of At-The-Money Implied Volatilities to the Spot Volatility." Journal of Applied Probability 45, no. 2 (June 2008): 542–50. http://dx.doi.org/10.1239/jap/1214950366.

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We study the convergence of at-the-money implied volatilities to the spot volatility in a general model with a Brownian component and a jump component of finite variation. This result is a consequence of the robustness of the Black-Scholes formula and of the central limit theorem for martingales.

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Durrleman, Valdo. "Convergence of At-The-Money Implied Volatilities to the Spot Volatility." Journal of Applied Probability 45, no. 02 (June 2008): 542–50. http://dx.doi.org/10.1017/s0021900200004411.

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We study the convergence of at-the-money implied volatilities to the spot volatility in a general model with a Brownian component and a jump component of finite variation. This result is a consequence of the robustness of the Black-Scholes formula and of the central limit theorem for martingales.

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Bucur, Carmen. "Checking the low track lattice girder bridges for robustness." Romanian Journal of Transport Infrastructure 5, no. 1 (July 1, 2016): 1–12. http://dx.doi.org/10.1515/rjti-2016-0038.

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Abstract When you say about a thing or a being that they are robust, you imagine a complete entity from the point of view of its component parts giving out force and safety. The notion of robustness is associated with a lot of activity domains. Consequently, there are many definitions individualizing the robustness notion depending on the study domain. The work aims to join in the present day studies with a new research in the bridge domain. The object of the study is represented by three low track steel lattice girder railway bridges. In the article there is also presented the way the structural robustness notion is to be found in the national codes.

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Pietrenko-Dabrowska, Anna, and Slawomir Koziel. "Optimization-based robustness enhancement of compact microwave component designs with response feature regression surrogates." Knowledge-Based Systems 240 (March 2022): 108161. http://dx.doi.org/10.1016/j.knosys.2022.108161.

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Batchelor, E., and M. Goulian. "Robustness and the cycle of phosphorylation and dephosphorylation in a two-component regulatory system." Proceedings of the National Academy of Sciences 100, no. 2 (January 9, 2003): 691–96. http://dx.doi.org/10.1073/pnas.0234782100.

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Lanning, Kevin. "Robustness is Not Dimensionality: On the Sensitivity of Component Comparability Coefficients to Sample Size." Multivariate Behavioral Research 31, no. 1 (January 1996): 33–46. http://dx.doi.org/10.1207/s15327906mbr3101_3.

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Journal articles on the topic 'Component robustness'

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