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Showing 1-25 of 154,794 resultsfor "Index Terms":robustness
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Populations exposed to a high mutation rate harbor abundant deleterious genetic variation, leading to depressed mean fitness. This reduction in mean fitness presents an opportunity for selection to restore fitness through the evolution of mutational robustness. In extreme cases, selection for mutational robustness can lead to flat genotypes (with low fitness but high robustness) outcompeting fit g...Show More
Sliding-mode control has the advantages of robustness, great steady and dynamic response, which also causes vibration, so the three-level buck converter based on integral sliding-mode control is proposed; the sliding-mode surface existent area, equivalent control, stability and existence are given. Simulink carried out show the advantages of the suggested approach: robustness, great steady and dyn...Show More
The Quantitative Relationship between Adversarial Training and Robustness of CNN Model
2020 7th International Conference on Dependable Systems and Their Applications (DSA)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
With the increasing application of deep neural networks in security-critical systems, robustness becomes an important property for deep learning. However, deep neural networks are very vulnerable to perturbations, especially adversarial attacks. The adversarial training by adding adversarial examples to the training set has become a common method to improve the performance of deep neural networks....Show More
Robustness during Network Evolution
2009 International Conference on Complex, Intelligent and Software Intensive Systems
Year: 2009 | Conference Paper |
Cited by: Papers (1)
Robust networks can maintain their characteristics under perturbation, which is rooted in the different topology structure.However, previous study of the relationship between network robustness and topology structure is based on a static context. We study the effects of network topology on the network robustness from an dynamic points of view. We explore robustness to two types of perturbation on ...Show More
Chaos Driven Development for Software Robustness Enhancement
2022 9th International Conference on Dependable Systems and Their Applications (DSA)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
The importance of software quality increases as software products become more intertwined with our daily lives. One critical software quality attribute is software robustness reflecting the confidence level that the software can still function correctly against stressful environment and invalid inputs. To build up a robust software system, we need start from the chaos to understand the system beha...Show More
Training Robust Deep Neural Networks via Adversarial Noise Propagation
Aishan Liu;Xianglong Liu;Hang Yu;Chongzhi Zhang;Qiang Liu;Dacheng Tao
In practice, deep neural networks have been found to be vulnerable to various types of noise, such as adversarial examples and corruption. Various adversarial defense methods have accordingly been developed to improve adversarial robustness for deep models. However, simply training on data mixed with adversarial examples, most of these models still fail to defend against the generalized types of n...Show More
Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness
2023 IEEE International Conference on Data Mining (ICDM)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Deep neural network ensembles hold the potential of improving generalization performance for complex learning tasks. This paper presents formal analysis and empirical evaluation to show that heterogeneous deep ensembles with high ensemble diversity can effectively leverage model learning heterogeneity to boost ensemble robustness. We first show that heterogeneous DNN models trained for solving the...Show More
Mitigating Accuracy-Robustness Trade-Off via Balanced Multi-Teacher Adversarial Distillation
Adversarial Training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performance towards clean examples is negatively affected after Adversarial Training, which means a trade-off exists between accuracy and robustness. Recently, some studies have tried to use knowledge distillation methods in Adver...Show More
A Robust Control Approach to AMSs by Using the Implementation of Strong and Weak Robustness
In automated manufacturing systems, the completion of all essential products must be guaranteed. Hence, each process of a system should have outputs. This outcome requires assurance that a system composed of a set of processes with shared and dedicated resources will operate smoothly. Shared resources can introduce deadlock problems through circular waits. In most methods to investigate the deadlo...Show More
Strong robust zero watermarking algorithm based on NSCT transform and image normalization
2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
Year: 2018 | Conference Paper |
Cited by: Papers (9)
In order to improve the robustness of the watermark, a strong robust zero watermarking algorithm based on NSCT transform and image normalization is proposed. In the proposed scheme, the cover image is mapped to the geometric invariant space by using the image normalization technique based on invariant moment. Then, the effective part of the normalized image is extracted by using the square of the ...Show More
Robust Remote Sensing Scene Classification by Adversarial Self-Supervised Learning
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Year: 2021 | Conference Paper |
Cited by: Papers (7)
Adversarial training is an effective method to enhance adversarial robustness for deep neural networks. However, it qequires large amounts of labeled data, which are often difficult to acquire. Recent research has shown that self-supervised learning can help to improve model performance and model uncertainty using unlabeled data. In this paper, we introduce a new adversarial self-supervised learni...Show More
Intelligent and Adaptive Mixup Technique for Adversarial Robustness
2021 IEEE International Conference on Image Processing (ICIP)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
Deep neural networks are generally trained using large amounts of data to achieve state-of-the-art accuracy in many possible computer vision and image analysis applications ranging from object recognition to natural language processing. It is also claimed that these networks can memorize the data which can be extracted from the network parameters such as weights and gradient information. The adver...Show More
A robust flyback converter based on high order sliding mode control for fuel cell
IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society
Year: 2014 | Conference Paper |
Cited by: Papers (5)
In the process of power generation by hydrogen energy, the application of fuel cell has been used widely. As we all known, it often appears that the randomness of hydrogen, oxygen input and power output load, which puts forward higher requirements for the robust design of power converters. The power converter based on traditional sliding mode due to its discrete control law exist inevitable chatte...Show More
Mutational Robustness and Structural Complexity in Grammatical Evolution
2019 IEEE Congress on Evolutionary Computation (CEC)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
A recent study in Artificial Life found that the need for mutational robustness can give rise to simpler structures in an evolving population. This begs the question, do we observe a similar phenomenon in Genetic Programming? Genetic Programming requires the search of structural space of solutions, usually requiring code growth to find fitter solutions. Typically Genetic Programming algorithms the...Show More
A real-time stereo vision sensor based on FPGA realization of orientation code matching
Isao Ohmura;Tomoyuki Mitamura;Hedenori Takauji;Shun'ichi Kaneko
2010 International Symposium on Optomechatronic Technologies
Year: 2010 | Conference Paper |
Cited by: Papers (2)
This paper describes a real-time stereo vision sensor based on the Field Programmable Gate Array (FPGA). It was developed using an image matching algorithm called Orientation Code Matching (OCM). Our implementation, using OCM, provides not only high performance, but also flexibility and a greater robustness, all of which are necessary to embed vision sensors in mobile robots and vehicles. The prot...Show More
A fast and low complexity zero-watermarking based on average subimage in multiwavelet domain
Xiaonian Tang;Jianchun Wang;Chengbao Zhang;Huiming Zhu;Yan Fu
2010 2nd International Conference on Future Computer and Communication
Year: 2010 | Conference Paper |
Cited by: Papers (2)
In this paper, a simple and robust zero-watermarking algorithm using balanced multiwavelet transform is proposed. The latter transform we use achieves simultaneous orthogonality and symmetry without requiring any input prefiltering or border extending. This reduction in computational complexity makes the transform a good candidate for real-time watermarking implementations. Novelty of the system l...Show More
Effective Node Resistance and its Implication on Network Robustness
2021 International Conference on Information Networking (ICOIN)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Robustness against failures and attacks is an important property of large scale systems such as power grids, transportation systems, communication systems and computer networks. It has been known that the largest connected component size and the diameter are related to network robustness. Also, it has been known that the effective graph resistance is useful for estimating the robustness of the net...Show More
Robust and rapid control for ABS based on global sliding mode
2012 24th Chinese Control and Decision Conference (CCDC)
Year: 2012 | Conference Paper |
Cited by: Papers (1)
To satisfy the stability and anti-jamming performance, robust theory must be adopted in the design for Antilock Braking System (ABS) controller. In this paper, the nonlinear and time-varying mathematical model of the antilock braking system (ABS) is established, the global sliding mode controller is designed based on slip-ratio, which make the system State always lies on the sliding surface, and t...Show More
Research on Sliding Mode Control of An Active Magnetic Bearing High-Speed Motor Rotor
2021 IEEE 2nd China International Youth Conference on Electrical Engineering (CIYCEE)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
With the improvement of high-speed motor technical requirements, the PID-controlled Active Magnetic Bearings (AMB)-rotor system gradually shows its shortcomings. In this regard, this paper proposes to use sliding mode controller (SMC) to constrain the magnetic bearing. Based on the introduction of the AMB structure, this paper establishes a mathematical model of a single-degree-of-freedom(DOF) mag...Show More
Multi-objective robust optimization based on NSGA-II and degree of robustness¯
2010 8th World Congress on Intelligent Control and Automation
Year: 2010 | Conference Paper |
Cited by: Papers (1)
This paper presents an approach to search robust optimal solutions with the concept of degree of robustness in multi-objective optimization problems. The definition of the degree of robustness is used in the calculation of the mean effective objective function(feff(X)) and in the evolutionary process, degree of robustness is considered as a factor to select robust solutions which can meet the dema...Show More
Exploring the Connection Between Neuron Coverage and Adversarial Robustness in DNN Classifiers
2023 IEEE International Conference on Image Processing (ICIP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The lack of robustness in neural network classifiers, especially when facing adversarial attacks, is a significant limitation for critical applications. While some researchers have suggested a connection between neuron coverage during training and vulnerability to adversarial perturbations, concrete experimental evidence supporting this claim is lacking. This paper empirically investigates the imp...Show More
Safety and Robustness for Deep Learning with Provable Guarantees
2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Year: 2020 | Conference Paper |
Computing systems are becoming ever more complex, with decisions increasingly often based on deep learning components. A wide variety of applications are being developed, many of them safety-critical, such as self-driving cars and medical diagnosis. Since deep learning is unstable with respect to adversarial perturbations, there is a need for rigorous software development methodologies that encomp...Show More
Super twisting sliding control based on active disturbance rejection technique for three-phase interleaved Buck converters
2024 3rd International Conference on Energy, Power and Electrical Technology (ICEPET)
Year: 2024 | Conference Paper |
In order to improve the dynamic performance and parameters' robustness of three-phase interleaved Buck converters under external disturbance, a composited cascade control strategy is proposed in this paper, which is consisted of super twisting sliding mode control and active disturbance rejection control. Compared with the traditional equivalent sliding mode control super-spiral sliding mode, it d...Show More
A C-Band Current-Steering-Based Variable Gain Low Noise Amplifier with High Temperature Robustness
Siyu Lin;Zhilin Chen;Yaxin Zhang;Min Lu;Jie Hu;Sheng Huang;Xingcun Li
2024 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT)
Year: 2024 | Conference Paper |
This paper introduces a C-band variable gain amplifier (VGA). The VGA demonstrates robust performance against gain control under temperature variations. To achieve this, this work modifies the switching signal generation circuit. The measured result indicates that the gain control range of the VGA ranges from 14.49 dB to 15.3 dB with a gain step RMS error of 0.25 dB to 0.57 dB at 5.7 GHz, over the...Show More
On the Role of Robustness in Multi-Objective Robust Optimization: Application to an IPM Motor Design Problem
IEEE Transactions on Magnetics
Cited by: Papers (27)
This paper discusses the different roles that robustness can assume when solving a multi-objective design optimization problem. A new role for robustness in multi-objective design optimization problems is proposed based on which an approach to conducting multi-objective robust optimization and finding the robust optima is suggested. This new approach is then tested on an internal permanent magnet ...Show More
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