Geometric characterization on the solvability of regulator equations

The solvability of the regulator equation for a general nonlinear system is discussed in this paper by using geometric method. The ‘feedback’ part of the regulator equation, that is, the feasible controllers for the regulator equation, is studied thoroughly. The concepts of minimal output zeroing control invariant submanifold and left invertibility are introduced to find all the possible controllers for the regulator equation under the condition of left invertibility. Useful results, such as a necessary condition for the output regulation problem and some properties of friend sets of controlled invariant manifolds, are also obtained

Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm

Disinfectant Performance of a Chlorine Regenerable Antibacterial Microfiber Fabric as a Reusable Wiper.

Rechargeable disinfectant performance of a microfiber fabric grafted with a halamine precursor, 3-allyl-5,5-dimethylhydantoin (ADMH), was tested in an actual use situation in a university student dining hall. The precursor was successfully incorporated onto the surfaces of polyester fibers by using a radical graft polymerization process through a commercial finishing facility. The N⁻H bonds of ADMH moieties on the fibers can be converted to biocidal N⁻Cl bonds, when the fabrics are washed in a diluted chlorine bleach containing 3000 ppm available chlorine, providing a refreshable disinfectant function. By wiping the surfaces of 30 tables (equivalent to 18 m²) with wet chlorinated fabrics, both Staphylococcus aureus and Escherichia coli in concentrations of 10⁵ CFU/mL were totally killed in a contact time of 3 min. The disinfectant properties of the fabrics were still superior after 10 times successive machine washes (equivalent to fifty household machine washes), and rechargeable after wiping 30 tables before each recharge. Recharging conditions, such as temperature, time, active chlorine concentration and pH value of sodium hypochlorite solution, as well as the addition of a detergent, were studied. The product has the potential to improve public safety against biological contaminations and the transmission of diseases

The Devil of Face Recognition is in the Noise

The growing scale of face recognition datasets empowers us to train strong convolutional networks for face recognition. While a variety of architectures and loss functions have been devised, we still have a limited understanding of the source and consequence of label noise inherent in existing datasets. We make the following contributions: 1) We contribute cleaned subsets of popular face databases, i.e., MegaFace and MS-Celeb-1M datasets, and build a new large-scale noise-controlled IMDb-Face dataset. 2) With the original datasets and cleaned subsets, we profile and analyze label noise properties of MegaFace and MS-Celeb-1M. We show that a few orders more samples are needed to achieve the same accuracy yielded by a clean subset. 3) We study the association between different types of noise, i.e., label flips and outliers, with the accuracy of face recognition models. 4) We investigate ways to improve data cleanliness, including a comprehensive user study on the influence of data labeling strategies to annotation accuracy. The IMDb-Face dataset has been released on https://github.com/fwang91/IMDb-Face.Comment: accepted to ECCV'1