For further studying the theories and applications of rough sets (RS), this paper proposes a new theory on RS, which mainly includes topological space, topological properties, homeomorphism, and its properties on RS by some new definitions and theorems given. The relationship between partition and countable open covering is discussed, and some applications based on the topological rough space and its topological properties are introduced. Moreover, some perspectives for future research are given. Throughout this paper, the advancements of the new theory on RS and topological algebra not only represent an important theoretical value but also exhibit significant applications of RS and topology.

Fitness trackers have broadened the healthcare ecosystem and made self-tracking everyday physical activities possible. Features like heart rate monitoring can help detect health ailments, yet there is little evidence that suggests tracking health indicators and physical activities leads to long-term health behavior change. This proceeding analyzes areas of Human Factors that could be used to increase long-term user engagement. Feedback, information display, and specific design principles and case studies are discussed.

Surface water samples were collected at 15 sampling sites in the southeastern Japan Sea along the Japanese Archipelago for analysis of polycyclic aromatic hydrocarbons (PAHs). Water samples were fractionated by filtration through a glass fiber membrane (pore size 0.5 µm) and analyzed by high-performance liquid chromatography with fluorescence detection. Thirteen PAHs having 3 to 6 rings were found in the dissolved phase (DP) and 12 were found in the particulate phase (PP). The total (DP PP) PAH concentration ranged from 6.83 to 13.81 ng/L with the mean standard deviation (S.D.) concentration of 9.36 1.92 ng/L. The mean S.D. PAH concentration in the DP and PP was 5.99 1.80 and 3.38 0.65 ng/L, respectively. Three-ring PAHs predominated in the DP, while the proportion of 4-ring PAHs was higher in the PP. The mean total PAH concentration in the southeastern Japan Sea was higher than the concentration in the northwestern Japan Sea (8.5 ng/L). The Tsushima Current, which originates from the East China Sea with higher PAH concentration, is considered to be responsible for this higher concentration.

Purpose This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy. Design/methodology/approach A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing. Findings Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy. Originality/value First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.

In this paper, a synthesized design of fault-detection filter and fault estimator is considered for a class of discrete-time stochastic systems in the framework of event-triggered transmission scheme subject to unknown disturbances and deception attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring deception attacks. To achieve a fault-detection residual is only sensitive to faults while robust to disturbances, a coordinate transformation approach is exploited. This approach can transform the considered system into two subsystems and the unknown disturbances are removed from one of the subsystems. The gain of fault-detection filter is derived by minimizing an upper bound of filter error covariance. Meanwhile, system faults can be reconstructed by the remote fault estimator. An recursive approach is developed to obtain fault estimator gains as well as guarantee the fault estimator performance. Furthermore, the corresponding event-triggered sensor data transmission scheme is also presented for improving working-life of the wireless sensor node when measurement information are aperiodically transmitted. Finally, a scaled version of an industrial system consisting of local PC, remote estimator and wireless sensor node is used to experimentally evaluate the proposed theoretical results. In particular, a novel fault-alarming strategy is proposed so that the real-time capacity of fault-detection is guaranteed when the event condition is triggered.

Aiming at solving the problems of overall darkness, uneven illumination and low contrast of image under low illumination conditions, we present a global and adaptive contrast enhancement algorithm for low illumination gray images in this paper. The proposed algorithm is based on the Bilateral Gamma Adjustment function and combined with the particle swarm optimization (PSO). For the PSO, the gray standard variance is integrated into the evaluation function. To reconcile the dilemma of promoting the gray values of dark areas and suppressing the gray values of local bright areas at the same time, the information of entropy, edge content, and gray standard variance are used as the objective function for each particle to evaluate the gray image enhancement results. Then, the algorithm globally enhances the quality of the image by determining the optimal α value. Meanwhile, the learning factors of the PSO are updated during the iteration of optimization in the proposed algorithm. Compared with histogram equalization (HE), double plateau histogram equalization (DPHE), contrast limited adaptive histogram equalization (CLAHE), linear contrast stretching (LCS), adaptive gamma correction weighting distribution (AGCWD), the traditional PSO and MSF-PSO algorithm, the proposed algorithm significantly enhances the visual effect of the low illumination gray images. The experimental results demonstrate the superior capabilities of the proposed algorithm in enhancing the contrast of the image, such as improving the overall visual effect of the low illumination gray image and avoiding over-enhancement in the local area (s). INDEX TERMS Low luminance images, bilateral gamma adjustment, uneven illumination, particle swarm optimization.

Noise-Induced Hearing Loss (NIHL) is the most common occupational disease in the USA. Impulse noise is a typical noise exposure in military and industrial fields, and generates severe hearing loss problem in these fields. This paper presents four key parameters of impulse noise that significantly affect on Auditory Risk Unit (ARU) in the Auditory Hazard Assessment Algorithm for Humans (AHAAH) model. The results show that ARUs increases monotonically with the peak pressure (both P(+) and P(-)) increasing. While the ARUs increase first and then decrease with time durations rising, and the peak of ARUs appears at about t = 0.2 msec (for both t(+) and t(-)). In addition, the auditory hazard of measured impulse noises generated by the lab noise exposure system was evaluated by using AHAAH model. Results from experiments indicate that the AHAAH model is suitable for impulse noise hazardous evaluation.

Purpose An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods. Design/methodology/approach A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection. Findings Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision. Originality/value First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.