The applications of barrier functions to safety analysis, diagnosis, and control have become a popular re-search direction. Barrier functions have been widely applied to solve the safety problems for different types of integer-order systems. This paper starts to discuss the relationship between barrier function and system state-safety for fractional-order nonlinear dynamical systems. First, by using Caputos fractional derivative, this paper revisits the safety theories via barrier functions (BFs). And based on less-zero barrier function, reciprocal barrier function and zeroing barrier function, three extended BFs under the Caputo fractional-order descriptions are designed. Then it establishes a framework by proposing the definitions and criteria of safety, safety-and-stability for a class of fractional-order nonlinear dynamical systems.

Currently, most advanced object detectors equip with deep convolutional neural network (CNN) backbones(e.g., ResNet-101) for capturing strong feature representation, but which leads to suffer heavy computational burden. Inversely, some detectors based on lightweight backbones implement real-time processing while accuracy is often not satisfactory. In this paper, we explore an approach to construct a light yet powerful detector by using efficient lightweight backbone (e.g., MobileNet) with our proposed Feature Fusion Block (FFB), composed of Feature Aggregation Block (FAB) and Dense Feature Pyramid (DFP). The extensive experiments confirmed that our proposed Feature Fusion Block Network (FFBNet) is able to improve the accuracy significantly of MobileNet-SSD as well as maintain a close processing speed. Specifically, on Pascal VOC datasets, FFBNet achieves 73.54 mAP at speed of 185 FPS and surpasses MobileNet-SSD five points. Moreover, we apply VGG16 as backbone to further indicate the effectiveness of FFB, which reaches 80.2 mAP on Pascal VOC benchmark. Code is available at https://github.com/fanbinqi/FFBNet.

In cloud computing, data is duplicated to prevent data loss. One way to achieve data consistency in such a distributed computing systems is to use a blockchain. Based on practical Byzantine fault tolerance (PBFT), a specific type of blockchain, this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains data consistency, but also has much higher efficiency than other general blockchain algorithms. We provide experimental results that demonstrate the algorithm's data consistency, efficiency, and reliability.

The realization of early detection of incipient faults makes great sense for the guarantee of system performance and security operation. Therefore, It is necessary to estimate the fault amplitude especially when the system security assessment is the main goal. Regarding the incipient fault with low Fault-Noise-ratio (FNR), in this paper, a practical online fault estimation method is presented for multivariate processes. Statistics based on Kullback-Leibler Divergence (KLD), that is characterized with a high sensitivity to incipient fault, are utilized for the realization of small deviation estimation. Based on the Gaussian assumption, KLDs of different principal components, that characterizes the difference between free-fault and fault case, are computed online through updating the mean and variance of scores. The simulation confirms the feasibility and effectiveness of the proposed incipient fault diagnosis method.

In this paper, we investigate the problem of robust stability for a class of delayed neural networks of neutral-type with linear fractional uncertainties. The activation functions are assumed to be unbounded, non-monotonic and non-differentiable, and the delay is assumed to be time-varying and belonging to a given interval, which means that the lower and upper bounds of the interval time-varying delay are available. By constructing a general form of the Lyapunov–Krasovskii functional, and using the linear matrix inequality (LMI) approach, we derive several delay-dependent stability criteria in terms of LMI. Finally, we give a number of examples to illustrate the effectiveness of the proposed method.

A robotic system for harvesting tomatoes in greenhouses is designed. Effective recognition of ripen tomatoes from complex background is the key technology of the harvesting robotic system. In this work, the color feature of ripen tomatoes is employed. The ripen tomato is segmented by K-means clustering using the L*a*b* color space. To extract a single integrity ripen tomato, mathematical morphology method is used to denoise and handle the situations of tomato overlapping and shelter. Experimental results show the effectiveness of the proposed method.