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Methods for all-at-once and also stepwise selection of cells with a number of innate manipulations.
Moreover, the lack of the direct state access does not incur any additional stabilizing bit rate. Simulations are done to confirm the effectiveness of the obtained stabilizing bit rate conditions.Indoor place category recognition for a cleaning robot is a problem in which a cleaning robot predicts the category of the indoor place using images captured by it. This is similar to scene recognition in computer vision as well as semantic mapping in robotics. Compared with scene recognition, the indoor place category recognition considered in this article differs as follows 1) the indoor places include typical home objects; 2) a sequence of images instead of an isolated image is provided because the images are captured successively by a cleaning robot; and 3) the camera of the cleaning robot has a different view compared with those of cameras typically used by human beings. Compared with semantic mapping, indoor place category recognition can be considered as a component in semantic SLAM. In this article, a new method based on the combination of a probabilistic approach and deep learning is proposed to address indoor place category recognition for a cleaning robot. Concerning the probabilistic approach, a new place-object fusion method is proposed based on Bayesian inference. For deep learning, the proposed place-object fusion method is trained using a convolutional neural network in an end-to-end framework. Furthermore, a new recurrent neural network, called the Bayesian filtering network (BFN), is proposed to conduct time-domain fusion. Finally, the proposed method is applied to a benchmark dataset and a new dataset developed in this article, and its validity is demonstrated experimentally.The present study concerns the dissipativity-based synchronization problem for the discrete-time switched neural networks with time-varying delay. Different from some existing research depending on the arbitrary and time-dependent switching mechanisms, all subsystems of the investigated delayed neural networks are permitted to be nondissipative. For reducing the switching frequency, the combined switching paradigm constituted by the time-dependent and state-dependent switching strategies is then constructed. In light of the proposed dwell-time-dependent storage functional, sufficient conditions with less conservativeness are formulated, under which the resultant synchronization error system is strictly (X,Y,Z)-θ-dissipative on the basis of the combined switching mechanism or the joint action of the switching mechanism and time-varying control input. Finally, the applicability and superiority of the theoretical results are adequately substantiated with the synchronization issue of two discrete-time switched Hopfield neural networks with time-varying delay, and the relationship among the performance index, time delay, and minimum dwell time is also revealed.This article studies the H∞ exponential synchronization problem for complex networks with quantized control input. An aperiodic sampled-data-based event-triggered scheme is introduced to reduce the network workload. Based on the discrete-time Lyapunov theorem, a new method is adopted to solve the sampled-data problem. In view of the aforementioned method, several sufficient conditions to ensure the H∞ exponential synchronization are acquired. Numerical simulations show that the proposed control schemes can significantly reduce the amount of transmitted signals while preserving the desired system performance.We present an adaptive force guidance system for laparoscopic surgery skills training. This system consists of self-adjusting fuzzy sliding-mode controllers and switching mode controllers to provide proper force feedback. Using virtual fixtures, the proposed system restricts motions or guides a trainee to navigate a surgical instrument in a 3-D space in a manner that mimics a human instructor who would teach the trainees by holding their hands. The self-adjusting controllers incorporate human factors, such as different force sensitivity and proficiency levels. The proposed system was implemented and evaluated using the computer-assisted surgical trainer (CAST). The effects of the force guidance system are presented based on the experimental test results.Evidential reasoning (ER) rule has been widely used in dealing with uncertainty. As an important parameter to measure the inherent property of evidence, the evidence reliability makes the ER rule constitute a generalized reasoning framework. In current research of the ER rule, the evidence reliability tends to be expressed in the form of quantitative value by certain methods or expert knowledge. see more The single quantitative value lacks the ability to describe the statistical property of reliability, which leads to unreasonable results. In this article, a new ER rule with continuous probability distribution of reliability denoted by ERr-CR is proposed. The combination of two pieces of evidence is discussed in detail, where the reliability is profiled as random variables with specific probability distribution. To characterize the output of ERr-CR, a novel concept of expectation of the expected utility is proposed. In addition, the ERr-CR is expanded to multiple pieces of evidence to show its universality. Further, the basic performances of the ERr-CR are explored to illustrate the rationality. Moreover, a case study of safety assessment of natural gas storage tanks (NGSTs) is conducted to show the potential applications of ERr-CR, which makes the proposed method more practical.In this article, we present a novel matrix-factorization-based model, labeled here as Vicinal vertex allocated matrix factorization (VVAMo), for uncovering clusters in network data. Different from the past related efforts of network clustering, which consider the edge structure, vertex features, or both in their design, the proposed model includes the additional detail on vertex inclinations with respect to topology and features into the learning. In particular, by taking the latent preferences between vicinal vertices into consideration, VVAMo is then able to uncover network clusters composed of proximal vertices that share analogous inclinations, and correspondingly high structural and feature correlations. To ensure such clusters are effectively uncovered, we propose a unified likelihood function for VVAMo and derive an alternating algorithm for optimizing the proposed function. Subsequently, we provide the theoretical analysis of VVAMo, including the convergence proof and computational complexity analysis.
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