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The GaN-based white light-emitting diodes (LEDs) increase the possibility of realizing an ideal spectrum for the growth of plants. At the same time, high photosynthetic action of white LEDs is very crucial for the plant lighting to increase the crops productivity in the indoor artificial lighting plant factory. In this paper, via designing a non-linear program (NLP) based on the common genetic algorithm (GA), we are capable of conducting the comprehensive spectral optimization for realizing high photosynthetic action of multi-primary white LEDs (from three-primary to five-primary) under three different correlated color temperatures (CCTs, 2700 K, 4500 K, and 6500 K, respectively) while taking the color rendering performances, such as Commission Internationale de l'Eclairage (CIE) color rendering index (CRI, Ra), two special color rendering indices (R9 and R12) and Illuminating Engineering Society of North America (IES) TM-30 (Rf and Rg), and color quality scale (CQS, Qa) into account. In addition, these mentioned performances are compared among different primary numbers, CCTs, and spectral bandwidths in the multi-primary LEDs. Finally, we believe that the obtained relevant results presented here could provide a useful guidance for the achievement of high photosynthetic and excellent color performance lighting for the indoor farming.

Automatic image registration of multimodal urban remote sensing images remains a critical challenging task in remote sensing image analysis due to significant nonlinear radiation distortions between multimodal image pairs; most of the traditional methods focus on the feature point detection and its local description and ignore the robust road information in multimodal urban remote sensing images. Motivated by this, we propose a fast and robust registration method for multimodal urban remote sensing images via road intersection triangular features. The proposed method obtains three main stages: Road lines extraction from images, intersection triangular feature construction, and triangular feature matching. The qualitative and quantitative experimental results show that the proposed method significantly outperforms other state-of-the-art methods, even when others completely fail to achieve the registration task of cross-modal images, our method still maintains good robustness and matching efficiency.

In multi-label learning, each object is represented by a single instance and associated with multiple labels simultaneously. Existing multi-label learning approaches mainly construct classification models with a fixed set of target labels (observed labels). However, in the big data era, it is difficult to provide a fully complete label set for a data set. In some real applications, there are multiple labels hidden in the data set, especially for those large-scale data sets. In this paper, a novel approach named MLLHL is proposed to not only discover the hidden labels in the training data but also predict these hidden labels and observed labels for unseen examples simultaneously. We assume that the observed labels are just a subset of labels which are selected from the full label set, and the rest ones are omitted by the annotators during the labeling stage. Extensive experiments show the competitive performance of MLLHL against other state-of-the-art multi-label learning approaches.

Single-cell RNA-sequencing (scRNA-seq) data provide opportunities to reveal new insights into many biological problems such as elucidating cell types. An effective approach to elucidate cell types in complex tissues is to partition the cells into several separated subgroups via clustering techniques, where the cells in a specific cluster belong to the same cell type based on gene expression patterns. In this work, we present a novel multiple kernel clustering framework for scRNA-seq data clustering via locality preserving kernel alignment. Specifically, we first generate a series of similarity kernel matrices by using different kernel functions. Then we transfer the clustering task to a multiple kernel k-means problem with the kernels aligned in a local manner, i.e., the similarity of a sample to its k-nearest neighbours are aligned with the ideal similarity matrix. In our method, the clustering process focuses on closer sample pairs that shall stay together, and avoids involving unreliable similarity evaluation for farther sample pairs. In addition, we construct a local Laplacian matrix for each sample to constrain that closer samples should be allocated similar labels. In such a manner, the local structure of the data can be well preserved and utilized to produce better alignment for clustering. An alternate updating algorithm with theoretical analysis is developed to solve the proposed problem. We evaluate the performance of the proposed method on various real scRNA-seq data, and the results show that our method can obtain superior results when compared with other state-of-the-art approaches.

The approximate range emptiness problem requires a memory-efficient data structure D to approximately represent a set S of n distinct elements chosen from a large universe U= {0,1,⋯,N-1} and answer an emptiness query of the form “S∩[a;b]=0?” for an interval [a;b] of length L (a,b∈U), with a false positive rate ε. The designed D for this problem can be kept in high-speed memory and quickly determine approximately whether a query interval is empty or not. Thus, it is crucial for facilitating online query processing in the information-centric Internet of Things applications, where the IoT data are continuously generated from a large number of resource-constrained sensors or readers and then are processed in networks. However, the existing works on the approximate range emptiness problem only consider the simple case when the set S is static, rendering them unsuitable for the continuously generated IoT data. In this paper, we study the approximate range emptiness problem over sliding windows in the IoT Data streams, denoted by ε-ARESD-problem, where both insertion and deletion are allowed. We first prove that, given a sliding window size n and an interval length L, the lower bound of memory bits needed in any data structure for ε-ARESD-problem is n log2 (nL/ε)+Θ(n). Then, a data structure is proposed and proved to be within a factor of 1.33 of the lower bound. The extensive simulation results demonstrate the advantage of the efficiency of our data structure over the baseline approach.

In multi-view multi-label learning, each object is represented by multiple data views, and belongs to multiple class labels simultaneously. Generally, all the data views have a contribution to the multi-label learning task, but their contributions are different. Besides, for each data view, each class label is only associated with a subset data features, and different features have different contributions to each class label. In this paper, we propose a novel framework VLSF for multi-view multi-label learning, i.e., multi-view multi-label learning with View-Label-Specific Features. Specifically, we first learn a low dimensional label-specific data representation for each data view and construct a multi-label classification model based on it by exploiting label correlations and view consensus, and learn the contribution weight of each data view to multi-label learning task for all the class labels jointly. Then, the final prediction can be made by combing the prediction results of all the classifiers and the learned contribution weights. The extensive comparison experiments with the state-of-the-art approaches manifest the effectiveness of the proposed method VLSF.

This article is concerned with the consensus formation control problem of a class of networked quadrotor aircraft partially bounded and state-dependent perturbations. A general distributed consensus error model is first developed to formulate the formation behavior of the networked quadrotor aircraft. Then, by using adaptive techniques, virtual position control strategies are proposed to eliminate the impacts of perturbations, so that the following quadrotor aircraft can boundedly track the desired position trajectory with a satisfying pattern. Furthermore, based on the designed virtual position control strategies, the attitude reference angles are constructed and adaptive attitude control strategies are further designed to guarantee that the attitude angles track the reference angles asymptotically. In terms of the asymptotic tracking results of attitude control systems, the bounded consensus formation results are obtained based on the Lyapunov stability theorem. Numerical simulations are carried out to verify the efficiency of the designed position formation as well as attitude tracking control strategies of the networked quadrotor aircraft. [ABSTRACT FROM AUTHOR]

The event-triggered model-free adaptive control (ET-MFAC) problem for nonlinear networked control systems (NNCSs) with presence of sensor faults and denial-of-service (DoS) attacks is investigated. The attack occured in sensor-to-controller networked channels is supposed to obey the Bernoulli distribution. While sensor faults are formulated as unknown functions which could be approximated by a radial basis function-based neural networks (RBF-NNs). Then an ET-MFAC algorithm is constructed to ensure the tracking performance under sensor faults and DoS attacks based on adaptive estimations. The designed control algorithm is independent with the system structure and only uses the system input and output data. An example with comparison is given to demonstrate the validity of the new ET-MFAC algorithm. [ABSTRACT FROM AUTHOR]

This article is concerned with the robust adaptive fault-tolerant control (FTC) circuit designs for a class of continuous-time disturbed systems. A circuit realization method is investigated to convert the robust adaptive FTC control schemes into analog control circuits. An adaptive compensation control scheme against state-dependent and partially bounded actuator faults and disturbances is first developed to demonstrate the approach clearly, then its equivalent control circuits are implemented by using the circuit theory. Compared with simulation results achieved by MATLAB and professional circuit simulation software, the effectiveness of the proposed robust adaptive FTC circuits is validated by a rocket fairing system and a Chua’s circuit system. [ABSTRACT FROM AUTHOR]