Farahsharp9482

In the field of data mining, how to deal with high-dimensional data is a fundamental problem. If they are used directly, it is not only computationally expensive but also difficult to obtain satisfactory results. JAK inhibitor Unsupervised feature selection is designed to reduce the dimension of data by finding a subset of features in the absence of labels. Many unsupervised methods perform feature selection by exploring spectral analysis and manifold learning, such that the intrinsic structure of data can be preserved. However, most of these methods ignore a fact due to the existence of noise features, the intrinsic structure directly built from original data may be unreliable. To solve this problem, a new unsupervised feature selection model is proposed. The graph structure, feature weights, and projection matrix are learned simultaneously, such that the intrinsic structure is constructed by the data that have been feature weighted and projected. For each data point, its nearest neighbors are acquired in the process of graph construction. Therefore, we call them adaptive neighbors. Besides, an additional constraint is added to the proposed model. It requires that a graph, corresponding to a similarity matrix, should contain exactly c connected components. Then, we present an optimization algorithm to solve the proposed model. Next, we discuss the method of determining the regularization parameter ɣ in our proposed method and analyze the computational complexity of the optimization algorithm. Finally, experiments are implemented on both synthetic and real-world datasets to demonstrate the effectiveness of the proposed method.The continuous development of intelligent traffic control systems has a profound influence on urban traffic planning and traffic management. Indeed, as big data and artificial intelligence continue to evolve, the traffic control strategy based on deep reinforcement learning (RL) has been proven to be a promising method to improve the efficiency of intersections and save people's travel time. However, the existing algorithms ignore the temporal and spatial characteristics of intersections. In this article, we propose a multiagent RL based on the deep spatiotemporal attentive neural network (MARL-DSTAN) to determine the traffic signal timing in a large-scale road network. In this model, the state information captures the spatial dependency of the entire road network by leveraging the graph convolutional network (GCN) and integrates the information based on the importance of intersections via the attention mechanism. Meanwhile, to accumulate more valuable samples and enhance the learning efficiency, the recurrent neural network (RNN) is introduced in the exploration stage to constrain the action search space instead of fully random exploration. MARL-DSTAN decomposes the large-scale area into multiple base environments, and the agents in each base environment use the idea of ``centralized training and decentralized execution to learn to accelerate the algorithm convergence. The simulation results show that our algorithm significantly outperforms the fixed timing scheme and several other state-of-the-art baseline RL algorithms.The new generation of the industrial cyber-physical system (ICPS) supported by the edge computing technology facilitates the deep integration of sensing and control. System observability is the key factor to characterize the internal relationship of them. In most existing works, the observability is regarded as the assumption for subsequent sensing and control. But, in fact, with the gradually expanded network scale, this assumption is more difficult to directly satisfy sensing design. For this problem, we propose the observability guaranteed method (OGM) for edge sensing and control co-design. Specifically, the nonconvex observability condition is transformed into the convex range of key parameters of the sensing strategy based on the graph signal processing (GSP) technology. Then, we establish the relationship between these parameters and control performance. In OGM, except the previous design from sensing to control, we reversely adjust the sensing design for control demands to satisfy observability. Finally, our algorithm is applied into the hot rolling laminar cooling process based on the semiphysical evaluation. The effectiveness is verified by the results.The tethered formation system has been widely studied due to its extensive use in aerospace engineering, such as Earth observation, orbital location, and deep space exploration. The deployment of such a multitethered system is a problem because of the oscillations and complex formation maintenance caused by the space tether's elasticity and flexibility. In this article, a triangle tethered formation system is modeled, and an exact stable condition for the system's maintaining is carefully analyzed, which is given as the desired trajectories; then, a new control scheme is designed for its spinning deployment and stable maintenance. In the proposed scheme, a novel second-order sliding mode controller is given with a designed nonsingular sliding-variable. Based on the theoretical proof, the addressed sliding variable from the arbitrary initial condition can converge to the manifold in finite time, and then sliding to the equilibrium in finite time as well. The simulation results show that compared with classic second sliding-mode control, the proposed scheme can speed up the convergence of the states and sliding variables.With the rapid development of digital information techniques, the use of DNA media for information storage is considered as the future direction of data storage. Existing DNA storage schemes simply map compressed binary multimedia data into DNA base data, which has the disadvantages of data loss, low logical storage density and high cost of synthesis. This paper presents an end-to-end high density DNA encoding algorithm(referred to as HD-code, where HD stands for high density). The novelty and contributions of this work contain three parts. First, by taking full advantage of the statistical characteristics of the original multimedia data and considering the biological constraints on the DNA bases, the proposed scheme achieves higher logical storage density and improves the flexibility and consistency in data storage. Second, by performing data conversion, the proposed scheme can effectively encode extreme images with large proportion of single color. Third, the proposed method can reconstruct high quality images and reduce synthesis costs by yielding better rate-PSNR(Peak Signal to Noise Ratio).A hallmark impairment in a hemiparetic stroke is a loss of independent joint control resulting in abnormal co-activation of shoulder abductor and elbow flexor muscles in their paretic arm, clinically known as the flexion synergy. The flexion synergy appears while generating shoulder abduction (SABD) torques as lifting the paretic arm. This likely be caused by an increased reliance on contralesional indirect motor pathways following damage to direct corticospinal projections. The assessment of functional connectivity between brain and muscle signals, i.e., brain-muscle connectivity (BMC), may provide insight into such changes to the usage of motor pathways. Our previous model simulation shows that multi-synaptic connections along the indirect motor pathway can generate nonlinear connectivity. We hypothesize that increased usage of indirect motor pathways (as increasing SABD load) will lead to an increase of nonlinear BMC. To test this hypothesis, we measured brain activity, muscle activity from shoulder abductors when stroke participants generate 20% and 40% of maximum SABD torque with their paretic arm. We computed both linear and nonlinear BMC between EEG and EMG. We found dominant nonlinear BMC at contralesional/ipsilateral hemisphere for stroke, whose magnitude increased with the SABD load. These results supported our hypothesis and indicated that nonlinear BMC could provide a quantitative indicator for determining the usage of indirect motor pathways following a hemiparetic stroke.In this study, we address the issue of whether vibrotactile feedback can enhance the motor cortex excitability translated into the plastic changes in local cortical areas during motor imagery (MI) BCI-based training. For this purpose, we focused on two of the most notable neurophysiological effects of MI - the event-related desynchronization (ERD) level and the increase in cortical excitability assessed with navigated transcranial magnetic stimulation (nTMS). For TMS navigation, we used individual high-resolution 3D brain MRIs. Ten BCI-naive and healthy adults participated in this study. The MI (rest or left/right hand imagery using Graz-BCI paradigm) tasks were performed separately in the presence and absence of feedback. To investigate how much the presence/absence of vibrotactile feedback in MI BCI-based training could contribute to the sensorimotor cortical activations, we compared the MEPs amplitude during MI after training with and without feedback. In addition, the ERD levels during MI BCI-based training were investigated. Our findings provide evidence that applying vibrotactile feedback during MI training leads to (i) an enhancement of the desynchronization level of mu-rhythm EEG patterns over the contralateral motor cortex area corresponding to the MI of the non-dominant hand; (ii) an increase in motor cortical excitability in hand muscle representation corresponding to a muscle engaged by the MI.In this paper, a comprehensive form of the range migration algorithm (RMA) is analytically derived for reconstructing the reflectivity function using synthetic aperture imaging techniques. Specifically, amplitude compensation, in addition to the typical phase compensation, is included in the development of the matched filter of the RMA, with the result herein referred to as the amplitude compensated RMA (AC-RMA). To illustrate the improvements offered by the AC-RMA, simulation and measurement (at Ka-band, 26.5 - 40 GHz) are performed to reconstruct the reflectivity function of a target using both RMA and AC-RMA algorithms. The results prove that the AC-RMA is a robust algorithm that can successfully reconstruct the reflectivity function of a target with higher accuracy, regardless of its dielectric properties, including scenarios with low contrast between the dielectric properties of the background and target in the presence of noise. This approach is also independent of the bandwidth of the imaging system and is applicable to multilayer media as well. In addition, while the formulation of the AC-RMA is more complicated than the traditional (phase compensation only) RMA, the processing time necessary for images created with the AC-RMA is just 1.2 times greater than that of the traditional RMA processing time.Automatic machine classification of concrete structural defects in images poses significant challenges because of multitude of problems arising from the surface texture, such as presence of stains, holes, colors, poster remains, graffiti, marking and painting, along with uncontrolled weather conditions and illuminations. In this paper, we propose an interleaved deep artifacts-aware attention mechanism (iDAAM) to classify multi-target multi-class and single-class defects from structural defect images. Our novel architecture is composed of interleaved fine-grained dense modules (FGDM) and concurrent dual attention modules (CDAM) to extract local discriminative features from concrete defect images. FGDM helps to aggregate multi-layer robust information with wide range of scales to describe visually-similar overlapping defects. On the other hand, CDAM selects multiple representations of highly localized overlapping defect features and encodes the crucial spatial regions from discriminative channels to address variations in texture, viewing angle, shape and size of overlapping defect classes.