Kernel Feature Extraction for Hyperspectral Image Classification Using Chunklet Constraints

A novel semi-supervised kernel feature extraction algorithm to combine an efficient metric learning method, i.e. relevant component analysis (RCA), and kernel trick is presented for hyperspectral imagery land-cover classification. This method obtains projection of the input data by learning an optimal nonlinear transformation via a chunklet constraints-based FDA criterion, and called chunklet-based kernel relevant component analysis (CKRCA). The proposed method is appealing as it constructs the kernel very intuitively for the RCA method and does not require any labeled information. The effectiveness of the proposed CKRCA is successfully illustrated in hyperspectral remote sensing image classification. Experimental results demonstrate that the proposed method can greatly improve the classification accuracy compared with traditional linear and conventional kernel-based methods

Electric Wheelchair Hybrid Operating System Coordinated with Working Range of a Robotic Arm

Electric wheelchair-mounted robotic arms can help patients with disabilities to perform their activities in daily living (ADL). Joysticks or keypads are commonly used as the operating interface of Wheelchair-mounted robotic arms. Under different scenarios, some patients with upper limb disabilities such as finger contracture cannot operate such interfaces smoothly. Recently, manual interfaces for different symptoms to operate the wheelchair-mounted robotic arms are being developed. However, the stop the wheelchairs in an appropriate position for the robotic arm grasping task is still not easy. To reduce the individual’s burden in operating wheelchair in narrow spaces and to ensure that the chair always stops within the working range of a robotic arm, we propose here an operating system for an electric wheelchair that can automatically drive itself to within the working range of a robotic arm by capturing the position of an AR marker via a chair-mounted camera. Meanwhile, the system includes an error correction model to correct the wheelchair’s moving error. Finally, we demonstrate the effectiveness of the proposed system by running the wheelchair and simulating the robotic arm through several courses

Probing thermally-induced structural evolution during the synthesis of layered Li-, Na-, or K-containing 3d transition-metal oxides

Layered alkali-containing 3d transition-metal oxides are of the utmost importance in the use of electrode materials for advanced energy storage applications such as Li-, Na-, or K-ion batteries. A significant challenge in the field of materials chemistry is understanding the dynamics of the chemical reactions between alkali-free precursors and alkali species during the synthesis of these compounds. In this study, in situ high-resolution synchrotron-based X-ray diffraction was applied to reveal the Li/Na/K-ion insertion-induced structural transformation mechanism during high-temperature solid-state reaction. The in situ diffraction results demonstrate that the chemical reaction pathway strongly depends on the alkali-free precursor type, which is a structural matrix enabling phase transitions. Quantitative phase analysis identifies for the first time the decomposition of lithium sources as the most critical factor for the formation of metastable intermediates or impurities during the entire process of Li-rich layered Li[Li0.2Ni0.2Mn0.6]O2 formation. Since the alkali ions have different ionic radii, Na/K ions tend to be located on prismatic sites in the defective layered structure (Na2/3-x[Ni0.25Mn0.75]O2 or K2/3-x[Ni0.25Mn0.75]O2) during calcination, whereas the Li ions prefer to be localized on the tetrahedral and/or octahedral sites, forming O-type structures

Hyperspectral Band Selection for Lithologic Discrimination and Geological Mapping

Classification techniques applied to hyperspectral images are very useful for lithologic discrimination and geological mapping. Classifiers are often applied either to all spectral channels or only to absorption spectral channels. However, it is difficult to obtain different lithology information using specific absorption regions from the narrow bandwidth and contiguous spectral channels due to spectral variability among rocks. In this article, we propose a band selection (BS) method for hyperspectral lithologic discrimination, in which the lithological superpixels are first gathered. A spectral bands selection criterion is learned by measuring the homogeneity and the variation of the lithological superpixels, and lithologic discriminating bands are identified by an efficient clustering algorithm based on affinity propagation. In this article, two geologic test sites, i.e., the Airborne Visible/Infrared Imaging Spectrometer data of the Cuprite, Nevada, USA, including 11 lithologic units (9 types of rocks) and the Hyperion data of Junggar, China, with 5 lithologic units, are chosen for validation. The performance of the proposed BS method is compared with those of using all the bands, specific absorption spectral channels, and two literature BS techniques. Experimental results show that the proposed method improves mapping accuracy by selecting fewer bands with higher lithologic discrimination capability than the other considered methods

The Crimping and Expanding Performance of Self-Expanding Polymeric Bioresorbable Stents: Experimental and Computational Investigation

Polymeric bioresorbable stents (PBRSs) are considered the most promising devices to treat cardiovascular diseases. However, the mechanical weakness still hampers their application. In general, PBRSs are crimped into small sheathes and re-expanded to support narrowed vessels during angioplasty. Accordingly, one of the most significant requirements of PBRSs is to maintain mechanical efficacy after implantation. Although a little research has focused on commercial balloon-expanding PBRSs, a near-total lack has appeared on self-expanding PBRSs and their deformation mechanisms. In this work, self-expanding, composite polymeric bioresorbable stents (cPBRSs) incorporating poly(p-dioxanone) (PPDO) and polycaprolactone (PCL) yarns were produced and evaluated for their in vitro crimping and expanding potential. Furthermore, the polymer time-reliable viscoelastic effects of the structural and mechanical behavior of the cPBRSs were analyzed using computational simulations. Our results showed that the crimping process inevitably decreased the mechanical resistance of the cPBRSs, but that this could be offset by balloon dilatation. Moreover, deformation mechanisms at the yarn level were discussed, and yarns bonded in the crossings showed more viscous behavior; this property might help cPBRSs to maintain their structural integrity during implantation

A Rolling Horizon Optimization Framework for Resilient Restoration of Active Distribution Systems

Network reconfiguration is an effective way to avoid severe, large-scale power outages and to improve the resilience of active distribution networks (ADNs). Furthermore, the rapid development of distributed energy resources (DERs) provides new perspectives for network reconfiguration. In this paper, the effect of network reconfiguration and DER collaboration on ADN’s resilient restoration are studied. The applications of DERs are fully explored. In order to achieve a better resilient performance, a detailed multiperiod model considering both reconfiguration and multiple DERs is established. Some linearization techniques are used to simplify the proposed model. Then, we build a rolling horizon optimization framework to solve the model. The framework eliminates the adverse effect of prediction errors and speeds up the calculation. By introducing predictions into strategy determination, the framework achieves a better restoration effect than the traditional greedy method. The proposed framework is tested on a 33-bus system. The simulations verify the efficiency of our work

Discriminative Feature Metric Learning in the Affinity Propagation Model for Band Selection in Hyperspectral Images

Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while pixels further away from each other in the space have a high probability of belonging to different classes. In this paper, we propose a novel discriminative feature metric-based affinity propagation (DFM-AP) technique where the spectral and the spatial relationships among pixels are constructed by a new type of discriminative constraint. This discriminative constraint involves chunklet and discriminative information, which are introduced into the BS process. The chunklet information allows for grouping of spectrally-close and spatially-close pixels together without requiring explicit knowledge of their class labels, while discriminative information provides important separability information. A discriminative feature metric (DFM) is proposed with the discriminative constraints modeled in terms of an optimal criterion for identifying an efficient distance metric learning method, which involves discriminative component analysis (DCA). Following this, the representative subset of bands can be identified by means of an exemplar-based clustering algorithm, which is also known as the process of affinity propagation. Experimental results show that the proposed approach yields a better performance in comparison with several representative class label and pairwise constraint-based BS algorithms. The proposed DFM-AP improves the classification performance with discriminative constraints by selecting highly discriminative bands with low redundancy