Neuro Control of Nonlinear Discrete Time Systems with Deadzone and Input Constraints

A neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of uncertain nonlinear systems with unknown deadzones and magnitude constraints on the input. The NN controller consists of two NNs: the first NN for compensating the unknown deadzones; and the second NN for compensating the uncertain nonlinear system dynamics. The magnitude constraints on the input are modeled as saturation nonlinearities and they are dealt with in the Lyapunov-based controller design. The uniformly ultimate boundedness (UUB) of the closed-loop tracking errors and the neural network weights estimation errors is demonstrated via Lyapunov stability analysis

Foreword to the special Issue on Hyperspectral remote sensing and imaging spectroscopy

The twenty six papers in this special issue focus on the technologies of hyperspectral remote sensing (HRS)and imaging spectroscopy. HRS has emerged as a powerful tool to understand phenomena at local and global scales by virtue of imaging through a diverse range of platforms, including terrestrial in-situ imaging platforms, unmanned and manned aerial vehicles, and satellite platforms. By virtue of imaging over a wide range of spectral wavelengths, it is possible to characterize object specific properties very accurately. As a result, hyperspectral imaging (also known as imaging spectroscopy) has gained popularity for a wide variety of applications, including environment monitoring, precision agriculture, mineralogy, forestry, urban planning, and defense applications. The increased analysis capability comes at a cost—there are a variety of challenges that must be overcome for robust image analysis of such data, including high dimensionality, limited sample size for training supervised models, noise and atmospheric affects, mixed pixels, etc. The papers in this issue represent some of the recent developments in image analysis algorithms and unique applications of hyperspectral imaging data

How do East African Communities Cope with the Impacts of Prosopis juliflora (Mesquite) Invasion? A Review

Prosopis juliflora is an evergreen invasive plant native to South America, the Caribbean, and Central America. The plant is well adapted to harsh environmental conditions. As a result, it has spread to most arid and semi-arid areas of the world causing both positive and negative impacts. This study reviewed the adaptation/coping strategies adopted by East African communities as a result of the invasion by the plant. The review results showed that East African communities cope by using the plant for human food and animal feed, leasing the infested land, renting land from uninvaded areas, clearing the plant from farming, grazing land, waterways, paths and homesteads, and using it as fuel in form of firewood and charcoal among others. The communities living in the infested areas now almost entirely depend on the plant for livelihood. Some of the employed adaptations/ coping strategies were found to be inadequate and to have negative environmental impacts. In order to enhance the adaptations/ coping strategies, we recommend commercialization of the plant’s seed for animal feed and human food production, sensitization of the communities on the medicinal use of the plant and that programs to manage the plant should take into account the adaptations the communities have developed over time to avoid negative impacts on the communities’ livelihoods. Keywords: Prosopis juliflora; Coping strategies; East Africa; Invasion; Po

Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection.

Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still lacking. The lack of proper understanding of the HSI data structure has resulted in the inconsistency in the outcome of UBS. Besides, most of the UBS methods are either relying on complicated measurements or rather noise sensitive, which hinder the efficiency of the determined band subset. In this article, an adaptive distance-based band hierarchy (ADBH) clustering framework is proposed for UBS in HSI, which can help to avoid the noisy bands while reflecting the hierarchical data structure of HSI. With a tree hierarchy-based framework, we can acquire any number of band subset. By introducing a novel adaptive distance into the hierarchy, the similarity between bands and band groups can be computed straightforward while reducing the effect of noisy bands. Experiments on four datasets acquired from two HSI systems have fully validated the superiority of the proposed framework

Mathematical modeling and simulation analysis of different structured cored wire feeding spheroidization by finite volume method

A three-dimensional dynamic heat transfer mathematical model of the process when cored wire feed in molten iron is established based on finite volume method (FVM). The calculation area is meshed with triangles and quadrilaterals to determine nodes and control volumes, and implicit time integration method is used to ensure the stability of calculating process. For exposing the dynamic heat transfer behavior, the variation of temperature field and explosion characteristics of cored wires are studied. In addition, the melt loss rate of the top end of cored wire and the correlation among melt explosion depth, molten iron temperature and feeding speed of cored wires are theoretically calculated. More importantly, the influence of different structures of cored wires on the absorption rate of magnesium are considered. The calculation results are in good agreement with the experimental data, which indicate that the existing theoretical model has good validity and can provide theoretical guidance for spheroidization process in molten iron

Weighted sparse graph based dimensionality reduction for hyperspectral images

Dimensionality reduction (DR) is an important and helpful preprocessing step for hyperspectral image (HSI) classification. Recently, sparse graph embedding (SGE) has been widely used in the DR of HSIs. In this letter, we propose a weighted sparse graph based DR (WSGDR) method for HSIs. Instead of only exploring the locality structure (as in neighborhood preserving embedding) or the linearity structure (as in SGE) of the HSI data, the proposed method couples the locality and linearity properties of HSI data together in a unified framework for the DR of HSIs. The proposed method was tested on two widely used HSI data sets, and the results suggest that the locality and linearity are complementary properties for HSIs. In addition, the experimental results also confirm the superiority of the proposed WSGDR method over the other state-of-the-art DR methods

An Efficient and Extensible Zero-knowledge Proof Framework for Neural Networks

In recent years, cloud vendors have started to supply paid services for data analysis by providing interfaces of their well-trained neural network models. However, customers lack tools to verify whether outcomes supplied by cloud vendors are correct inferences from particular models, in the face of lazy or malicious vendors. The cryptographic primitive called zero-knowledge proof (ZKP) addresses this problem. It enables the outcomes to be verifiable without leaking information about the models. Unfortunately, existing ZKP schemes for neural networks have high computational overheads, especially for the non-linear layers in neural networks. In this paper, we propose an efficient and extensible ZKP framework for neural networks. Our work improves the performance of the proofs for non-linear layers. Compared to previous works relying on the technology of bit decomposition, we convert complex non-linear relations into range and exponent relations, which significantly reduces the number of constraints required to prove non-linear layers. Moreover, we adopt a modular design to make our framework compatible with more neural networks. Specifically, we propose two enhanced range and lookup proofs as basic blocks. They are efficient in proving the satisfaction of range and exponent relations. Then, we constrain the correct calculation of primitive non-linear operations using a small number of range and exponent relations. Finally, we build our ZKP framework from the primitive operations to the entire neural networks, offering the flexibility for expansion to various neural networks. We implement our ZKPs for convolutional and transformer neural networks. The evaluation results show that our work achieves over $168.6\times$ (up to $477.2\times$) speedup for separated non-linear layers and $41.4\times$ speedup for the entire ResNet-101 convolutional neural network, when compared with the state-of-the-art work, Mystique. In addition, our work can prove GPT-2, a transformer neural network with $117$ million parameters, in $287.1$ seconds, achieving $35.7\times$ speedup over ZKML, which is a state-of-the-art work supporting transformer neural networks

Ameliorative Effect of D-α-Tocopherol Acetate Complexes on D-Galactose-Induced Aging in Mice

To investigate the ameliorative effect of the D-α-tocopheryl acetate compound on D-galactose-induced aging in mice, the in vitro antioxidant capacity of the compound of natural oils+phytosterols (VEO), the compound of D-α-tocopheryl acetate+phytosterol (VEZ), and the compound of D-α-tocopheryl acetat+phytosterol+astaxanthin (VEX) were measured. The aging model was established using mice injected with D-galactose on the back of the neck, while the intervention was carried out with different compounds. The results showed that all three groups of compounds had strong antioxidant effects, with the VEZ group showing better in vitro antioxidant effects. Compared with the aging model mice, the intervention of the three compounds increased glutathione peroxidase (GSH-Px) and total antioxidant capacity (T-AOC), decreased malondialdehyde (MDA) (P<0.01), and a decrease in the serum inflammatory factors interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor (TNF-α) and liver function indicators alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were significantly reduced (P<0.01). After the intervention, the mRNA and protein expression of nuclear factor-erythroid 2-related factor 2 (Nrf2), quinone oxidoreductase (NQO-1) and heme oxygenase-1 (HO-1) in mice were significantly enhanced (P<0.0001). This indicated that the different combinations exerted their antioxidant effects through up-regulating the expression of Nrf2, NQO-1 and HO-1, thus achieving anti-aging effects, with the VEZ group showing the best expression effect. In conclusion, D-α-Tocopheryl acetate complex achieved their anti-aging effects by increasing the expression of antioxidant-related mRNAs and proteins, thus enhancing the levels of downstream antioxidant enzymes, among which D-α-tocopheryl acetate was more effective when combined with phytosterols

Vitamin C Enhances the Generation of Mouse and Human Induced Pluripotent Stem Cells

SummarySomatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs) by defined factors. However, the low efficiency and slow kinetics of the reprogramming process have hampered progress with this technology. Here we report that a natural compound, vitamin C (Vc), enhances iPSC generation from both mouse and human somatic cells. Vc acts at least in part by alleviating cell senescence, a recently identified roadblock for reprogramming. In addition, Vc accelerates gene expression changes and promotes the transition of pre-iPSC colonies to a fully reprogrammed state. Our results therefore highlight a straightforward method for improving the speed and efficiency of iPSC generation and provide additional insights into the mechanistic basis of the reprogramming process