Sliding Mode Control (SMC) of Image‐Based Visual Servoing for a 6DOF Manipulator

The accuracy and stability are two fundamental concerns of the visual servoing control system. This chapter presents a sliding mode controller for image‐based visual servoing (IBVS) which can increase the accuracy of 6DOF robotic system with guaranteed stability. The proposed controller combines proportional derivative (PD) control with sliding mode control (SMC) for a 6DOF manipulator. Compared with conventional proportional or SMC controller, this approach owns faster convergence and better disturbance rejection ability. Both simulation and experimental results show that the proposed controller can increase the accuracy and robustness of a 6DOF robotic system

One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks

Unlearnable examples (ULEs) aim to protect data from unauthorized usage for training DNNs. Error-minimizing noise, which is injected to clean data, is one of the most successful methods for preventing DNNs from giving correct predictions on incoming new data. Nonetheless, under specific training strategies such as adversarial training, the unlearnability of error-minimizing noise will severely degrade. In addition, the transferability of error-minimizing noise is inherently limited by the mismatch between the generator model and the targeted learner model. In this paper, we investigate the mechanism of unlearnable examples and propose a novel model-free method, named \emph{One-Pixel Shortcut}, which only perturbs a single pixel of each image and makes the dataset unlearnable. Our method needs much less computational cost and obtains stronger transferability and thus can protect data from a wide range of different models. Based on this, we further introduce the first unlearnable dataset called CIFAR-10-S, which is indistinguishable from normal CIFAR-10 by human observers and can serve as a benchmark for different models or training strategies to evaluate their abilities to extract critical features from the disturbance of non-semantic representations. The original error-minimizing ULEs will lose efficiency under adversarial training, where the model can get over 83\% clean test accuracy. Meanwhile, even if adversarial training and strong data augmentation like RandAugment are applied together, the model trained on CIFAR-10-S cannot get over 50\% clean test accuracy

Application of mixed collectors on quartz-feldspar by fluorine-free flotation separation and their interaction mechanism : a review

Quartz and feldspar are usually exist in symbiosis in nature, and they are difficult to be separated effectively by conventional physical methods owing to their similarities in crystal structures and surface characteristics. Flotation is the most resultful method, and especially, flotation with hydrofluoric acid (HF) is the most efficient way. Because HF may cause serious environmental and health problems, the effective and environmentally friendly separation of quartz and feldspar remains a formidable challenge. The crystal structure, surface broken bonds, surface energy, and solid–liquid interface properties of quartz and feldspar are investigated in this paper. In particular, some types of mixed cationic/anion collectors and their interaction mechanism on the quartz and feldspar surfaces with acidic, alkaline, and neutral media in the absence of fluorine are discussed, and the grade and scheme of quartz and feldspar for the practical application are illustrated. This review proposes concrete research approaches and provides perspectives for the advanced processing of quartz and feldspar in an environmentally friendly and economical way

Predicting the Surface Soil Texture of Cultivated Land via Hyperspectral Remote Sensing and Machine Learning: A Case Study in Jianghuai Hilly Area

Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture’s spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R2) of 0.948 and 0.908 and a root mean square error (RMSE) of 26.51 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R2 and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture

High SPINK1 Expression Predicts Poor Prognosis and Promotes Cell Proliferation and Metastasis of Hepatocellular Carcinoma

Background Serine protease inhibitor Kazal type I (SPINK1) is highly expressed and promotes tumor progress in different cancers. This study aimed to evaluate SPINK1’s prognostic value and its role in hepatocellular carcinoma (HCC) progress. Methods We use tissue micro-arrays containing 273 tumor and paired para-tumor tissues to evaluate SPINK1’s prognostic value in HCC. CCK8 cell proliferation assay, wound healing assays, transwell migration and invasion assays were performed to explore the effect of SPINIK1 on HCC cells. The Cancer Genome Atlas (TCGA) database and Gene set enrichment analysis (GSEA) were used to verify the prognosis value of SPINK1 in HCC and explore the underlying mechanisms. Results SPINK1 expression was significantly higher in tumor tissues than paired para-tumor tissues (P < 0.001). Higher SPINK1 expression in tumor was significantly associated with portal vein tumor thrombus formation (P = 0.019) and shorter overall survival (P = 0.029). SPINK1 expression in tumor tissue was an independent predictor for overall survival. SPINK1 increased proliferation (P < 0.001), enhanced migration and invasion ability of HCC cell lines (P < 0.001). GSEA revealed that glycine, serine, threonine and bile acid metabolism may be the underlying mechanism of SPINK1 in HCC. Conclusions In conclusion, high SPINK1 expression is associated with poor prognosis of HCC. SPINK1 promotes proliferation, migration and invasion ability of HCC cells