Search

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pixel-value differencing (PVD) steganography is a popular spatial domain technology. Several PVD-based studies have proposed extended PVD steganography methods. The majority of these studies have verified their security against the regular-singular (RS) analysis. However, RS analysis is aimed at the feature of the least significant bit substitution method, which is relatively less significant for PVD steganography. The pixel difference histogram (PDH) is generally utilized to attack PVD steganography. If the embedding capacity is high, then the features on the PDH are evident; otherwise, the features are less obvious. In this paper, we propose a statistical feature-based steganalysis technique for the original PVD steganography. Experimental results demonstrate that, compared with existing steganalysis technique with weighted stego-image (WS) method, the proposed method effectively detects PVD steganography at low embedding ratios, such that there is no need of using the original embedding parameters. Furthermore, the accuracy and precision of the method are better than those of existing PVD steganalysis techniques. Therefore, the proposed method contributes to the security analysis of the original PVD steganography as an alternative to the commonly used RS, PDH and WS attack techniques. [ABSTRACT FROM AUTHOR]

Pixel-value differencing (PVD) steganography can embed a large quantity of information and effectively resist regular/singular (RS) detection. Recent steganography techniques that are based on PVD have only focused on improving embedding capacity and image quality. In terms of security, PVD steganography has typically only been tested against well-known RS detection analysis or pixel difference histogram attacks. Modified PVD (MPVD) is an improved method that eliminates certain detectable features of PVD, thereby increasing security. State-of-the-art studies in steganalysis have attempted to detect MPVD steganography by using features of the differences between cover images and stego images, and they have generated models to test suspicious images. However, these methods have low accuracy when the embedding capacity is low. In this study, we propose a chi-square goodness-of-fit-based steganalysis technique that can be applied to MPVD steganography. Experimental results demonstrate that the method effectively detects MPVD steganography even when the embedding ratio is low. Furthermore, the accuracy, precision, and implementation of the method are better than those of existing state-of-the-art techniques in detecting data hidden using MPVD steganography. Therefore, the proposed method contributes to the security analysis of PVD-extended steganography. [ABSTRACT FROM AUTHOR]

Objective: To develop a practical method to rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively evaluate Alzheimer's disease (AD). Methods: For the properties of low cost and convenient access in general clinics, Tc-99-ECD SPECT imaging data in brain perfusion detection was used in this study for AD detection. Two-stage transfer learning based on the Inception v3 network model was performed using the ImageNet dataset and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning. The effect of pre-training parameters for Tc-99m-ECD SPECT image to distinguish AD from normal cognition (NC) was investigated, as well as the effect of the sample size of F-18-FDG PET images used in pre-training. The same model was also fine-tuned for the prediction of the MMSE score from the Tc-99m-ECD SPECT image. Results: The AUC values of w/wo pre-training parameters for Tc-99m-ECD SPECT image to distinguish AD from NC were 0.86 and 0.90. The sensitivity, specificity, precision, accuracy, and F1 score were 100%, 75%, 76%, 86%, and 86%, respectively for the training model with 1000 cases of F-18-FDG PET image for pre-training. The AUC values for various sample sizes of the training dataset (100, 200, 400, 800, 1000 cases) for pre-training were 0.86, 0.91, 0.95, 0.97, and 0.97. Regardless of the pre-training condition ECD dataset used, the AUC value was greater than 0.85. Finally, predicting cognitive scores and MMSE scores correlated (R2 = 0.7072). Conclusions: With the ADNI pre-trained model, the sensitivity and accuracy of the proposed deep learning model using SPECT ECD perfusion images to differentiate AD from NC were increased by approximately 30% and 10%, respectively. Our study indicated that the model trained on PET FDG metabolic imaging for the same disease could be transferred to a small sample of SPECT cerebral perfusion images. This model will contribute to the practicality of SPECT cerebral perfusion images using deep learning technology to objectively recognize AD. [ABSTRACT FROM AUTHOR]

This article presents the design and implementation of a super-twisting second-order sliding-mode controller (SOSMC) for a synchronous reluctance motor. Second-order sliding-mode control is an effective tool for the control of uncertain nonlinear systems since it overcomes the main drawbacks of the classical sliding-mode control, i.e., the large control effort and the chattering phenomenon. Its real implementation implies simple control laws, and ensures an improvement in the sliding accuracy with respect to conventional sliding-mode control. This article proposes a novel scheme that is based on the technique of super-twisting second-order sliding-mode control. First, SOSMC is derived mathematically, and then the performance of the proposed method is verified by simulations. The proposed SOSMC shows robustness for variations in the motor parameters and an improvement in the chattering phenomenon. [ABSTRACT FROM AUTHOR]

This article proposes the PC-based LabVIEW as the software to develop the algorithm of the robust complex extended Kalman filter (RCEKF) to detect the parameters of the voltage signal in power systems. The hardware used is a sample-and-hold card and a data acquisition (DAQ) card to extract the data from an outside system to the PC, and the program will compute the amplitude, frequency, and phase of the voltage signal with RCEKF. To validate the performance of RCEKF, the voltage signal from a function generator was applied to check the feasibility of the algorithm. This application was also used in the Taiwan Power Company (TPC) secondary substation in Sijhou, Taiwan. [ABSTRACT FROM AUTHOR]

In this article, a sliding mode control (SMC) design based on a Gaussian radial basis function neural network (GRBFNN) is proposed for the synchronous reluctance motor (SynRM) system in electrical motorcycle applications. The conventional SMC assumes that the upper lumped boundaries of parameter variations and external disturbances are known, and the sign function is used. This causes high-frequency chattering and the high-gain phenomenon. In order to avoid these drawbacks, the proposed method utilizes the Lyapunov stability method and the steep descent rule to guarantee the convergence asymptotically, and reduce the magnitude of the chattering or avoid it completely. Finally, numerical simulations are shown to illustrate the good performance of our controller design. [ABSTRACT FROM AUTHOR]

In this article, a sliding mode control (SMC) design based on a Gaussian radial basis function neural network (GRBFNN) is proposed for a synchronous reluctance motor (SynRM) system robust stabilization and disturbance rejection. This method utilizes the Lyapunov function and the steep descent rule to guarantee the convergence of the SynRM drive system asymptotically. Finally, we employ experiments to validate the proposed method. [ABSTRACT FROM AUTHOR]

The compound N, N′-di(6-methyl-2-pyridyl)formamidine (HDMepyF) has been exploited in preparation of porous materials of divalent metal complexes of the formulae M(HDMepyF)2(NO3)2 (M=Cd, 1; Co, 2; Ni, 3) and M(HDMepyF)2X2 (M=Mn, X=Cl, 4; M=Mn, X=Br, 5; M=Ni, X=Cl, 6; M=Ni, X=Br, 7). Their structures have been determined by X-ray crystallography. Each metal center of these complexes is approximately octahedrally coordinated by four nitrogen and two halogen or oxygen donor atoms. Complexes 1– 5 and 7 self-assemble through similar hydrogen bonding motifs which involve the C–H⋅ sX (X=Cl, Br or O) hydrogen bondings and π–π stacking interactions between the HDMepyF ligand and the X atoms to form porous structures. [ABSTRACT FROM AUTHOR]

Catalytic synthesis of propionitrile from methanol and acetonitrile was achieved by using activated-carbon-supported sodium catalysts. The activity of catalysts is determined by the precursor of the sodium; the catalysts prepared from sodium hydrogen carbonate show the highest activity. At high conversion of acetonitrile (70%), a propionitrile yield selectively exceeding 80% is achieved with a minor amount of methacrylonitrile as a secondary product. The order of catalytic activity of catalysts correlates well with the adsorption capacities for methanol and acetonitrile measured by the TPD method. [ABSTRACT FROM AUTHOR]

Based on the statistics of the peaks in the random Gaussian field, we put forward a method to model the evolution of quasars, which are assumed to be a phenomenon of the early stages for some massive galaxies. Our calculations show that the cold-hot mixed dark matter model with Οh = 0.3 cannot match the observed quasar abundance at redshift z = 4.5, and other prevailing CDM models, such as the mixed dark matter model with Οh = 0.2, the tilted CDM models with n = 0.7 or 0.8, and the flat ΛCDM models with ΟΛ = 0.6 or 0.7, are compatible with the observation of the abundance of QSOs on the range of z = 2-5. [ABSTRACT FROM AUTHOR]