Smart grid (SG) facilitates our lives by providing more reliable electricity and enabling better integration of renewable energy sources. Currently, numerous authentication and key agreement (AKA) protocols have been proposed to secure SG communication. However, these solutions often result in considerable cost, making them inappropriate for resource-constrained SG environment. In this paper, we propose a secure and resource-efficient AKA protocol by employing lightweight cryptography primitives including authenticated encryption with associated data (AEAD) primitive ASCON, hash function and XOR operation. The ASCON primitive simultaneously provides data confidentiality, integrity and authenticity with low computation cost, making it suitable for employing in resource-constrained SG environment. The secret intermediate values in the protocol are designed as hash values that incorporate both long-term and short-term secrets, thereby providing enhanced security while further reducing cost. Moreover, a dynamic indexing method is deployed in the protocol to resist de-synchronization attack. The designed protocol performs secure mutual authentication and session key establishment between entities without relying on a central trusted authority. The proposed protocol is proven secure through rigorous security proof under the real-or-random model and formally verified by AVISPA tool. Theoretical performance analysis and simulation results indicate that the proposed protocol outperforms other related protocols due to its lightweight nature and adherence to all fundamental security attributes, making it suitable for deployment in smart grid environment.

Nondestructive testing (NDT) plays a vital role in the production and quality control of the casting process. Due to the complexity of inspection procedures and the extensive scale of mass production, it becomes imperative to develop fast and precise automatic detection methods. This paper introduces a deep learning-based approach for detecting defects in X-ray images of aluminum castings. Firstly, we introduce the Gain-Adaptive Multi-Scale Retinex (GAMSR) algorithm, which is designed to enhance the low-contrast and noisy X-ray raw data. To address the problem of minor blowhole defects being overlooked during detections, we combine the Feature Pyramid Network (FPN) with the Convolutional Block Attention Module (CBAM) to extract high-level semantic information from the X-ray images. It can also promote the feature extraction network to focus more on the casting defect features. Furthermore, we employ Weighted Region of Interest pooling (W-RoI pooling) in place of RoIAlign. This strategy eliminates area misalignment and significantly enhances the precision of defect identification. Experiment results demonstrate that the proposed approaches can improve the performance of defect detection for aluminum casting DR images, with the accuracy increasing by 20.08

The Industrial Internet of Things (IIoT) is the application of the Internet of Things (IoT) in the industrial field. IIoT allows users to remotely access industrial equipment and the data in it, which also brings certain challenges to the security of industrial data. Authentication and key agreement protocols are very effective security technologies in the matter of protecting industrial data. There is a large amount of research work on authentication protocols in IIoT, but most of the protocols have security weaknesses. Recently, Rafique et al. proposed a multi-factor protocol in IIoT that can accomplish authentication and session key establishment through a gateway. Rafique et al. claimed that their protocol is secure, unfortunately, we carefully analyze the protocol of Rafique et al. and find some security flaws, i.e., it is vulnerable to insider attack and known session-specific temporary information (KSSTI) attack, and unable to provide forward security. We explore the factors of insecurity and propose an enhanced multi-factor secure authentication and key agreement protocol in IIoT. The new protocol improves the security of the protocol while using only symmetric cryptography, hash function, and XOR operation. Formal security analysis and informal security discussions demonstrate that the new protocol is resistant to a variety of known attacks. After performance analysis, our protocol has lower computational cost, and increases no significant communication cost, while providing more secure and robust properties.

X-ray images of various complicated components have the issue of poor contrast, which prevents the structural information from being completely portrayed. A straightforward and efficient enhancement method based on multi-grayscale fusion and edge-weight is proposed to improve the quality of X-ray images. This approach makes use of several established image processing techniques. First, multiple derived images with various grayscales are obtained using three contrast adjustment techniques. The Laplacian pyramid, Gaussian-weighted pyramid, and principal component analysis (PCA) algorithm are then utilized to create a fusion image that displays overall structural information. The bilateral and guided filters are employed to generate an edge-weighted image that contains a lot of edge information. Finally, the edge-weighted image is performed by an exponential operation on the fusion image to obtain a high-contrast image, which is crucial to enhance the detailed features of the image. The experimental results demonstrate that the proposed method beats previous comparison algorithms in the quantitative index and is capable of fully presenting the internal structures of the samples. Additionally, the enhanced image is satisfactory in detail enhancement, local contrast improvement, and visual sensation.

With the enablement of Internet of Things technology, the electrical grid is currently undergoing a drastic revolution, which is known as smart grid. Since massive sensitive data and control commands transmitted via public channels, the smart grid is challenged by various cyber threats. Authenticated key agreement protocols in smart grid effectively ensure the confidentiality and authentication of communication through mutual authentication and establishing session keys. In this article, we review the existing elliptic curve cryptography (ECC)-based authentication and key agreement protocols in smart gird and perform a security analysis of Hu et al.'s protocol. We exhibit that the protocol fails to resist key compromise impersonation (KCI) attack and cannot provide untraceability. Furthermore, we propose a security-enhanced authentication and key agreement protocol based on ECC, which performs registration, authentication, and key agreement phases over public channels to enable mutual authentication and to establish session keys. The protocol is also proved to be security-enhanced by formal proof and informal analysis. The performance analysis results demonstrate that the proposed protocol is comparable to other existing protocols while achieving enhanced security. Therefore, the protocol satisfies the deployment requirements for resource-constrained smart grid.

The ring artifact is a typical artifact in X-ray Computed Tomography (CT) images, the existence of which can reduce the image's quality, change the structure and details and affects the interpretation of image information. This paper conducts research from two aspects: tomographic images and sinograms, and designs a dual-domain artifact suppression network. By introducing the Inception module and the residual module, the network's ability to extract features is improved, and the gradient diffusion is suppressed. By customizing the loss function, the network's recognition of ring artifacts is improved and the network's convergence is accelerated. Finally, the outputs of the dual-domain are fused based on the Nonsubsampled Contourlet Transform (NSCT) fusion theory to improve the quality of the image. In addition, three experiments of CT scanning are designed based on microfocus cone-beam CT system to verify the effectiveness. The model is trained and tested using real datasets. Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are employed as evaluation indicators of image quality. Finally, the proposed method is compared with typical algorithms for artifact suppression. The experimental results show that the method effectively protects the structure information in CT images while suppressing ring artifacts to the greatest extent. The PSNR, SSIM, and MSE of the processed images can respectively reach 36.167 dB, 0.9847 and 6.87 × e-4.

During the security check, the metal in the baggage engenders serious metal artifacts on the Computed Tomography (CT) image. To reduce the effect of metal artifacts on the judgment of prohibited items during such activity, a Deep Learning (DL) method combined with dual domain information in CT images is proposed in this work. As for the methodology of work, the metal areas are segmented, in the first phase, from the metal artifact CT image and they are then projected into the sinogram domain using the Forward Projection (FP) algorithm. As the trace of the metal-corresponding projection area in the sinogram domain is considered to be missing data, the linear interpolation method is adopted to correct the metal missing trace, and the Sino-Inpainting Network (SIN) is deployed to repair the metal erosion trace. By adopting the Filtered Back Projection (FBP) algorithm to reproduce the results of the sinogram restoration, the mutual information between the sinogram domain and the image domain is completed. In the second phase, the sinogram inconsistent artifacts are repaired using the Partial Refine Network (PRN) after the corrected image restoration. The PRN only depends on the effective pixels outside the metal damaged area to restore the trace area; thus, this technique can be more effective to refine the image details. Finally, the metal mask, obtained by threshold segmentation, is inserted into the repaired reconstructed image. Using both simulated data and real data, a comparison between the proposed method, the conventional method, and the DL method is performed. Quantitative results show that the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM) in the reconstructed image of the baggage, obtained through the proposed network, are 31.0722 dB and 0.9718, respectively; thus, they are better than the other tested methods. Moreover, the results of the experiments demonstrate the effectiveness of the suggested method in recovering the lost data of the metal corrosion area in the sinogram and in suppressing the secondary artifacts in the reconstructed image.

The IoT-enabled Smart Grid uses IoT smart devices to collect the private electricity data of consumers and send it to service providers over the public network, which leads to some new security problems. To ensure the communication security in a smart grid, many researches are focusing on using authentication and key agreement protocols to protect against cyber attacks. Unfortunately, most of them are vulnerable to various attacks. In this paper, we analyze the security of an existent protocol by introducing an insider attacker, and show that their scheme cannot guarantee the claimed security requirements under their adversary model. Then, we present an improved lightweight authentication and key agreement protocol, which aims to enhance the security of IoT-enabled smart grid systems. Furthermore, we proved the security of the scheme under the real-or-random oracle model. The result shown that the improved scheme is secure in the presence of both internal attackers and external attackers. Compared with the original protocol, the new protocol is more secure, while keeping the same computation efficiency. Both of them are 0.0552 ms. The communication of the new protocol is 236 bytes, which is acceptable in smart grids. In other words, with similar communication and computation cost, we proposed a more secure protocol for smart grids.

With Wireless Sensor Networks(WSNs) rapid development, it is a hot topic that designing a secure and effective authentication protocol to guarantee the safety of WSNs. Many two-factor multi-gateway authentication protocols have been proposed for WSNs. However, almost all the known protocols were found insecure. Especially, all the existing two-factor multi-gateway authentication protocols cannot resist the password and identity(ID) guessing together with the smart card loss attack. We present a novel two-factor multi-gateway authentication protocol based on password and smart card in multi-gateway wireless sensor networks in this paper. Thanks to “fuzzy-verifier” and “honeywords” technique, the new protocol can effectively resist the joint password and identity guessing with the smart card loss attack. In addition, key negotiation is based on discrete elliptic curve encryption, which ensures forward security of the protocol and resists sensor capture attack. Subsequently, we use BAN-logic to ensure the authentication is implemented, and the session key is negotiated. Moreover, a formal security proof of the new protocol based on the game sequence is conducted in Random Oracle model (RO model). Finally, we compare our work with other schemes in the same field in terms of security, communication efficiency and computational efficiency.

Electrode deformation can cause high local strain and serious capacity degradation in lithium-ion batteries (LIBs) during cycling. Risk reduction in many applications requires an understanding of the effects of the charging/discharging rate on the electrode structure during the battery life cycle. Cyclic charging/discharging experiments of wound 18 650 cylindrical LIBs were conducted at different charging/discharging rates (1C and 2C) to determine the effect of rate on electrode deformation. The charging/discharging capacity as well as battery voltage and time were analyzed during cycling. To acquire electrode deformation images and meet the requirements of computed tomography (CT) within 2 min during the charging/discharging process, sparse-view CT was performed at fixed cycle intervals. Subsequently, a sparse-view reconstruction network was proposed to generate a slice image. Finally, the electrode displacement and strain fields were calculated using the augmented Lagrangian digital image correlation algorithm. The causes of electrode deformation were analyzed and discussed from the perspective of molecular and macroscopic structure. Experimental results show that the structural similarity, peak signal-to-noise ratio and root mean square error in the reconstructed image of the axial section within the battery obtained via the proposed network were 0.9616, 38.7411 dB and 0.0108, respectively, which were better than the other methods of comparison. After 100 cycles, the capacity decay of the battery at 2C was 9.23-fold higher than that at 1C. After 100 cycles at 2C, the maximum displacement of the electrode reached 0.46 mm along the x -direction. The electrode structural deformation of the battery can be intuitively understood at different rates, which facilitates reasonable utilization and structural optimization of the battery.

In the preparation process of cylindrical lithium-ion batteries, a rigorous manufacturing process demands that the position distances between positive and negative pole-pieces must be kept within a reasonable range of variation. Otherwise, a too small position distance may cause safety problems, such as short circuits and thermal runaway. To inspect the position distances between positive and negative pole-pieces automatically, and to decrease the risk of safety and economic losses during the subsequent use, this paper proposes a method to identify the position distance defects of a cylindrical lithium-ion battery on the base of x-ray digital radiography (DR) images. According to this method, the DR image is firstly enhanced by the GPU-accelerated homomorphic filtering algorithm to intensify its contrast and detailed information. Then through the Shi-Tomasi corner detection algorithm, corners of all the positive and negative pole-pieces are preliminarily detected in a region of interest which is defined in advance. To delete the false corners and find the lost corners, one-dimension region growing and curve-fitting methods are adopted. Finally, the minimum position distance, repeated accuracy and alignment metrics are calculated at the base of the detected corners. The experimental results show that the corner positions of five 26 650 cylindrical lithium-ion batteries with different pole-piece structural characteristics can be effectively identified by the proposed method, which provides a useful approach to filtrate unqualified batteries during the process of manufacture.

Indirect neutron imaging is an effective method for nondestructive testing of spent nuclear fuel elements. Considering the difficulty of obtaining experimental data in a high-radiation environment and the characteristic of high noise of neutron images, it is difficult to use the traditional FBP algorithm to recover the complete information of the sample based on the limited projection data. Therefore, it is necessary to develop the sparse-view CT reconstruction algorithm for indirect neutron imaging. In order to improve the quality of the reconstruction image, an iterative reconstruction method combining SIRT, MRP, and WTDM regularization is proposed. The reconstruction results obtained by using the proposed method on simulated data and actual neutron projection data are compared with the results of four other algorithms (FBP, SIRT, SIRT-TV, and SIRT-WTDM). The experimental results show that the SIRT-MWTDM algorithm has great advantages in both objective evaluation index and subjective observation in the reconstruction image of simulated data and neutron projection data.

X-ray computed laminography (CL), as a popular nondestructive testing technology, has unique advantages for the detection of plate-type structures. However, with the limitation of the detector size, the specimen, i.e., large flat-like components, may be beyond the field of view (FOV) of the CL system. For the cone-beam computed tomography (CBCT) system, which is analogous to the CL system, a traditional way to enlarge the FOV is the displaced sample stage scanning mode. In this article, a new CL reconstruction method with the displaced sample stage scanning mode is proposed to enlarge the FOV of the CL system. According to this method, a virtual detector is established to obtain a new CBCT system under the displaced sample stage. Then, the projection data are converted into the virtual detector and scanning geometric parameters are calculated. The Feldkamp–Davis–Kress (FDK) algorithm combined with the Parker weighting function can well reconstruct the cross sections of the specimen with acceptable quality. The numerical simulation and experiments demonstrate that the proposed approach can achieve image reconstruction with sufficient quality and low computational cost. The FOV of the CL system is enlarged effectively without changing the original scanning geometry, and thereby, it can be conveniently utilized in engineering applications.