Detecting surface defects plays a crucial role in ensuring the quality, functionality, and security of the production process. Traditional image processing techniques and machine learning models rely on manual analysis and feature extraction for specific vision inspection tasks. Deep learning approaches, which can automatically extract features from images, have demonstrated outstanding performance in computer vision tasks, including detecting surface defects. Motivated by this consideration, a Systematic Literature Review (SLR) method is employed for the comprehensive analysis of studies published between 2020 and 2023 in the field of deep learning-based surface defect detection applications in industrial products. The study provides a technical taxonomy for deep learning models according to the content of current studies through the SLR process, including Convolutional Neural Networks (CNN), encoder–decoder models, pyramid network models, Generative Adversarial Networks (GAN), attention-based models, and other models for surface defect detection. Then, the commonly used datasets for surface defect detection are discussed, and a comparative analysis of deep learning models’ performance is provided. Our comparative analysis reveals that pyramid network models and CNN models are the most frequently used deep learning models for surface defect detection. These models yield reasonable results in surface defect detection due to their exceptional feature extraction capabilities. Finally, some hints for addressing future research directions and identifying open issues in surface defect detection applications are presented.

Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability . In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy modeling for Sustainable Hypersonic Vehicles (SHVs) with Markovian jump parameters and H ∞ attitude control in three channels was conducted. Initially, the reentry dynamics were transformed into a control–oriented affine nonlinear model . Then, the original T–S local modeling method for SHV was projected by primarily referring to Taylor's expansion and fuzzy linearization methodologies. After the estimation of precision and controller complexity was assumed, the fuzzy model for jump nonlinear systems mainly consisted of two levels: a crisp level and a fuzzy level. The former illustrates the jumps, and the latter a fuzzy level that represents the nonlinearities of the system. Then, a systematic method built in a new coupled Lyapunov function for a stochastic fuzzy controller was used to guarantee the closed–loop system for H ∞ gain in the presence of a predefined performance index. Ultimately, numerical simulations were conducted to show how the suggested controller can be successfully applied and functioned in controlling the original attitude dynamics.

This study discusses the new stochasticmaximum power point trackingcontrol approach toward thephotovoltaic cells(PCs). A PC generator is isolated from the grid, resulting in adirect currentmicrogrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposednetworked control systemsbeneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into theTakagi–Sugeno(T--S) fuzzy model is another option. The T--S fuzzy model is presented in a unified framework, for which 1) the fuzzy observer based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer-based controller can be created using the same premises being the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulation in the PC generator model for the real-time climate data obtained in China demonstrates the importance of our method. In addition, by using a newLyapunov–Krasovskii functionalfor combining with the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the tensor-product (T-P)transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators.

In the last few decades, technology advancements have paved the way for the creation of intelligent and autonomous systems that utilize complex calculations which are both time-consuming and central processing unit intensive. As a consequence, parallel processing systems are gaining popularity to enhance overall computer performance. Programmers should be able to efficiently utilize available hardware resources with parallelization in an ideal world. Through the automatic parallelization of sequential code, multithreading can be executed without extra supervision. However, a wide range of software dependencies prevents this from being feasible. An architectural framework for speculative parallelization along with an efficient memory analysis and computational algorithms for the code generation are proposed that can provide optimal performance. Furthermore, a suitable support of hardware design as a runtime library to the proposed architectural framework is presented which can be used to recover misspeculated results during execution to minimize speculative parallelism overhead. The implementation makes use of the Low-Level Virtual Machine compiler infrastructure and is tested on numerous benchmarks, thus making it highly scalable in terms of programming languages and architectures. According to our experimental results, there is significant potential for speedup increase. In comparison to the overall function speedup, that is, geomean speedup of 5.2× approximately when using the proposed architecture without hardware support, the proposed architectural framework and algorithm with hardware support give an average geomean speedup of 7.0× approximately on the given benchmark which is written in C/C++.

Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing and collecting of IoT data, and defend against cyber threats. To address these issues, the authors present a distributed and decentralized blockchain-software-defined networking (SDN)-based energy-aware architecture for IoT in smart cities. Thus, SDN is continuously observing, controlling, and managing IoT devices activities and detects possible attacks in the network; blockchain provides adequate security and privacy against cyber attacks, and reduces the central point of failure issues; network function virtualization (NFV) is used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a cluster head selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g., throughput, response time, gas consumption, and communication overhead) and demonstrate the result that provides higher throughput, lower response time, and lower gas consumption than existing works for smart cities.

Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In the field of gene expression analysis, several matrix factorization-based dimensionality reduction methods have been developed. However, such methods can still be improved in terms of efficiency and reliability. In this paper, an innovative approach to feature selection, called Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy (DR-FS-MFMR), is introduced. The major focus of DR-FS-MFMR is to discard redundant features from the set of original features. In order to reach this target, the primary feature selection problem is defined in terms of two aspects: (1) the matrix factorization of data matrix in terms of the feature weight matrix and the representation matrix, and (2) the correlation information related to the selected features set. Then, the objective function is enriched by employing two data representation characteristics along with an inner product regularization criterion to perform both the redundancy minimization process and the sparsity task more precisely. To demonstrate the proficiency of the DR-FS-MFMR method, a large number of experimental studies are conducted on nine gene expression datasets. The obtained computational results indicate the efficiency and productivity of DR-FS-MFMR for the gene selection task.