Engineering reports : open access最新文献

The rapid proliferation of Internet of Things (IoT) devices has underscored the critical need to safeguard the data they store and transmit. Among various data types, digital images often carry highly sensitive information, making their protection against breaches essential. This study introduces a novel image encryption algorithm specifically designed to bolster the security of images in resource-constrained IoT ecosystems. Leveraging the randomness of a 5D multi-wing hyperchaotic map, the proposed method employs pairs of non-overlapping rectangles to induce confusion by swapping the pixels they encompass. Repeated iterations of this operation achieve significant confusion effects, enhancing encryption strength. To validate the robustness of the proposed algorithm, standard benchmark images were utilized, and rigorous security metrics —including information entropy, correlation coefficient, histogram uniformity, and resistance to differential attacks —were analyzed. Results demonstrate that the algorithm not only ensures strong protection against unauthorized access but also maintains low computational complexity, making it ideal for IoT applications. This research provides a foundational step toward ensuring the confidentiality and integrity of visual data in an increasingly interconnected digital world.

This work deals with optimizing the synthesis error in an eight-bar Peaucellier—Lipkin mechanism, for its dimensional synthesis and applications in load-lifting machines. A new method for the formulation of the problem of maximizing the objective function is proposed and makes it possible to obtain from the PSO algorithm a minimum synthesis error e_min = 9.07E−06 mm for the generation of the straight trajectory when the search interval for the lengths of the bars is [1 mm, 15 mm] and a minimum error e_min = 1.47E−04 mm when the search interval is [1000 mm, 15,000 mm]. For 10 simulations in Case 1 the average convergence time is t_m = 55 s with the largest iteration at 10 (for t = 159 s); for 100 simulations in Case 2, the t_m = 229 s with the largest iteration at 136 (for t = 2294 s). The minimum error of Case 1 is compared with the results of authors in the literature on the generation of the right trajectory because the search space is approximately equal. In the literature, e_min = 0.648358 mm with the GA-DE algorithm in 2010, e_min = 2.3667E−005 mm with the MKH algorithm in 2016, e_min = 0.027145 mm with the SAP-TLBO algorithm in 2017, e_min = 3.7E−4 with the GA algorithm in 2019. This new method brings a plus, because even when the search space is very large, the algorithm converges quickly and it allows the study to be extended to the generation of circular trajectories by just modifying the ratio between the frame bar and the crank bar. The practical implications of achieving an error as low as 9.07E−06 mm are the design of high-precision industrial machines with reduced vibration, noise, and premature wear of joints. The results of the post-design FEM analysis show that for a 1.4571 steel (X6CrNiMoTi17-12-2) with a thickness of 50 mm and a joint with a radius of 500 mm, the mechanical device obtained can support a load of 1500 kg.

The pursuit of sustainable energy solutions is crucial in meeting global sustainable development goals (SDGs). This experimental study explores enhancing a box-type solar cooker's thermal performance through the integration of a hybrid nano-enhanced phase change material (PCM). Specifically, multi-walled carbon nanotubes (MWCNTs) and silicon oxide (SiO₂) nanoparticles were incorporated into the PCM at a 2% concentration, with 1% each of MWCNT and SiO₂. The hybrid nano-PCM was meticulously prepared using ultrasonication to ensure optimal dispersion and homogeneity. This innovative approach significantly improved the cooker's efficiency, achieving a peak PCM temperature of 128.9°C, a cooking power of 47.6 W, an average efficiency of 28.5%, and an energy efficiency of 6.2%. Notably, the cooking time was halved, from 36.3 min to just 18 min, demonstrating the ultrafast capabilities of the solar cooker. These findings underscore the potential of the MWCNT/SiO₂ hybrid nano-PCM in revolutionizing solar cooking technology, offering a cost-effective, environmentally friendly, and highly efficient solution for sustainable energy harvesting.

Brain tumors pose a significant health risk and require immediate attention. Despite progress, accurately classifying these tumors remains challenging due to their location, shape, and size variability. This has led to exploring deep learning and machine learning in biomedical imaging, particularly in processing and analyzing Magnetic Resonance Imaging (MRI) data. This study compared a newly developed Convolutional Neural Network model to pre-trained models using transfer learning, focusing on a comprehensive comparison involving VGG-16, ResNet-50, AlexNet, and Inception-v3. VGG-16 model outperformed all other models with 95.52% test accuracy, 99.87% training accuracy, and 0.2348 validation loss. ResNet-50 model got 93.31% test accuracy, 98.78% training accuracy, and 0.6327 validation loss. The CNN model has a 0.2960 validation loss, 92.59% test accuracy, and 98.11% training accuracy. The worst model seemed to be Inception-v3, with 89.40% test accuracy, 97.89% training accuracy, and 0.4418 validation loss. This approach facilitates deep-learning researchers in identifying and categorizing brain cancers by comparing recent papers and assessing deep-learning methodologies.

Load-frequency control (LFC) is essential for maintaining system stability and ensuring high power quality in microgrids (MGs), particularly those heavily reliant on renewable energy sources (RES) and operating independently of the main grid. This paper introduces a novel control strategy aimed at improving LFC performance in interconnected MGs by correcting the error signal. The proposed controller, denoted as TIDA+1, combines tilt, integrator, derivative, and acceleration operators in a parallel configuration to refine the incoming error signal. The controller parameters are optimized using a modified particle swarm optimization (PSO) algorithm with nonlinear time-varying acceleration coefficients (NTVAC). The controller's effectiveness is validated through four distinct scenarios, including sudden load variations, system modeling uncertainties, fluctuations in RES outputs, and the impact of nonlinearities. Additionally, a lab-scale evaluation of the controller has been conducted to further assess its practical applicability. Comparative results demonstrate that the TIDA+1 controller outperforms traditional controllers such as PID and FOPID, especially under complex operational conditions. The study highlights the TIDA+1 controller as a robust and viable solution for LFC in MGs, with potential for future scalability and application in larger systems.

The study aims to investigate the irreversibilities of a Carreau nanofluid flow over, unsteady stretching cylindrical sheet exposed to radiation, non-Darcy porous medium, viscous dissipation, joule heating, etc. It provides how energy produced in the nanofluid flow is used efficiently by minimizing the irreversibilities. The governing partial differential equations are transformed into first-order initial value problems by similarity transformation and linearization. The shooting technique and an open-source Python programming package are used to solve the initial value problems using the Runge–Kutta sixth-order, and the numerical approach is validated using published articles. Basic flow profiles and, most importantly, entropy generation are examined using graphs in relation to relevant parameters. Skin friction and the behavior of heat and mass fluxes in response to various parameters are also examined. The results of the study demonstrated that the entropy creation is initiated by an increase in the magnetic and curvature parameters, as well as the Eckert, Brinkman, and porosity parameters. However, when the Forchheimer number increases, entropy generation decreases. An increase in the Eckert number, Prandtl number, and radiation parameter motivates the irreversibility due to heat transfer, whereas as the Weissenberg number rises, the irreversibility of heat transfer falls around the wall. According to the numerical values in the table, growth in Weissenberg number, thermal Biot number, Forchheimer number, curvature parameter, and radiation parameter initiate the magnitude of the rate of heat and mass transfers. In contrast, the rates fell as the Eckert and Prandtl values rose. Analysis of energy conversions and system efficiency can be done using this study, particularly in heat engines, refrigeration systems, and other thermodynamic processes.

Artificial intelligence, especially deep learning, has attracted significant interest in bioinformatics, with prominent applications in precision agriculture. A significant threat to the agricultural sector is the rapid propagation of diseases from affected to healthy plants, which, if undetected, may culminate in significant crop losses. This research focusses on employing multi-model deep-learning techniques to identify diseases in the leaves of economically significant crops that are potatoes, tomatoes, grapes, apples, and peaches. These crops are widely grown and crucial for food security, with disease outbreaks threatening yield and quality. This study evaluates the performance of deep learning models, including VGG16, MobileNetV2, Xception, and ResNet, using four metrics, that is, Accuracy, Precision, Recall, and F1-Score. Furthermore, consumer research was undertaken to evaluate user trust in AI-driven multi-model systems, collecting feedback from farmers to inform future research directions. The results demonstrate that the VGG16 model outperformed all others in every evaluation criterion. Experimental simulations were performed in Jupyter Notebook utilizing Anaconda and Python. The findings indicate that the proposed multi-model approach allows a scalable, non-invasive, and contactless machine vision solution for the early detection of diseases in plant leaves, achieving an efficiency of 99% via multimodal classification techniques that incorporate statistical variables including mean, median, mode, skewness, and kurtosis.

In the present research, the entropy generation (EG) for a nanofluid through vertical channels is analyzed numerically. The water-based suspension of alumina nanoparticles with different volume fractions is considered a heat transfer fluid. The nanofluid flow in the channel is under the effects of forced and natural convection simultaneously. For EG assessment, the values of EG number, Bejan number, EG ratio, and average EG number are evaluated comprehensively. The effect of changes in some parameters including Brinkman (Br) number, temperature difference, and concentration of nanoparticles in EG evaluation will be assessed. The results show that by increasing the temperature difference, the EG number is enhanced. Also from the results, the Bejan number increases by reducing the temperature difference.

In fuzzy regression, estimation of centers and spreads of triangular fuzzy numbers are both performed using the same methods, such as linear programming (LP), quadratic programming (QP), fuzzy weighted linear programming (FWLP), and adaptive neuro-fuzzy inference system (ANFIS). In this article, ANFIS and Bayesian methods have been adopted to estimate centers and spreads of a triangular fuzzy numbers are applied, respectively, for estimation of centers and spreads of the triangular fuzzy data. To illustrate the efficiency of the proposed approach, three numerical examples have been used to verify the superiority of proposed method to other existing ones.

This paper proposes a controlled signal technique for visible light non-orthogonal multiple access (VL-NOMA) communication in an interference-controlled environment with intelligent reflecting surfaces (IRS) for beyond 5G (B5G) and 6G communication networks. The light-emitting diode (LED) is used for carrier signal generation to transmit signals to the two users (photodiodes, PDs) due to its advantages, such as its programmable nature and flexibility. The potential challenge is how the signals could be controlled with an IRS approach, which prompted this research. We have used IRS, which is a cutting-edge enabling technology that modifies the signal's reflection by utilizing numerous inexpensive passive reflecting elements to improve the signal's performance. Furthermore, deep reinforcement learning (DRL) is deployed to control the reflected signals, simulate, make decisions, and link LED-IRS-PDs, redirecting the signals. The entire system is successfully synchronized, and then the bit error rate (BER), line of sight (LOS), and non-line of sight (NLOS) performances are investigated. Furthermore, we place a blocker at the center of the model as a NLOS to check how the transmitted signals will perform. We observed that the propagated signal improved the BER as per LOS, hence, the NLOS blocker reduced the signal's performance. Furthermore, we optimized the signals to investigate BER, LOS, and NLOS signal performance. We observed that LOS signals performed better than NLOS signals.