Ain Shams Engineering Journal最新文献

This paper presents the power-on signature graph of analog circuits for fault classification. This graph can be attained using the simulation mechanism through the practical circuit simulator and the hardware mechanism through the mixed-signal design. The presented signature graph is influenced by changes in pass-band transmission and bandwidth as a result of device under test (DUT) component modifications. In order to exercise the frequency band of the DUT for fault stimulation, sinusoidal waveforms wiped at their frequencies are produced using the analog waveform generator (AWG). The analog compactor is devised to accumulate the output samples from the DUT for signature generation, compared with global signature boundaries derived from the worst-case analysis. The built-in self-test controller is devised to properly synchronize the process of analog test cycle for proper signature generation. Two DUTs chosen from a variety of analog circuits in frequency bands used in medical applications are subjected to this testing mechanism. Due to the difference between the wiped sinusoidal frequencies of the AWG in the simulation mechanism and the hardware mechanism, the normalized signature graphs of each component in DUTs using both mechanisms are developed to attain the approved convergences between the two mechanisms.

A new controllable rotary bending cracking cropping method, based on V-notch stress concentration, is proposed to address the drawbacks of the metal bar separation process, such as poor cross-section quality and low material utilization. The controlled rotary bending cracking cropping experimental device is developed to implement this method. Subsequently, a mechanical model for the precision cropping process is established using material strength theory, and a load control strategy based on fracture mechanics theory is applied to predict cropping efficiency. Experimental verification is then conducted, with results indicating that increasing notch depth improves cropping efficiency and cross-section quality. Additionally, maintaining constant stress intensity factor amplitude (ΔK = 0.4K_c) and notch depth of 5 mm yields optimal cropping effects. The precision cropping process leverages the fatigue crack propagation mechanism to explain the cropping process, offering theoretical guidance for selecting appropriate parameters in subsequent precision cropping processes.

Software-Defined Radio (SDR) systems have become pivotal in modern communication, offering unparalleled flexibility and adaptability. This study focuses on the queuing process within SDR architectures, exploring methods to optimize performance and address challenges in real-time signal processing. Queuing theory, traditionally applied in telecommunications and computer science, is adapted to the unique characteristics of SDR, where signals traverse a dynamic processing pipeline. The queuing process in SDR involves stages such as RF front-end reception, digital down conversion, baseband processing, and higher-layer protocol handling. Challenges, including noise, interference, frequency offset, multipath fading, and sampling rate mismatches, can impact the efficiency of the queuing system. The study investigates techniques for dynamic spectrum access, adaptive filtering, and signal reprocessing to mitigate these challenges.

Objective of the research

By leveraging queuing theory principles, the research aims to optimize resource allocation, reduce latency, and enhance the overall efficiency of the SDR queuing process. This involves dynamic adjustment of queuing parameters, adaptive scheduling algorithms, and the integration of intelligent decision-making mechanisms. Additionally, the study explores the impact of queuing processes on the system’s ability to support various communication standards and adapt to changing environmental conditions.

Research findings

The findings from this research contribute to a deeper understanding of the queuing dynamics in SDR systems, providing insights into potential improvements for real-time signal processing. The optimized queuing process enhances the SDR system’s responsiveness, adaptability, and reliability, making it well-suited for diverse applications in wireless communication, military operations, and beyond.

Choosing by Advantages (CBA) is a multi-criteria decision-making method, based on the importance of advantages, under a collaborative and transparent context, avoiding basing decisions on assumptions or previous experiences. Currently, the CBA application process excludes the uncertainty and variability inherent to the performance of alternatives involved in a decision-making process. Therefore, the objective of this work was to create a new proposal of the CBA method, which incorporates probabilistic on the attributes of the alternatives in the process of choosing, seeking to close the gap that the traditional CBA has in terms of the lack of incorporation of the uncertainty to which certain data could be affected. To validate the probabilistic CBA method, a simulation focused on an application case framed within the architecture, engineering and construction (AEC) industry, related to energy consumption in residential buildings, is carried out to select a heating system. The study demonstrates that the probabilistic CBA method enhances transparency and collaboration in decision-making by quantifying uncertainty and involving stakeholders in a structured process. Monte Carlo simulations provided a comprehensive view of potential outcomes, helping to identify the most advantageous alternative with a high probability of achieving significant benefits. This research seeks to contribute to the knowledge of the CBA method and provide greater versatility to its application, reaching use in any situation or area of performance, allowing to be a guide for decision-makers involving multiple criteria and variability in the attributes of the alternatives.

Solar-powered electric vehicles play a pivotal role in the forthcoming era of eco-friendly transportation, offering significant ecological advantages and addressing challenges posed by escalating fuel costs. Despite these advantages, these vehicles often encounter a disparity between available photovoltaic power and the required load power, necessitating reliance on energy storage systems. This situation gives rise to several challenges, including maximizing the lifespan of storage systems, identifying shiftable and non-shiftable secondary systems in real-time scenarios, ensuring road and driver safety, and navigating the urban environment with obstacles causing shading. In response to these challenges, this paper presents pioneering solutions aimed at pushing the boundaries of solar-powered electric vehicle technology. First, a novel approach to PV power converter control is introduced, leveraging an adaptive control strategy within the maximum power point tracking algorithm. This innovative technique dynamically adjusts the algorithm’s step size, particularly crucial when traversing shaded areas during vehicle movement, thus maximizing energy capture efficiency. Complementing this breakthrough, the paper proposes a cutting-edge decentralized energy management strategy. This strategy is characterized by its versatility and autonomy, featuring four parallel functions designed to optimize signal frequency allocation to each storage component, determine shedding percentages for secondary systems based on PV and battery power availability, identify optimal secondary systems for shedding, and manage their activation and deactivation seamlessly. To validate the performance and efficacy of these groundbreaking methodologies, extensive simulations were conducted using Matlab software, supplemented by real-time validation on the OPAL-RT platform within a hardware-in-the-loop application. The results obtained from both simulation and real-time testing provide compelling empirical evidence of the superior effectiveness and high-performance capabilities of the proposed solutions.

Drone technology has the potential to disrupt and augment our quality of life, as it is rapidly growing in popularity and being utilized in various applications, such as agriculture, emergency response, border control, asset inspection, and intelligent transportation. On the other side, Artificial intelligent instruments that possess a variety of input and output (I/O) mechanisms are employed to achieve model stabilizing with data estimation. Firstly, in the present study, a linear mathematical model was developed for a quad–copter Unmanned Aerial Vehicle (UAV), in which the Takagi–Sugeno (T–S) Fuzzy logic framework was integrated. The crisp variables have been used to make the interference between the input and output of the T–S fuzzy system. Secondly, to control a quadcopter model with inherent dynamic instability, these state space models are crucial. Inputs of fuzzy controller are data generated by sensors and Bluetooth connected to IoT. The state–space model of the quad copter, which consists of six Degrees Of Freedom (6–DOF), is derived by utilizing fundamental Newtonian equations. This establishment of the model holds significant value in effectively governing the quad copter system. Thirdly, the system stabilizing has been proved by linear matrix inequalities (LMIs) with an associated Lyapunov function with the γ performance index. Simulation results have been presented to demonstrate the efficiency of our proposed algorithm with additional computational burden analysis.

The extension of wireless sensor network (WSN) lifetime and reduction of power consumption are now important objectives in sensor network research. Energy-efficient communication networks are required when using a WSN. WSNs are additionally constrained in terms of energy by clustering, storage, communication capacity, high configuration complexity, low communication speed, and limited computing. Furthermore, choosing a cluster head is still difficult when minimizing WSN energy. In this study, the Bacterial Foraging Optimization with Harmony Search Algorithm (BFO-HSA) is used to cluster sensor nodes (SNs). Eliminating latency, reducing distance, and stabilizing energy consumption are the main goals of research in order to maximize the choice of cluster heads. In WSNs, maximizing the use of energy resources is a crucial issue due to these limitations. The quickest path is found dynamically by decreasing network overhead through the use of a cross-layer-based opportunistic routing protocol (CORP). PDR, packet latency, throughput, power consumption, network lifetime, packet loss rate, and error estimation are all assessed using the suggested method; the outcomes outperformed those of previous approaches. Results for quality-of-service parameters include PDR (98.5 %), packet latency (0.019 s), throughput (0.98 Mbps), power consumption (9.75 mJ), network lifespan (5250 cycles), and PLR (1.5 %) for 100 nodes.

The integration of metaheuristics with machine learning methodologies presents significant advantages, particularly in optimization and computational intelligence. This amalgamation leverages the global search capabilities of metaheuristics alongside the pattern recognition and predictive prowess of machine learning, facilitating enhanced convergence rates and solution quality in complex problem spaces. The Quantum Long Short-Term Memory (QLSTM) emerges as a highly efficient deep learning model tailored to tackle such intricate engineering problems. The QLSTM's architecture, comprising data encoding, variational, and quantum measurement layers, facilitates the effective encoding and processing of civil engineering data, leading to heightened prediction accuracy. However, the task of determining optimal values for QLSTM parameters presents challenges due to its NP-problem nature and time-consuming characteristics. To address this, we propose an alternative technique to optimize the QLSTM based on a modified Electric Eel Foraging Optimization (MEEFO). The MEEFO is a modified version of the original EEFO that applies triangular mutation operators to boost the search capability of the traditional EEFO. Thus, the MEEFO optimizes the QLSTM and boosts its prediction performance. To validate the efficacy of our proposed method, we conduct comprehensive experiments utilizing five real-world engineering datasets related to construction and structure engineering. The evaluation outcomes unequivocally demonstrate that the MMEFO significantly enhances the performance of the QLSTM.

A well-designed exhaust manifold has a positive effect on the efficiency of an engine and exhaust emissions. If the dimensions and geometric structure of the exhaust manifold are not designed in accordance with the pressure fluctuations of the fluid, this will have a negative effect on the velocity, temperature, density and pressure of the flow. In view of this and the high production costs of locomotive diesel engines, the pressure and velocity distributions in the exhaust manifold of a six-cylinder locomotive engine are investigated numerically in this study. Two different designs for the diesel engine are studied, taking into consideration the area in which the exhaust manifold will be mounted and the other engine parts. The pressure and velocity variations of the exhaust manifolds are compared via a computational fluid dynamics analysis, and analyses are performed using test data from a heavy-duty diesel engine at different power values (225, 450, 675, and 900 HP) and 1500 rpm, with the aim of finding the optimal design. Since the diameters at the cylinder outlets cannot be changed, the designs are created to fit within the existing area of the engine area The exhaust outlet is located in the middle of the manifold in the first model examined here, and is positioned close to the right-hand side of the manifold in the second model (the existing configuration). It is found that the flow becomes more efficient in the model in which the outlet is in the middle of the exhaust manifold.

Construction Quality Management (CQM) is important for achieving project quality objectives. Currently, CQM is mainly achieved through cyclical inspections and various tests and subsequent analysis of the generated text records. These texts record various construction quality problems (CQPs) that need to be categorized and analyzed by quality managers. However, the current classification and analysis of CQPs is mainly achieved by manual analysis or natural language processing (NLP), the former is time-consuming and labor-intensive, while the latter improves the processing efficiency but is limited by the classification perspective and fails to fully capture the root causes of the CQPs. CQPs text usually describes the problems based on the inspection area, and multiple types of problems may exist simultaneously in each record. The previous classification model of CQPs based on sub-projects can only distinguish the frequent quality problems of sub-projects but cannot analyze the essential characteristics of CQPs, and ignores the comprehensive characteristics of CQPs, such as short text and unbalanced data. Therefore, aiming at the problem of mixed text information and diverse categories of CQPs, this study constructs a TDA-WV-TextCNN model for automatic text categorization by combining the characteristics of unbalanced data and short text of CQPs, taking the actual on-site inspection reports from multiple sources as the data base, and determining the classification labels based on the perspective of CQPs result orientation. The model combines the part-of-speech-based Text Data Augmentation (TDA) method, Word2vec (WV) technique and Text Convolutional Neural Network (TextCNN) algorithm. The results show that the TDA-WV-TextCNN model has a short training time and a high accuracy in short text classification; the part-of-speech-based TDA method expands the small sample data by extracting the core feature words and the word position change, realizing the text data equalization and subsequently improving the accuracy of the model; multiple sources of data increase the diversity of data, redundant text increases the amount of data, both play an important role in improving the performance of the model, so the deletion of duplicate text is related to the model’s demand for the amount of data The research results provide a method to categorize quality reports quickly and accurately, which helps to construct the engineering quality knowledge system.