Engineering Science and Technology-An International Journal-Jestech最新文献

Vehicle suspension systems have become increasingly crucial for both driving safety and comfort. Active suspension systems can dynamically adjust suspension characteristics in real-time by introducing force into the system. Designing a controller for the real-time adjustment of the control force in active suspension systems is essential to meet challenging control objectives, including body acceleration, suspension deflection, and tire deflection. This article proposes a hybrid optimization approach named Coati–Grey Wolf Optimization (COAGWO), which combines the strengths of the coati optimization algorithm and grey wolf optimization to tune the gains of linear quadratic control applied to vehicle suspension systems. The COAGWO algorithm incorporates a unique strategy inspired by the Coati Optimization Algorithm, allowing wolves to climb trees. This enhancement significantly improves the wolves’ ability to explore the global search space and reduces the likelihood of being trapped in local optima. Initially, we conduct extensive experiments using a suite of challenging optimization problems from the CEC2019 benchmark to evaluate the effectiveness of the COAGWO algorithm. The effectiveness of COAGWO is compared against several state-of-the-art algorithms, including grey wolf, coati, aquila-grey wolf, whale, reptile search, tunicate swarm, and seagull optimization algorithms. The experimental results demonstrate that COAGWO consistently outperforms these algorithms in terms of solution quality and convergence speed. For the optimal weight selection problem of linear quadratic control applied to the control of vehicle suspension systems, the excellent performance of the proposed method is illustrated through comparative simulation studies under various road disturbance conditions. The results indicate that the COAGWO algorithm achieves a more efficient active suspension system compared to competitor algorithms by reducing the overall acceleration of the driver’s body, thereby enhancing ride comfort.

The global energy landscape is evolving, with an increasing focus on sustainability and the transition towards cleaner energy sources. As countries strive to meet their climate goals and reduce carbon emissions, the energy transition has become a critical imperative. In this endeavor, the role of regulatory sandboxes has emerged as a promising approach to foster innovation, accelerate technological advancements and overcome regulatory barriers in the energy sector. Many regulatory authorities introduce legislations incorporating the concept of ‘sandbox’ to create a regulatory framework and an experimental environment that encourages participation and integrates infrastructure with new technologies and business processes for facilitating and accelerating the energy transition. With the help of sandbox applications, the designated test or pilot implementations provide protected spaces for projects to allow regulatory trials and the development of required regulations. This study aims to examine and compare regulatory sandbox and pilot project experiences in selected countries to explore technological requirements, and recommendations for effective regulation. A four-dimensional/key perspectives framework was designed to analyze and compare selected countries. The reviewed projects were qualitatively summarized and categorized in a thematic synthesis based on a combined systematic review approach. Key findings underscore the significance of regulatory frameworks and provide clear guidance to stakeholders in innovative energy initiatives. Furthermore, collaboration, precise project delineations, and the preservation of technology neutrality emerge as crucial factors. Addressing challenges like grid integration, resource limitations, and regulatory adherence prove pivotal for the prosperous evolution of the energy domain. For this reason, the study includes a policy needs framework through experiences and insights in particular.

Conducting thorough research, analysis, and detection of cyber-threatening malware with the right parameters is crucial for safeguarding a country’s security and economy. Increasingly sophisticated cyber-attacks directly affect individual welfare, social dynamics, and political stability. So, due to the evolving nature of malware, which continuously improves itself to evade detection, it is even more essential to select effective and decisive parameters, considering interactions among various malware features. As malware evolves with new technologies and techniques, signature-based detection systems are becoming inadequate. Instead of relying on these still widely used but insufficient systems, in this study a new system was established focusing on malware behavior and the relationships between malware features resulting from these behaviors. In this system, rather than using a uniform approach, multi-objective genetic algorithms (MOGAs) are employed to select critical and decisive features for malware detection. These selected features are then utilized by machine learning (ML) algorithms within the implemented hybrid framework to accurately detect and classify malware.

The aim of this paper is to identify the optimal feature selection and classification methods yielding the highest accuracy within the Cuckoo Sandbox environment. Specifically, the J48 Decision Tree (J48), Reduced Error Pruning Tree (REP Tree), Adaptive Boosting Model 1 (AdaboostM1), Multilayer Perceptron (MLP), and Naive Bayes (NB) classifiers were assessed. Through our analysis, the feature set was refined from 335 to 200, considering inter-feature relationships, resulting in a peak accuracy of 93.33% and a corresponding 40% performance enhancement due to the reduction in the number of features. The obtained metrics were meticulously compared and evaluated with respect to the employed algorithms and methodologies. Additionally, Mc Nemar’s test was utilized to evaluate the performance of different malware detection classifiers by comparing their correct and incorrect classifications. Notably, the Mc Nemar’s test revealed significant improvements upon analysis of the results.

Microwave imaging techniques can identify abnormal cells in early development stages. This study introduces a microstrip patch antenna coupled with artificial magnetic conductor (AMC) to realize improved sensor for non-invasive (early-stage) breast cancer and brain cancer diagnosis. The frequency selectivity of the proposed antenna has been increased by the presence of AMC by creating an additional resonance at 2.276 GHz associated with peak gain of 8.15 dBi and 10.02 dBi, with and without AMC, respectively. High precision and high-quality imaging in the field of medical diagnostics are ensured by the directive radiation pattern of the sensor, emitted from the center of the sensor’s front surface. The antenna has been manufactured and experimentally validated with measurement results being in good agreement with the full-wave simulations. In particular, the measured broadside gain at the operating frequency is 11.7 dBi. The presented structure has been incorporated in the microwave imaging system for breast and brain cancer identification. Extensive simulation studies corroborate its suitability for the task based on the analysis of multiple scenarios of tumor detection. Furthermore, our antenna has been favorably compared to state-of-the-art designs reported in the literature showing its competitive performance, especially in terms of size, impedance matching bandwidth, and gain trade-offs.

The increasing interest in solar energy has led to the development of various connection types designed to enhance the efficiency of photovoltaic (PV) systems under different environmental conditions. Addressing the challenges associated with these connection types is crucial for improving the reliability of solar energy utilization. Accordingly, the significance of electrical faults in PV systems with different array configurations has grown. This study aims to comprehensively evaluate Line-to-Different Line (LDL), Line-to-Line (LL), and Line-to-Neutral (LN) electrical fault types using the Series–Parallel (SP), Total-Cross-Tie (TCT), Bridge-Link (BL), Honey-Comb (HC), SP-TCT, BL-TCT, BL-HC, and HC-TCT PV array connection types on the TMS320F28338 DSP kit based Processor-in-Loop (PIL) platform. To do this, The Reliability Index (RI) and Efficiency Index (EI) are defined and calculated for all possible fault types based on the array’s fault current and generated power. Additionally, the current paths of the system under different fault conditions for eight configuration types are formalized. With this, the electrical fault states of a PV array in any dimension can be easily analyzed with the proposed method, and it will also facilitate the electrical fault analysis of a new PV configuration to be proposed in the future. The results of this work indicate that among the eight configurations, the TCT achieves the highest RI value of 3.59 under all electrical fault types. In contrast, the SP configuration has the lowest RI of 1.28. Conversely, the SP array configuration shows a significantly higher average EI of 81.86%, while the TCT configuration has the lowest EI of 56.56%. These indicate that the TCT configuration is the most reliable under electrical faults; however, the SP configuration is the most efficient. In addition, among the three electrical fault types, the LN fault causes more deterioration in the RI and EI across almost all connection types.

Controlling wheel slip during braking in vehicle tires is a challenging task due to the complex behavior and highly nonlinear dynamics involved. Uncertainties arising from parameter variations and un-modeled dynamics further complicate the control process, along with actuator saturation. This article introduces a novel approach for controlling vehicle antilock braking systems (VABSs) using a robust adaptive (RA) beta basis function (BBF) neural network (NN) framework. The BBF-NN is capable of approximating complex functions and is employed as both an online approximator for unknown nonlinear functions and an actuator saturation compensator. This framework addresses the challenges posed by undefined models, nonlinearity, and uncertainties associated with VABS. The BBF-NN is trained online and its stability is verified using the Lyapunov theory. The performance of the BBF-NN is greatly influenced by its parameter tuning. To address this, the Coronavirus disease optimization algorithm (COVIDOA) is employed to determine the constant parameters of the RA-BBF-NN. The optimization results demonstrate that COVIDOA outperforms other optimization algorithms. The hybrid RA-BBF-NN framework, optimized by COVIDOA, exhibits superior performance compared to alternative methods, as confirmed by the results.

This article analyzes the potential effects of sub–cycle impedance variations on NB–PLC according to PRIME v1.4 in a laboratory setup, where the loads are connected at different locations. First, in order to characterize the impedance variations, some metrics are defined and calculated. Then, the attenuation difference and degradation of the quality of communications are studied. Although the results show that the transmitted signal does not suffer high attenuation difference, the quality of communications is considerably affected if there is variability between the ON/OFF impedance states within the 20 ms period. Besides, higher degradations have been observed when the NIEs generated by these devices are also considered.

This paper introduces an innovative tri-band metamaterial absorber designed for advanced sensing applications, demonstrating significant achievements in electromagnetic absorption and sensitivity across multiple frequencies. The proposed metamaterial absorber operates effectively at targeted resonant frequencies of 2.46 GHz, 6.25 GHz, and 12.45 GHz, exhibiting excellent reflection coefficients, indicating reduced energy loss and enhanced signal integrity at these frequencies. The compactness of the design is achieved through accurately calculated unit cell dimensions of 0.121${\lambda }_0$ × 0.121 ${\lambda }_0$, where ${\lambda }_0$ is the wavelength at a resonance frequency of 2.46 GHz, resulting in a total size of 18 × 18 mm2. One of the most notable features of this proposed design is the high absorption rate, achieving up to 99.99 % at its operational frequencies. The proposed design was validated numerically and experimentally, ensuring the theoretical models accurately predict practical behavior. The design also offers substantial flexibility for frequency-selective applications in sensing technology, enhanced by the sensor’s high sensitivity. This sensitivity was thoroughly verified using food color-water mixtures with concentrations up to 99.99 %, effectively demonstrating the metamaterial absorber’s ability to respond to the changes in dielectric properties with shifts in resonant frequencies that perfectly match theoretical expectations. Therefore, the proposed metamaterial absorber is an efficient solution for modern sensing challenges.

This paper proposes compact and wideband crossover and monopulse comparator based on the new multi-layer vertical transition of substrate integrated waveguide (SIW). Through etching slot lines in the internal layers and arranging vias to connect the top and bottom layers of the stacked SIW, the transition structure can excite the TE₁₀-like mode and stacked TE₁₀-like mode, simultaneously. By properly designing the physical size of the transition, good transmission and isolation characteristics of the dual-layer SIW crossover and quad-layer monopulse comparator are readily achieved. Finally, two samples are fabricated for demonstration. The bandwidth and size of the proposed crossover are 28.6% and 10.5λ_g², and that of the comparator are 20% and 12.05λ_g², respectively. Compared with the conventional prototypes, the bandwidths of proposed ones increased by nearly 50% at comparable dimensions, especially the comparator, which has tremendous potential utilization value in multi-port multilayer SIW beamforming networks and radar tracking systems.

Rapid advancement in robotics technology has paved the way for developing mobile service robots capable of human interaction and assistance. In this paper, we propose a comprehensive approach to design, fabricate, and optimize the overall structure of a dual-arm service robot. The conceptual design phase focuses on both critical components, the mobile platform and the manipulation system, essential for seamless navigation and effective task execution. In the proposed system, the distribution of the robot payload in terms of region, maximum stress, and displacement is examined, comprehensively analyzed, and compared with the relevant works. In addition, to enhance the system’s efficiency while minimizing its weight, we introduce a lightweight design approach in which Finite Element Analysis is utilized to optimize the frame structure. Subsequently, we fabricate a physical prototype based on the derived model. Finally, we provide a kinematic model for our dual-arm service robot and demonstrate its efficacy in both control and human–robot interaction (HRI) tasks. Experimental results indicate that the proposed dual arm design can achieve a significant weight reduction of 25% from the original design while still performing actions smoothly for HRI tasks.