Search

The extraction algorithm of target characteristics resonance set at the high-frequency band plays a significant role in defense applications like early warning. The paper proposes a new method to extract the characteristic resonance set of radar targets in the resonance frequency region using an autoregressive moving average model optimized by root mean square propagation particle swarm optimization. Considering that the total scattering response in the resonant region consists of early-time and late-time responses, the autoregressive moving average model approximate the total scattering responses to avoid errors caused by intercepting the late-time response. Further, the paper investigated the impact of the swarm optimization algorithm on the accuracy of moving average model parameters when obtaining the resonance set at different target aspects. The extracted characteristic resonance results within a range of azimuth directions through an aircraft target paradigm indicate that the new method is more convenient and precise than the matrix beam prediction method. [ABSTRACT FROM AUTHOR]

Regarding the issue of information freshness in systems that aid in data collection using unmanned aerial vehicles (UAVs), a data collection algorithm that is based on freshness and UAV assistance is proposed. Under the limitations of wireless sensor node communication distance and UAV parameters, the optimization problem of minimizing the average spatial correlation age of information (SCAoI) of all nodes in the area is set up. This problem is solved by optimizing the number of clusters, UAV flight trajectories, and the order of data collection from cluster member nodes. The maximum communication distance of the nodes is used as the cluster formation radius, and the maximum-minimum distance clustering algorithm is used to cluster the nodes in the region to obtain the minimum number of clusters. After it has been proven that the trajectory optimization problem in this study is NP-hard, the ant colony algorithm is applied to obtain the minimum flight time and the corresponding trajectory. By using the greedy algorithm to determine the member nodes in the sequence of data collection for a cluster, the instantaneous SCAoI of the UAV arriving at the cluster head is solved. Simulation results show that the proposed algorithm in this paper can effectively improve the freshness of data and reduce the average SCAoI of the system compared with the algorithm in the comparative literature, reducing the average SCAoI by about 61%. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for determining the optimal parameters of a photocatalysis process for treating pharmaceutical wastewater. Three parameters are considered: pH value, catalyst dosages, and reaction time to boost the amoxicillin degradation efficiency (ADE). The proposed methodology contains two stages: fuzzy modelling and a parameter determination process using the marine predators algorithm (MPA). Firstly, based on the experimental dataset of ADE in terms of pH value, catalyst dosages, and reaction time, a robust fuzzy model is produced to model the photocatalysis/ozonation process. The target is reducing the root mean square error (RMSE) between the actual data and the experimental dataset. Using fuzzy, the RMSE decreased from 2.0248 using ANOVA to 0.3148 using fuzzy (decreased by 84%). Next, using the MPA, the optimal parameters of pH value, catalyst dosages, and reaction time corresponding to maximum ADE are determined. The suggested strategy boosted the ADE from 88.23% to a rate of 11.68% compared with the experimental and RSM approaches. Under this condition, the optimal solutions are 11, 384 mg/L, and 33.615 min, respectively, for pH, catalyst dosages, and reaction time. [ABSTRACT FROM AUTHOR]

Optimization challenges effectively mirror numerous complex real‐world problems. These are typically addressed using two methodologies: approximate methods and exact methods. Within the approximate category, meta‐heuristic methods are prominent. These algorithms employ systematic principles to avoid local optima or prevent their occurrence altogether. The evolution of computing technology has spurred the development of various meta‐heuristic algorithms designed for precise and expedient resolution of optimization issues. This paper conducts a comparative analysis of meta‐heuristic algorithms applied to structural optimization. It encompasses 11 distinct meta‐heuristic optimization algorithms: atomic orbital search algorithm, chaos game optimization, energy valley optimizer, firefly algorithm, genetic algorithm, gray wolf optimization, harmony search, material generation algorithm, particle swarm optimization, social network search, and whale optimization algorithm. The efficacy of these algorithms is assessed through their application to structural problems such as trusses, cantilever‐cracked beams, welded beams, tubular columns, and I‐shaped beams. Evaluation criteria include computational efficiency, accuracy, and statistical analysis based on reports among the 50 iterations. The findings confirm the effectiveness of the algorithms, with particle swarm optimization and social network search frequently delivering superior results across most problem sets. [ABSTRACT FROM AUTHOR]

The rice‐fish system has gained significant interest in recent years because of its effective usage of limited land and freshwater resources. To scientifically guide the improvement of rice field fishery production, the data in this study were selected from the latest China Fishery Statistical Yearbook, and therefore the development of paddy aquaculture was investigated. In order to more precisely predict the production of rice‐fish in China, this paper introduces an artificial neural network with the SSA‐BP model, which solves the drawbacks of the BP neural network such as easy to fall into local optimum and slow convergence speed when it is used for prediction. Firstly, the SSA‐BP model incorporates the aquaculture area (split by water area), the national freshwater fish seedling output, the national end‐of‐year ownership of inland fishing vessels, the number of freshwater fisheries practitioners as input variables, and the production of rice‐fish as an output variable; secondly, the SSA optimization algorithm was used to find the optimal initial thresholds and weights for the BP neural network, and finally the SSA‐BP prediction model was constructed. The results revealed that the overall expansion of the rice field fishery was swift in the last five years, and the output of cultivated fish in China's rice fields rose by nearly 20% yearly in the past five years. Compared with the BP neural network and GA‐BP models, the accuracy of the SSA‐BP prediction model was enhanced by 61.01% and 16.15%, respectively, which was more suited for predicting the production of rice‐fish. [ABSTRACT FROM AUTHOR]

Device‐to‐device (D2D) communication is a promising technology in fifth‐generation (5G) wireless networks, offering enhanced system capacity, spectrum performance, and energy efficiency. However, D2D links can introduce interference with cellular links, posing challenges in spectrum allocation and network quality assurance. This paper presents a novel approach using multiagent reinforcement learning with a proximal policy optimization algorithm to address the resource allocation problem in D2D networks. The proposed algorithm aims to optimize overall throughput and maximize the signal‐to‐interference noise ratio (SINR) while ensuring low computational complexity. The study introduces the following two key techniques: staggered training and decentralized execution. Staggered training improves agent performance and minimizes computational complexity by training agents one at a time in a sequential manner. This allows agents to learn from each other's mistakes and avoid local minima. Decentralized execution enhances scalability and system robustness by enabling agents to learn and act independently without relying on communication with other agents. In the event of agent failure, the remaining agents can continue operating. The findings of this work demonstrate a significant improvement in energy efficiency (EE) and an enhancement in the quality of service (QoS) of the network. Overall, the algorithm proves to be a promising solution for resource allocation in multiagent D2D networks, offering notable improvements in EE and QoS while maintaining scalability for large networks. [ABSTRACT FROM AUTHOR]

To solve the problem of collaborative search mission planning (CSMP) for a heterogeneous unmanned aerial vehicle (UAV) swarm for dynamic targets, this paper proposes a new CSMP scheme for a heterogeneous UAV swarm. First, a new polar coordinate system motion method is used to establish the UAV motion model and decision input solution model, which effectively solves the path‐unified coding problem of a heterogeneous UAV swarm. Then, in the mission area, a dynamically updated multisearch situation map model is established. Finally, to improve the global searching capability of a UAV, an improved path optimization algorithm PSO‐Rolling Horizon Control (PSO‐RHC) is designed. Multiple Monte Carlo simulations are performed for three time‐sensitive moving target types and three constraint types. The simulation results show that the task execution efficiency indexes of the proposed scheme for the decision input solution model, pheromone update mechanism, and optimization algorithm are improved by 188%, 72%, and 102%, respectively, and the overall performance is improved by 227%. [ABSTRACT FROM AUTHOR]

The security of communication networks can be compromised through both known and novel attack methods. Protection against such attacks may be achieved through the use of an intrusion detection system (IDS), which can be designed by training machine learning models to detect cyberattacks. In this paper, the KOMIG (knapsack optimization and mutual information gain) IDS was developed to detect network intrusions. The KOMIG IDS combined the strengths of optimization and machine learning together to achieve a high intrusion detection performance. Specifically, KOMIG IDS comprises a 2-stage feature selection procedure; the first was accomplished with a knapsack optimization algorithm and the second with a mutual information gain filter. In particular, we developed an optimization model for the selection of the most important features from a network intrusion dataset. Then, a new set of features was synthesized from the selected features and combined with the selected features to form a candidate features set. Next, we applied an information gain filter to the candidate features set to prune out redundant features, leaving only the features that possess the maximum information gain, which were used to train machine learning models. The proposed KOMIG IDS was applied to the UNSW-NB15 dataset, which is a well-known network intrusion evaluation dataset, and the resulting data, after optimization operation, were used to train four machine learning models, namely, logistic regression (LR), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN). Simulation experiments were conducted, and the results revealed that our proposed KNN-based KOMIG IDS outperformed comparative schemes by achieving an accuracy score of 97.14%, a recall score of 99.46%, a precision score of 95.53%, and an F1 score of 97.46%. [ABSTRACT FROM AUTHOR]

In the realm of structural health monitoring (SHM), understanding the expected behavior of a structure is vital for the timely identification of anomalous activities. Existing methods often model only the physical quantities of monitoring data, neglecting the corresponding temporal information. To address this, this paper presents an innovative deep learning framework that synergistically combines a BiLSTM model, fortified by a temporal pattern attention (TPA) mechanism, with time-encoded temperature and traffic-induced deflection-temporal patterns. The arithmetic optimization algorithm (AOA) is employed for optimal hyperparameter tuning, and incremental learning was implemented to enable real-time updates of the model. Based on the proposed framework, an anomaly detection method was subsequently developed. This method is bidirectional: it uses quantile loss to provide expected ranges for structural behavior, identifying isolated anomalies, while the windowed normalized mutual information (WNMI) based on multivariate kernel density estimation (MKDE) helps detect trend variability caused by decreases in structural stiffness. This framework and the anomaly detection method were validated using data from an operational cable-stayed bridge. The results demonstrate that the method effectively predicts structural behavior and detects anomalies, highlighting the critical role of temporal information in SHM. [ABSTRACT FROM AUTHOR]

Mathematical models that represent food processing operations are characterized by the nonlinearity of their dynamic behavior with possible discrete events, the existence of several variables of interest that are usually distributed in space, and the presence of nonlinear constraints. These features require robust optimization methods to resolve these models and to identify the optimum operating conditions of the processes. Stochastic optimization methods, often referred as metaheuristics, are effective and reliable tools to perform the global and multiobjective optimization of process units and operations involved in food engineering. In this way, this paper surveys recent advances and contributions that have applied stochastic methods for solving global and multiobjective optimization problems in food engineering. The description of the most used stochastic algorithms in food engineering is provided including the application of those methods classified as random search techniques, evolutionary methods, and swarm intelligence methods. It was observed that evolutionary methods are the most applied in solving food engineering optimization problems where the genetic algorithm and differential evolution stand out. Finally, remarks on the limitations and current challenges to improving the numerical performance of stochastic optimization methods for food engineering applications are also discussed. [ABSTRACT FROM AUTHOR]

The rolling bearing is one of the commonly used mechanical components in rotating machinery, and its health directly affects the normal operation of equipment. However, the fault signal of rolling bearing is susceptible to noise interference, which makes it difficult to extract the fault characteristics of the rolling bearing and thus affects the accuracy of the diagnosis results. To address this problem, this paper proposes a method by using a snake optimization algorithm to optimize variational mode decomposition (SOA-VMD) and applies it to the extraction of the fault feature of rolling bearing. First, the minimum Shannon entropy to kurtosis ratio (EKR) is used as the fitness function of SOA to search for the best parameter combination of VMD. Second, the optimized VMD is used to decompose the vibration signal of rolling bearing to obtain K intrinsic mode functions (IMFs). Then, the IMF with the most fault information is selected for reconstruction through EKR. The Teager–Kaiser energy operator (TKEO) spectrum analysis is performed on the reconstructed signal. Finally, this method is used to analyze the simulation signal and rolling bearing vibration signal and compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithms to verify the feasibility and effectiveness of the SOA-VMD method. [ABSTRACT FROM AUTHOR]

Digital twin (DT) is a prolific buzzword in this era, where digitization plays a significant role. The perception of solar energy harvesting has been gaining popularity with the advent of solar panels. Solar asset maintenance is a need of the hour for investors because of the smart city scheme and green building certificate evaluation for all industries, educational institutions, etc. Among the list of factors that reduce PV system efficiency, the issue of partial shading is a vital distress that must be resolved. This paper focuses on the development of a digital twin framework that is proactively driven by shading patterns and a proposed optimization-based reconfiguration embedded controller that electronically relocates the panel in the physical world. The real-time system has been created for a three-by-three series parallel panel arrangement. The proposed switching matrix controller achieves the maximum power, i.e., 40% of increased power output, row current difference is made almost zero from 3 to 4 A, and fill factor increases by 20% by reconfiguring the solar array. It is done based on a decision taken by a nature-inspired (equal irradiation distribution algorithm) or puzzle-based (skyscraper) optimization algorithm. Switching matrix controllers overcome the disadvantages of physical relocation. The users can query the digital twin build to know the historical performance and current operating conditions of the system. It can trigger alarms as early warnings and make predictions about possible system anomalies, if and when they occur using a digital twin. [ABSTRACT FROM AUTHOR]

Accurate fault detection in high-voltage direct current (HVDC) transmission lines plays a pivotal role in enhancing operational efficiency, reducing costs, and ensuring grid reliability. This research aims to develop a cost-effective and high-performance fault detection solution for HVDC systems. The primary objective is to accurately identify and localize faults within the power system. In pursuit of this goal, the paper presents a comparative analysis of current and voltage characteristics between the rectifier and inverter sides of the HVDC transmission system and their associated alternating current (AC) counterparts under various fault conditions. Voltage and current features are extracted and optimized using a metaheuristic approach, specifically Harris Hawk's optimization method. Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. With a substantial volume of data employed for learning and training, the Harris Hawks optimization method exhibits faster convergence compared to other metaheuristic methods examined in this study. The research findings are applied to simulate diverse fault types and unknown fault locations at multiple system points. Evaluating the fault detection system's effectiveness, quantified through metrics such as specificity, accuracy, F1 score, and sensitivity, yields remarkable results, with percentages of 99.01%, 98.69%, 98.64%, and 98.67%, respectively. This research underscores the critical role of accurate fault detection in HVDC systems, offering valuable insights into optimizing grid performance and reliability. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective framework for simultaneous reconfiguration and allocation of photovoltaic (PV) energy resources in radial distribution networks is performed for minimizing the power losses, lowering network loading factor, and reducing cost of energy resources as well as increasing the voltage stability index. A novel algorithm called the improved clouded leopard algorithm is used to find the set of optimum decision factors in the combined execution of restructuring and also PV resource distribution. The clouded leopard optimization (CLO) algorithm, which is widely used, takes its cues from the sleeping and foraging habits of the animal, and the improved CLO (ICLO) is formed using adaptive inertia weight to overcome premature convergence. In five cases of base distribution network and different contribution of reconfiguration and PV allocation, single- and multiobjective approach has been applied on 33- and 69-bus distribution networks. According to the findings, the best case includes simultaneous reconfiguration and PV allocation in the radial networks based on the multiobjective approach unlike the single-objective method which obtained the highest network performance with the best compromise between different goals. Also, in the best lowest losses, the decrease in the network loading and cost of energy resources and the better voltage profile and stability are obtained satisfying the constraints. The losses, network loading, voltage profile, and voltage stability are improved by 60.40%, 37.89%, 6.17%, and 27.5% for 33-bus network and also enhanced by 71.77%, 41.23%, 4.76%, and 20.48% for 69-bus network, respectively. Also, the results showed that network reconfiguration only has the weakest performance among other cases based on reconfiguration or photovoltaic sources. Moreover, the superior capability of the ICLO in addressing the aim of the study is proved in comparison with the traditional CLO and well-known particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) to obtain the better objective value and statistical criteria to solve the best case. [ABSTRACT FROM AUTHOR]

This paper uses an augmented Lagrangian method based on an inexact exponential penalty function to solve constrained multiobjective optimization problems. Two algorithms have been proposed in this study. The first algorithm uses a projected gradient, while the second uses the steepest descent method. By these algorithms, we have been able to generate a set of nondominated points that approximate the Pareto optimal solutions of the initial problem. Some proofs of theoretical convergence are also proposed for two different criteria for the set of generated stationary Pareto points. In addition, we compared our method with the NSGA-II and augmented the Lagrangian cone method on some test problems from the literature. A numerical analysis of the obtained solutions indicates that our method is competitive with regard to the test problems used for the comparison. [ABSTRACT FROM AUTHOR]

Substation automation systems (SAS) are critical infrastructures whose design and maintenance must be optimised to guarantee a suitable performance. In order to provide a collection of solutions that balance availability and cost, this paper explores the optimisation of the design and maintenance of a section of SAS. Multiobjective evolutionary algorithms are combined with discrete event simulation while the performance of two state-of-the-art multiobjective evolutionary algorithms is studied. On the one hand, the nondominated sorting genetic algorithm II (NSGA-II), and on the other hand, the S-metric selection evolutionary multiobjective optimisation algorithm (SMS-EMOA). Such a problem is solved from 2 and 3-objective approaches by attending to the multiobjectivisation concept. The robustness of the methodology is brought to light, and benefits were observed from the multiobjectivisation approach. Decision-makers can employ this knowledge to make informed decisions based on economic and reliability criteria. [ABSTRACT FROM AUTHOR]

Aerial manipulators, integrating a quadrotor base and a multifaceted manipulator, represent a frontier in robotic innovation. This study introduces an identification methodology for the comprehensive dynamics of a cable‐driven aerial manipulator, incorporating both rigidity and flexibility. The initial section delineates the conception of an inventive cable‐driven aerial manipulator. Subsequent to the design, separate dynamics models for the quadrotor and the cable system are formulated using the Newton–Euler approach. The manipulator's dynamics incorporate joint flexibility, resulting in a hybrid rigid‐flexible dynamics model. A synthesized dynamics model emerges upon the integration of the quadrotor and manipulator models. For the system's identification, a backpropagation neural network is utilized. Enhancement of the neural network's performance is achieved through an augmented butterfly optimization algorithm (BOA), which fine‐tunes thresholds and weights. This algorithm operates effectively owing to a clustering‐based competitive learning and a chaotic elite learning strategy, demonstrating adeptness in data extraction. Experimental validation confirms the superior accuracy and precision of the model derived from the algorithm herein, in comparison to two alternative methodologies. The findings underscore the algorithm's exceptional accuracy, robustness, and stability, providing an accurate dynamic representation of the aerial manipulator. [ABSTRACT FROM AUTHOR]

Telesurgery and robotic surgery are two real‐time mission‐critical applications where Artificial Intelligence (AI) has a lot of perspective. In this work, a blockchain‐ and AI‐powered telesurgery system for 6 G communication is suggested, which describes a transparent, safe, self‐managing, and trustworthy structure having massive Ultra‐Reliable Low‐Latency Communication (mURLLC). The condition is categorized using AI methods like Enhanced Light GBM, whose criticality scores range from 0 to 1 (after the predicted output, the criticality score of the corresponding disease is divided into high critical, medium critical, and low critical on the basis of the scores that range from 0 to 1). Here, the parameter tuning in light GBM is performed using the Tasmanian Devil Optimization (TDO) with the consideration of attaining the fitness function and thus referred to as Enhanced Light GBM. This proposed novel Enhanced Light GBM predicts the final output based on the criticality scores. In the future, recent deep learning algorithms can be considered for drone‐assisted telesurgery framework together with the consideration of hybrid optimization algorithms. The proposed Enhanced Light GBM‐TDO model for the drone‐oriented telesurgery framework with respect to prediction accuracy is 3.22%, 3.11%, 1.84%, 3.40%, 2.26%, and 1.15% advanced than Aayush, Habits, BATS, CSIMH, MGA, and heuristic approach, respectively. [ABSTRACT FROM AUTHOR]

Facing the escalating global energy demand and environmental pressures, efforts to enhance ship efficiency and reduce carbon emissions have become pivotal strategies in addressing energy challenges. This study focuses on optimizing propeller boss cap fin (PBCF), proposing a strategy that integrates a backpropagation neural network (BPNN) with an intelligent optimization algorithm. PBCF's positions relative to the propeller are expressed parametrically, and the sample space for PBCF parameters is constructed using Latin hypercube sampling. Building upon this, a BPNN model is established, with design parameters as inputs and hydrodynamic performance calculated by computational fluid dynamics as outputs. Simultaneously, the approximate model trained by the neural network serves as the objective function for the northern goshawk optimization algorithm, significantly enhancing the iteration speed. After determining the PBCF's position parameters, the optimal performance airfoil is chosen as the PBCF profile from seven airfoil types. Finally, visualization techniques were employed to compare the flow field distributions of the optimized fin and the original propeller. The results demonstrate that the optimized fins effectively absorb tangential kinetic energy from blade flow, reduce hub cap pressure differences, and suppress post‐hub vortices. Under specified design conditions, the optimized fins enhance the propulsion system's efficiency by 1.5%. This study underscores the transformative potential of intelligent optimization techniques in shaping the future of maritime engineering, providing sustainable solutions to global energy challenges. [ABSTRACT FROM AUTHOR]

The ability to change lanes safely, efficiently, and comfortably is an important prerequisite for the application of Connected‐Automated Vehicles (CAVs). Based on the five‐order polynomial trajectory planning for CAVs, the 2D‐Action Asynchronous Lane Change (AALC) trajectory planning model is constructed by further considering the longitudinal and lateral driving action execution time parameters. This is done to improve the applicability of the lane change model and increase the CAV lane change success rate. The continuous collision space algorithm is constructed by determining the continuity condition of collision trajectory parameter solution space through the monotonicity of trajectory curve parameters and collision form classification. AALC trajectory safety judgment is realized through this algorithm. A cooperative lane change trajectory evaluation objective function is constructed, considering multivehicle comfort and efficiency. Finally, the AALC model is solved in the continuous collision space according to the optimal objective function, and the lane change is divided into free, cooperative, and refused according to the optimization. The results indicate that the AALC model achieves the transfer of collision space between lanes through asynchronous process of behavior execution time window, thereby reducing the possibility of vehicle collision. The AALC model reduces the degree of change of cooperative lane change parameters by asynchronous process of behavior, increasing the number of free lane change trajectories by about 17%, effectively reducing the occurrence of lane change refusal, improving the successful rate of lane change, and enhancing the overall evaluation of the lane change. The AALC model realizes the reallocation of collision space between different lanes through asynchronous process, making it more suitable for environments with large differences in vehicle gaps such as ramp merging. The collision‐based trajectory optimization algorithm can quickly obtain the corresponding safety space and optimal trajectory. The maximum calculation time for a single cooperative lane change is 0.073 s, thus enabling real‐time trajectory planning. [ABSTRACT FROM AUTHOR]

The advent of automated vehicles (AVs) and connected automated vehicles (CAVs) creates significant uncertainties in infrastructure planning due to many unknowns, such as performance variability and user adaptation. As technologies are still emerging with low market penetration, limited observational data hinder validation and escalate prediction uncertainty. This study addresses these gaps by employing diverse vehicle models and wide performance ranges in Aimsun microsimulations. It involved three AV/CAV car‐following models with the default Gipps human‐driven vehicle (HDV) model. We evaluated the performance of a mixed fleet in three well‐calibrated real‐world corridor models, including two highways and one freeway. Vehicle parameters in Aimsun are commonly drawn from a corresponding truncated normal distribution with fixed mean, min, and max values. However, to account for future uncertainty and heterogeneity, our AV/CAV models were given truncated normal distributions with variable means for important parameters to incorporate broader performance ranges. The variable means are drawn from intervals with uniform probability, and some of the interval extended below HDV values to account for scenarios where riders opt for smoother rides at the cost of traffic flow. Recognizing that precise future prediction is unattainable, we aimed to establish traffic performance boundaries that define best‐ and worst‐case scenarios in a mixed‐fleet environment. Enumerating all possible combinations is impractical, so a refined optimization algorithm was employed to expedite solution discovery. Our findings suggest that AVs/CAVs, even with conservative performance parameters, can improve traffic operations by reducing peak delays and enhancing travel time reliability. Freeways benefited more than arterial roads, especially with full CAV penetration, although the authors speculate this could create bottlenecks at off‐ramps. The added capacity may induce traffic demand that is difficult to estimate. Instead, we conducted a demand sensitivity analysis to gauge additional traffic accommodation without worsening delays. Compared to point predictions, establishing the range of possibilities can help us future‐proof infrastructure by considering uncertainties in the planning process. Our framework can be adopted to test alternative models or scenarios as more data becomes available. [ABSTRACT FROM AUTHOR]

The main objective of this research project is to demonstrate a valuable method for scheduling an SSSC damping controller installed in a single‐machine infinite bus (SMIB) power system. Over time, a damping controller has been used in power systems to reduce low‐frequency oscillation. The configuration challenge associated with this configuration is regarded as an optimization problem. A modified optimization algorithm (ZOA) that can be used to adjust the controller parameters is constructed by modifying the original ZOA algorithm. The suggested study distinguishes between the mZOA method, traditional ZOA, and conventional PSO algorithms. Based on the simulation findings, it can be inferred that the modified approach that has been suggested is the most effective method for defining the said damping controller by considering the percentage improvement in the objective function value. [ABSTRACT FROM AUTHOR]

Machine learning‐assisted electromagnetic simulation has become an effective acceleration tool for designing microwave components by introducing high‐precision models and optimization algorithms, featuring fast design and high efficiency. However, enormous amount of data generated from the blind preliminary and computationally expensive simulation is required to predict the accuracy response. An efficient geometric parameter optimization method for microstrip bandpass filter (BPF) based on a one‐dimensional convolutional neural network is proposed. Nonlinear convergence factor, adaptive weight, and Gaussian difference mutation strategies are integrated using the whale optimization algorithm to avoid the local optimum and improve optimization accuracy. Computational efficiency is improved significantly with small‐scale training data. The validity and efficiency of the proposed method are confirmed by fifth‐order microstrip BPFs, and the performance of the predicted structure parameters is significantly improved, which shows great promise for application in optimization and performance improvement in microwave electromagnetic simulation. [ABSTRACT FROM AUTHOR]

This study introduces a novel Black Widow‐Optimized Dense CNN (BW‐DCNN) framework for the automated detection and segmentation of brain tumors in MRI scans, aiming to enhance early diagnosis and treatment planning. With the increasing frequency of brain tumors, early and accurate detection becomes crucial for effective treatment. Magnetic Resonance Imaging (MRI) is a diagnostic technique owing to its excellent sensitivity. However, manual MRI data analysis is time‐consuming and prone to errors. Leveraging advancements in Convolutional Neural Networks (CNNs) and optimization algorithms, the proposed BW‐DCNN framework utilizes a unique integration of preprocessing, segmentation enhancement, and classification techniques optimized through Black Widow Optimization to improve diagnostic accuracy and efficiency. Evaluating the BW‐DCNN framework against existing methodologies, including DCNN, DNN, and DBN, demonstrates superior performance across a comprehensive suite of metrics. These results highlight the potential of the BW‐DCNN approach to significantly advance the capabilities of computer‐aided diagnostic systems in medical imaging, offering a promising direction for future research and application in clinical settings. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) faces a significant challenge in accurately detecting damage due to noise in acquired signals in composite plates, which can adversely affect reliability. Specific noise reduction techniques tailored to SHM signals are developed to tackle this issue. Gaussian smoothing proves effective in reducing noise and enhancing signal features, thereby facilitating the identification of damage‐related information. Optimization algorithms play a crucial role in damage detection, especially when integrated with smoothing and fusion techniques, as they provide optimal solutions to SHM challenges. A model‐updating‐based optimization algorithm is proposed for detecting damage in structures using condensed frequency response functions (CFRFs), even in the presence of various types of noise and measurement errors. The CFRF signals are first smoothed using an optimized Gaussian smoothing technique as part of the proposed method. Then, the proposed methodology integrates diverse smoothed signals using a raw data fusion approach, including those from different excitations, frequency ranges, and sensor placements. Fused smoothed signals are then fed into a new objective function, incorporating mutual information (MI) and Gaussian smoothing to mitigate correlated coloured noise. The proposed objective function also introduces a hyperparameter tuning of Gaussian smoothing to enhance its performance. Optimization via the improved reptile search algorithm (IRSA) updates the objective function, optimizing damage and smoothing parameters. The hybrid method detects damage in numerical composite laminated plates with different layers and boundary conditions, demonstrating its effectiveness as an SHM technique. Comparative evaluations of other state‐of‐the‐art methods show that the proposed method outperforms its counterparts, making it a promising damage detection approach to address the noise challenge in the SHM field. [ABSTRACT FROM AUTHOR]

In online mode of teaching—learning process, which is prevailing in large in the recent years, the levels of involvement while taking part in these instructional activities—ranges from boredom to learning gain. It is a significant role of online educators to accurately and effectively determine their online learners' engagement status in order to offer them individualized pedagogical support through interventions. This work thus focuses on processing the online video classes to analyze emotional engagement of the learners. On the basis of video face processing, an innovative pipeline is suggested. First, the face in the facial video of the dataset is detected using a multitask cascaded convolutional neural networks (MTCNN) framework designed for face detection. Then, using a single efficient convolutional neural network (CNN), the emotional features of each frame are obtained, and the appropriate emotions are forecasted. The engagement level is then determined using the weighted average of the assessed probabilities of the predicted emotions. This network is pretrained on face detection and fine‐tuned for identifying emotions on static images using a newly designed robust optimization technique. The three levels of student engagement—highly engaged, engaged, and disengaged—and their seven different emotions, happy, sad, angry, neutral, scared, surprise, and disgust, are all been quickly and simultaneously predicted using the generated facial features. The students' facial recordings may all be processed secretly and instantly on their mobile device with this technology, so there is no need to transfer them elsewhere. The proposed model detects emotions and levels of engagement with a better accuracy of 97.45%. [ABSTRACT FROM AUTHOR]

In the context of integrating Renewable Energy Sources, Microgrid (MG) development is pivotal, particularly as a foundational technology for Smart-Grid evolution. Despite advancements in control techniques, challenges persist in ensuring system stability and accurate power sharing across diverse operational conditions and load types. The objective of this research is to control numerous paralleled inverters-based distributed generators (DGs) that contribute to power sharing in an island MG. The proposed methodology involves developing an innovative small-signal model for islanding MGs that incorporate virtual impedances. Subsequently, optimization algorithms based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are proposed and compared for designing the virtual impedances. These algorithms analyze all potential operating points, aiming to minimize reactive power mismatches while maximizing MG stability. The suggested objective function facilitates the simultaneous achievement of these objectives. The proposed approaches were tested using MATLAB-Simulink software, and the comparison of the results between conventional approach and the proposed optimal approaches shows significant improvement in terms of the dynamic response during load changes, such as a decrease in response time by up to 20%, a reduction in overshoot percentage by approximately 15%, and a settling time improvement of nearly 25%. These quantified improvements highlight the effectiveness of the GA and PSO methods in minimizing the reactive power-sharing error while optimizing MG performance and stability. [ABSTRACT FROM AUTHOR]

To sustain a system frequency within acceptable limits, it is widely conceded that retaining a power balance between generation and demand is necessary. In order to regulate the frequency of power systems (PSs) this article proposes a novel cascaded based fractional-order controller termed as fractional-order integer- (FOI-) fractional-order proportional integral with double derivative (FOPIDD2). In addition to redox flow batteries and capacitive energy storage, the recommended control strategy has been validated with gas, thermal reheat, hydro, and nuclear power systems. Additionally, a newly designed algorithm known as squid game optimizer (SGO) optimizes the gains of the new FOI-FOPIDD2 controller. The squid game optimizer technique is inspired by the fundamental principles of a conventional Korean sport. It employs a population of candidate solutions and iteratively adjusts the control parameters to discover the optimal set that reduces frequency abnormalities and improves system stability. A comparison is also made between the controller's performance and benchmarks, including the jellyfish search algorithm, the firefly algorithm, the grey wolf optimizer, and the particle swarm algorithm. The proposed algorithms reduced peak overshoot as compared to grey wolf optimizer algorithm by 35.34%, 46.78%, and 76.89%; jellyfish search optimization algorithm by 34.76%, 77.22%, and 82.56%; and firefly algorithm by 82.67%, 89.23%, and 29.67% for frequency variations in area 1, area 2 and tie line power, respectively. Furthermore, SGO-FOI-FOPIDD2 controllers under different loading circumstances and conditions were evaluated and endorsed for their ability to withstand uncertainties in power system parameters. [ABSTRACT FROM AUTHOR]