Time-Domain Reflectometry (TDR) generally consists of injecting a signal into the Network Under Test (NUT), and then collecting the multiple reflections that occur from each instance of junction or termination. Howeve...
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Time-Domain Reflectometry (TDR) generally consists of injecting a signal into the Network Under Test (NUT), and then collecting the multiple reflections that occur from each instance of junction or termination. However, since reflectometers combine primary reflections with multiple and intermediate reflections, pulses in the reflectometry response often overlap. Therefore, reconstructing wiring networks using only TDR responses is not feasible. In this paper, the Wiring Network Reconstruction (WNR) process is formulated as an optimisation problem. The optimisation algorithm used in this paper to solve the formulated problem is the forensic-basedinvestigation (FBI) algorithm. Solving the WNR problem consists of finding the topology and the length of branches of the NUT. In the objective function, the optimisation algorithm compares the TDR obtained from the NUT with the TDR of the predicted solutions, where all TDRs are generated analytically to save time. The obtained results using the FBI algorithm are tested against ten well-known optimisation algorithms over a set of seven experiments. In these experiments, five cases are simulation-based, while the last two are real-world cases. The results provided in the paper clearly show the effectiveness and resilience of the proposed approach for reconstructing wiring networks with different degrees of complexity.
The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional *** Selection(FS)methods can abate the complexity of the data and enhance the accuracy,generaliza...
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The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional *** Selection(FS)methods can abate the complexity of the data and enhance the accuracy,generalizability,and interpretability of ***-heuristic algorithms are often utilized for FS tasks due to their low requirements and efficient *** paper introduces an augmented forensic-based investigation algorithm(DCFBI)that incorporates a Dynamic Individual Selection(DIS)and crisscross(CC)mechanism to improve the pursuit phase of the ***,a binary version of DCFBI(BDCFBI)is applied to *** conducted on IEEE CEC 2017 with other metaheuristics demonstrate that DCFBI surpasses them in search *** influence of different mechanisms on the original FBI is analyzed on benchmark functions,while its scalability is verified by comparing it with the original FBI on benchmarks with varied *** is then applied to 18 real datasets from the UCI machine learning database and the Wieslaw dataset to select near-optimal features,which are then compared with six renowned binary *** results show that BDCFBI can be more competitive than similar methods and acquire a subset of features with superior classification accuracy.
Project managers often face some challenges resulting from the scarcity of resources in construction management. Levelling the used resources in multiple projects is a frequently encountered problem in construction ar...
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Project managers often face some challenges resulting from the scarcity of resources in construction management. Levelling the used resources in multiple projects is a frequently encountered problem in construction areas and manufacturing sectors. Tn this regard, the current study proposes a robust forensic-Rased Tnvestigation (FRT) algorithm for resource leveling in multiple projects, considering different objective functions of the resource graphs. To this end, Fuzzy C-Means (FCM) clustering approach was fused into the main operation of the FRT to enhance the convergence rate using the population information. The proposed scheduling examines different objective functions to efficiently optimize the resource profile selection. Two case studies were taken into account in this research to elaborate on the performance of the improved optimization algorithm while dealing with the resource-leveling problem in multiple projects. The experimental findings and statistical comparisons revealed that the developed Fuzzy clustering forensic-Rased Tnvestigation (FFRT) could acquire solutions of high quality and outperform the compared optimization algorithms. (c) 2024 Sharif University of Technology. All rights reserved.
forensic-basedinvestigation (FBI) is recently developed metaheuristic algorithm inspired by the suspect investigation-location-pursuit operations of police officers. This study focuses on the search processes of the ...
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forensic-basedinvestigation (FBI) is recently developed metaheuristic algorithm inspired by the suspect investigation-location-pursuit operations of police officers. This study focuses on the search processes of the FBI algorithm, called Step A and Step B, to improve and increase its performance. For this purpose, opposition-based learning is adopted to Step A to enhance diversity, while Cauchy-based mutation is integrated with Step B to guide the search to different regions and to jump out of local minima. To show the effectiveness of these improvements, the proposed algorithm has been tested with two different benchmark sets. To verify the performance of the new modified algorithm, the statistical test is carried out on numerical functions. This study also investigates the application of the proposed algorithm to a set of six real-world problems. The proposed and adapted/integrated methods appear to have a significant impact on the FBI algorithm, which augments its performance, resulting in better solutions than the compared algorithms in most of the functions and real-world problems.
This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic-basedinvestigation (FBI) an...
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This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic-basedinvestigation (FBI) and Archimedes optimization algorithm (AOA), named the FBIAOA technique. The objective of the proposed method is to rise the profit of fast charging stations and lessen the rising energy demand on the grid that is made up of storage systems and renewable energy generation (wind and PV). The demand for EVs and renewable generation is calculated using the FBI algorithm method. The growth of the proposed method is to examine the reliability of SG depending on the aggregation of the state matrices of EV stochastic parameters. The proposed method can help accelerate the reliability calculations by determining the desired count of EV states. The proposed strategy is run in MATLAB and is evaluated in its performance with existing methods. The proposed method gives a lower cost than the existing genetic algorithm, cuttlefish algorithm, and tunicate swarm algorithm methods. This manuscript introduces FBIAOA, a novel method combining forensic-basedinvestigation (FBI) and Archimedes optimization algorithm (AOA) for precise electric vehicle (EV) modeling in smart grid (SG) reliability studies. The approach aims to maximize fast charging station (FCS) profits, reduce grid energy demand using storage and renewable sources, and assess SG reliability through aggregated stochastic EV parameters. Implemented in MATLAB, the proposed FBIAOA method demonstrates cost-effectiveness compared to existing techniques. image
This study developed a novel optimization algorithm, called forensic-basedinvestigation (FBI), inspired by the suspect investigation-location-pursuit process that is used by police officers. Although numerous unwield...
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This study developed a novel optimization algorithm, called forensic-basedinvestigation (FBI), inspired by the suspect investigation-location-pursuit process that is used by police officers. Although numerous unwieldy optimization algorithms hamper their usability by requiring predefined operating parameters, FBI is a user-friendly algorithm that does not require predefined operating parameters. The performance of parameter-free FBI was validated using four experiments: (1) The robustness and efficiency of FBI were compared with those of 12 representations of the top leading metaphors by using 50 renowned multidimensional benchmark problems. The result indicated that FBI remarkably outperformed all other algorithms. (2) FBI was applied to solve a resource-constrained scheduling problem associated with a highway construction project. The experiment demonstrated that FBI yielded the shortest schedule with a success rate of 100%, indicating its stability and robustness. (3) FBI was utilized to solve 30 benchmark functions that were most recently presented at the IEEE Congress on Evolutionary Computation (CEC) competition on bound-constrained problems. Its performance was compared with those of the three winners in CEC to validate its effectiveness. (4) FBI solved high-dimensional problems, by increasing the number of dimensions of benchmark functions to 1000. FBI is efficient because it requires a relatively short computational time for solving problems, it reaches the optimal solution more rapidly than other algorithms, and it efficaciously solves high-dimensional problems. Given that the experiments demonstrated FBI's robustness, efficiency, stability, and user-friendliness, FBI is promising for solving various complex problems. Finally, this study provided the scientific community with a metaheuristic optimization platform for graphically and logically manipulating optimization algorithms. (C) 2020 Elsevier B.V. All rights reserved.
An intriguing area in the IP (image processing) is the recovery of noisy photographs from the noise caused by the salt and pepper. As the mistake rate rises and the image format varies, the issue with the current task...
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An intriguing area in the IP (image processing) is the recovery of noisy photographs from the noise caused by the salt and pepper. As the mistake rate rises and the image format varies, the issue with the current task does not go away. In this study, Salt and Pepper Denoising, Hybrid Balancing Composite motion Optimization with Adaptive Switching Modified Decision based Unsymmetric Trimmed Median Filter and forensics-basedinvestigationalgorithm is proposed (R-SPN-ASMD-UTMF-Hyb-BCO-FBIA). Initially, the input images are obtained from boat-types-recognition dataset, cat-breeds-dataset, cars-image-dataset, butterfly-images 40-species dataset and birds-200 dataset. The images are pre-processed through an ASMD-UTMF filter. ASMD-UTMF does not expose any adoption of optimization systems to calculate the optimal parameters. Therefore, the proposed Hyb-BCO-FBIA is employed for optimizing the ASMD-UTMF weight parameters. The suggested system is implemented on MATLAB and the assessment metrics as Mean Square Error (MSE), Structural similarity index measurement (SSIM), Peak signal to noise ratio (PSNR), Normalized cross-correlation (NC), Image Enhancement Factor (IEF), Mean Square Error (MSE) are analysed. The proposed method attains higher PSNR, NC related with other SOTA (State-Of-The Art) methods.
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