In this study, an algorithm to the detection and imaging of hidden arms for passive millimeter-wave (PMMW) imaging systems is proposed. This technique is; in fact, an improved version of our previously developed auto-classification algorithm by extending it by exploiting the Otsu’s multi-level thresholding method. The detailed derivation and the brief steps of the proposed algorithm are given. The proposed algorithm is tested and validated by real PMMW images obtained by a real radiometric imaging system. Resultant measured images are obtained with the employment of signal and image processing procedures of the suggested technique. It is demonstrated by the constructed PMMW images that proposed technique successfully detects a concealed metal threat and also predicts its size by drawing the shape outline based on Otsu’s multi-level thresholding routine that was specially tailored to our auto-classification technique.

The optimal power flow (OPF) problem remains a popular and challenging work in optimizing power systems. Although researchers have suggested many optimization algorithms to solve this problem in the literature, their comparison studies lack fairness and transparency. As these studies increase in number, they deviate from a standard test system, considering a common security and technical constraints., there is a growing trend away from a standard test system. Different studies used different search ranges for the same decision and constraint parameters, different than the standard ranges suggested by IEEE systems. This caused many unfair comparisons in literature. Furthermore, these studies are generally not transparent enough so that their results cannot be verified. This has resulted in numerous infeasible solutions in the literature, violating the limits of constraint parameters. The recent incorporating of renewable energy sources in OPF studies has made this situation more complicated. Sorting through the literature and identifying those OPF applications having exactly the same test conditions is a challenging process. The main contribution is this paper adapts the modified effective butterfly algorithm (MEBO) to solve OPF problem under the common parameter constraints and sufficient transparency. The focus is on a transparent comparison with works in the literature with the same constraint values. This paper compares the performance of the proposed algorithm with other state-of-the-art algorithms in the literature, focusing on the wind energy and without wind energy IEEE 30-bus and IEEE 57-bus systems and the most commonly used constraints. The results demonstrate the efficiency and superiority of the proposed algorithm. For instance, in the 30-bus test system, compared to the initial case, fuel cost has been reduced by 11.42 %, emission by 14.33 %, L-index by 45.10 %, active power losses by 51.60 %, and voltage deviation by 92.70 %.

This paper introduces three new variations of the HFO-1 (Honey Formation Optimization with Single Component) algorithm, namely HFO-1a, HFO-1b and HFO-1c, adapted to address the optimal power flow (OPF) problem. The original HFO-1 algorithm has shown success in solving various numerical problems in recent years; however, it assumes a single search range for all dimensions of the solution space, making it unsuitable for direct application to the OPF problem. Modifications to both the honey formation and mixing phases of the HFO-1 algorithm were made to improve solution quality and convergence speed, resulting in three new variants of HFO-1. The newly developed variants aim to minimize even the most challenging objective functions of the complex OPF problem, which has been further complicated by the integration of renewable energy sources into power systems. The paper provides a comprehensive and transparent comparison of the three types of IEEE 30-bus test systems and 118-bus test systems with existing methods, meticulously adhering to practical, technical, operational, and safety constraints. Following successful results on the CEC 2021 standard benchmark functions, the proposed HFO-1 variants have been thoroughly validated through extensive analysis. Experimental results demonstrate that the proposed approach can achieve lower costs ($800.5972/hour and $800.3871/hour) in two types of IEEE 30-bus systems without integrating renewable resources while maintaining system constraints. Furthermore, HFO-1a (achieving 3.0776261 MW) and HFO-1b achieve the lowest values in the literature with a multi-fuel cost of (646.375893 $/h) and a valve point effective fuel cost of (823.981360 $/h), respectively, while HFO-1c exhibits a voltage deviation of (0.083498 p.u.) and Prohibited Operating Zones (POZ) cost of generator (800.665078 $/h).

The Journal of Applied Remote Sensing (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications.