Topology optimization is gaining popularity as a primary tool for engineers in the initial stages of design. Essentially, the design domain is broken down into individual pixels, with the material density of each element or mesh point serving as a design variable. The optimization problem is then tackled through mathematical programming and optimization methods that rely on analytical gradient calculation. In this study, topology optimization using honeycomb tessellation elements is explored. Hexagonal elements have the ability to flexibly connect two adjacent elements. The use of the hexagonal element limits the occurrence of the checkerboard pattern to the finite elements of the quadrilateral standard Lagrangian type. A mathematical model is developed with the objective function being the minimum compliance value of the design domain. The element stiffness matrix is constructed using the strain-displacement matrix and the constitutive matrix, assuming a unit Young's modulus. Additionally, optimal conditions are established using Lagrangian multipliers. Two sensitivity and density filtering filters are employed to increase optimization efficiency, prevent the algorithm from reaching a local optimal state, and speed up convergence. If the suggested filter is employed, the objective function achieves a value of c=173,0293 and convergence is attained after 200 iterations. In contrast, without using the filter, the objective function has a larger value (c=186,7922) and convergence occurs at the 27th iteration. The results are significant for optimizing topology to meet specific boundary condition requirements. This paper proposes a novel approach using a combination of filters to advance topology optimization using hexagonal elements in future applications.

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Automated human tracking in real time has been applied in many areas such as security, surveillance, traffic control, and robots. In this paper, an improvement of the Camshift human tracking algorithm based on deep learning and the Kalman filter is proposed. To detail an approach by using YOLOv4-tiny to detect a human in real time, Camshift is used to track a particular person and the Kalman filter is applied to enhance the performance of this algorithm in case of occlusion, noise, and different light conditions. The experiments show that the combination of YOLOv4-tiny and the improved Camshift algorithm raises the standard of speed as well as robustness. The proposed algorithm is suitable for running in real time and adapts well to the same color and different light conditions.

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Multi-criteria decision-making (MCDM) is well known as one of the most important solutions that seeks to identify the best alternative among several options. It has a significant effect on the effectiveness of many technical disciplines within the industrial field, especially electrical discharge machining. This paper presents the findings of a multi-criteria decision-making study involving the powder-mixed electrical discharge milling (PMEDM) of cylindrically shaped tool steel 90CrSi components. As performance measures for the PMEDM process, the material removal rate (MRS) and surface roughness (SR) are selected. Three different techniques (CRITIC, MEREC, and IDOCRIW) are applied in sequence to determine the weight value of the quality indicators. The key MCDM methods used for ranking the alternatives are the Combined Compromise Solution (COCOSO) method and the Stable Preference Ordering Towards Ideal Solution (SPOTIS). The experimental study indicates the ranking tables under different scenarios and proposes the best alternative for the PMEDM procedure.