Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
This paper presents the development of an Intelligent Virtual Instrument (VI) for detecting and characterizing corrosion on aluminum and steel surfaces. Implemented within the LabVIEW® environment, the system utilizes colorimetric computer vision techniques tailored for the metalworking industry. The methodology integrates colorimetric and roughness analysis with Artificial Intelligence, specifically employing Fuzzy Logic for decision-making and Deep Learning algorithms for image processing. This system enables personnel without specialized training to perform rapid, objective diagnostics. The results demonstrate a high correlation between the color spectra of processed images and standard industry patterns, validating the instrument as an efficient and reliable alternative for diverse industrial environments....
Modern robotic tasks often require interaction with the surrounding elements in the workspace. In some high-precision tasks, it is essential to stabilize the contact force on a smooth yet rigid surface, which can be modeled as a unilateral constraint. This challenge becomes increasingly complex in the presence of disturbances. This study addresses these issues using a robust fuzzy force-position controller that combines the approximation capabilities of fuzzy inference systems with the nonlocal properties of fractional operators. The proposed approach extends the error integration to include proportionalintegral- derivative (PID) components of the position error, along with the integral of the contact force error. This formulation leverages the orthogonality between force and velocity subspaces to achieve accurate force-position stabilization. Additionally, an adaptive mechanism enhances closed-loop performance and robustness. The effectiveness of the proposed controller is validated through analytical derivations and simulations, thereby demonstrating its reliability in constrained environments....
We developed a hybrid system combining deep learning‑based recognition with fuzzy inference to enhance the detection, recognition, and identification of maritime targets. In the system, deep learning provides strong feature extraction, while fuzzy logic mitigates uncertainty in low‑visibility or occluded conditions. The system uses confidence score, screen ratio, and estimated distance as input and processes them through fuzzy inference with triangular membership functions and center of area defuzzification. This integration improves decision robustness and suppresses input noise. Experimental results demonstrate enhanced stability and reduced misjudgment in dynamic maritime environments, highlighting the applicability of a hybrid deep learning–fuzzy inference systems to intelligent ships and unmanned maritime vehicle sensing tasks....
A novel engineering approach for assessing the robustness of fuzzy logic control (FLC) systems with modified parallel distributed compensation (MPDC) is presented. It addresses the problem of successful implementation and operation in industrial environment of designed systems for the control of complex plants with model uncertainty. The research steps on modified Takagi–Sugeno–Kang (MTSK) plant models MTSKn and MTSKlow already derived and validated for normal and low plant loads from experimental data for the level of the solution in an industrial carbonisation column for soda ash production. MPDC with PI linear local controllers are developed based on the MTSKn plant model, which differ in the parameters that are optimised by genetic algorithms for fitness functions with and without robustness requirements and different random initial parameter values. The MTSKn and each of the designed MPDC are represented according to suggested criteria by a nominal and varied linear plant model and controller, respectively. Then, robust stability and robust performance criteria are derived for the linearised MPDC– MTSKn systems. The system performance and robustness are investigated in the frequency domain and from the simulated reference step responses for MTSKn and MTSKlow, with the results benchmarked against an existing adaptive FLC....
Young’s modulus is one of the geomechanical properties used in the design phase of different rock engineering applications. Difficulties in sample preparation and the high cost of experimental equipment lead researchers to perform studies on the estimation of Young’s modulus. However, previous studies on this topic are often limited in terms of rock type and/or number of data. Therefore, a comprehensive database covering a wide variety of rock types is needed for reliable estimation of Young’s modulus. To address this deficiency, a large database including Schmidt rebound value, uniaxial compressive strength, and porosity was compiled from the literature to derive equations and models for Young’s modulus estimation. Multivariate regression analysis and adaptive-neuro-fuzzy inference system (ANFIS) were used to predict Young’s modulus of rock materials. The reliability of the derived multivariate regression equations was verified using F- and t-tests, and the equations were found to be statistically reliable. The prediction pperformance of multivariate regression analysis and neuro-fuzzy models was compared using root mean square error (RMSE) and mean absolute percentage error (MAPE). The ANFIS models yielded considerably lower absolute prediction errors than the regression models. Thus, the neuro-fuzzy method provided significantly higher prediction accuracy than the multivariate regression approach. The results indicated that the neuro-fuzzy model constructed in this study using the uniaxial compressive strength (σc), Schmidt rebound value (R), and porosity (n) as input parameters yielded the best predictions of E when compared to those predicted in some previous studies....
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