Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

Wi-Fi-based indoor positioning offers significant opportunities for numerous applications. Examining the Wi-Fi positioning systems, it was observed that hundreds of variables were used even when variable reduction was applied. This reveals a structure that is difficult to repeat and is far from producing a common solution for real-life applications. It aims to create a common and standardized dataset for indoor positioning and localization and present a system that can perform estimations using this dataset. To that end, machine learning (ML) methods are compared and the results of successful methods with hierarchical inclusion are then investigated. Further, new features are generated according to the measurement point obtained from the dataset. Subsequently, learning models are selected according to the performance metrics for the estimation of location and position. These learning models are then fused hierarchically using deductive reasoning. Using the proposed method, estimation of location and position has proved to be more successful by using fewer variables than the current studies. This paper, thus, identifies a lack of applicability present in the research community and solves it using the proposed method. It suggests that the proposed method results in a significant improvement for the estimation of floor and longitude

Production fault simulation and forecasting from time series data with machine learning in glove textile industry

Although textile production is heavily automation-based, it is viewed as a virgin area with regard to Industry 4.0. When the developments are integrated into the textile sector, efficiency is expected to increase. When data mining and machine learning studies are examined in textile sector, it is seen that there is a lack of data sharing related to production process in enterprises because of commercial concerns and confidentiality. In this study, a method is presented about how to simulate a production process and how to make regression from the time series data with machine learning. The simulation has been prepared for the annual production plan, and the corresponding faults based on the information received from textile glove enterprise and production data have been obtained. Data set has been applied to various machine learning methods within the scope of supervised learning to compare the learning performances. The errors that occur in the production process have been created using random parameters in the simulation. In order to verify the hypothesis that the errors may be forecast, various machine learning algorithms have been trained using data set in the form of time series. The variable showing the number of faulty products could be forecast very successfully. When forecasting the faulty product parameter, the random forest algorithm has demonstrated the highest success. As these error values have given high accuracy even in a simulation that works with uniformly distributed random parameters, highly accurate forecasts can be made in real-life applications as well

Evaluation of Participant Success in Gamified Drone Training Simulator Using Brain Signals and Key Logs

The risk of accidents while operating a drone is quite high. The most important solution is training for drone pilots. Drone pilot training can be done in both physical and virtual environments, but the probability of an accident is higher for pilot trainees, so the first method is to train in a virtual environment. The purpose of this study is to develop a new system to collect data on students' educational development performance of students during the use of Gamified Drone Training Simulator and objectively analyze students' development. A multimodal recording system that can collect simulator, keystroke, and brain activity data has been developed to analyze the cognitive and physical activities of participants trained in the gamified drone simulator. It was found that as the number of trials increased, participants became accustomed to the cognitive load of visual/auditory tasks and therefore the power in the alpha and beta bands decreased. It was observed that participants' meditation and attention scores increased with the number of repetitions of the educational game. It can be concluded that the number of repetitions lowers stress and anxiety levels, increases attention, and thus enhances game performance

Artificial Intelligence for Non-Destructive Imaging in Composite Materials

(1) Background: The purpose of this review is to explore how advanced sensor technologies and AI-driven methods, like machine learning and image processing, are shaping non-destructive imaging (NDI) systems. NDI plays a vital role in ensuring the strength and reliability of composite materials. Recent advancements in sensor technologies and AI-driven methods, such as machine learning and image processing, have opened up new ways to improve NDI systems, offering exciting opportunities for better performance. (2) Methods: This review takes a close look at how advanced sensor technologies and machine learning techniques are being integrated into NDI systems. The review evaluates how effective these technologies are at detecting defects and examines their strengths, limitations, and challenges. (3) Results: Combining sensor technologies with AI methods has shown a clear boost in defect detection accuracy and efficiency. However, challenges like high computational requirements and integration costs remain. Despite these hurdles, the potential for these technologies to revolutionize NDI systems is significant. (4) Conclusions: By synthesizing the latest research, this review offers a comprehensive understanding of how sensor technologies are enhancing NDI. The findings highlight their importance for improving defect detection and their broader impact on research and industry, while also pointing out areas where further development is needed for future growth

Thermophysiological comfort properties of woven fabrics produced from hybrid yarns containing copper wires

The aim of this study was to determine the thermophysiological comfort behavior of fabrics based on copper wire that can be used for electro-textile applications. For this purpose, hybrid folding yarns were produced by twisting cotton/polyester yarn with copper wire. These electrically conductive hybrid yarns were then used to produce upholstery fabrics with different weave types as plain, 2/1 twill and sateen weave in three different density levels as tight, medium and loose. Thermophysiological comfort properties such as air permeability, thermal and water vapor properties of the hybrid fabrics were measured. In addition, the heat transfer properties of the fabrics were investigated with thermal camera videos, and porosity values were determined from microscope images. In this way, the main thermophysiological comfort properties of the basic electro-textile structures were revealed. According to the results obtained, it was found that the use of conductive wire in the fabric structure did not negatively affect the thermophysiological comfort properties of the fabrics, and fabric density was a determining parameter in relation to the thermophysiological comfort properties of the fabrics. The obtained results of this study may be used to improve the design of electro-textile structures taking into account the thermophysiological comfort

Thermophysiological comfort properties of woven fabrics produced from hybrid yarns containing copper wires

The aim of this study was to determine the thermophysiological comfort behavior of fabrics based on copper wire that can be used for electro-textile applications. For this purpose, hybrid folding yarns were produced by twisting cotton/polyester yarn with copper wire. These electrically conductive hybrid yarns were then used to produce upholstery fabrics with different weave types as plain, 2/1 twill and sateen weave in three different density levels as tight, medium and loose. Thermophysiological comfort properties such as air permeability, thermal and water vapor properties of the hybrid fabrics were measured. In addition, the heat transfer properties of the fabrics were investigated with thermal camera videos, and porosity values were determined from microscope images. In this way, the main thermophysiological comfort properties of the basic electro-textile structures were revealed. According to the results obtained, it was found that the use of conductive wire in the fabric structure did not negatively affect the thermophysiological comfort properties of the fabrics, and fabric density was a determining parameter in relation to the thermophysiological comfort properties of the fabrics. The obtained results of this study may be used to improve the design of electro-textile structures taking into account the thermophysiological comfort. </jats:p