Voting Scheme Nearest Neighbors by Difference Distance Metrics Measurement

K-Nearest Neighbor (KNN) is a widely used method for both classification and regression cases. This algorithm, known for its simplicity and effectiveness, relies primarily on the Euclidean formula for distance metrics. Therefore, this study aimed to develop a voting model where observations were made using different distance calculation formulas. The nearest neighbors algorithm was divided based on differences in distance measurements, with each resulting model contributing a vote to determine the final class. Consequently, three methods were proposed, namely k-nearest neighbors (KNN), Local Mean-based KNN, and Distance-Weighted neighbor (DWKNN), with an inclusion of a voting scheme. The robustness of these models was tested using umbilical cord data characterized by imbalance and small dataset size. The results showed that the proposed voting model for nearest neighbors consistently improved performance by an average of 1-2% across accuracy, precision, recall, and F1 score when compared to the conventional non-voting method

Anomaly detection on flight route using similarity and grouping approach based-on automatic dependent surveillance-broadcast

Flight anomaly detection is used to determine the abnormal state data on the flight route. This study focused on two groups: general aviation habits (C1)and anomalies (C2). Groups C1 and C2 are obtained through similarity test with references. The methods used are: 1) normalizing the training data form, 2) forming the training segment 3) calculating the log-likelihood value and determining the maximum log-likelihood (C1) and minimum log-likelihood (C2) values, 4) determining the percentage of data based on criteria C1 and C2 by grouping SVM, KNN, and K-means and 5) Testing with log-likelihood ratio. The results achieved in each segment are Log-likelihood value in C1Latitude is -15.97 and C1Longitude is -16.97. On the other hand, Log-likelihood value in C2Latitude is -19.3 (maximum) and -20.3 (minimum), and log-likelihood value in C2Longitude is -21.2 (maximum) and -24.8 (minimum). The largest percentage value in C1 is 96%, while the largest in C2 is 10%. Thus, the highest potential anomaly data is 10%, and the smallest is 3%. Also, there are performance tests based on F-measure to get accuracy and precision

Design and testing of test rigs for fault detection in energy systems

This portfolio summarizes the development of an automated system capable of detecting operational faults within energy systems. The system includes water transport, heating and refrigeration. Multiple sensors monitor the sub-systems in the prototype. For example, when heating water, temperature was measured to identify deviations from a steady state value...Este portafolio resume el desarrollo de un sistema automatizado capaz de detectar fallas operacionales en sistemas energéticos. El sistema incluye transporte de agua, calentamiento y refrigeración. Múltiples sensores monitorean los subsistemas en el prototipo. Por ejemplo, al calentar agua, se midió la temperatura para identificar desviaciones de un valor en estado estable..

Dynamic spatial segmentation strategy based magnetic field indoor positioning system

In this day and age, it is imperative for anyone who relies on a mobile device to track and navigate themselves using the Global Positioning System (GPS). Such satellite-based positioning works as intended when in the outdoors, or when the device is able to have unobstructed communication with GPS satellites. Nevertheless, at the same time, GPS signal fades away in indoor environments due to the effects of multi-path components and obstructed line-of-sight to the satellite. Therefore, numerous indoor localisation applications have emerged in the market, geared towards finding a practical solution to satisfy the need for accuracy and efficiency. The case of Indoor Positioning System (IPS) is promoted by recent smart devices, which have evolved into a multimedia device with various sensors and optimised connectivity. By sensing the device’s surroundings and inferring its context, current IPS technology has proven its ability to provide stable and reliable indoor localisation information. However, such a system is usually dependent on a high-density of infrastructure that requires expensive installations (e.g. Wi-Fi-based IPS). To make a trade-off between accuracy and cost, considerable attention from many researchers has been paid to the range of infrastructure-free technologies, particularly exploiting the earth’s magnetic field (EMF). EMF is a promising signal type that features ubiquitous availability, location specificity and long-term stability. When considering the practicality of this typical signal in IPS, such a system only consists of mobile device and the EMF signal. To fully comprehend the conventional EMF-based IPS reported in the literature, a preliminary experimental study on indoor EMF characteristics was carried out at the beginning of this research. The results revealed that the positioning performance decreased when the presence of magnetic disturbance sources was lowered to a minimum. In response to this finding, a new concept of spatial segmentation is devised in this research based on magnetic anomaly (MA). Therefore, this study focuses on developing innovative techniques based on spatial segmentation strategy and machine learning algorithms for effective indoor localisation using EMF. In this thesis, four closely correlated components in the proposed system are included: (i) Kriging interpolation-based fingerprinting map; (ii) magnetic intensity-based spatial segmentation; (iii) weighted Naïve Bayes classification (WNBC); (iv) fused features-based k-Nearest-Neighbours (kNN) algorithm. Kriging interpolation-based fingerprinting map reconstructs the original observed EMF positioning database in the calibration phase by interpolating predicted points. The magnetic intensity-based spatial segmentation component then investigates the variation tendency of ambient EMF signals in the new database to analyse the distribution of magnetic disturbance sources, and accordingly, segmenting the test site. Then, WNBC blends the exclusive characteristics of indoor EMF into original Naïve Bayes Classification (NBC) to enable a more accurate and efficient segmentation approach. It is well known that the best IPS implementation often exerts the use of multiple positing sources in order to maximise accuracy. The fused features-based kNN component used in the positioning phase finally learns the various parameters collected in the calibration phase, continuously improving the positioning accuracy of the system. The proposed system was evaluated on multiple indoor sites with diverse layouts. The results show that it outperforms state-of-the-art approaches and demonstrate an average accuracy between 1-2 meters achieved in typical sites by the best methods proposed in this thesis across most of the experimental environments. It can be believed that such an accurate approach will enable the future of infrastructure–free IPS technologies