Predictive maintenance of induction motors continues to be a significant challenge in ensuring industrial reliability and minimizing downtime. In this study, machine learning techniques are utilized to enhance fault diagnosis through the use of the Machinery Fault Database (MAFAULDA). A detailed extraction of statistical features was performed on multivariate time-series data to capture essential patterns that could indicate potential faults. Three machine learning algorithms—deep neural networks (DNNs), support vector machines (SVMs), and K-nearest neighbors (KNNs)—were applied to the dataset. Optimization strategies were carefully implemented along with oversampling techniques to improve model performance and handle imbalanced data. The results achieved through these models are highly promising. The SVM model demonstrated an accuracy of 95.4%, while KNN achieved an accuracy of 92.8%. Notably, the combination of deep neural networks with fast Fourier transform (FFT)-based autocorrelation features produced the highest performance, reaching an impressive accuracy of 99.7%. These results provide a novel approach to machine learning techniques in enhancing operational health and predictive maintenance of induction motor systems.

In an intelligent multi-target tracking (MTT) system, the tracking filter cannot track multi-targets significantly through occlusion in a low-altitude airspace. The most challenging issues are the target deformation, target occlusion and targets being concealed by the presence of background clutter. Thus, the true tracks that follow the desired targets are often lost due to the occlusion of uncertain measurements detected by a sensor, such as a motion capture (mocap) sensor. In addition, sensor measurement noise, process noise and clutter measurements degrade the system performance. To avoid track loss, we use the Markov-chain-two (MC2) model that allows the propagation of target existence through the occlusion region. We utilized the MC2 model in linear multi-target tracking based on the integrated probabilistic data association (LMIPDA) and proposed a modified integrated algorithm referred to here as LMIPDA-MC2. We consider a three-dimensional surveillance for tracking occluded targets, such as unmanned aerial vehicles (UAVs) and other autonomous vehicles at low altitude in clutters. We compared the results of the proposed method with existing Markov-chain model based algorithms using Monte Carlo simulations and practical experiments. We also provide track retention and false-track discrimination (FTD) statistics to explain the significance of the LMIPDA-MC2 algorithm.

In order to apply coastal acoustic tomography (CAT) efficiently to a specific coastal area, it is necessary to design a measurement system in consideration of various variables related to the marine environment of the area. In particular, to consider seasonal variations in the water temperature structure quantitatively is important regarding the formation of sound rays. In this study, the feasibility of performing CAT according to the seasonal variations in the water temperature structure of Yeosu and Gwangyang Port was studied through simulation using BELLHOP model. First, BELLHOP model simulations were performed using the precise chart data and seasonal water temperature structure data of Yeosu and Gwangyang Port as input data. As a result, signal received during transmitting and receiving between candidate locations of stations could be calculated for each season. And, by estimating the minimum sound source level required to obtain an identifiable peak from the SNR value of the signal, the feasibility of tomography measurement could be assessed. It is expected that some important information, in constructing a tomography system with specifications appropriate for any coastal area of interest, can be provided in advance utilizing the method used in this study.

In the era of industrial evolution, satellites are being viewed as swarm intelligence that does not rely on a single system but multiple constellations that collaborate autonomously. This has enhanced the potential of the Global Navigation Satellite System (GNSS) to contribute to improving position, navigation, and timing (PNT) services. However, multipath (MP) and non-line-of-sight (NLOS) receptions remain the prominent vulnerability for the GNSS in harsh environments. The aim of this research is to investigate the impact of MP and NLOS receptions on GNSS performance and then propose a Received Signal Quality Monitoring (RSQM) algorithm. The RSQM algorithm works in two ways. Initially, it performs a signal quality test based on a fuzzy inference system. The input parameters are carrier-to-noise ratio (CNR), Normalized Range Residuals (NRR), and Code–Carrier Divergence (CCD), and it computes the membership functions based on the Mamdani method and classifies the signal quality as LOS, NLOS, weak NLOS, and strong NLOS. Secondly, it performs an adaptive navigation strategy to exclude/mask the affected range measurements while considering the satellite geometry constraints (i.e., $DOP\le 2$). For this purpose, comprehensive research to quantify the multi-constellation GNSS receiver with four constellation configurations (GPS, BeiDou, GLONASS, and Galileo) has been carried out in various operating environments. This RSQM-based GNSS receiver has the capability to identify signal quality and perform adaptive navigation accordingly to improve navigation performance. The results suggest that GNSS performance in terms of position error is improved from 5.4 m to 2.3 m on average in the complex urban environment. Combining the RSQM algorithm with the GNSS has great potential for the future industrial revolution (Industry 5.0), making things automatic and sustainable like autonomous vehicle operation.

In the era of industrial evolution satellites are being viewed as swarm intelligence that do not relies on single system but multiple constellations collaborate autonomously. This has enhanced the potential of the GNSS system to contribute in the improved position, navigation and timing (PNT) services. However, Multipath (MP) and Non-line-of-sight (NLOS) receptions remain the prominent vulnerability for GNSS in harsh environments. The aim of this research is to investigate its impact of MP and NLOS on GNSS performance and then proposed a Received Signal Quality Monitoring (RSQM) algorithm. RSQM works twofold: Initially it performs a signal quality test based on fuzzy inference system. The input parameters are Carrier-to-Noise ratio (CNR), Range Residuals (RR) and Code-Carrier Divergence (CCD) and it computes the membership functions based on mamdani method and classify the signal quality as LOS, NLOS, weak NLOS, and strong NLOS. Secondly, it performs an adaptive navigation strategy to exclude/mask the affected range measurements, while considering the satellite geometry constraints (i.e., DOP≤2).. For this purpose, comprehensive research to quantify the multi-constellation GNSS receiver with four constellation configuration (GPS,BeiDou, GLONASS and Galileo) has been carried out in various operating environments. This RSQM based GNSS receiver has capability to identify signal quality and perform adaptive navigation accordingly to improve the navigation performance. The results suggest that GNSS performance in terms of position error is improved from 5.4m to 2.3m averagely in the complex urban environment. Accumulating RSQM with GNSS has great potential for future industrial revolution (Industry 5.0) making things automatic and sustainable like autonomous vehicle operation.

The influence of station-to-station line orientation on sea current speed observations using Coastal Acoustic Tomography (CAT) was quantitatively investigated. For this purpose, we conducted CAT experiments at five stations in Yeosu Bay, South Korea. Through these experiments, the sea current speeds were estimated along a total of six tomographic observation lines with different orientations, and the results were compared with current speeds measured simultaneously by an Acoustic Doppler Current Profiler (ADCP). The comparison showed that the concordance between tomography-estimated sea current speed and ADCP-measured sea current speed tended to decrease as the acute angle between the predominant tidal current direction in Yeosu Bay and a tomographic observation line increased. This tendency is interpreted as arising because the smaller the difference between the two one-way travel times obtained during tomographic observations, the greater the effect of the travel time measurement error whose magnitude is relatively direction-independent. This interpretation was supported by a simple numerical simulation. Furthermore, quantitative analysis of these simulation results indicated that a smaller acute angle between the predominant sea current direction in the survey area and a tomographic observation line enhances the robustness of sea current speed estimation against travel time measurement errors. The results show that the station-to-station line in CAT should be arranged considering the predominant sea current direction in the survey area, which can provide an important guideline for selecting station locations.