Identifying stator imbalance problems in induction machines (IMs) is crucial to preserving operating efficiency, reliability, and safety. This identification contributes to the IM’s overall health, prolongs its lifespan, and helps organizations meet regulatory and performance standards. Moreover, it enables the execution of maintenance tasks when required, improving resource use and decreasing shutdown periods. This research presents a Non-Destructive Technique (NDT) that utilizes a thermography-based machine learning algorithm for diagnosing Stator Imbalance Defects (SID) within IMs experimentally. This technique relies on assessing temperature distribution on the outer surface of the IM stator. Then, the evaluation of various statistical variables that provide valuable insights into the normality of temperature distribution within the designated area referred to as the Region of Interest (ROI) is presented. Initially, thermal images of the stator are captured under various operating conditions, including a healthy state, SID presence, and phase missing. Then, all thermal images are converted into thermographic data matrices for statistical analysis using the Machine Learning Algorithm Gaussian Naïve Bayes (GNB). This analysis aimed at classifying SID in IMs arising from deviations in the normalcy of temperature distribution within the ROI. To demonstrate the efficacy of the suggested GNB approach, a comparison with the Subspace Discriminant Classifier and Cubic Support Vector Machine is provided. The GNB algorithm exhibits exceptional accuracy of 95.2% in classification tasks while maintaining a minimal processing time, irrespective of the operational duration of the IM.

Stator Imbalance Defects Diagnosis of Induction Machine Using Thermography and Machine Learning Algorithms

Recent progress in hand gesture recognition has introduced several natural and intuitive approaches to drone control. However, effectively maneuvering drones in complex environments remains challenging. Drone movements are governed by four independent factors: roll, yaw, pitch, and throttle. Each factor includes three distinct behaviors—increase, decrease, and neutral—necessitating hand gesture vocabularies capable of expressing at least 81 combinations for comprehensive drone control in diverse scenarios. In this paper, we introduce a new set of hand gestures for precise drone control, leveraging an RGB camera sensor. These gestures are categorized into motion-based and posture-based types for efficient management. Then, we develop a lightweight hand gesture recognition algorithm capable of real-time operation on even edge devices, ensuring accurate and timely recognition. Subsequently, we integrate hand gesture recognition into a drone simulator to execute 81 commands for drone flight. Overall, the proposed hand gestures and recognition system offer natural control for complex drone maneuvers.

Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control

UAV gesture recognition, a novel human-computer interaction form, offers an intuitive approach to controlling UAVs in various environments. However, there is a lack of comprehensive datasets for AI-powered UAV gesture recognition. This paper contributes in several ways: (i) We introduce MD-UHGRD, a unique UAV static gesture dataset with 20, 000 images and annotations, collected from a diverse group of participants in different environmental conditions. This dataset is expected to bridge a significant gap in UAV gesture recognition algorithms. (ii) We propose SA-YOLO, a multifunctional UAV gesture recognition method that not only enables gesture recognition but also includes face and pedestrian tracking, optimizing UAV control in complex scenarios. SA-YOLO incorporates the Spatial Asymptotic Feature Pyramid Network (SAFPN), Scale Pyramid Pooling with Cross Stage Partial Networks Convolution (SPPCSPC), and Space-to-Depth Convolution (SPD-Conv). (iii) Extensive evaluation of SAYOLO on MD-UHGRD establishes it as a benchmark in this domain. Our method demonstrates high accuracy, processing speed, and a compact model size, achieving a 93.2% mean Average Precision (mAP) with 10.3 million parameters and 48 frames per second (FPS). Among competing models, SA-YOLO not only achieves the highest mAP but also maintains a balance in model size and FPS. The database and code are available at: https://github.com/ijcnn2024/SA-YOLO.

Intuitive UAV Operation: A Novel Dataset and Benchmark for Multi-Distance Gesture Recognition

A Two-Stage Real-Time Gesture Recognition Framework for UAV Control

Gesture recognition is a very important technology in human-computer interaction. In classroom teaching, gesture recognition technology can also be used to improve the digital level of teaching, thus replacing the role of the page turning pen. In response to the complexity of the background and individual differences of gestures, and based on the characteristics of gesture interaction in classroom teaching, a static gesture recognition algorithm under a simple background can be selected to effectively detect and recognize the control gestures of multimedia courseware in classroom teaching, thereby achieving the goal of improving the level of classroom teaching informatization. This article proposes a method for controlling the flipping of multimedia courseware using static gesture segmentation and gesture feature extraction techniques. Firstly, a gesture model is constructed using an SVM classifier to classify and recognize gestures. Secondly, coded by the OpenCV Python library, opening the camera, collecting hand images, and fusing the OSTU threshold processing method based on the YCrCb color space, threshold segmentation gesture is selected. Thirdly, based on edge detection, elliptical Fourier descriptors are used to fit edges and extract gesture features. Finally, the SVM classifier is used to determine the gesture type, and then the internal communication mechanism is used to call the interface that controls the flipping of the PPT. The experimental results show that the above methods can be applied in classroom teaching under a simple background, and the average recognition accuracy rate of common gestures used to control multimedia courseware page turning reaches 100%, which can meet the requirements for the accuracy of gesture recognition and the stability of multimedia courseware control.

Application of Static Gesture Recognition Based on OpenCV

Recent advances in hand gesture recognition have produced more natural and intuitive methods of controlling unmanned aerial vehicles (UAVs). However, in unknown and cluttered environments, UAV motion planning requires the assistance of hand gesture interaction in complex flight tasks, which remains a significant challenge. In this paper, a novel framework based on hand gesture interaction is proposed, to support efficient and robust UAV flight. A cascading structure, which includes Gaussian Native Bayes (GNB) and Random Forest (RF), was designed, to classify hand gestures based on the Six Degrees of Freedom (6DoF) inertial measurement units (IMUs) of the data glove. The hand gestures were mapped onto UAV’s flight commands, which corresponded to the direction of the UAV flight.The experimental results, which tested the 10 evaluated hand gestures, revealed the high accuracy of online hand gesture recognition under asynchronous detection (92%), and relatively low latency for interaction (average recognition time of 7.5 ms; average total time of 3 s).The average time of the UAV’s complex flight task was about 8 s shorter than that of the synchronous hand gesture detection and recognition. The proposed framework was validated as efficient and robust, with extensive benchmark comparisons in various complex real-world environments.

/pdf/online-hand-gesture-detection-and-recognition-for-uav-motion-3pzqakow.pdf

Online Hand Gesture Detection and Recognition for UAV Motion Planning

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Online Hand Gesture Detection and Recognition for UAV Motion Planning