Outer rotor mechanical and dynamic performance analysis for high-speed machine

Due to high-speed rotation, the problems about rotor mechanics and dynamics for outer rotor high-speed machine are more serious than conventional ones, in view of above problems the mechanical and dynamics analysis for an outer rotor high-speed permanent magnet claw pole motor are carried out. The rotor stress analytical calculation model was derived, then the stress distribution is calculated by finite element method also, which is coincided with that calculated by analytical model. In addition, the stress distribution of outer rotor yoke and PMs considering centrifugal force and temperature effect has been calculated, some influence factors on rotor stress distribution have been analyzed such as pole-arc coefficient and speed. The rotor natural frequency and critical speed were calculated by vibration mode analysis, and its dynamics characteristics influenced by gyroscope effect were analyzed based on Campbell diagram. Based on the analysis results above an outer rotor permanent magnet high-speed claw pole motor is design and verified.

3D Navigation with Six Degrees-of-Freedom using a Multi-Touch Display

With the introduction of new input devices, such as multi-touch surface displays, the Nintendo WiiMote, the Microsoft Kinect, and the Leap Motion sensor, among others, the field of Human-Computer Interaction (HCI) finds itself at an important crossroads that requires solving new challenges. Given the amount of three-dimensional (3D) data available today, 3D navigation plays an important role in 3D User Interfaces (3DUI). This dissertation deals with multi-touch, 3D navigation, and how users can explore 3D virtual worlds using a multi-touch, non-stereo, desktop display. The contributions of this dissertation include a feature-extraction algorithm for multi-touch displays (FETOUCH), a multi-touch and gyroscope interaction technique (GyroTouch), a theoretical model for multi-touch interaction using high-level Petri Nets (PeNTa), an algorithm to resolve ambiguities in the multi-touch gesture classification process (Yield), a proposed technique for navigational experiments (FaNS), a proposed gesture (Hold-and-Roll), and an experiment prototype for 3D navigation (3DNav). The verification experiment for 3DNav was conducted with 30 human-subjects of both genders. The experiment used the 3DNav prototype to present a pseudo-universe, where each user was required to find five objects using the multi-touch display and five objects using a game controller (GamePad). For the multi-touch display, 3DNav used a commercial library called GestureWorks in conjunction with Yield to resolve the ambiguity posed by the multiplicity of gestures reported by the initial classification. The experiment compared both devices. The task completion time with multi-touch was slightly shorter, but the difference was not statistically significant. The design of experiment also included an equation that determined the level of video game console expertise of the subjects, which was used to break down users into two groups: casual users and experienced users. The study found that experienced gamers performed significantly faster with the GamePad than casual users. When looking at the groups separately, casual gamers performed significantly better using the multi-touch display, compared to the GamePad. Additional results are found in this dissertation.

Applying Augmented Reality to Outdoors Industrial Use

Augmented Reality (AR) is currently gaining popularity in multiple different fields. However, the technology for AR still requires development in both hardware and software when considering industrial use. In order to create immersive AR applications, more accurate pose estimation techniques to define virtual camera location are required. The algorithms for pose estimation often require a lot of processing power, which makes robust pose estimation a difficult task when using mobile devices or designated AR tools. The difficulties are even larger in outdoor scenarios where the environment can vary a lot and is often unprepared for AR. This thesis aims to research different possibilities for creating AR applications for outdoor environments. Both hardware and software solutions are considered, but the focus is more on software. The majority of the thesis focuses on different visual pose estimation and tracking techniques for natural features. During the thesis, multiple different solutions were tested for outdoor AR. One commercial AR SDK was tested, and three different custom software solutions were developed for an Android tablet. The custom software solutions were an algorithm for combining data from magnetometer and a gyroscope, a natural feature tracker and a tracker based on panorama images. The tracker based on panorama images was implemented based on an existing scientific publication, and the presented tracker was further developed by integrating it to Unity 3D and adding a possibility for augmenting content. This thesis concludes that AR is very close to becoming a usable tool for professional use. The commercial solutions currently available are not yet ready for creating tools for professional use, but especially for different visualization tasks some custom solutions are capable of achieving a required robustness. The panorama tracker implemented in this thesis seems like a promising tool for robust pose estimation in unprepared outdoor environments.

CONCURRENT LOCALIZATION AND MAPPING WITH SONAR SENSORS AND CONSIDERATION OF VEHICLE MOTION

Simultaneous Localization and Mapping (SLAM) is a procedure used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. A typical SLAM consists of four main components, namely, experimental setup (data gathering), vehicle pose estimation, feature extraction, and filtering. Feature extraction is the process of realizing significant features from the unknown environment such as corners, edges, walls, and interior features. In this work, an original feature extraction algorithm specific to distance measurements obtained through SONAR sensor data is presented. This algorithm has been constructed by combining the SONAR Salient Feature Extraction Algorithm and the Triangulation Hough Based Fusion with point-in-polygon detection. The reconstructed maps obtained through simulations and experimental data with the fusion algorithm are compared to the maps obtained with existing feature extraction algorithms. Based on the results obtained, it is suggested that the proposed algorithm can be employed as an option for data obtained from SONAR sensors in environment, where other forms of sensing are not viable. The algorithm fusion for feature extraction requires the vehicle pose estimation as an input, which is obtained from a vehicle pose estimation model. For the vehicle pose estimation, the author uses sensor integration to estimate the pose of the mobile vehicle. Different combinations of these sensors are studied (e.g., encoder, gyroscope, or encoder and gyroscope). The different sensor fusion techniques for the pose estimation are experimentally studied and compared. The vehicle pose estimation model, which produces the least amount of error, is used to generate inputs for the feature extraction algorithm fusion. In the experimental studies, two different environmental configurations are used, one without interior features and another one with two interior features. Numerical and experimental findings are discussed. Finally, the SLAM algorithm is implemented along with the algorithms for feature extraction and vehicle pose estimation. Three different cases are experimentally studied, with the floor of the environment intentionally altered to induce slipping. Results obtained for implementations with and without SLAM are compared and discussed. The present work represents a step towards the realization of autonomous inspection platforms for performing concurrent localization and mapping in harsh environments.

An adaptive orientation misalignment calibration method for shoulder movements using inertial sensors : a feasibility study

Qualitative assessment of the progress in physical rehabilitation largely depends on accurate measurement of the range of movements and other kinematic parameters. In clinical practice, wearable inertial sensors have proved to be a potential candidate for such measurements, over the traditional marker based optical systems due to cost and space considerations. The accuracy of wearable sensors have a significant dependence on the initial orientation calibration and the assumption that the sensor will not slip or move with respect to the attached limb. This article introduces a novel calibration algorithm to correct initial orientation misalignment, as well as to track and correct subsequent alignment errors progressively throughout the experiment. The theoretical assertions are validated through controlled experiments with simulated accelerometer and gyroscope measurements.

Smartwatch: performance evaluation for long-term heart rate monitoring

Recent advancement in wearable technologies, particularly smart watches embedded with powerful processors, memory subsystems with various built-in sensors such as ac-celerometer, gyroscope and optical sensor in one single package has opened a whole new application space. One of the main applications of interest is the monitoring of movement patterns, heart rate, ECG and PPG particularly for longer duration's in natural environments. In this study, we conducted a performance evaluation on the optical heart rate sensor of the smartwatch with respect to the commonly used ECG and PPG devices. Results have shown that the heart rate acquired from the smartwatch is reasonably accurate with a high degree of correlation. Further, we conducted a preliminary exerise to evaluate sleep quality using the heart rate readings and accelerometer readings captured from the smartwatch and compared with a commercially available and clinically used non-contact sleep sensor, RESMED S+.