This paper presents the development of a vision-based neuro-fuzzy controller for a two axes gimbal system mounted on a small Unmanned Aerial Vehicle (UAV). The controller uses vision-based object detection as input and generates pan and tilt motion and velocity commands for the gimbal in order to keep the interest object at the center of the image frame. A readial basis function based neuro-fuzzy system and a learning algorithm is developed for the controller to address the dynamic and non-linear characteristics of the gimbal movement. The controller uses two separate, but identical radial basis function networks, one for pan and one for tilt motion of the gimbal. Each system is initialized with a fixed number of neurons that act as rules basis for the fuzzy inference system. The membership functions and rule strengths are then adjusted with the feedback from the visual tracking system. The controller is trained off-line until a desired error level is achieved. Training is then continued on-line to allow the system to accommodate air speed changes. The algorithm learns from the error computed from the detected position of the object in image frame and generates position and velocity commands for the gimbal movement. Several tests including lab tests and actual flight tests of the UAV have been carried out to demonstrate the effectiveness of the controller. Test results show that the controller is able to converge effectively and generate accurate position and velocity commands to keep the object at the center of the image frame. 相似文献
Multimedia Tools and Applications - Copy-move image forgery is a type of digital image forgery where content is copied and pasted within the same image, either by hiding foreground objects within... 相似文献
Neural Processing Letters - Emerging Trends in the use of smart portable accessories, particularly within the context of the Internet of Things (IoT), where smart sensor devices are employed for... 相似文献
Supraglacial debris (SGD) cover on mountain glaciers is known to greatly influence various glacier processes and alter their response to climate change. In this study, vital glacier parameters of five glaciers with varying debris coverage (about 7–26%), located in Zanskar basin, Ladakh Himalaya, were monitored using Landsat imagery (from 1977 to 2013) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM). Results reveal how varying amounts of SGD induce categorically distinct responses on glaciers, sharing same geographical settings. The clean glaciers (CG) in the area were found to have higher retreat rates (about 8–19 m year?1), comparable areal shrinkage (about 13–15%), higher accumulation area ratios (AARs) (>0.5), rapid increase in SGD (about 1.6–3.0-fold), and association with increasing numbers of peri- and proglacial lakes (2–8 per glacier). The debris-covered glaciers (DCG) showed minimal frontal changes (about 2–5 m year?1), higher areal shrinkage (about 14–21%), low AARs (<0.5), slow SGD changes (about ≤1.2-fold), and association with increasing numbers of peri- and supraglacial lakes (SGLs) (2–39 per glacier). Moreover, while changes in SGD had a strong negative correlation with changes in glacier area, retreat rates, and AAR, they were positively related with increase in area of SGLs. 相似文献
Different nanostructures of TiO2 play an important role in the photocatalytic and photoelectronic applications. TiO2 nanotubes (TNTs) have received increasing attention for these applications due to their unique physicochemical properties. Focusing on highly functional TNTs (HF‐TNTs) for photocatalytic and photoelectronic applications, this study describes the facile hydrothermal synthesis of HF‐TNTs by using commercial and cheaper materials for cost‐effective manufacturing. To prove the functionality and applicability, these TNTs are used as scattering structure in dye‐sensitized solar cells (DSSCs). Photocatalytic, optical, Brunauer‐Emmett‐Teller (BET), electrochemical impedance spectrum, incident‐photon‐to‐current efficiency, and intensity‐modulated photocurrent spectroscopy/intensity‐modulated photovoltage spectroscopy characterizations are proving the functionality of HF‐TNTs for DSSCs. HF‐TNTs show 50% higher photocatalytic degradation rate and also 68% higher dye loading ability than conventional TNTs (C‐TNTs). The DSSCs having HF‐TNT and its composite‐based multifunctional overlayer show effective light absorption, outstanding light scattering, lower interfacial resistance, longer electron lifetime, rapid electron transfer, and improved diffusion length, and consequently, JSC, quantum efficiency, and record photoconversion efficiency of 10.1% using commercial N‐719 dye is achieved, for 1D‐based DSSCs. These new and highly functional TNTs will be a concrete fundamental background toward the development of more functional applications in fuel cells, dye‐sensitized solar cells, Li‐ion batteries, photocatalysis process, ion‐exchange/adsorption process, and photoelectrochemical devices. 相似文献
In this work, a low temperature aqueous chemical growth methodology was used for the fabrication of CuO nanostructures. The as-synthesised nanostructures were then elaborately characterised by number of analytical techniques such as scanning electron microscopy (SEM) and X-ray powder diffraction (XRD). The obtained nanostructures were observed to possess interlaced rice-shaped structural features with the length and width of individual rice determined to be in the range of 200–300 nm and 50–100 nm respectively. The unique nanostructures when utilised as electrode material exhibited excellent electro-catalytic potential towards oxidation of hydrazine in alkaline media. The excellent conductive of CuO added by the high surface area of obtained nanorice-like structures enabled development of highly sensitive (3087 µA mM−1 cm−2), selective and stable electrochemical sensor for hydrazine. In addition, the successfully application of the developed sensor in spiked tap, bottled and industrial water samples for the detection of hydrazine suggested its feasibility for practical environmental application.
The effect of metal(Ti,Ni,and Au) electrodes on humidity sensing properties of electrospun TiO2 nanofibers was investigated in this work.The devices were fabricated by evaporating metal contacts on SiO2 layer thermally grown on silicon substrate.The separation between the electrodes was 90μm for all sensors.The sensors were tested from 40%to 90%relative humidity(RH) by AC electrical characterization at room temperature. When sensors are switched between 40%and 90%RH,the corresponding response and recovery time are 3 s and 5 s for Ti-electrode sensor,4 s and 7 s for Ni-electrode sensor,and 7 s and 13 s for Au-electrode sensor.The hysteresis was 3%,5%and 15%for Ti-,Ni-,and Au-electrode sensor,respectively.The sensitivity of Ti,Ni,and Au-electrode sensors are 7.53 MΩ/%RH,5.29 MΩ/%RH and 4.01 MΩ/%RH respectively at 100 Hz.Therefore Ti-electrode sensor is found to have linear response,fast response and recovery time and higher sensitivity as compared with those of Ni- and Au-electrode sensors.Comparison of humidity sensing properties of sensors with different electrode material may propose a compelling route for designing and optimizing humidity sensors. 相似文献
Live virtual machine migration is one of the most promising features of data center virtualization technology. Numerous strategies have been proposed for live migration of virtual machines on local area networks. These strategies work perfectly in their respective domains with negligible downtime. However, these techniques are not suitable to handle live migration over wide area networks and results in significant downtime. In this paper we have proposed a Machine Learning based Downtime Optimization (MLDO) approach which is an adaptive live migration approach based on predictive mechanisms that reduces downtime during live migration over wide area networks for standard workloads. The main contribution of our work is to employ machine learning methods to reduce downtime. Machine learning methods are also used to introduce automated learning into the predictive model and adaptive threshold levels. We compare our proposed approach with existing strategies in terms of downtime observed during the migration process and have observed improvements in downtime of up to 15 %. 相似文献