305 results about "Massive gravity" patented technology

The present invention relates to the field of battery and capacitor charging. In particular, the present invention provides pulsed current charging using changes, regardless of polarity, in the local energy environment to obtain power. The present invention relates, for example, to ambient energy charging thin film batteries, other batteries, or capacitors, via, for example, polyvinyladine fluoride homopolymer (PVDF), PVDF bi-axially poled, or other piezoelectric materials. Ambient energy may be defined as any change in energy within the local environment. Charging can be accomplished with, and is not limited to, positive or negative changes of the following energy types: thermal; visible light, including infrared and ultraviolet; mechanical motion or impact; triboelectric, including airflow or physical contact; movement in relation to a gravitational plane (increase or decrease in gravitational potential energy); and radio frequency (RF) electromagnetic energy, regardless of specific frequency.

The invention provides an underwater releaser, consisting of an electromotion part, a trigger mechanism, an energy storage mechanism, a speed reduction mechanism, a bidirectional overflow mechanism, a dynamic sealing transmission mechanism and a releasing arm, wherein the electromotion part is positioned inside a pressure-resistant cabin; a lower bottom plate of the pressure-resistant cabin is connected with an isolating cabin so as to jointly form a seawater isolating cabin segment in a circle; the seawater isolating cabin segment is provided with the bidirectional overflow mechanism; and the trigger mechanism, the energy storage mechanism and the speed reduction mechanism are all arranged inside the seawater isolating cabin segment. The underwater releaser adopts the design of combining two blocks of U-shaped magnetic steel, ensuring that the maximum trigger power is close to the scalar sum of the gravitational force and repulsive force between the U-shaped magnetic steel; by using a plane volute spring to store energy and using a gear set to reduce speed, the underwater releaser can provide great releasing power; a seawater isolating cabin is built through the bidirectional overflow design, which avoids seawater impact and the adverse effect of marine organisms; through a rotary unhooking mechanism, the releaser can have greater bearing capacity. The underwater releaser is not only better in watertight reliability, but also can work underwater for a long period of time without replacing components.

The present invention provides a multi-unmanned aerial vehicle cooperation sequential coupling task distribution method of a mixing gravitation search algorithm, and relates to the unmanned aerial vehicle cooperation task distribution field. The method comprises: a multi-unmanned aerial vehicle cooperation task distribution model is constructed in the time coupling constraint, a fitness function and a task constraint are obtained, in the gravitation search algorithm based on genetic operators, the individual discretization coding and the population are initialized, the individual is decoded, and the fitness function is employed to calculate the fitness and perform individual update. Because the genetic operators are added in the gravitation search algorithm, the multi-unmanned aerial vehicle cooperation sequential coupling task distribution method of the mixing gravitation search algorithm has good general applicability, the number of times of long-term simulation tests and data statistics constructs a more improved database to allow the model to be more improved; and compared to the discrete particle swarm algorithm, the mixing gravitation search algorithm can be rapidly converged, the searching optimization result is optimal, the iteration process is brief, and the convergence speed is fast.

Disclosed is a spatial gravity model based fuzzy c-means remote sensing image automatic classification method which is suitable for automatic segmentation and classification of remote sensing images, medical images and other image. The spatial gravity model based fuzzy c-means remote sensing image automatic classification method comprises the steps of determining the number of pixels of a remote sensing digital image and performing clustering on the image through a standard FCM (Fuzzy C-Means) model to obtain an initial fuzzy membership matrix and clustering center; solving the spatial gravity and spatial constraint penalty factors between every pixel and other pixels in neighborhood windows of the pixel in turn to obtain a fuzzy factor as the formula finally; adding the fuzzy factor as the formula to the standard FCM model to obtain a new clustering objective function; solving the fuzzy matrix as the formula and the clustering center as the formula in a circulating mode until the clustering center does not continue to be changed any longer or the operation reaches the maximum number of iterations; performing class marking on every pixel point of the remote sensing image through the maximum membership matrix criterion according to the finally solved fuzzy membership matrix as the formula to determine the class of every pixel to form a remote sensing image classification thematic map so as to implement the automatic classification of remote sensing digital images. The spatial gravity model based fuzzy c-means remote sensing image automatic classification method is simple, high in automation degree, small affected by image noise and high in image segmentation classification accuracy.

The invention discloses a method for navigation positioning by using a gravitation vector and gradient tensor. The method comprises the following steps: firstly, establishing a background field three-dimensional information databank of a target area according to beforehand observation, wherein basic elements comprise position coordinates of different points, gravitation vector invariants and gravitation gradient tensor invariants, subsequently observing and calculating according to the gravitation and the gravitation gradient in real time so as to obtain three invariants, judging whether navigation positioning can be performed directly or not by judging whether a resolving matrix is of full rank, if navigation positioning can be performed directly, performing iterative computation to obtain the position of a point to be positioned according to a least squares algorithm, if navigation positioning cannot be performed directly, performing matching search with the combination of an inertial navigation positioning technique, and performing iterative search for multiple times so as to obtain the position of the point to be positioned. The method has the outstanding characteristics that the gravitation vector invariants and gravitation gradient tensor invariants are adopted, and not only is contribution of all components of gravitation field information considered, but also the components are not related to the posture, so that compared with a conventional algorithm, the method can greatly reduce the influence of posture errors.

The invention discloses a wind speed section prediction method and system based on an artificial neural network, and the method and system are used for the short-time wind speed prediction of a wind field. The method comprises the steps: decomposing an initial complex time sequence into a plurality of time sequences in a simple structure through VMD (variational mode decomposition); carrying out the feature selection through GSO (Gram-Schmidt orthogonal); taking a processed wind speed sequence as the input of the ANN (artificial neural network), wherein the output of the ANN is the upper and lower bounds of wind speed at a future moment; finally training the weight and bias of the ANN through an MOGSA (Multi objective gravitational search algorithm), taking two contradictory indexes (coverage rate and section width) as an optimization target, and obtaining an optimal scheme set. The wind speed section predicted through the method has a higher coverage rate for the actual wind speed section, and is small in section width. The above combined model enables the prediction accuracy to be improved to a very high level.