43 results about "Cross-training" patented technology

An exercise device includes a frame, a pair of foot supports, and at least one four-bar linkage assembly coupled to the frame. The at least one linkage assembly is coupled to at least one of the foot supports. The four-bar linkage assembly directs the foot support in a generally elliptical motion while in use. The generally elliptical motion defines a major dimension extending at an angle from horizontal that is within the range of about thirty degrees (30°) to about seventy-five degrees (75°) and the major dimension having a length that is within the range of about ten inches to about eighteen inches.

In an elliptical step exercise apparatus where stride length can be varied the various user programs can take advantage of this feature to provide for an enhanced workout. A control system can be used to implement a preprogrammed exercise routine such as a hill program where stride is shortened as the user goes up a simulated hill and lengthened as the user goes down the hill. In an interval training program, stride length can be increased and decreased at periodic intervals. In a cross training program, stride length can be decreased when the user is pedaling backwards and increased when the user is pedaling forwards.

An exercise device includes a frame, first and second foot links, first and second foot supports, a coupling system and a guide system. Each foot link has a first portion and a second portion. The first and second foot supports are carried by the first and second foot links, respectively. The coupling system is associated with the first portion of each foot link for coupling the first portion to the frame so that the first portion of each foot link travels in a closed path relative to the frame. The guide system supports the second portions along a preselected reciprocating path of travel as the first portions of the respective foot links travel along their paths of travel. The guide system is selectably positionable at an angle from horizontal within the range of 45 degrees to 75 degrees.

An exercise device includes one or more foot supports movable along an arcuate path defined around a point of rotation. The arcuate path is divisible into machine defined user selectable arc segments and the movement of the foot support(s) is limited to the machine defined user selected arc segment. The exercise device can include a frame, a linkage movably engaged with the frame, a foot support movably engaged with the linkage, a crank arm movably engaged with the frame, a motor operative to move the crank arm location with respect to the frame, and a drive linkage movably engaging the linkage.

An integrated method for achieving short-term activity resource staffing levels and long-term resource action planning for a portfolio of services projects is presented. The method establishes the short-term resource-staffing level for each project activity in a portfolio of services projects, subject to short-term resource availability in each skill type and substitutability among skill types. It further establishes the long-term resource-staffing level and long-term resource action planning using combination of a plurality of resource actions that include hiring, contracting, and cross-training of skill types.

An exercise apparatus is designed specifically to provide a cross training apparatus for mixed martial arts training. The exercise apparatus combines separate training methodologies and apparatuses that are desirable for mixed martial arts training, then arranges each of those apparatuses into a desired spatial orientation at separate stations. Stations include a close combat station, two stations that are directed upper body strength training, and a core training station. Users of the apparatus go through an exercise routine at each station, then move immediately to the next station and go through a routine for that station. The exercise routines may be timed.

An exercise apparatus is designed specifically to provide a cross training apparatus for mixed martial arts training. The exercise apparatus combines separate training methodologies and apparatuses that are desirable for mixed martial arts training, then arranges each of those apparatuses into a desired spatial orientation at separate stations. Stations include a close combat station, two stations that are directed upper body strength training, and a core training station. Users of the apparatus go through an exercise routine at each station, then move immediately to the next station and go through a routine for that station. The exercise routines may be timed.

The invention belongs to the technical field of radio signal reconstruction, and discloses a signal reconstruction method based on a generative adversarial network. The method includes the following steps: under the framework of the generative adversarial network, constructing a generator for generating a signal and a discriminator for judging whether the signal is real data, and updating and optimizing parameters of the generator through the cross training of the generator and the discriminator. The scheme of the invention is suitable for the signal reconstruction in a complex electromagneticenvironment, has the characteristics of simple operating process, high similarity of generated data and the like, and effectively overcomes the shortcomings of low similarity of generated samples andinsufficient sample diversity existing in a current signal reconstruction method; and the scheme of the invention proposes a method of implementing the signal reconstruction by using the generative adversarial network, the cross-game training of the generative adversarial network is adopted, signal features are extracted through the mapping of a network layer, the cumbersome and inefficient process of performing parameter measurement, feature extraction and the like on the signal can be eliminated, and the problem of difficulties in signal analysis in the complex electromagnetic environmentscan be solved.

The invention discloses a synthetic aperture radar (SAR) target identification method based on a multi-parameter optimization generative adversarial network, and mainly solves the problems that the identification rate is not high during classifier training and the classifier parameters obtained by training cannot be ensured to be an optimal solution in the prior art. According to the implementation scheme, an initial training sample set and a test sample set are generated, and initial training samples are expanded to generate a final training sample set; Setting a structure and a parameter group number of the generative adversarial network; Training the generative adversarial network by adopting a multi-group network parameter cross training method, and training a discriminator in the generative adversarial network by utilizing the training set samples and the pseudo samples generated by the generator at the same time; And identifying the target model by using the trained discriminators in the plurality of groups of generative adversarial networks, adding the results obtained by the plurality of groups of discriminators, and averaging to obtain an identification result of the target model. According to the method, the accuracy of SAR target identification is improved, and the method can be used for identifying the static SAR target.

The invention discloses a method for establishing an oil deposit development effect prediction model. The method includes the following steps: 1. Using known oil deposit development data to conduct K-fold cross training on an initial neural network, obtaining k sets of network parameters; 2. Based on the k sets of network parameters and the K sets of validation data, computing k sets of error data corresponding to the k set of validation data; 3. Based on the k sets of error data, computing the weight of each set of network parameter, and based on the weight, conducting weighted average on the k sets of network parameters, obtaining an average network parameter, based on the average network parameter, determining an optimized neural network so as to establishing an oil deposit development effect prediction model. Compared with traditional BP neural network algorithm, the method herein can repeatedly use samples, and the limited sample data can be sufficiently used, and achieves training and estimation from different angles.

The invention relates to a critical region detection based accurate complex target identification method. The critical region detection based accurate complex target identification method comprises the following steps: using a cross training method to fuse and train the whole neural network, using the convolutional neural network to extract the target characteristic, using a detection sub-networkto detect a critical region of a complex target by taking an anchor block as a reference, using a regional standard pond to convert a critical region pond into a characteristic pattern at fixed size,classifying the critical region by using a classification sub-network, and fusing the classification results of various critical regions to accurately identify the target. The whole network comprisesthe critical region detection sub-network and the critical region classification sub-network, the detection sub-network detects the critical region having a distinction degree of the complex target, the classification sub-network classifies the critical region, the classification results of the various regions are fused to identify the whole target. The two sub-networks share the characteristic extracted by the VGG convolutional neural network, so that the effect of rapidly accurately identify the complex target is achieved.

An apparatus used for cardiovascular workouts and strength training workouts contains a treadmill, a first weight-engaging pulley mechanism, a second weight-engaging pulley mechanism, a first guide channel, and a second guide channel. The treadmill is used to complete the cardiovascular workouts. The first weight-engaging pulley mechanism and the second weight-engaging pulley mechanism are used for strength training. Since the weight arrangement of the apparatus is movable along the first guide channel and the second guide channel, the user can complete a wide variety of strength training workouts. The apparatus also contains an incline-adjustment mechanism, which orients the treadmill belt track to simulate an incline or a decline. A belt direction-reversing mechanism of the apparatus helps change the direction a belt that is layered along the treadmill belt track.

The embodiment of the invention discloses a target recognition model training method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. One specific embodiment of the method comprises the steps: acquiring a training sample set, wherein training samples in the training sample set are labeled target sample images; constructing a first deep convolutional neural network and a second deep convolutional neural network which are different; performing positive and negative sample sampling on the training sample set by using a first deep convolutional neural network and a second deep convolutional neural network to obtain a positive sample set and a negative sample set respectively; and performing cross training on the first deep convolutionalneural network and the second deep convolutional neural network based on the positive sample set and the negative sample set to obtain a target recognition model. According to the embodiment, a weaksupervision target recognition technology based on positive and negative learning is provided, weak supervision learning can be carried out by fully utilizing error annotation samples, and the robustness of the model is improved.

The invention discloses a micro-seismic signal classification and identification method based on deep learning, and belongs to the field of signal analysis and identification. The method includes following steps: step 1, establishing a sample database of micro-seismic signals and blast signals; step 2, extracting characteristics of the dominant frequency, an after-peak attenuation coefficient, andan energy gravity center coefficient of sample signals, and forming a sample characteristic data training set and a test set; step 3, training a deep neural network classification and identificationmodel by employing the sample characteristic data training set, verifying a classification and identification effect of the signal classification and identification model by employing data of the testset, and continuously improving the classification precision through crossed training; and step 4, extracting a characteristic vector of a to-be-identified signal, inputting the signal into the signal classification model, and obtaining an identification result. The method has characteristics of simple algorithm, high adaptability and timeliness, and high identification accuracy, the coal mine micro-seismic signals and the blast signals can be effectively classified, and the technical value and the application prospect are very good.

A multi-functional basketball cross-training apparatus, system, and method are provided. The apparatus includes at least two substantially vertical members. At least two bases are connected to a lower portion of the at least two substantially vertical members, respectively. The two bases provide support for the at least two substantially vertical members on a ground surface. At least one cross bar is removably connectable between the at least two substantially vertical members. The cross bar is position-adjustable along a vertical height of each of the two substantially vertical members. An upper portion of each of the at least two substantially vertical members has a plurality of members having a humanoid configuration.

An accessory device for use with an elliptical cross training machine comprises two like-configured attachments, each of with generally comprises a handle attachment bracket, a substantially horizontal stabilizer bar, a combined palm push pad/handle attachment bracket, a fixed handle, and a push palm pad. The fixed handle and the palm pad are fully and rotatably movable around and about the stabilizer bar. Each is also longitudinally movable along the stabilizer bar to provide inward and outward movement of the fixed handle and the push palm pad relative to the original handle positions of the elliptical cross trainer to which they are attached. The accessory device allows the user to have a combined platform to push from, in a form that is similar to doing a push up from the floor, as well as a fixed point handle that presents to the user at a more natural shoulder angle.

The disclosure relates to a process which determines a cross-training plan in order to ensure a company or business unit will be better prepared for a pandemic event by cross-training additional employees in the most critical services or functions. The pandemic cross-training plan is determined by utilizing such criteria such as geography, experience-level, and employee preference to strategically and systematically cross-train an employee in a critical function. This can be completed either manually through a criteria matrix approach or automatically by a computing device and utilizing mathematical formulas.

The embodiment of the invention discloses an identification method and device for junk short messages and a storage medium. The method can comprise the steps of: generating a first fingerprint libraryand a first classifier according to short message samples in a short message sample library and indication information corresponding to each short message sample, wherein the indication information is used for indicating whether the corresponding short message samples are junk short messages; training the short message sample library, the first fingerprint library and the first classifier according to a set butterfly cross training strategy to obtain a second fingerprint library after training and a second classifier after training; and based on a set serial verification strategy, the secondfingerprint library and the second classifier, verifying a to-be-verified short message, and determining a verification result of the to-be-verified short message, wherein the verification result comprises that the to-be-verified short message is a junk short message or the to-be-verified short message is not a junk short message. Complementation of two junk short message identification technologies can be implemented, and a success rate of identification can also be improved.