34 results about "Adaptive integration" patented technology

An adaptive integration activity management framework for on demand business process integration provides a mechanism to enable easy integration of legacy and new applications. The framework minimizes the effort need to integrate a new application into an existing business process environment such that the new activity is a “plug-in” into an action manager by implementing a standard adaptation layer. Activity integration is implemented in the principle of “on-demand” because it is invoked as required, so the communication and collaboration between partners become much more flexible.

Particular techniques for improving the scalability of concurrent programs (e.g., lock-based applications) may be effective in some environments and for some workloads, but not others. The systems described herein may automatically choose appropriate ones of these techniques to apply when executing lock-based applications at runtime, based on observations of the application in the current environment and with the current workload. In one example, two techniques for improving lock scalability (e.g., transactional lock elision using hardware transactional memory, and optimistic software techniques) may be integrated together. A lightweight runtime library built for this purpose may adapt its approach to managing concurrency by dynamically selecting one or more of these techniques (at different times) during execution of a given application. In this Adaptive Lock Elision approach, the techniques may be selected (based on pluggable policies) at runtime to achieve good performance on different platforms and for different workloads.

The invention discloses a gas adaptive integration valve with double air sources, which is composed of a valve body (1), wherein one end of the valve body (1) is provided with a low-pressure air source air inlet (3) and a high-pressure air source air inlet (4); a self-sealing valve group (10) is installed in each of the low-pressure air source air inlet (3) and the high-pressure air source air inlet (4); a self-regulating voltage-stabilizing valve (5), an ODS (Ozone Depleting Substances) air passage selection component (6) and a main air passage selection component (7) are installed in the valve body (1); the self-regulating voltage-stabilizing valve (5), the ODS air passage selection component (6) and the main air passage selection component (7) respectively abut against the corresponding pressure regulating reed by the respective pressure regulating component; the pressure regulating reed is abutted with a regulating pulling rod (13); the regulating pulling rod (13) is provided witha convex-concave structure which causes the pressure regulating reed to act; two ends of the regulating pulling rod (13) are supported in chutes or holes on the valve body (1); and the valve body (1)is also provided with a locating slot (15) for locating an operating lever (14) on the regulating pulling rod (13). The gas adaptive integration valve with double air sources has simple structure, a manual selection switch is omitted, safety is improved, and the production cost is lowered.

The invention relates to a MIMO-SCFDE adaptive transmission scheme based on model-driven deep learning. The invention establishes an adaptive transmission model based on the MIMO‑SCFDE system. AMNet and ADNet are used to replace the signal modulation and modulation identification parts in the traditional system respectively. AMNet adopts a combined network with 2D CNN, LSTM and FC-DNN as sub-networks to form an integrated neural network model, and adjusts the modulation mode of the sending end according to the channel conditions of the receiving end, and inputs the feature information extracted from the received signal into multiple sub-networks , and realize the conversion of features and optimal modulation schemes according to the network parameters obtained through training. At the same time, the received power under different path delays is selected as the adaptive factor to realize the adaptive integration of the results of each sub-network. According to the cyclic spectrum, ADNet has the advantage of accurately detecting the signal type under low signal-to-noise ratio, and based on the complexity of the cyclic spectrogram, it completes the adaptive selection of the modulation recognition scheme. This system is more suitable for the performance requirements of the 5G communication system.

The invention proposes an adaptive integral backstepping control method for anti-load disturbance of PMSM used in elevators. Compare the motor speed w with the given motor speed w * By comparison, the speed error e is obtained; according to the feedback motor speed w and electromagnetic torque T e Estimated load torque τ L ;The estimated load torque τ L and the rotational speed error e are input into the adaptive integral backstepping controller for adjustment to eliminate the steady-state error of the speed so as to obtain the virtual control quantity of the q-axis of the stator current in the rotating coordinate system d-q and use the excitation current component as a reference value to input the current In the ring, and the stator current i after the coordinate transformation d Make a difference to get the d-axis stator current error e d ;According to the q-axis stator current error e q and d-axis stator current error e d Calculate the control voltage u d and u q ; will u d and u q After inverting through the park, it is input to the SVPWM pulse width modulation module to generate the pulse signal required to drive the inverter, thereby driving the motor to run. The invention improves the robustness of the electric system of the elevator, and enhances the comfort and speed of the elevator.

The invention discloses an infrared focal plane array non-uniformity correction method adapted to the dynamic adjustment of integration time based on a neural network, which belongs to the field of non-uniformity correction of an infrared system. The method includes: calibrating at multiple working points of the system, calculating Average gray-scale response; use mathematical regression to calculate the interpolation curve of average gray-scale response and pixel gray-scale response under different integration times; build and train a forward neural network for non-uniformity correction; use the trained neural network to perform Non-uniformity correction, the integration time can be adjusted dynamically during the correction process. The present invention overcomes the disadvantages of the previous infrared system, such as few steps of integration time, large adjustment span, and troublesome switching, makes the adjustment of integration time more delicate and flexible, expands the adaptability of the infrared system to scenes with a large dynamic temperature range, and is beneficial to infrared systems. The system performs intelligent imaging on the target.

The invention discloses an intelligent driving vehicle environment adaptive merging method under an urban environment. The method comprises steps: an initial state vector is extracted; an action variable is calculated according to a greedy strategy, a merging scene is updated while a merging action is executed, if the action variable adopts a random action, a merging gap and a merging action are selected with a uniform probability, if an intelligent method is adopted, candidate gaps comprise a front vehicle, a following vehicle and a merging vehicle, the maximum action value functions of all candidate gaps are compared, the maximum value function is selected, the gap and the action corresponding to the maximum value are picked out, and a target merging gap and an intelligent merging actionare returned; the state vector at a next moment is sensed; a reward value is calculated according to the environmental feedback information; the initial state vector, the action variable, the state vector at the next moment and the reward value are saved to a sample set, and after enough samples are obtained, evaluation and improvement are carried out according to an LSQ method; and the above steps are repeated until merging succeeds. The sample set and the learning time are lower than a Q learning algorithm, and the success rate is high.