689results about How to "Achieve adaptive" patented technology

The invention discloses a self-adaption alarming method for preventing tailgating with a front car on an expressway. The method comprises the following steps of: adapting to driver characteristic and driving environment based on a safety time logic algorithm; and performing self-adaption adjustment on a safety time threshold value, namely an alarming threshold to effectively avoid false-alarm. According to the algorithm, an alarm inhibition strategy is set by determining the intension of a driver to reduce false-alarm rate according to the selection of an initial mode and vehicle driving relevant information acquired by a vehicle-mounted sensor so as to calculate collision time (TTC) of the own car and the front car; the current alarm safety threshold value (TW) is calculated in real time according to the driver characteristic and the current driving environment; and finally, whether the potential danger of tailgating with the front car exists is determined according to the calculated collision time (TTC) of the own car and the front car and the alarm safety threshold value (TW); and an alarm instruction is sent when the potential danger exists. By utilizing the method, the potential tailgating danger can be accurately found out in real time according to the driving age of the driver, the driving environment, the vehicle brake type and the like so as to alarm the driver; and therefore, the occurrence of the tailgating accidents of the own car and the front car on the expressway can be effectively prevented.

The invention relates to an SOC estimation method for a series-wound battery pack having an equalization circuit. According to the estimation method, adaptive extended Kalman filtering SOC estimation is carried out on the single batteries with the lowest voltage or the highest voltage in the charge-discharge stage so as to obtain the SOC of the series-wound battery pack. For eliminating influence on the measurement signal from noise, and for analyzing the unstable and sharply-changed voltage and current signals, wavelet transform is performed before the implementation of the adaptive extended Kalman filtering so as to realize denoising and analyzing of the signal; and the condition that the curves of the battery parameters and the open circuit voltage-state of charge (OCV-SOC) are changed along with the changes of temperatures and the changes of the SOC is taken into consideration. By adoption of the estimation method, online updating of the parameters can be performed, so that the SOC estimation precision can be improved; the SOC of the battery pack can be accurately estimated under the premise of ensuring the safety operation of the series-wound battery pack; therefore, the estimation method is applicable to active equalization and passive equalization; and meanwhile, by adoption of the estimation method, the influence from noise can be effectively eliminated, and the voltage and current signals can be effectively analyzed.

The invention relates to an adaptive method of and intelligent wristband. Whether the intelligent wristband is in an off state or a worn state is judged according to operation data, heart rate data and blood oxygen saturation data; when the intelligent wristband is in the off state, an operation sensing module and a control unit are switched into a power saving mode, and power of other modules except for the operation sensing module and the control unit is switched off; when the intelligent wristband is in the worn state, an operation sensing module, a heart rate sensing module and a blood oxygen saturation sensing module are switched into a normal operating mode, respectively detect the operation data, the heart rate data and the blood oxygen saturation data, and store the data to a storage unit. Thus, adaptiveness of the off state and the worn state of the intelligent wristband is realized, operation is facilitated and simple, frequent operations during usage of the intelligent wristband are avoided, user experience is improved, battery power is saved, and service life of the intelligent wristband is prolonged.

The invention discloses a self-adaptive configuration method and device for a mobile phone terminal, aiming at solving the technical problem that network parameters are required to be manually reconfigured when the terminal is switched among different operators. By acquiring the network parameters, recognizing the network operators and then acquiring operating parameters related to the network operators, the operating parameters such as emergency call numbers, short message centers, access point names, homepages and the like of the mobile phone terminal can be self-adaptively configured, the network self-adaptation of the mobile phone terminal can be realized, convenience is provided for users and the situation that certain functions of the terminal are unavailable because of setting errors can be avoided when the users roam among different countries, regions or operators.

The invention discloses a control method for an electronic expansion valve, a self-adaptive control method for a heat pump, and a device thereof; wherein, the control method for the electronic expansion valve comprises the steps as follows: S110: the electronic expansion valve is started and initiated; a controller arranges the openness of the electronic expansion valve as an initial value; S120: after starting prearranged time, the controller determines the practical over-heat degree of the electronic expansion valve; S130: controller compares the practical over-heat degree with a destination over-heat degree so as to determine the optimum openness value domain of the electronic expansion valve, and S140: the controller adjusts the openness of the electronic expansion valve to the optimum openness value domain. Therefore, automatic regulation of refrigerant flux and throttling degree of the heat pump device according to the working conditions can be ensured, the system is protected and runs at a best state, the optimum openness of the electronic expansion valve can be quickly gained, the lag time is greatly reduced, and the throttling degree of the system can be adjusted according to the running state.

The invention provides a central air-conditioning control system and a control method thereof, and belongs to the technical field of air-conditioning systems. The central air-conditioning control system comprises a universal equipment model library, an equipment performance testing module, a comfort level evaluating module, a genetic algorithm multi-objective optimization control module and an execution module. An actual measured method is used, a system model is established, and the central air-conditioning control system is high in precision and high in reliability and is adaptive; a genetic algorithm is used, multi-objective optimizing can be realized, the optimizing efficiency is high, the comfort level is high, energy is saved, and the cost is low; whether a user feels comfortable or not can be intelligently judged by a user behavior recognition mode, and control objectives are adjusted in time; and the control method can be optimal on the basis of environmental change, particularly at the transition season, the equipment efficiency can be greatly improved, and energy conservation and emission reduction are realized.

The invention provides a method and device for recognizing malicious users, and belongs to the field of network technology. The method includes the steps that historical behavior characteristics of a user to be recognized are acquired; a user classification model is acquired, wherein the user classification model is generated through classifier model training based on historical behavior characteristics of malicious users and historical behavior characteristics of normal users; the historical behavior characteristics of the user to be recognized are input into the user classification model to determine whether the user to be recognized is a malicious user or not. Through the user classification model obtained through classifier model training, the behavior characteristics of malicious users can be automatically analyzed according to the characteristics of a machine learning algorithm; compared with the prior art, the method and device have the advantages that the recognition process is independent of the subjective cognition of the human, and the coverage scope of behavior characteristics is wider, so that misjudgment and missed judgment are effectively reduced, and the accuracy of recognizing malicious users is improved.

Provided is a self-adaptive pneumatic flexible grabbing mechanical gripper based on a metamorphic mechanism. A support is fixedly connected with a base to form a frame-shaped structure, an air cylinder is located in the frame-shaped structure and is fixedly connected with the base, the gripper comprises a slider crank mechanism and the metamorphic mechanism, chunks are fixedly connected with the metamorphic mechanism, a workpiece is a grabbing object, and contact force sensors are arranged on the surfaces, making contact with the workpiece, of the chucks. Connecting rods are connected to an air cylinder piston through revolute pairs A, the connecting rods are movably connected with the metamorphic mechanism, and the slider crank mechanism is formed by the connecting rods, the air cylinder piston and connecting rods 1 in the metamorphic mechanism, the air cylinder piston is the slider of the slider crank mechanism, the connecting rods are movably connected with the air cylinder piston, and a force feedback control system is connected with the contact force sensors and the air cylinder. Compared with the prior art, the self-adaptive pneumatic flexible grabbing mechanical gripper based on the metamorphic mechanism achieves the purposes of being little in drive, simple in control, low in cost and high in reliability, having certain flexibility, and being capable of self-adaptively grabbing objects in complex space geometrical shapes or simulating the movement characteristics of human hands.

The invention discloses a trusted network access and access control system. The control system comprises a network access requester, a network access decision-maker and a network access controller. When the network access requester accesses to the network, platform identity authentication is carried out, and secure and trusted states of the requester are detected; the secure and trusted states are sent to the network access decision-maker, and the decision-maker judges the secure and trust level of the requester and gives a network access decision; the network access decision is sent to the network access controller, and the controller limits the requested network access. The invention further discloses a trusted network access and access control method. The control system carries out the platform identity authentication on the basis of TCM and ensures the network access control while avoids identity forging. Network areas are divided on the basis of a digital label technology and dynamically updated, network self-adaption is realized, and the access security of different logical areas is ensured. Secure and trusted strategies are configured, and combined with the TCM cryptographic algorithm, so that the security and credibility of the platform state of the requester are ensured.

The invention relates to a piezoelectric-static composite micro machine vibration energy collector and a manufacture method of the collector, and relates to an energy collecting device. The piezoelectric-static composite micro machine vibration energy collector comprises a chip main body and a chip external circuit, wherein the chip main body is of a three-layer laminated structure, and the chip main body comprises an upper glass piece device, an SOI (Silicon On Insulator) piece device, a lower glass piece device, a piezoelectric ceramic wafer device, a piezoelectric ceramic wafer electrode and a variable capacitance plate electrode. The chip external circuit is provided with a rectification circuit, a filter capacitor, a first switch, a second switch and an energy storage capacitor, or is provided with a rectification circuit, a filter capacitor, a first switch, a second switch and an energy storage capacitor and a variable capacitance plate starting power supply. The manufacture method comprises the following steps of: manufacturing the lower glass piece device; manufacturing the SOI piece device; manufacturing the upper glass piece device; assembling; and connecting the chip external circuit. According to the collector and the manufacture method, the process is simple, the defects of poorer device stability and performance and the like caused by,manufacturing a PZT (Piezoelectric Transducer) by using the traditional sol gel can be overcome, and wide band and high efficiency collection of energy can be realized.

The invention discloses a strong tracking Kalman filter method for target tracking, belongs to the field of target tracking, and relates to a maneuvering target method based on a strong tracking Kalman filter. The method comprises the steps of building a disperse nonlinear dynamic system model; carrying out system initialization; carrying out time updating, and introducing a time varying and fading factor [lambda k]; measuring and updating; and finally carrying out filtering updating. According to the method, the time varying and fading factor is introduced into a Kalman filter, therefore, the method has the advantages that the Kalman filter is simple to realize and high in filter precision; meanwhile, the strong tracking Kalman filter has a real-time tracking capacity on the sudden change of the system.

The invention relates a self-adaptive semi-supervised network traffic classification method, system and equipment. The method comprises the following steps: acquiring a network stream, extracting thestream feature with the preset fixed quantity in each network stream to obtain a network stream feature vector; computing the centroid of the network stream feature vector in each type according to the marked network stream feature vector, thereby obtaining a vector set M; performing self-adaptive semi-supervised k-means clustering by taking the vector set M as an initial center point; mapping theobtained network stream in each type of cluster to the belonged traffic type according to the maximum posterior probability; taking the traffic cluster of the known type as the training data to trainan online traffic classifier. The invention further relates to a system, the system comprises an acquisition module, a vector set processing module, a clustering module, a classification module, andan output module. The invention relates to the equipment. The equipment comprises a processor, a memorizer, and a computer program stored on the memorizer and capable of being run on the memorizer.

A method for managing allocation of paths for transmission, in packets of symbols, of at least a first data content, each content being transmitted from a source node up to a destination node in a meshed communications network comprising a plurality of nodes, each destination node receiving a plurality of copies of a same packet of symbols of the content transmitted to said destination node by a source node, each copy being received by the destination node via a distinct transmission path directly from the source node or via at least one relay node of the meshed communications network, the packets being encoded with an error correction code enabling decoding by erasure of packets symbols. Such a method comprises the following steps: selecting a set of transmission paths from the source node up to the destination node for the transmission of copies of at least one packet of the first content, for which an estimated first number of erasures of symbols of the at least one packet is smaller than a determined threshold, the first number of erasures being estimated from a piece of information on quality of transmission that is determined and associated with each of the selected transmission paths, the threshold being determined as a function of an erasure correction capability of the error correction code; allocating the selected transmission paths to the transmission of the first content from said source node up to said destination node.

The invention discloses a high-definition video aerial photography multimode sensor self-outer-sensing intelligent navigation system. The system comprises a self-outer-sensing module, an information fusion module and a master control module, wherein the sell-outer-sensing module is used for detecting and sensing the internal state and the external state of an aerial photography aircraft in real time through fusion of multiple sensors and acquiring internal state information and external state information of the aerial photography aircraft; the information fusion module is used for gathering the internal state information and the external state information of the aircraft into a decision making layer through a centralized topological structure, making a consistent decision after the information of the multiple sensors is fused in the decision making layer and outputting a navigation control signal; the master control module is used for adjusting and controlling the state of the aerial photography aircraft in real time in a three-dimensional navigation mode according to the navigation control signal, and therefore horizontal autonomous navigation and vertical autonomous navigation of the aerial photography aircraft are achieved. The invention further discloses a high-definition video aerial photography multimode sensor self-outer-sensing intelligent navigation method.

The invention discloses a video multi-target tracking method based on multi-Bernoulli characteristic covariance which belongs to the technical field of artificial intelligent and intelligent information processing and aims mainly to solve the problems that in complex environment, the targets in video multi-target tracking are very close to each other and that the size change and tracking to them are not accurate. This method, according to the multi-Bernoulli filtering framework, is introduced by an integration graph and employs a particle filter method to track video multi-targets whose number are in constant change in combination with the characteristic covariance technology. Based on that, a self-adaptive mechanism and a size self-adaptive mechanism for targets in close contact are proposed to realize the self-adaptive processing of targets in close contact and the tracking windows respectively. Finally, a particle filter method is utilized to realize the self-adaptive recognition and tracking of the video multi-targets through their movement trajectories. The method of the invention is highly robust and has a good anti-interference ability. The method meets the design requirement of a practical engineering system and has good engineering application value.

The invention provides a computer system based on a brain-like coprocessor. The neuronmorphic network based coprocessor included by the system is integrated with both storage and processing, employs a distributed storage and parallel cooperative processing mode, is especially suitable for processing non-formal problems and unstructured information and can also process formal problems and structured information, such that the speed of a computer in processing such problems as brain-like calculation, artificial intelligence and the like is substantially accelerated, the energy consumption is reduced, the fault tolerance capability is greatly improved, the programming complexity is reduced, and the computer performance is enhanced.

The invention discloses a data collecting method applied to a multihop wireless sensor network, which belongs to the wireless communication network field and mainly solves the problems that the efficiency is low, the time lapse is big, and the network service life is short in prior network data collection. The data collecting steps include as following: executing clock synchronization to the local network nodes, and updating the local network information of the network nodes; dividing the network work time into a plurality of network interception periods; utilizing the updated localized network information to work out the interception signal channel probability of the nodes; executing interception at each network interception period according to the interception signal channel probability; executing data transmission according to the contest transmission nodes transmitted by the generating data; executing the data receiving according to the frames hearing the busy signal contest receiving node; and transmitting the data to the syntaxis nodes SINK used for collecting the data through multihops. The method has the advantages that the data collection is in time, and the nodes are high in efficiency and good in expansibility, thus being applicable for collecting data information in a large-scale wireless sensor network for monitoring emergency accidents.

The invention discloses an image processing method for quickly and automatically detecting soldered dot unsatisfied products of electric connectors. The image processing method comprises the following steps: 1) converting an original image into a gray image, determining a section center and the outer radius of an inner circle, and accordingly, extracting a square image within a section region; 2) binarizing the square image of the section region, separating out the section and the background and performing morphological filtering; 3) obtaining a contour line width in each direction of the binary value of the section, and obtaining the average width to determine the radius of a center circle; 4) converting an original connector enclosing rectangle RGB image into HSV, segmenting out an H-channel image and carrying out binarization by use of a color threshold of soldered dots; 5) performing morphological filtering on the H-channel binary image to obtain a soldered dot image; and 6) detecting the number of the soldered dots in the center circle of the section by traversing, and comparing the number of the soldered dots with a standard number of the soldered dots to judge whether the soldered dots are satisfied or not. The image processing method is capable of eliminating the subjective error caused by manual examination and improving the consistency of detection, and also capable of improving the efficiency and the accuracy and guaranteeing the safety of the personnel, and has the advantages of non-contact and the like.

The invention relates to an adaptive segmentally controlled tunnel illumination control system and method; the system comprises a tunnel illumination controller, a signal detection unit, a Zigbee network unit, and a plurality of illumination lamp sets; the signal detection unit comprises a vehicle sensor, a vehicle speed sensor, a brightness sensor, a temperature sensor, and a raindrop sensor; the Zigbee network unit comprises Zigbee main node module connected with the tunnel illumination controller, and a plurality of Zigbee node modules respectively connected with the illumination lamp sets; the illumination lamp sets comprise an introduction segment lamp set arranged outside the tunnel, an illumination lamp set arranged in the entrance segment in the tunnel, a transition segment illumination lamp set, a middle segment illumination lamp set, an exit segment illumination lamp set, and a road lamp illumination lamp set arranged outside the tunnel, and also comprise an emergency illumination lamp set. The adaptive segmentally controlled tunnel illumination control system can realize tunnel illumination, satisfies illumination curve demands of human eyes, thus eliminating black frame, black hole and white hole effects.

The present invention discloses a NAT characteristic self adapting method, which comprises the following steps; looking for dialogue table using data packet IP five tuple or part of IP five tuple as index to match the dialogue tables items that contains NAT_ID; besides looking for the regulation table using the data packet as key works to match the regulation table items that contains NAT_ID; judging the NAT_ID in the dialogue tables items and regulation table items, if is the same, processing NAT according to the dialogue tables items; otherwise, deleting dialogue tables items and building new dialogue tables items according to the regulation table items and processing the NAT according to the new dialogue tables items. The present invention can renew the dialogue tables immediately after the NAT regulations changing, so as to realize NAT attribution self adapting. Corresponding to the above method, the present invention also provides a NAT charactersitic self adapting device.

The present invention relates to a city expressway multi-ramp coordination control method based on chaos. The city expressway multi-ramp coordination control method based on chaos takes a city expressway system consisting of a multi-entry ramp, a multi-exit ramp, multiple interchange overpasses and an expressway mean line as a research subject. The method comprises the following steps: 1) performing parameter initialization; 2) performing real-time traffic flow data preprocessing: obtaining the real-time traffic flow data according to an information collection module, wherein the traffic flow data is taken as the input of a traffic flow chaos intelligent identification module; 3) performing the identification of each subsystem chaos state: allowing the traffic flow chaos intelligent identification module to determine whether a weaving area is located at a chaos state or not; 4) performing the single-ramp control grade control based on the chaos; 5) performing the multi-ramp coordination control grade control based on the chaos; and 6) determining the regulation of each entry ramp participating in the coordination in an alternate ramp set R2. The method practicable, low in cost and good in control effect and is applicable to the large-scale popularization and application.

The invention discloses a BP neural network-based ultra-short load prediction method. The method includes the following steps of: step 1, microgrid load data identification and completion; step 2, microgrid load normalization processing; step 3, microgrid data de-noising processing; step 4, load sample difference calculation; step 5, RBF neural network load prediction mathematical model establishment and on-line prediction. According to the BP neural network-based ultra-short load prediction method, based on a traditional RBF neural network load prediction mathematical model, load sample difference is defined through a regression analysis method; and the prediction model is adjusted according to the load sample difference, so that the load prediction model can automatically adapt to load change.

The invention belongs to the technical field of lower limb exoskeleton robots, and discloses an ankle joint exoskeleton based on an elastic element rigidity switching mechanism. The ankle joint exoskeleton based on the elastic element rigidity switching mechanism comprises a shank brace, a foot support, a spring support, a motor support, a motor, a ball screw mechanism, an extension spring, a return spring, a penetrating type electromagnet, a ratchet wheel support and a ratchet wheel mechanism, wherein the ratchet wheel mechanism comprises a ratchet wheel and a pawl; the penetrating type electromagnet is mounted on the ratchet wheel support; one end of the return spring is connected with the pawl while the other end of the return spring is mounted on the ratchet wheel support; the lower end of the extension spring is mounted on the spring support while the upper end of the extension spring is connected and wound onto a pulley through a rope; and the pulley is arranged on a ratchet shaft. By the extension spring, a unique energy storage process of achilles tendon and an energy releasing mode can be simulated effectively, an elastic element rigidity switching function can be fulfilled on the basis of a human gait movement rule, thus, an elastic element conforms to time-variant characteristics of in-gait and inter-gait of human movement, three-dimensional geometrical parameter limitation can be broken through, and deformation of the elastic element is improved.

A distributed multi-robot containment control method comprises the following steps: (1) determining leader and follower sets in a directed network according to the communication relationship between robots in the network; (2) designing a position control operator of the follower robots when the leaders are static; (3) introducing a potential energy function to ensure that collision between the follower robots in the process of movement is prevented, and forming a containment control rate when the leaders are static; (4) designing a position control operator of the follower robots when the leaders are dynamic; (5) introducing an exponential estimator to a control strategy formulated for the follower robots to estimate the speed of the leader robots, realize the transformation from locally knowable to globally knowable and determine a speed control operator of the follower robots; and (6) integrating the position control operator and a direction control operator of the robots in a weighted manner, introducing the potential energy function to ensure that collision between individuals in the process of movement is prevented, and forming a distributed containment control rate of the dynamic robots.