126 results about "Butterworth filter" patented technology

The invention provides a control method of a cell energy storage system inhibiting renewable energy output power fluctuation. The control method is characterized in that: based on a low pass filtering principle, through a first order Butterworth filter, a renewable energy output power value is subjected to filtering, a renewable energy output power object value is obtained, by utilizing charge and discharge control of the cell energy storage system, a difference between the renewable energy output power object value and a renewable energy output power measured value is compensated, and a purpose of inhibiting the renewable energy output power fluctuation is achieved. According to the invention, based on a basic filtering principle, by utilizing a good renewable energy output power inhibition effect and adding of an SOC (state of charge) feedback link, over charge and over discharge of a cell are avoided, innovation is carried out, when adding the SOC feedback link, through adjusting a size of a filtering time constant, an output power of the energy storage system is changed indirectly, thus when inhibiting the inhibiting the renewable energy output power fluctuation, the over charge and over discharge of the cell are effectively avoided, stable operation of the cell energy storage system is maintained, and a service life of the cell is prolonged.

The invention relates to an apoplexy recovery degree index calculation method based on brain electrical signals. The method includes the following steps of firstly, enabling a preferred examinee to execute a left/right hand motor imagery task according to a prompt, utilizing multi-channel brain electrical signal collecting equipment to collect the brain electrical signals produced when the examinee executes the motor imagery task, then, utilizing a public average eliminating reference method to lower the level of public noise, utilizing an independent component analysis method to eliminate ocular artifacts, improving the signal to noise ratio of the brain electrical signals, utilizing a Butterworth filter to draw data of frequency ranges which are closely relevant to the motor imagery task for later analysis, finally, calculating a coefficient of polymerization and an overall efficiency property of a brain function network, calculating accuracy of the execution of the motor imagery task, utilizing the brain function network property and motor imagery task accuracy as recovery degree indexes, and storing calculation results in a special data base. The apoplexy recovery degree index calculation method based on the brain electrical signals has the advantages of being free of will interference and strong in objectivity. An objective assessing basis can be obtained through the method.

The invention discloses a method for restraining speed errors of a single-shaft rotation optical fiber gyro strapdown inertial navigation system. The method comprises the following steps of: 1. acquiring carrier position information through a global positioning system (GPS); 2. acquiring data output by an optical fiber gyro and a quartz accelerometer; 3. driving an inertial component to perform single-shaft forward and backward rotation and stop motion by a rotary mechanism, and adopting a rotation scheme that eight rotation and stop sequences are used as a rotation period; 4. acquiring information of linear velocity and angular velocity of the motion of measurement carriers of the optical fiber gyro and the quartz accelerometer, and performing navigation solution to obtain navigation information; and 5. constructing a Butterworth band elimination filter, and performing Butterworth filter processing on the carrier velocity obtained under a navigation system. According to the method, the Butterworth band elimination filter is designed according to the rotation angular velocity, oscillation error items related to the rotation angular velocity under the navigation system are filtered and removed, and the velocity information accuracy is improved.

The invention discloses a multiple-grating monochrometer. The monochrometer mainly comprises a mono-light optical splitting system, a control system, a computer and a printer, wherein the splitting system and the control system are designed as one, the upper part of the device is a grating chamber, the lower part is a controller chamber; the grating chamber mainly comprises an entrance slit, two exit slits, a color filter group, two concave mirrors, a grating turntable, a reflector and a stepper motor, wherein the open or close size of the entrance and exit slits are controlled manually; the hardware circuit adopts a second-order Butterworth filter to perform anti-aliasing filtering, and the hardware 8-bit pulse width modulation method is used to realize the control of the stepper motor. The multiple-grating monochrometer overcomes the bottleneck of low data transmission speed caused by the use of the RS232 interface before and the USB2.0 interface is used to connect with the computer. A power distribution system, a grating turntable control system, a filtering wheel control system, a reflector control system and a high speed and high accuracy data acquisition system are integrated to increase the integration degree, reliability, control precision and scanning precision of the system. The newly provided automatic scanning multiple-grating monochromatic spectrometer system has wide development prospect.

The invention discloses a nuclear power plant loose part positioning method based on Hilbert transform and data screening. The method comprises the steps of dividing grids on an impacted object; mounting acceleration sensors; calculating a distance different between a distance from the center point of each grid to one acceleration sensor and a distance from the center point of each grid to another acceleration sensor; acquiring impact signals received by all the acceleration sensors; eliminating low-frequency noise of the signals by adopting a moving average method, and filtering environmental background noise by a Butterworth filter; performing Hilbert transform on the impact signals subjected to the noise elimination to obtain oscillation starting point moments of the impact signals; calculating a nominal transmission speed of the center point of each grid according to a transmission distance difference from the center point of the corresponding grid to the sensors and arrival time differences of all channels; and performing data screening on the nominal transmission speeds of the center points of all the grids, and calculating a variance; and finding out a minimum value in all the variances, recording coordinates of the center point, and displaying a positioning result. The nuclear power plant loose part positioning method has the advantages of high positioning precision and high searching speed.

The invention relates to animal muscle diagnosis and human body bioelectricity acquisition and recognition. The invention relates to electrode conductors for collecting surface electromyography signals, a functional buffer stage circuit, a preposing differential amplifying circuit, a filter circuit, a double T trap circuit and a back-stage amplifying circuit; the preposing differential amplifying circuit comprises a preceding stage differential amplifying circuit and a secondary differential amplifying circuit; the output end of the preceding stage differential amplifying circuit is also connected with a voltage follower circuit and an inverse amplifying feedback circuit which is communicated with the voltage follower circuit; the output of the inverse amplifying feedback circuit is communicated with the root of the electrode conductor; the filter circuit is the fifth-order Butterworth filter circuit; the electrode conductors are two silver electrodes which are pasted on the surface of skin; and the distance between the two silver electrodes is 8-12mm. The active double electrode surface electromyography sensor has good fixed mode, stable collected signals and strong signal filtering function, causes the capability of the circuit resisting the interfering of the power frequency to be increased and overcomes the defects of the high noise brought about by the electrode wiring, larger whole volume of the sensor and the like.

The invention relates to a classification and recognition method for P300 event-related potential based on deep learning, and belongs to the technical field of medical and physiological signal detection and processing analysis. According to the invention, a Butterworth filter is adopted to successively perform high-pass filtering and low-pass filtering on original signals to remove artifacts and power frequency interference; the data is amplified by using a one-time superposition averaging technique, normalization and time domain truncation are performed on EEG signals, and corresponding supervisory signals are formulated according to the signal types; the EEG data is divided into a training set and a verification set after data preprocessing, a deep learning network capable of classifyingand recognizing the P300 event-related potential is constructed, and the feature extraction ability of the network is improved; the probability that an input signal contains the P300 event-related potential is finally predicted through the trained network; and finally, a target character is predicted according to an experimental paradigm and the probability value outputted by the network. The experiment shows that the algorithm is good in performance and can also obtain good character recognition accuracy under the condition of reducing the number of experiments.

The invention discloses a laser radar weak signal extraction and decomposition method based on fractional order Fourier transformation, and belongs to the laser radar signal processing technical field; the method comprises the following steps: 1, carrying out fractional order Fourier transformation for noise-containing laser radar echo signals, obtaining a FRFT spectrum, and converting one dimension signals into a 2D image; 2, using a mean filtering, wavelet filtering and Butterworth filtering compound filtering method to make image enhancement for the FRFT spectrum; 3, clustering enhanced FRFT spectrum image data, and determining a clustering quantity and separating each piece of cluster data; 4, recording position and intensity of separated each piece of cluster data, screening valid data points, carrying out least square fitting for reserved data so as to obtain a flight time, a pulse width and an amplitude, thus finishing laser radar echo signal extraction and component decomposition. The 2D time-frequency analysis method for the laser radar signal processing has a stronger anti-noise ability and a more accurate decomposition result.

An image processing apparatus (1) comprises: a projection image data input unit (10) for inputting nuclear medicine image data; a Butterworth filter (12) for filtering the nuclear medicine image data; and a cutoff frequency setting unit (20) for setting a cutoff frequency of the Butterworth filter (12). The cutoff frequency setting unit (20) has an optimum cutoff frequency table (22) storing a relation between counts in nuclear medicine image data and an optimum cutoff frequency. The image processing apparatus (1) determines counts in the nuclear medicine image data, determines based on the optimum cutoff frequency table (22) a cutoff frequency corresponding to the determined counts, and sets a cutoff frequency of the Butterworth filter (12) at the determined cutoff frequency.

The invention adds return difference control circuit into the regenerative loop of comparator, connects the second integrating device between the original negative feedback integrating device and the comparator, the sampling point of the negative feedback is at the output end of the first grade Butterworth filter of the output grade low pass filter, and adds in the skewness correcting circuit, and the skewness and radiation are decreased greatly.

The invention discloses an infrared image denoising method based on a Butterworth filter. At first, that image is divided into high frequency component and low frequency component by Butterworth filter, a partial differential equation is used to denoise the image of inverse Fourier transform of high frequency component, the low-frequency component is transformed by inverse Fourier transform, and the image enhancement and median filtering result are fused to denoise. Finally, the processed high-frequency component and low-frequency component are transformed to frequency domain for fusing, and then the denoised image is obtained by inverse Fourier transform to spatial domain. The method can obviously improve the signal-to-noise ratio of the infrared image, thereby removing noise in the image, clearly separating the image from the background, and improving the quality of the infrared image.

The invention relates to the field of wireless information technology processing, and discloses an indoor fire detection and alarming system based on wireless signals. Steps for realizing the system mainly comprise: a wireless receiving end receives wireless electromagnetic signals from a wireless transmitting end, CSI (channel state information) is evaluated, and a data filtering technology (Butterworth filter) is adopted to carry out denoising processing on the data; an anomaly detection algorithm (sequence anomalies) is used for recognizing anomalies of channel state information changes; a random forest algorithm is adopted, five feature extraction methods (such as standard deviation and an interquartile range) are selected, indoor fire signal information is stored, and if signal features in the anomaly mode are matched with stored signal features, an indoor fire is judged to appear, and fire alarming signals are further given. The method and the system of the invention have the beneficial effects that the existing wireless network and the device are mainly used, no other detection or sensing device needs to be additionally arranged in the room, no additional laying price is needed, the fire can be monitored in real-time, and universality is extremely high.

The invention discloses an sEMG gesture recognition method based on a wavelet width learning system. The sEMG gesture recognition method comprises: 1, defining the number d of gesture action types according to a corresponding recognition scene; 2, acquiring an sEMG signal s by using electromyographic signal acquisition equipment; 3, filtering and denoising the sEMG original signal according to thefrequency characteristic of the sEMG signal, and removing noise outside the frequency band of 10Hz-500Hz by using a Butterworth filter; 4, detecting an active section in the sEMG signal by adopting amoving window method; step 5, carrying out feature extraction on the detected movable section. Compared with an algorithm based on a traditional deep learning network, the method has the advantages that model training and parameter determination can be completed more quickly, so that the working efficiency is improved; nodes can be dynamically expanded, so that the recognition rate of the systemis improved, and a model does not need to be completely rebuilt and trained.

The invention relates to a drill-o-graph, which consists of a master control unit MCU, a rechargeable storage regulated power supply unit, an external sensor, a keyboard port, an electric warning module, a display screen, a communication unit, a sludge sensor signal interface unit, a weight transducer signal interface unit, a manual drill stopping interrupt signal generating circuit, a pump stopping interrupt signal generating circuit, a connection making interrupt signal generating circuit and a positive and negative rotation interrupt signal generating circuit. The drill-o-graph is characterized in that the keyboard port is extended into an external I/O port of the MCU through a 138 decoder and a 373 latch; ATmega128 controls the I/O port by reading and writing I/O; a winch sensor signal interface circuit, the sludge sensor signal interface unit, the weight transducer signal interface unit, the manual drill stopping interrupt signal generating circuit, the pump stopping interrupt signal generating circuit, the connection making interrupt signal generating circuit and the positive and negative rotation interrupt signal generating circuit realize signal detection by using an ATmega128 external interrupt port; an external interrupt signal of the external sensor unit performs signal shaping by adopting LM111; and LM324 performs second order Butterworth filter, and is connected to an interrupt pin of the MCU through a photoelectric coupler. The drill-o-graph has the advantages of complete functions, accurate recorded information and relatively low developing difficulty.

The invention discloses a unified power quality conditioner. The unified power quality conditioner comprises a voltage and current collection module, an analog-to-digital conversion module, a DSP operation control module, a drive level conversion module and a main circuit which is composed of a series-type APF, a parallel-type APF and a direct-current-side capacitor. Aiming at the problem that a traditional detecting algorithm based on an instantaneous reactive power theory cannot accurately detect the distortion amount when network voltage is distorted, the invention further provides a methodfor using the unified power quality conditioner to detect the harmonic distortion amount and an improved comprehensive detection method of the synchronous rotation coordinate transformation deformation amount. Meanwhile, a novel digital lowpass filter is adopted by the method, a traditional lowpass filter is replaced through series connection of an improved moving averaging filter and a Butterworth filter, the calculating amount and the storage space are greatly reduced, the dynamic response speed is increased, and the detection of the network voltage and the load current distortion amount can be quickly and accurately conducted.

The invention discloses an electronic cloth defect segmentation method. The method comprises the following specific steps: firstly, converting a to-be-detected electronic cloth image into a gray levelimage; secondly, carrying out ButterWorth filtering processing on the gray level image; clustering the filtered images by using a K-means algorithm; and finally, marking the pixel points of the clustered image according to the clustered category, and carrying out binarization processing according to the marking result, so that fabric defects can be segmented. According to the method, the ButterWorth low-pass filter is used for processing the electronic cloth image, detail information of defects can be reserved to the maximum extent while background textures are restrained, and the contrast ratio of the background and defect areas is enhanced. Meanwhile, the filtered images are clustered through the K-means algorithm, finally, the electronic cloth images can be clustered into different classes, then binary segmentation is conducted on the images according to the marking result, and accurate positioning and accurate segmentation of defects of the electronic cloth are achieved.

The embodiment of the invention discloses an analog band pass filter which is used for improving the out-of-band rejection capacity under the condition of keeping the volume of the filter constant. The analog band pass filter provided by the embodiment of the invention comprises a first inductor, a second inductor, a first capacitor, a second capacitor, a third inductor and a third capacitor, wherein the second inductor and the second capacitor form a series circuit; the third inductor and the third capacitor form a parallel circuit; the series circuit is connected with the parallel circuit; the component value of the series circuit and the parallel circuit is the same as that of a Butterworth filter circuit; the first inductor and the first capacitor are respectively connected on the series circuit in parallel; the first inductor and the first capacitor are connected with the parallel circuit; the first inductor and the first capacitor form a coupling channel between the connected two ends; and crossed coupling is provided between the two ends connecting the first inductor and the first capacitor.

The invention discloses a leakage current detection circuit for ADC sampling and can acquire voltage signals suitable for ADC sampling. The leakage current detection circuit for ADC sampling comprises a current transformer detection circuit used for outputting a differential voltage signal, a differential filtering circuit which is electrically connected with the current transformer detection circuit and is used for filtering high frequency ripples in the current transformer detection circuit when a power supply power source is coupled to a leakage current, a differential proportion amplification circuit which is electrically connected with the current transformer detection circuit and is used for converting the differential voltage signal outputted by the current transformer detection circuit into a single-end signal for proportion amplification, a four-order Butterworth filter which is electrically connected with the differential proportion amplification circuit and is used for filtering the high frequency ripples and carrying out proportional signal amplification, and an absolute value circuit which is electrically connected with the four-order Butterworth filter and is used for converting a power frequency AC signal inputted into the absolute value circuit into a steamed bread wave, and a voltage satisfying ADC sampling requirements is outputted.

The invention discloses a non-interactive monocular living body detection method, and belongs to the field of security and protection recognition, being applied to living body detection of RGB face images and IR face images, for solving the strong requirements of hardware equipment only provided with a monocular RGB camera for living body detection in face recognition and identity verification, such as the fields of man-machine verification, mobile applications, web applications, numerous mobile intelligent terminals and the like. The non-interactive monocular living body detection method mainly comprises the steps of collecting continuous images, performing face detection to obtain a Face set and a Background set, carrying out equalization filtering denoising, using a Butterworth filter to filter out signals in a non-heart-rate frequency band, obtaining a two-dimensional power signal-to-noise spectrum image through a face PPG-Map scheme, finally establishing a classifier for data of living body and prosthesis faces in a deep learning model, and completing the living body detection target in the real scene. The non-interactive monocular living body detection method achieves good balance in use cost and effectiveness, and can effectively prevent forged face identity attacks of portrait photos, portrait videos, masks and the like.

The invention relates to a closed-loop ultrasonic pain physiotherapy system based on electromyographic signal feedback, which is characterized by comprising the following steps: (Step 1) ultrasonic physiotherapy is carried out on a subject, and electromyographic electrodes are placed; (Step 2) electromyographic signals are acquired in real time, wavelets are decomposed, and electromyographic signals within 500Hz are extracted; (Step 3) a Butterworth filter is used for extracting 50-150Hz electromyographic signals; (Step 4) the amplitude of the electromyographic signals within the fragrance band is calculated; (Step 5) the amplitude of physiotherapeutic ultrasound is regulated according to the change of signal amplitude. The closed-loop ultrasonic pain physiotherapy system based on electromyographic signal feedback which is provided by the invention can qualitatively analyze the pain degree of the treated part of the subject from the source, process and analyze extracted signals and judge the physiotherapeutic effect of the subject according to the average amplitude change of electromyographic signals of the subject, so that physiotherapeutic intensity can be regulated to provide more effective physiotherapy for the subject.

The invention discloses an image defogging method with high fidelity. The method comprises the steps of S1, enhancing color in an HSV space, and thus obtaining a color enhanced image; S2, enhancing details in a RGB space, using a Butterworth filter to filter the image in the RGB space, after filtering, converting the image from the RGB space to the HSV space, and thus obtaining a detail enhanced image; and S3, implementing weighted fusion on the obtained color enhanced image and the obtained detail enhanced image by using a rule. According to the method provided by the invention, the color enhancement is implemented in the HSV space, the detail enhancement is implemented in the RGB space, and at last the two parts are fused. In the HSV space, by introducing an adjustment factor, the problems of color distortion and noise amplification are avoided. In the RGB space, the Butterworth filter is adopted for reserving more detail information. According to the method of the invention, under the condition of obtaining the same effect, the computation speed is increased by double, and the processing real-time performance is better; the obtained image comprises more details and information, and more uniform illumination is provided.

The invention discloses a rolling bearing fault classification method based on CNN and LSTM. The method comprises the steps: acquiring and preprocessing bearing vibration data, and constructing a dataset; denoising the processed vibration data by using a Butterworth Filter, and carrying out fast Fourier transform (FFT) to convert the preprocessed time domain signal into a frequency domain signal;employing a CNN network for learning to obtain image features of a time domain graph and a frequency domain graph, and carrying out image feature fusion through an Add layer; inputting image featuresobtained by CNN learning into an LSTM network, and realizing a classification function through a full connection layer and a Softmax function according to time sequence features contained in the LSTMnetwork learning features. The trained network is used for fault classification of test samples, and the method has important practical significance and practical value for early detection and analysis of weak faults of the rolling bearing.

The invention relates to a current characteristic frequency extraction method of a machine tool transmission system with immune random resonance comprises the following steps: collecting a current signal of a servo driving motor of the transmission system by a current sensor; calculating the gyro frequency and the meshing frequency of each output shaft of the transmission system; processing the collected current signal by a Butterworth filter-type bandpass filter; processing the filtered current signal by frequency shift; optimizing the current signal processed by frequency shift by a multi-mode immune clonal selection method to obtain secondary sampling random resonance system parameter; selecting a parameter with the largest affinity as the optimal secondary sampling scale and structuresystem parameter; finally, achieving the compression of the input signal according to the optimal secondary sampling scale, wherein the largest peak component of the corresponding spectrum can be transformed to the characteristic frequency component of the machine tool transmission system. By using the current characteristic frequency extraction method, the optimization quality of a secondary sampling random resonance system of the current signal of the servo motor with large frequency scale can be improved, and the weak characteristic frequency of the transmission system can be effectively extracted.