205 results about "Window function" patented technology

The invention belongs to the field of structural modal identification, and particularly relates to an automatic modal parameter identification method based on a multi-scale space, which comprises the following steps: collecting a vibration data sample from a target structure, the vibration data sample comprising an acceleration signal of the structure; selecting power spectrum calculation parameters including a window function and a fast Fourier transform (FFT) length; performing frequency spectrum estimation on the acceleration signal by using interpolation power spectrum estimation to obtain frequency spectrum data; analyzing the spectrum data by using a multi-scale space algorithm, and detecting a peak value in the spectrum data to obtain a peak value frequency; through a frequency domain decomposition method, according to the peak frequency, modal parameters of the structure are calculated, the modal parameters comprise the modal frequency and the damping ratio, interpolation power spectrum estimation and a multi-scale space algorithm are innovatively combined, automatic and accurate identification of the modal parameters is achieved, and the identification precision and efficiency are remarkably improved.

The invention discloses a wide-spectrum interference weak signal enhancement method for high aspect ratio micro-nano structure measurement, and relates to the technical field of semiconductor measurement. The method comprises the following steps: acquiring a wide-spectrum interferogram, and performing two-layer two-dimensional discrete wavelet transform to obtain a first-stage wavelet coefficient group; performing nonlinear enhancement on the wavelet coefficient, and outputting a nonlinear enhanced wide-spectrum interferogram; performing three-layer two-dimensional discrete wavelet transform on the wide-spectrum interferogram after nonlinear enhancement to obtain a second-stage wavelet coefficient group, and calculating a Bayesian threshold value; a neighborhood sliding window function is set, and a Bayesian threshold is combined to carry out local denoising enhancement on the second-stage wavelet coefficient; reconstructing the second-stage wavelet coefficient after local de-noising enhancement to obtain a wide-spectrum interference pattern after local de-noising enhancement; and determining an optical path position by adopting an improved gravity center method according to the wide-spectrum interferogram after local denoising enhancement, and calculating a depth value of the to-be-measured sample. According to the method, the stripe contrast is enhanced, the noise is suppressed, and the depth measurement capability is improved at the same time.

The invention provides a Paillier homomorphic encryption hardware acceleration method and a Paillier homomorphic encryption hardware acceleration device based on a residual number system (RNS) and number theory transformation (NTT), which are suitable for high throughput parallel computing of large integer modular operation. The system adopts a modular architecture, comprises an RNS conversion cluster, an NTT core array, a modular domain multiply-accumulate unit and a CRT reconstruction module, and supports homomorphic addition, multiplication and modular exponent operation on a ciphertext. Low latency and high energy efficiency of encryption operation are realized by mapping a large integer to a plurality of small module domains for parallel processing and combining NTT fast convolution and a window function table look-up strategy. The method can be widely applied to privacy computing, cloud security and edge encryption scenes.

The invention relates to the field of GNSS-R SAR image cross-correlation registration, in particular to a GNSS-R SAR image cross-correlation registration method based on an improved window function. The scheme comprises the following steps: extracting a matching region from a main image, and extracting a reference region from a slave image; performing sliding traversal in the matching region and the reference region by using an improved elliptical weighted window function, and calculating coherence coefficients under translation positions one by one to obtain a coarse registration offset; performing multiple cubic interpolation on the time sequence image to obtain a primary image and secondary image interpolation result with sub-pixel precision, re-selecting a matching area and a reference area on the interpolated image, and repeating sliding traversal and coherence coefficient calculation to obtain a fine registration offset; and performing interpolation resampling on all the slave images based on the final registration offset to obtain a time sequence image set of which the registration precision is a sub-pixel level. The GNSS-R SAR image cross-correlation registration method is suitable for GNSS-R SAR image cross-correlation registration.

The invention relates to the technical field of audio sound calibration, and discloses a room sound calibration method based on intermediate frequency reverberation time and related equipment, which are used for improving the accuracy of a sound calibration result. The room sound calibration method based on the intermediate frequency reverberation time comprises the following steps: acquiring a room impulse response of a target room and calculating the intermediate frequency reverberation time; according to the intermediate frequency reverberation time, the recommended window length of the frequency correlation window is determined by querying a predefined mapping relation, and the mapping relation is established based on acoustic association between the room reverberation time and the analysis window length; based on the recommended window length, determining the actual window length of the actual application in combination with the user setting mode; performing time domain windowing and frequency-related transfer function estimation processing on the room impulse response according to the actual window length to obtain window function weighted transfer function estimation; filter calibration coefficients are generated based on the transfer function estimates and applied for audio calibration.

The invention provides a control panel circuit module fault detection method, which relates to the technical field of electronic fault detection, and comprises the following steps: acquiring key electrical parameters of each functional unit of a control panel circuit module, establishing a dependency graph of the key electrical parameters, and constructing a multi-level parallel detection sequence; a variable-length window function is adopted to carry out synchronous sampling on detection items of the same level, a level feature matrix is generated, improved short-time Fourier transform is executed, a frequency spectrum deviation curve of each level is generated through sliding cross-correlation operation, a fault transmission chain is established, and a specific fault type is judged through a topological structure of the fault transmission chain; according to the method, the problems of low fault positioning efficiency and poor accuracy caused by difficulty in capturing fault transfer characteristics and insufficient capability of a traditional detection method in multi-level feature extraction and analysis can be solved.

A deviated well anisotropic formation three-dimensional induction logging data inversion method, comprising: carrying out azimuth angle conversion on pre-acquired three-dimensional induction logging data to generate a three-dimensional induction logging curve under a wellbore coordinate system (100); constructing a window function on the basis of morphological characteristics of the three-dimensional induction logging curve (200); segmenting the three-dimensional induction logging curve by means of the window function (300); and performing full-parameter inversion on each section of the three-dimensional induction logging curve to obtain a horizontal resistivity and vertical resistivity of each formation layer, formation boundaries and a well deviation angle, wherein an objective function of the full-parameter inversion is to minimize the ZZ component fitting error of the three-dimensional induction logging curve (400). The present application further relates to a deviated well anisotropic formation three-dimensional induction logging data inversion apparatus.

The invention belongs to the technical field of extrusion process monitoring, and particularly relates to a cable extrusion process state online monitoring method and system based on acoustic sensing, so as to solve the technical problem of inaccurate cable extrusion process state monitoring in the prior art. The monitoring method comprises the following steps: S1, when a transient impact factor is greater than a preset threshold value, windowing a signal frame by adopting an asymmetric window function with a main lobe in front and a side lobe in back; s2, performing fast Fourier transform on each windowed signal frame to obtain a frequency spectrum of each windowed signal frame; filtering the frequency spectrum of each signal frame by using a filter bank and calculating logarithmic energy of each filtering channel; s3, selecting cepstrum coefficients of which the ratio is greater than a selection threshold to form a process state feature vector; and inputting the process state feature vector into a state classifier to obtain a current cable extrusion process state. According to the method, redundancy and noise interference are eliminated, so that the accuracy of process state monitoring is improved.

The invention requests to protect an ADC dynamic parameter test method based on spectrum correction, and the method comprises the following core steps: a primary spectrum correction stage: carrying out the spectrum error correction of an ADC output signal through employing a spectrum three-interpolation algorithm, and extracting a fundamental frequency; the sine wave amplitude, the phase and the direct current offset are solved through least square fitting, an initial ideal sine reference sequence is constructed, and noise separation is carried out for the first time. In the secondary spectrum optimization stage, a classic Hanning window function and a frequency domain two-point interpolation algorithm are adopted; and a sine wave error compensation sequence is constructed, and secondary noise extraction is carried out. In the coherent parameter reconstruction stage, the ADC output sequence is restored to a standard test state through amplitude and frequency conversion. According to the invention, the risk of test failure caused by the fact that the amplitude of the input signal exceeds the ADC range can be effectively avoided. On the basis, the secondary spectrum optimization significantly improves the recognition precision of the test algorithm on the harmonic distortion of the ADC output sequence, and effectively reduces the measurement error caused by nonlinear distortion.

The invention relates to an electrocardio atrial fibrillation classification detection method, system and device capable of resisting individual difference and a storage medium. The system comprises a signal acquisition module used for acquiring ECG signals; the invention discloses an anti-individual-difference preprocessing circuit. The signal segment reading module is used for reading a signal segment according to an overlapping segmentation strategy, the Hanning window weighting module is used for performing window function weighting on the signal segment and only storing a first half coefficient through address mapping multiplexing by utilizing the symmetry of a window function, and the FFT frequency domain transformation module is used for converting a time domain signal into a frequency domain; the QRS frequency domain enhancement filtering module is used for enhancing QRS spectrum components and attenuating individual difference frequency components, the IFFT time domain reconstruction module is used for converting frequency domain signals back to a time domain, and the amplitude normalization output module is used for eliminating signal amplitude differences among individuals; and the deep learning classification module is used for performing atrial fibrillation detection on the standardized signal. Based on a CPU and FPGA heterogeneous architecture, the accuracy and robustness of atrial fibrillation detection are improved, and the method is suitable for the fields of wearable monitoring, telemedicine and the like.

The present disclosure relates to the technical field of power system frequency measurement, and discloses a method, device, and electronic device for measuring ultra-high voltage high harmonic frequency, comprising: dynamically selecting the optimal window function type, window length, and sliding step size for the power system operating state based on the time-varying frequency signal characteristics of the power system using an adaptive window information design algorithm based on reinforcement learning; performing a short-time Fourier transform on the time-varying frequency signal based on the selected window function type, window length, and sliding step size, retaining the positive frequency components and deleting the negative frequency components during the short-time Fourier transform process, and converting the time-varying frequency signal from a time domain representation to a two-dimensional time-frequency matrix represented in the time-frequency domain; determining the spectral line position based on the two-dimensional time-frequency matrix, and determining the ultra-high voltage high harmonic frequency based on the main spectral line position, the sampling rate for the time-varying frequency signal, and the selected window length. The above method can improve the accuracy of the measurement results of the ultra-high voltage high harmonic frequency.

This invention relates to a method for extracting guided wavenumbers in variable thickness structures, belonging to the field of nondestructive testing technology. It utilizes an excitation signal to generate guided wave signals on the surface of the object under test. Based on a set window function and a set window length, the two-dimensional wavefield signal obtained by sampling the guided wave signal is divided into multiple local wavefield signals. A short-space Fourier transform is then performed on each local wavefield signal to obtain the amplitude-frequency-wavenumber array corresponding to each local space. A frequency range is then selected by summing the amplitude-frequency-wavenumber arrays of each local space, using the center frequency of the excitation signal as the midpoint. The weighted average wavenumber of each frequency point within the selected frequency range is calculated using the amplitude corresponding to the wavenumber sequence at each frequency point as the weight. The mean of the sum of the weighted average wavenumbers at each frequency point is then used as the wavenumber corresponding to the center position of each local signal space, and these values ​​are sequentially spliced ​​to obtain the wavenumber curve of the guided wave signal. This method solves the problem in existing technologies where the obtained guided wavenumbers cannot accurately reflect the thickness changes of variable thickness structures.

The application discloses a space-time-frequency wave number domain synchronous extrusion superimposed dense channel reservoir characterization method, which comprises the following steps: S1, inputting original seismic data s(t, x) to be analyzed, wherein x is the number of channels, and t is time; S2, obtaining short-time Fourier transform V2(t, x, omega, k) of the signal by using a Gaussian window function g(t, x), wherein omega is frequency, and k is wave number; S3, calculating an instantaneous frequency and an instantaneous wave number estimation of the signal according to S2; S4, defining an extrusion operator TDSTO according to S3; S5, constructing a space-time-frequency wave number domain synchronous extrusion transform characterization method TDSST(t, omega, x, k) according to the extrusion principle; S6, fixing the wave number by maximizing the energy of TDSST, so as to obtain a TDSST time-space-frequency domain three-dimensional data body TDSST(t, x, omega, k(t, x, omega)); and S7, performing feature extraction of frequency f0 in S6, so as to obtain a single-frequency profile to characterize the morphological structure characteristics of a complex superimposed dense channel sandstone reservoir. The application can provide a space-time reservoir morphological characterization method considering the lateral characteristics of a complex superimposed dense channel sandstone.

The invention provides a mental stress analysis method and equipment based on pulse waves. The method is applied to physiological signal processing. The method comprises the following steps: acquiring a pulse wave signal of a target object, wherein the pulse wave signal is a signal acquired by pulse wave acquisition equipment; extracting pulse rate change information from the pulse wave signal by using a window function with the length of 0, so as to obtain a forward pulse rate; reversely correcting a problem pulse rate in the forward pulse rate by adopting a path growth mode to obtain a target pulse rate; performing forward heartbeat point search on the target pulse rate, and performing reverse correction on the heartbeat point to obtain a heartbeat interval; calculating a heart rate variability parameter according to the heartbeat interval; and calculating the heart rate variability parameter by using a pre-constructed mental stress model to obtain a mental stress parameter. According to the invention, mental stress monitoring with low burden, high reliability and high accuracy is realized.

The invention discloses an SAR (Synthetic Aperture Radar) water depth inversion method based on a self-adaptive elliptic window function, which comprises the following steps of: realizing self-adaptive adjustment in the direction of an elliptic window, the proportion of a long axis to a short axis and the type of a window function by analyzing the energy distribution characteristics of a main peak of a two-dimensional frequency spectrum of an SAR sub-graph; therefore, the directivity expression of the spectrum main peak and the accuracy of wavelength extraction are remarkably improved. According to the method, the definition of the main peak of the frequency spectrum and the water depth inversion precision can be remarkably improved, and a more stable and accurate technical path is provided for SAR water depth inversion in a complex near-shore environment.

The application discloses a method for sidelobe suppression of single-bit quantization Chirp signal, and the main technical content of the method comprises sidelobe suppression of single-bit quantization Chirp signal based on a convolution window function. The technical content performs sidelobe suppression through the following steps: step S1, generating a Chirp echo signal; step S2, single-bit quantization of the Chirp signal; step S3, single-bit quantization of a Chirp signal matching filter and adding a convolution window; and step S4, pulse compression of the single-bit quantization Chirp signal. The application can achieve a good compromise between quantization order and pulse compression effect, single-bit quantization can reduce quantization complexity to the maximum extent, and the sidelobe suppression method based on the convolution window function can effectively solve the problem of the decrease of the main sidelobe ratio caused by single-bit quantization. For Chirp signals and other radar signals with large time-bandwidth product, the application can improve the pulse compression effect, improve the range resolution of the radar system, and has certain reference significance for improved design in radar engineering technology.

The application discloses a kind of self-adapting downsampling time series encoding method and system.The method includes providing step prediction module based on learnable network, dynamically generating prediction window parameters based on the global feature of multidimensional time series signal, generating smoothing window function according to the prediction window parameters, weighting multidimensional time series signal and smoothing window function to obtain multiple groups of soft block signals, performing two-dimensional self-attention calculation on each group of signals and extracting feature vectors, and outputting sequence representation after reorganization through global self-attention calculation.The system corresponds to each corresponding functional module, including step learner based on learnable network, and introducing regularization term to optimize training loss.The application gets rid of artificial experience dependence, realizes the adaptive downsampling of time series signal, avoids boundary information loss and extreme downsampling strategy, improves encoding efficiency, accuracy and universality, and can be widely applied to astronomical gravitational wave monitoring, medical treatment, industrial and other cross-domain time series signal processing scenarios.

The invention discloses a sparse inversion time-frequency analysis method based on a semi-norm, and the method comprises the steps: designing a window function for original seismic data, so as to reduce the influence of a window truncation effect on time-frequency analysis; constructing a Fourier forward matrix and a windowed Fourier matrix according to the size of the window function and the size of the frequency spectrum; in-window seismic data extraction is carried out on sample points of single-channel seismic data, and a semi-norm constrained time-frequency spectrum sparse inversion objective function is constructed; performing time-frequency spectrum coefficient inversion on the time-frequency spectrum sparse inversion target function to obtain a sparse inversion time-frequency spectrum result of the seismic data in the window; and sliding the sample points of the seismic data to repeat the process, and performing time-frequency spectrum sparse inversion on all the seismic data to obtain a final time-frequency analysis result. According to the method, the time-frequency spectrum precision and the time-frequency focusing property of the seismic signal can be effectively improved, and a time-frequency analysis result with higher precision is provided for subsequent reservoir identification and oil and gas prediction work.

The application discloses a channel estimation method and device, electronic equipment, storage medium and program, and applies to the field of wireless communication, and the method comprises the steps of adding a window function on the basis of a pilot signal and a received signal of an obtained target channel, constructing a windowed channel estimation model, determining a posterior distribution probability density function of a channel estimation parameter in the windowed channel estimation model, obtaining an auxiliary manifold and a target manifold which are approximate to a nonlinear term in the posterior distribution probability density function, determining an auxiliary manifold parameter, generating a target manifold parameter based on the auxiliary manifold parameter, and generating a channel estimation parameter of the target channel based on the target manifold parameter. The embodiment of the application can reduce the calculation complexity of the channel estimation process, improve the channel estimation efficiency, reduce the signal transmission delay, and enhance the user experience.

The application discloses a trigonometric function window generating method based on DSP, which comprises the following steps: converting the cosine term of the trigonometric function window into the n-th power form of the complex exponential fixed value based on the Euler formula; after the program initializes the calculation of the fixed value, generating the complex exponential term of each point sequence by recursive multiplication and storing the complex exponential term in the first-level storage space of the DSP; then, the SIMD parallel operation is realized by using the in-line function of the DSP, the first half of the window function values are calculated and stored; the first half of the window function values are inversely symmetrical copied to generate the second half of the window function values; finally, the hardware adaptive memory allocation is completed by combining the hierarchical storage architecture of the DSP, the stack is allocated to the first-level storage space, and the window function generation is completed by summarizing the data. The application greatly reduces the operation amount and the storage occupation, guarantees the calculation precision, deeply adapts to the hardware characteristics of the DSP, can efficiently generate the Hanning, Hamming and Blackman trigonometric function windows, and meets the real-time and high-precision requirements of the signal processing with large bandwidth and high sampling rate.

A signal transmission method and a communication device, a sending end device applies different sending end window functions to a plurality of symbols to be transmitted in a frequency domain spectrum shaping process. The sending end window functions corresponding to different symbols are related to the distance of a target to be perceived, so that within the distance range of the target to be perceived, the range ambiguity function of one or more symbols after applying the sending end window function has a notch or a sidelobe less than a threshold within the distance range. After the receiving end device receives the echo signal reflected by the target to be perceived, it can determine whether the target to be perceived exists according to whether the range image generated by one or more symbols has a peak or a sidelobe greater than a threshold within the distance range, thereby reducing the sidelobe level (peak) in the range image, effectively reducing the false alarm probability and the missed detection probability of the perceived target.

The invention discloses a dynamic boundary window function design method for a memristive system mathematical model, and belongs to the technical field of memristive systems. The memristor mathematical model comprises a state equation and a state-based volt-ampere equation; the state equation comprises a state change function for reflecting a state variable change rule under the action of a signal by the memristor and a window function for limiting a state variable change range for the state variable of which the boundary can change dynamically; the window function comprises two independent variables which are respectively used for reflecting the state change corresponding to the state equation and the direction of the state change. According to the method, the window function is used for defining the memristor mathematical model, the definition interval of the state variable of the memristor mathematical model is more flexible, the state equation can describe the memristive system behavior of which the definition interval dynamically changes, and model design of the memristive system with complex dynamic behaviors is facilitated.

This invention provides a method for preprocessing and classifying satellite photometric curve data. The method includes: converting phase angle-magnitude curves with discontinuous observations and uneven sampling frequencies into fixed-dimensional window function feature vectors using a frame-segmentation and windowing preprocessing strategy; employing a random erasure data augmentation strategy on these window function feature vectors to generate different photometric curve feature vectors through multiple random erasures while simulating phase observation gaps; performing positional encoding of the phase angle features on the photometric curve feature vectors to obtain photometric features containing phase information; constructing query vectors, index vectors, and key value vectors using a transformer model to extract global features; and classifying the photometric curve feature vectors using a fully connected layer. This method effectively captures both local and global feature information of the photometric curves, exhibiting superior performance compared to one-dimensional convolutional neural networks.

A method for optimizing flip angles in a magnetic resonance variable flip angle pulse sequence, a CEST imaging method, a medium, and a device are presented. A signal-to-noise ratio (SNR) enhancement problem is modeled as a flip angle optimization problem, and a total objective function composed of an SNR maximization objective term and a resolution penalty term is constructed, wherein the total objective function may be solved for an optimal solution by finding derivatives with respect to flip angles, so as to obtain an optimal flip angle capable of maximizing SNR. The objective function includes the resolution penalty term, so that resolution may also be considered in variable flip angle CEST when the SNR is optimized. Compared with a conventional filtering method, the present disclosure avoids noise amplification caused by filtering, and does not require manual presetting of a window function.

The invention discloses a simple and convenient method for improving time-frequency estimation precision, which comprises the following steps of: carrying out segmented cross-correlation operation on a local copy of a main synchronization sequence and a received signal; carrying out secondary filtering on a segmented cross-correlation operation result by utilizing a window function; the window function can be a predefined static window, such as a Gaussian window, a triangular window, a rectangular window and the like, and can also be calculated by using a delay power spectrum of a channel; the maximum value is found in the result of the secondary filtering, and the position of the maximum value is the timing position; and obtaining phase differences of correlation results of different segments at a timing position, and calculating a mean value of the phase differences to obtain frequency offset estimation. According to the method, timing estimation performance and frequency offset estimation performance can be improved, and the synchronization signal detection probability is also improved.

The invention discloses a two-dimensional image feature extraction method under adaptive polar coordinate mapping. The method comprises the following steps: step 1, intercepting a short-time sequence by using a short-time sliding window function; step 2, generating a time domain signal short-time matrix energy-time distribution map; step 3, positioning a key signal sequence representing fault characteristics through a peak screening algorithm; step 4, adaptively adjusting scale parameters of polar coordinate mapping, and constructing a multi-scale polar coordinate space; 5, generating a two-dimensional image under adaptive polar coordinate mapping; and step 6, eliminating the influence of rotation speed fluctuation on image features through normalization processing. And step 7, extracting spatial features by using a convolutional layer, and realizing adaptive diagnosis under a variable-speed working condition. According to the method, the fault features of the rotating equipment under the variable-speed working condition can be effectively extracted, and the features have robustness in a time-varying speed strong noise environment.

The invention discloses a self-adaptive overlapped framing and variable window phase measurement method, device and system based on signal state perception, and belongs to the technical field of digital signal processing and phase measurement. Aiming at the technical problems that a fixed overlapping ratio and a single window function are adopted in an existing phase measurement method, resolution ratios of a time domain and a frequency domain cannot be considered under a time-varying signal, and frequency spectrum leakage and phase jump are easily introduced, the invention provides a joint self-adaptive preprocessing method. The method comprises the following steps: firstly, framing collected signals, and judging whether the signal state is a steady state or an unsteady state according to smoothness detection of absolute value sums of adjacent sampling points; according to the determined signal state, adaptively configuring an overlapping rate between adjacent data frames and window function type parameters, and then, before fast Fourier transform is carried out, introducing a window function coherent gain compensation mechanism to eliminate an amplitude reference error caused by window type switching; and finally calculating the phase difference based on the corrected spectrum result. According to the method, the overlapping framing and window function parameters are adaptively adjusted according to the signal state, the phase measurement continuity is ensured, the phase measurement precision and stability under the multi-frequency signal are improved, and the method has good real-time performance and engineering application value.

The present invention provides an efficient scheme for improved cross-product high frequency reconstruction (HFR), where a new frequency component of Q Ω + r Ω 0 is generated based on the existing components Ω and Ω + Ω 0.SOLUTION: The present invention performs a block-wise harmonic transposition, wherein time blocks of complex subband samples are processed with a common phase modification. The superposition of the modified samples has the advantageous effect of limiting unwanted intermodulation products, thereby allowing a coarser frequency resolution and / or a lower degree of oversampling to be used. The present invention in one embodiment uses a window function suitable for use with an improved block-based cross product HFR. A hardware embodiment of the present invention includes an analysis filter bank (101), a subband processor (102) configured by control data (104), and a synthesis filter bank (103).SELECTED DRAWING: Figure 1

The invention belongs to the technical field of intelligent transportation, and provides a truck stop point prediction method based on directed graph spatial-temporal feature fusion, comprising the following steps: S1, preprocessing truck trajectory data through a multi-modal trajectory preprocessing subsystem; s2, constructing a spatial-temporal characteristic matrix with the stay point as a core; s3, encoding the data by using a space-time directed graph encoder, and extracting related features of a stay point; s4, constructing a graph neural network architecture to predict a track and a truck stop point; the step S1 comprises dynamic interruption detection, semantic stay identification and intention analysis, adaptive interpolation and sparsification processing, and stay point determination by using a space-time window function. According to the method, refined staying point classification prediction facing business requirements can be provided. And effective early warning of future key staying behaviors of the truck can be realized.