CN114121305B - A sensor safety detection method and system based on frequency sweep technology - Google Patents
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Abstract
本发明公开了一种基于扫频技术的传感器脆弱性检测方法和系统，包括以下步骤：步骤一，扫频激励信号生成；步骤二，带外信号生成；步骤三，带外信号测试；步骤四，传感器数据收集；步骤五，传感器数据预处理；步骤六，模型训练阶段；步骤七，带外脆弱性检测；步骤八，测试报告生成。采用该系统可以对传感器的带外脆弱性进行检测，泛化能力好，可方便对任何类型的传感器进行一个较为全面的带外脆弱性检测，可覆盖等全频谱范围内的传感器漏洞。本发明系统采用数字扫频技术，结合机器学习算法实现传感器脆弱性的自动化检测与分析，为物联网中感知系统的安全防护提供了很好的参考依据。
The invention discloses a sensor vulnerability detection method and system based on frequency sweep technology, which includes the following steps: step one, frequency sweep excitation signal generation; step two, out-of-band signal generation; step three, out-of-band signal testing; step four , sensor data collection; step five, sensor data preprocessing; step six, model training phase; step seven, out-of-band vulnerability detection; step eight, test report generation. This system can be used to detect the out-of-band vulnerability of sensors. It has good generalization ability and can facilitate a more comprehensive out-of-band vulnerability detection for any type of sensor, covering sensor vulnerabilities in the full spectrum range. The system of the present invention adopts digital frequency scanning technology and combines it with machine learning algorithms to realize automatic detection and analysis of sensor vulnerability, which provides a good reference for the security protection of sensing systems in the Internet of Things.
Description
Technical Field
The invention belongs to the field of vulnerability detection, and particularly relates to a sensor safety detection method and system based on a frequency sweeping technology.
Background
Along with the continuous development of the Internet of things and the continuous intellectualization of the terminal equipment, the sensor is used as the terminal equipment to sense the 'eyes' of the physical world, is a tie for the interactive connection of the Internet of things and the physical world, and is widely used in an Internet of things system. However, the security risk of the sensor is not paid enough attention, and in general, the upper layer defaults that all data acquired by the sensor are trusted, and once the sensor is attacked, the measured data of the sensor are tampered, so that the blind trust of hardware causes a huge threat to the security operation of the whole internet of things. If the automatic driving system makes driving decisions based on the sensing information of the sensor, when the sensing signal of the sensor is wrong, the wrong driving decisions are led out, so that serious consequences are caused. Therefore, ensuring the testing accuracy of the sensor is important to the safe operation of the Internet of things system.
Traditional sensor tests focus on performances such as sensitivity, precision and the like, and test is performed by using in-band signals, and the fact that the sensor is possibly affected by out-of-band signals is not considered, so that safety problems occur. The in-band (in-band) signal refers to a detection signal within the design range of the sensor, otherwise referred to as an out-of-band (out-of-band) signal, for example, the speed detection of the accelerometer is in-band signal detection, and the acoustic detection of the accelerometer is out-of-band signal detection. In a real application scenario of the internet of things based on sensor perception, most emergencies or malicious attacks are caused by out-of-band vulnerabilities of sensors. In addition, except for a hardware factory test, the existing multi-sensor fusion technology can reduce risks caused by abnormal operation of a single sensor to a certain extent, but does not solve the problem of out-of-band vulnerability of the sensor from the source.
In conclusion, how to realize the out-of-band vulnerability detection system of the sensor, which is convenient to operate, strong in portability, accurate and reliable in measurement, has great significance for safety research and protection of the Internet of things system based on sensor perception.
Disclosure of Invention
The invention aims to provide a sensor safety detection method and system based on a frequency sweeping technology, and aims to solve the problem that the existing sensor performance detection only detects in-band signals and extreme bearing environments of a sensor, but lacks out-of-band signal vulnerability detection and has limitation in practical application scenes.
The invention adopts the following technical scheme for solving the problems:
a sensor safety detection method based on sweep frequency technology comprises the following steps:
step one, an excitation signal generation stage: setting excitation signal types, sweep initial frequency, sweep cut-off frequency, equal interval sweep frequency point number, excitation signal amplitude and same frequency signal detection duration parameters by adopting a digital sweep frequency technology, and outputting a periodic excitation electric signal by an arbitrary waveform generator;
step two, an out-of-band signal generation stage: converting the periodic excitation electric signal into an out-of-band signal to be tested by using a transducer;
step three, an out-of-band signal testing stage: according to the contact type of the out-of-band signal to be tested and the sensor, the out-of-band signal to be tested is acted on the sensor and equipment for installing the sensor;
step four, test data collection stage: acquiring output data of a sensor and characteristic data of equipment provided with the sensor, and preprocessing the output data to obtain normal sample data;
step five, adding interference in the step three, and repeating the step three and the step four to obtain abnormal sample data;
step six, constructing a neural network model, and training the model by using normal sample data and abnormal sample data;
and seventhly, aiming at the sensor to be detected, performing safety detection by using a trained model, and in the safety detection process, firstly, executing frequency sweep in a wide frequency band range, and searching the optimal vulnerability frequency point according to the frequency sweep result and a dichotomy.
Further, the type of the out-of-band signal includes any one of acoustic, optical, electrical, magnetic, thermal, and chemical signals.
Further, the characteristic data of the sensor-mounted device comprises the working temperature and the vibration amplitude of the device.
Further, the preprocessing process of the data comprises the following steps:
4.1 Taking the sensor output data and the characteristic data of the equipment provided with the sensor as raw data, and carrying out filtering and normalization processing on the raw data;
4.2 Dividing the normalized data into a plurality of samples according to the number of the frequency sweeping frequency points at equal intervals.
Further, the length of the abnormal sample is the same as the length of the normal sample.
Further, in the training of the model in the step six, the probability value in the range of (0, 1) is outputted from the model, and the probability value is smaller, the probability of abnormality of the sensor is greater.
Further, the step seven specifically includes:
7.1 Firstly, setting the number of m equally-spaced frequency sweeping frequency points in a broadband range [ L, R ], wherein L is the frequency sweeping initial frequency, R is the frequency sweeping cut-off frequency, and m is an even number; taking the output value of the to-be-detected sensor, the vibration amplitude of equipment installed by the to-be-detected sensor and the working temperature data which are obtained in the frequency band range as raw data, and carrying out filtering and normalization preprocessing on the raw data;
7.2 Dividing the preprocessed data into m sections according to the number of the equidistant frequency sweeping points, wherein the length of a sample to be tested of each section is consistent with the length of a training sample when training a neural network model, and m sample data to be tested are obtained;
7.3 Taking m pieces of sample data to be detected as input of a trained neural network model according to the time sequence of segmentation to obtain m output results;
7.4 And (3) taking the average value of the output of the normal sample in the training process as a standard value, respectively calculating the average value of the output of the first half part and the average value of the output of the second half part of the model, comparing the average value with the standard value, resetting the frequency band range in the first half part or the second half part by a bisection method if the average value of the first half part or the second half part is smaller than the standard value and the difference value of the average value and the average value exceeds the detection threshold value, setting the frequency sweep range of the first half part as [ L, (L+R)/2 ], or setting the frequency sweep range of the second half part as [ (L+R)/2, R ], and repeating the steps 7.1) to 7.4) until the frequency sweep range is lower than the frequency sweep threshold value, and outputting the corresponding fragile frequency point.
Further, the method also comprises a step eight of generating a detection report according to a detection result; the detection report comprises a sweep frequency detection object, a sweep frequency range, test precision, a vulnerability frequency point and a sensor output value under the vulnerability frequency point.
A sensor safety detection system based on a sweep frequency technology is used for realizing the sensor safety detection method.
The sensor out-of-band vulnerability detection system based on the sweep frequency technology is reasonable in design, simple in structure, convenient to operate, strong in generalization capability and reliable in detection result. The main beneficial effects include:
(1) Aiming at the out-of-band vulnerability detection system of the sensor, the invention breaks through the defect that the traditional performance detection is only carried out on an in-band signal domain, perfects the performance detection system of the sensor, and has important significance for guaranteeing the reliability of perceived data in the Internet of things system.
(2) The invention adopts a digital sweep frequency technology to provide stable excitation signals for the transducer element, the transducer type in the prior art can cover the out-of-band leak detection of the sensor in the category 104 of the sound, light, electricity, magnetism, heat and chemistry, and the detection can be divided into contact detection and non-contact detection according to whether the detection signals directly contact the sensor to be detected, wherein the contact detection is to directly apply the out-of-band signals to the sensor and equipment for installing the sensor, and the non-contact detection is to apply the out-of-band signals to the sensor and equipment for installing the sensor in a non-contact manner, such as through air propagation and the like. Therefore, the invention establishes a complete out-of-band vulnerability detection system which is irrelevant to the sensor type and has good generalization performance.
(3) In the out-of-band vulnerability detection system of the sensor, a high-efficiency digital sweep frequency strategy is adopted to provide stable excitation signals, so that the out-of-band signal detection efficiency of the sensor is effectively improved; an abnormal detection model based on a machine learning algorithm is introduced, detection tasks are converted into classification problems, and detection accuracy and detection speed are further improved.
Drawings
FIG. 1 is a schematic overall flow diagram of a sensor safety detection system based on frequency sweep technology of the present invention;
FIG. 2 is a schematic diagram of out-of-band signal generation based on a swept frequency technique in accordance with the present invention;
FIG. 3 is a schematic diagram of the data acquisition and preprocessing process of the present invention;
FIG. 4 is a schematic illustration of vulnerability detection based on machine learning algorithm of the present invention;
FIG. 5 is a flow chart of the present invention employing a modified dichotomy to find fragile points;
FIG. 6 is a diagram illustrating the content structure of a test report in accordance with an embodiment of the present invention.
Detailed Description
The invention provides a sensor safety detection method and a sensor safety detection system based on a sweep frequency technology, which utilize the characteristic that a sensor outputs abnormal data at an out-of-band weak point, realize automatic vulnerability mining and detection by combining a machine learning classification algorithm, can effectively detect the vulnerability of the sensor in out-of-band physical signals, and overcome the defect that the traditional sensor detection system only aims at in-band signal detection. The detection method and the detection system complement the defect of out-of-band signal loopholes of the sensor in the whole frequency spectrum range of acoustic wave resonance loopholes, filter loopholes, saturation loopholes, frequency mixing loopholes, photoelectric coupling loopholes, electromagnetic coupling loopholes, nonlinear intermodulation distortion loopholes, envelope extraction loopholes and the like.
The following describes embodiments and technical schemes of the present invention in further detail with reference to the accompanying drawings:
as shown in fig. 1, the following describes an embodiment of the present invention for safety detection of a sensor based on a frequency sweep technology, taking a triaxial acceleration sensor for out-of-acoustic-band vulnerability detection as an example. In this embodiment, the sweep signal source module uses an arbitrary waveform generator (DG 4012, supporting maximum frequency of 100MHz, sampling rate of 500 MSa/s) and a high-voltage amplifier (high-power amplifier (HAS 4051, supporting maximum signal frequency of 500 KHz) of NF corporation, a high-voltage radio-frequency amplifier (ZHL-100W-gan+, supporting amplification frequency of 20-500 MHz) of coaxal corporation. The out-of-band signal transmitting module is a high-power loudspeaker, the sensor to be detected is a triaxial acceleration sensor, the sensor data acquisition platform and the microcontroller module are integrated on the raspberry group 4B, and the display module adopts a high-definition liquid crystal display screen.
Step 1: and generating an excitation signal. The sound wave frequency sweep test system on the control host is turned on, sound wave frequency sweep test parameters are set in a system interface, and the sound wave frequency sweep test parameters comprise excitation signal types, frequency sweep starting frequency, frequency sweep cut-off frequency, the number of frequency sweep frequency points at equal intervals, excitation signal amplitude and same-frequency signal detection duration, as shown in figure 2. Clicking a start button, transmitting a control instruction to a sweep frequency signal source module, namely an arbitrary waveform generator, and enabling the arbitrary waveform generator to enter a sweep frequency mode to generate an original excitation signal periodically changing at equal intervals. The original excitation signal is amplified by a high-voltage amplifier, and the high-voltage amplifier transmits the amplified excitation signal to an out-of-band signal transmitting module, namely a high-power loudspeaker.
Step 2: out-of-band acoustic wave signal generation. The out-of-band sound wave signal generation stage mainly refers to that the signal output end of the high-voltage amplifier is connected with the input end of the loudspeaker, and the loudspeaker converts a periodic sweep frequency excitation signal into a periodic variable-frequency out-of-band sound wave signal.
Step 3: the out-of-band signal test, because the acoustic wave signal mainly propagates through the air, a non-contact test mode is adopted in the test stage, the loudspeaker is close to the sensor module to be tested, namely the anti-shake module of the camera, the out-of-band acoustic wave signal propagates to the receiving end of the sensor to be tested through the air and acts on the MEMS vibration module of the triaxial accelerometer, so that abnormal sensor output and platform shake and heat are caused.
Step 4: sensor data collection. As shown in fig. 3, the data collection stage includes accessing the triaxial acceleration sensor to be tested to a sensor data collection platform and fixing the sensor data collection platform on a mechanical device, wherein the data collection platform integrates an analog-to-digital conversion module supporting high input frequency and having high linearity and ultra-low power consumption. Measuring the vibration amplitude of the sensor by adopting a vibration monitoring device, and connecting the output of the vibration monitoring device with a data acquisition platform; the infrared temperature measuring device is used for collecting the real-time working temperature of the sensor, and the output of the infrared temperature measuring device is connected with the data acquisition platform.
After the sensor to be tested is electrified to work, the data acquisition platform adopts a high-speed analog-to-digital converter to convert the analog output signals of the sensor triaxial, the vibration monitoring device and the infrared temperature measuring device into stable digital signals in real time, and stores the stable digital signals in time sequence. The collected data are divided into normal sample data and abnormal sample data, the normal sample data refers to data collected in a state that the sensor is stable and normal in operation, and the normal sample data are obtained by converting analog output signals of the sensor into stable digital signals in real time by adopting a high-speed analog-to-digital converter and storing the stable digital signals; abnormal sample data refers to data acquired when the sensor output is abnormal due to the artificial external interference, such as abnormal output of the sensor caused by the short-time vibration accelerometer.
Step 5: sensor data preprocessing, as shown in fig. 3, since the triaxial accelerometer is affected by ambient noise in a static state, a filtering operation is performed on raw data in a preprocessing stage. Considering the difference of the magnitude and magnitude of the output signals of different sensors, vibration monitoring and temperature monitoring platforms, the original data x is needed to improve the generalization of the detection algorithm 1 ,x 2 ,x 3 ,x 4 ,x 5 Normalization processing is carried out to obtain a preprocessed data sample x' 1 ,x′ 2 ,x′ 3 ,x′ 4 ,x′ 5 In this example, a normalization method is used, as shown in the following formula.
The preprocessed 50% of the data are used as training sets, and the rest are used as test sets.
Wherein x is 1 ,x 2 ,x 3 ,x 4 ,x 5 Respectively representing the x-axis output, the y-axis output, the z-axis output, the vibration amplitude and the working temperature of the acceleration sensor; x ' represents the normalized data set x ' = [ x ] ' 1 ,x′ 2 ,x′ 3 ,x′ 4 ,x′ 5 ]X represents the original data set x= [ x ] 1 ,x 2 ,x 3 ,x 4 ,x 5 ]Mu represents the mean set mu= [ mu ] of the raw data 1 ,μ 2 ,μ 3 ,μ 4 ,μ 5 ]σ represents the variance set σ= [ σ ] of the original data 1 ,σ 2 ,σ 3 ,σ 4 ,σ 5 ]。
Step 6: and in the model training stage, the preprocessed sensor training set data under a certain test frequency is segmented into a plurality of samples at equal intervals, the samples are input into the input end of the machine learning model, the weight is initialized randomly, and the gradient descent algorithm is adopted to optimize the model parameters. The method comprises the following specific steps:
step 6.1: as shown in fig. 4, a neural network model consisting of an input layer, a hidden layer and an output layer is built, the vulnerability detection problem is converted into a classification problem, and as each group of data has 5 inputs, the input layer consists of 5 neural units;
step 6.2: initializing parameters of a neural network model, wherein the weight w of each neuron is generated randomly by generating initialization weights n And offset value b n Initialized to a random number, where w n Representing the weight matrix at the nth iteration, b n Representing a neural network bias value matrix at the nth iteration; setting an activation function, namely 6.3: pre-training a neural network model by using a training set, and firstly calculating a neural network activation value:
where n represents the number of iterations,representing an output value of the hidden layer of the sample under the nth iteration; o (O) n Representing an activation function at the nth iteration to enhance model nonlinearity, outputting hidden layerThe values are concentrated to a probability value in the (0, 1) range and are used as the final output of the neural network model.
Step 6.4: and calculating errors, carrying out error reverse transmission on the neural network model according to the loss function value, and optimizing parameters and learning rate by adopting a gradient descent method. Let the model predicted output value of the sample data be P (where the model output value of the normal sample data p=1, the model output value of the abnormal sample data p=0), the output layer error Err for the nth iteration n Can be expressed as:
the updating weight is as follows by error reverse transmission:
wherein, beta represents the learning rate,the neuron weights at the nth iteration are represented. When the output result of the proposed neural network is higher than 0.5, the sample to be tested is considered to be normal, namely the sensor to be tested has no weak point in the out-of-band signal of the current frequency point; otherwise, the sample is abnormal, namely the out-of-band signal of the current frequency point is the out-of-band fragile frequency point of the sensor. The training process is not stopped until the predicted error rate is lower than a certain threshold value, so that a trained network is obtained; b n+1 Is the neural network bias value, b at the n+1th iteration n Is the neural network bias value at the nth iteration.
Step 7: out-of-band vulnerability detection, in the detection stage, a trained neural network model is adopted to convert the abnormal detection problem into the classification problem, and a dichotomy is combined to search the optimal vulnerability frequency point, so that the detection efficiency is effectively improved, and a specific process is shown in a flowchart 5.
Step 7.1: first in the wide frequency band range [ L, R]Setting m equally-spaced frequency sweeping frequency points, preprocessing output values of to-be-detected sensors, vibration amplitude and working temperature data of equipment installed by the to-be-detected sensors, which are obtained in the frequency band range, according to the method of step 5, dividing the preprocessed data into m sections at equal intervals according to the frequency points, wherein the length of to-be-detected samples of each section is consistent with the length of training samples when training a neural network model, sequentially inputting m pieces of to-be-detected sample data obtained under the m frequency points into the trained neural network model to obtain m model output O= [ O ] 1 ,O 2 ,...,O m-1 ,O m ]M is an even number.
Step 7.2: the improved binary search method searches for fragile frequency points. Because the vulnerability frequency points appear in a certain narrow bandwidth range and a plurality of vulnerability points possibly exist, the workload is increased if each narrow-band frequency point is subjected to one-by-one frequency sweep detection in a full frequency band, and the frequency sweep frequency can be effectively reduced by adopting an improved binary search method, the search speed is improved, the vulnerability points can be accurately positioned, and the detection performance is enhanced.
The specific implementation method comprises the following steps: and (3) calculating the average value of the front half part output and the average value of the rear half part output of the model, comparing the average value of the front half part output and the average value of the rear half part output with the average value of the normal sample model output, if the front half part (or the rear half part) is smaller than the average value of the normal sample model output and the difference value of the front half part (or the rear half part) is larger than a detection threshold value, redefining the frequency sweep range in the front half part (or the rear half part), for example, setting the frequency sweep range as [ L, (L+R)/2 ] if the average value of the front half part output is smaller than the average value of the normal sample model output, setting the frequency sweep range as [ (L+R)/2, R ] if the average value of the rear half part output is smaller than the average value of the normal sample model output, repeating the steps 7.1 to 7.2 until the frequency sweep range is lower than the threshold value, and outputting the corresponding fragile frequency point, namely outputting the final frequency sweep range. Note that in the detection process, there may be a plurality of fragile frequency points, so if both the front and rear portions are smaller than the output average value of the normal sample model, it is necessary to perform dichotomy detection on both the front and rear portions.
In this embodiment, through out-of-band vulnerability detection in step 7, the sensor is swept from a frequency range of 1-100KHz, the number of equally spaced frequency sweeping points is set to 10, it is found by a binary search method that the difference between the output average value of the model output average value in the first half frequency range and the output average value of the normal sample model output average value exceeds a detection threshold, and the difference between the output average value in the second half frequency range and the output average value of the normal sample model output average value does not exceed the detection threshold, so that the vulnerability can be determined to be in the range of 1-50 KHz. Similarly, frequency subdivision is further carried out on [1KHz,50KHz ], frequency sweeping and judgment are carried out, and finally the vulnerability frequency point of the out-of-band sound wave signal of the accelerometer is detected to be about 0.8KHz and about 3KHz, the vulnerability of the accelerometer at about 0.8KHz is caused by the resonance of the Y axis, and the vulnerability at about 3KHz is caused by the resonance of the X axis, so that the vulnerability type is sound wave resonance vulnerability.
Step 8: a test report is generated. After the sweep test is finished, a complete test report is generated. The test report content comprises a sweep frequency detection object (a triaxial acceleration sensor), a test frequency point range (1 Hz-100 KHz), test accuracy (100 Hz), vulnerability frequency points (0.8 KHz and 3 KHz) and sensor output values under the vulnerability frequency points (when the frequency is 0.8KHz, the Y-axis output value of the sensor is 0.19g/s, and when the frequency is 3KHz, the X-axis output value of the sensor is 0.42 g/s). Through further professional analysis, the test report can also comprise the type of the loopholes of the sensor to be tested, the application field, the test principle, the protection suggestion and the like, and the specific content is shown in fig. 6. Aiming at the out-of-band acoustic wave signal vulnerability detected by the acceleration sensor in the case is an acoustic wave resonance vulnerability, the protection proposal is provided by adopting an acoustic wave blocking method, namely, adding a layer of acoustic wave shielding material on the periphery of the sensor to shield or attenuate malicious acoustic wave signals.
Corresponding to the foregoing embodiment of a method for detecting sensor security based on sweep frequency technology, the present application further provides an embodiment of a system for detecting sensor security based on sweep frequency technology, which includes:
the frequency sweep signal source module generates a periodic excitation electric signal according to the excitation signal type, the frequency sweep starting frequency, the frequency sweep cut-off frequency, the number of frequency points of equidistant frequency sweep, the amplitude of the excitation signal and the detection duration parameter of the same-frequency signal.
The out-of-band signal transmitting module is used for converting the periodic excitation electric signal into an out-of-band signal to be tested, and applying the out-of-band signal to be tested to the sensor and equipment for installing the sensor according to the contact type of the out-of-band signal to be tested and the sensor.
And the sensor data acquisition platform acquires output data of the sensor and characteristic data of equipment provided with the sensor, and preprocesses the data.
And the neural network model training module is used for training the model by using the normal sample data and the abnormal sample data.
And the detection module is used for carrying out safety detection by utilizing the trained model, and in the safety detection process, firstly, frequency sweep is carried out in a wide frequency band range, and the optimal vulnerability frequency point is searched according to the frequency sweep result and the dichotomy.
And the display module is used for displaying the detection result.
For system embodiments, reference is made to the description of method embodiments for the relevant points, since they essentially correspond to the method embodiments. The system embodiments described above are merely illustrative, and the detection modules may or may not be physically separate. In addition, each functional module in the present invention may be integrated in one processing unit, each module may exist alone physically, or two or more modules may be integrated in one unit. The integrated modules or units can be realized in a hardware form or a software functional unit form, so that part or all of the modules can be selected according to actual needs to realize the purpose of the scheme.
The foregoing list is only illustrative of specific embodiments of the invention. Obviously, the invention is not limited to the above embodiments, but many variations are possible. All modifications directly derived or suggested to one skilled in the art from the present disclosure should be considered as being within the scope of the present invention.
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| CN115021971B (en) * | 2022-05-10 | 2023-06-09 | 电子科技大学 | Hardware security testing method for FPGA cloud platform |
| CN115130503B (en) * | 2022-06-21 | 2025-04-15 | 浙江大学 | A touch screen vulnerability analysis and protection method and system |
| CN117890709A (en) * | 2024-01-18 | 2024-04-16 | 浙江大学 | A sensor vulnerability detection method and system based on wired injection technology |
| CN118300824A (en) * | 2024-03-15 | 2024-07-05 | 浙江大学 | A sensor vulnerability detection method and system based on common mode signal ground line injection |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104374468A (en) * | 2014-10-24 | 2015-02-25 | 哈尔滨工业大学深圳研究生院 | Intrinsic frequency measuring method for long and thin object |
| CN105136281A (en) * | 2015-10-14 | 2015-12-09 | 基康仪器股份有限公司 | Vibrating wire transducer frequency measurement device and method |
| CN107525849A (en) * | 2017-08-25 | 2017-12-29 | 北京航空航天大学 | A kind of single-input single-output test modal analysis system and method based on fiber grating |
| CN111157099A (en) * | 2020-01-02 | 2020-05-15 | 河海大学常州校区 | Distributed optical fiber sensor vibration signal classification method and identification classification system |
| CN111325095A (en) * | 2020-01-19 | 2020-06-23 | 西安科技大学 | Method and system for intelligent detection of equipment health status based on acoustic signal |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10271233B2 (en) * | 2013-03-15 | 2019-04-23 | DGS Global Systems, Inc. | Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum |
| CN109154489B (en) * | 2016-06-08 | 2020-10-23 | 齐戈股份有限公司 | Precision positioning system using wavelength tunable laser |
-
2021
- 2021-11-23 CN CN202111394732.XA patent/CN114121305B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104374468A (en) * | 2014-10-24 | 2015-02-25 | 哈尔滨工业大学深圳研究生院 | Intrinsic frequency measuring method for long and thin object |
| CN105136281A (en) * | 2015-10-14 | 2015-12-09 | 基康仪器股份有限公司 | Vibrating wire transducer frequency measurement device and method |
| CN107525849A (en) * | 2017-08-25 | 2017-12-29 | 北京航空航天大学 | A kind of single-input single-output test modal analysis system and method based on fiber grating |
| CN111157099A (en) * | 2020-01-02 | 2020-05-15 | 河海大学常州校区 | Distributed optical fiber sensor vibration signal classification method and identification classification system |
| CN111325095A (en) * | 2020-01-19 | 2020-06-23 | 西安科技大学 | Method and system for intelligent detection of equipment health status based on acoustic signal |
Non-Patent Citations (2)
| Title |
|---|
| Taxonomy and Challenges of Out-of-Band Signal Injection Attacks and Defenses;Ilias Giechaskiel等;《IEEE Communications Surveys & Tutorials》;全文 * |
| 正弦动态压力校准中扫频速率对校准结果的影响研究;邰寒松;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;全文 * |
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