CN106096625A - Point long-range loss assessment system of vehicle and a method is set up based on artificial intelligence's KNN learning method - Google Patents
Point long-range loss assessment system of vehicle and a method is set up based on artificial intelligence's KNN learning method Download PDFInfo
- Publication number
- CN106096625A CN106096625A CN201610364185.3A CN201610364185A CN106096625A CN 106096625 A CN106096625 A CN 106096625A CN 201610364185 A CN201610364185 A CN 201610364185A CN 106096625 A CN106096625 A CN 106096625A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- data
- collision
- model
- training
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
Abstract
Point long-range loss assessment system of vehicle and a method is set up based on artificial intelligence's KNN learning method, belong to car damage identification field, after solving vehicle collision, problem collision rift being collided to the vehicle detection of vehicle, there is vehicle detection subsystem, it is judged that collided type during vehicle collision；Described vehicle detection subsystem, learns vehicle training data thus generates vehicle model, and described vehicle model is set up and used K arest neighbors sorting algorithm.Effect is: technique scheme, it is possible to achieve for vehicle collision vehicle detect, the method that this technical field in long-range setting loss employs machine learning, for machine learning method, during setting loss, it determines accuracy rate on promoted.
Description
Technical field
The invention belongs to car damage identification field, relate to setting up the long-range setting loss of point vehicle based on artificial intelligence's KNN learning method
System and method.
Background technology
During low-speed motion (including low speed links traveling, vehicle parking etc.), take place frequently collision accident for vehicle and lead
The Claims Resolution dispute problem caused, long-range setting loss technology by multi-signal in collection vehicle driving process (as speed, acceleration,
Angular velocity, sound etc.) and analyzed with signal processing and machine learning techniques, to judge whether collision occurs and collision rift
The damage situation of vehicle.
After vehicle crashes, headend equipment is capable of detecting when the generation of collision and intercepts the signal of collision process,
Being sent to high in the clouds by wireless network, remote server extracts the eigenvalue of design in advance from the signal received, and uses machine
Learning algorithm is analyzed, and first judges the accuracy of crash data, then judges collision object and operating mode situation, to determine Collision Number
Create the damage of which kind of grade according to what part of set pair, then go out with reference to amount for which loss settled concurrent according to part injury rating calculation
Deliver to insurance company.The detection for vehicle, operating mode, target, part and region can be related to during this.
Summary of the invention
After solving vehicle collision, problem collision rift being collided to the vehicle detection of vehicle, the present invention proposes
A kind of set up point long-range loss assessment system of vehicle based on artificial intelligence's KNN learning method, to realize the vehicle detection during setting loss
And judgement.
In order to solve above-mentioned technical problem, the technical scheme that the present invention provides is characterized by: a kind of based on artificial intelligence KNN
Learning method sets up point long-range loss assessment system of vehicle, including:
Truck type choice subsystem, selects the model data corresponding to vehicle as total data set；
Data classification subsystem, reads CAE emulation data and real vehicle data, and classifies data accordingly；
Collision detection subsystem, it is judged that the most whether vehicle collides；Described collision detection subsystem pair
Collision training data carries out learning thus generates collision model, and described collision model is set up and used K arest neighbors sorting algorithm；
Operating mode detection subsystem, it is judged that all work informations that collision occurs；Operating mode is instructed by described operating mode detection subsystem
White silk data carry out learning thus generate condition model, and described condition model is set up and used K arest neighbors sorting algorithm；
Vehicle detection subsystem, it is judged that collided type during vehicle collision；Described vehicle detection subsystem, trains number to vehicle
Generating vehicle model according to carrying out learning, described vehicle model is set up and is used K arest neighbors sorting algorithm.
The invention still further relates to a kind of set up the long-range damage identification method of point vehicle based on artificial intelligence's KNN learning method, including with
Lower step:
Step one. select the model data corresponding to vehicle as total data set；
Step 2. read CAE emulation data and real vehicle data, and accordingly data are classified；
Step 3. judge the most whether vehicle collides；Described collision detection subsystem is to collision training
Data carry out learning thus generate collision model, and described collision model is set up and used K arest neighbors sorting algorithm；
Step 4. judge all work informations that collision occurs；Operating mode training data is entered by described operating mode detection subsystem
Row learns thus generates condition model, and described condition model is set up and used K arest neighbors sorting algorithm；
Step 5. judge collided type during vehicle collision；Described vehicle detection subsystem, to vehicle training data
Practising thus generate vehicle model, described vehicle model is set up and is used K arest neighbors sorting algorithm.
Beneficial effect: technique scheme, it is possible to achieve the vehicle for vehicle collision detects, at this of long-range setting loss
Technical field employs the method for machine learning, for machine learning method, during setting loss, it determines accuracy rate on
To promote；The present invention is by selecting vehicle to import the data corresponding to this vehicle, and data classification is then for model training
With the purpose of test and the step that adds；The detection of vehicle is the purpose that the program realizes, and is to obtain through sequence of operations
The result arrived.
Accompanying drawing explanation
Fig. 1 is the structural schematic block diagram of system of the present invention.
Detailed description of the invention
In order to the present invention being made apparent explanation, below the technical term that the present invention relates to is made definitions:
Operating mode: all collision informations such as collision angle, direction, target, region；
Vehicle: automobile model；
Target: collision target；
Region: position of collision；
Part: auto parts；
Operating mode detects: detect all collision informations such as this car collision angle, direction, target, region；
Vehicle detects: the automobile model that detection collides with this car；
Target detection: detect this car collision target；
Region detection: detect this car position of collision；
Piece test: detect this car auto parts.
Embodiment 1:A kind of foundation based on artificial intelligence's KNN learning method divides the long-range loss assessment system of vehicle, including:
Truck type choice subsystem, selects the model data corresponding to vehicle as total data set；
Data classification subsystem, reads CAE emulation data and real vehicle data, and classifies data accordingly；
Collision detection subsystem, it is judged that the most whether vehicle collides；Described collision detection subsystem pair
Collision training data carries out learning thus generates collision model, and described collision model is set up and used K arest neighbors sorting algorithm；
Operating mode detection subsystem, it is judged that all work informations that collision occurs；Operating mode is instructed by described operating mode detection subsystem
White silk data carry out learning thus generate condition model, and described condition model is set up and used K arest neighbors sorting algorithm；
Vehicle detection subsystem, it is judged that collided type during vehicle collision；Described vehicle detection subsystem, trains number to vehicle
Generating vehicle model according to carrying out learning, described vehicle model is set up and is used K arest neighbors sorting algorithm.
As a kind of embodiment: described collision detection subsystem includes, collide training module, crash tests module, collision
Authentication module, described collision training module generates collision model for learning collision training data, crash tests
Module detects the result of collision model for crash tests data being brought in collision model, collision authentication module uses true race
The reliability of car data checking collision model and accuracy rate；
Described operating mode detection subsystem includes, operating mode training module, working condition measurement module, operating mode authentication module, described work
Condition training module generates condition model for learning operating mode training data, and described working condition measurement module is for by work
Condition test data bring the result detecting condition model in model into, and operating mode authentication module uses true sport car data verification operating mode mould
The reliability of type and accuracy rate；
Described vehicle detection subsystem includes, vehicle training module, vehicle test module, vehicle authentication module, described car
Type training module generates vehicle model for carrying out learning by vehicle training data, and vehicle test module is for surveying vehicle
Examination data bring the result detecting vehicle model in model into, and vehicle authentication module uses true sport car data verification vehicle model
Reliability and accuracy rate.
As another kind of embodiment, described K arest neighbors sorting algorithm comprises the following steps:
It is maximum that step 1---initializes distance；
Step 2---calculates unknown sample and distance dist of each training sample；
It is individual closest to ultimate range maxdist in sample that step 3---obtains K up till now；
If step 4---dist is less than maxdist, then using this training sample as K-nearest samples；
Step 5---repeat step 2,3,4, until the distance of unknown sample and all training samples is the completeest；
The number of times that in step 6---statistics K-nearest samples, each class label occurs；
Step 7---selects the class label class label as unknown sample of frequency of occurrences maximum.
The present embodiment, detects for vehicle, and we use K arest neighbors (k-Nearest Neighbor, KNN) to classify at present
Algorithm, the thinking of the method is: if the k that sample is in feature space most like (i.e. closest in feature space)
Great majority in sample belong to some classification, then this sample falls within this classification.
In KNN algorithm, selected neighbours are the objects of correctly classification.The method is determining class decision-making on foundation
The classification of one or several closest samples determines the classification belonging to sample to be divided.Although KNN method from principle also
Depend on limit theorem, but when classification decision-making, the most relevant with minimal amount of adjacent sample.Owing to KNN method is mainly by surrounding
Limited neighbouring sample rather than by differentiate class field method determine generic, therefore for class field intersection or
For overlapping more sample set to be divided, KNN method relatively additive method is more suitable for.
KNN algorithm can be not only used for classification, it is also possible to is used for returning.By finding out k nearest-neighbors of a sample,
The meansigma methods of the attribute of these neighbours is assigned to this sample, it is possible to obtain the attribute of this sample.More useful method is by not
The neighbours of same distance give different weights (weight) to the impact that this sample produces, and (combine as weights are directly proportional to distance
Function).
K-NN can be described as a kind of the most directly for the method for unknown data of classifying.
In simple terms, K-NN can regard as: has the data of so your already known classification of a pile, then when a new data
The when of entrance, begin to each point in training data seeks distance, then choose from the point of view of the K that this training data is nearest is individual
See what type these points belong to, then by the principle that the minority is subordinate to the majority, sort out to new data.
Embodiment 2:A kind of foundation based on artificial intelligence's KNN learning method divides the long-range damage identification method of vehicle, including following step
Rapid:
Step one. select the model data corresponding to vehicle as total data set；
Step 2. read CAE emulation data and real vehicle data, and accordingly data are classified；
Step 3. judge the most whether vehicle collides；Described collision detection subsystem is to collision training
Data carry out learning thus generate collision model, and described collision model is set up and used K arest neighbors sorting algorithm；
Step 4. judge all work informations that collision occurs；Operating mode training data is entered by described operating mode detection subsystem
Row learns thus generates condition model, and described condition model is set up and used K arest neighbors sorting algorithm；
Step 5. judge collided type during vehicle collision；Described vehicle detection subsystem, to vehicle training data
Practising thus generate vehicle model, described vehicle model is set up and is used K arest neighbors sorting algorithm.
As a kind of embodiment, comprising the concrete steps that of said method scheme:
Step 3 includes:
S3.1. use collision detection subsystem that CAE collision simulation data are processed, then classify to produce collision to it
Training data and crash tests data；
S3.2. in collision training module, collision training data learnt and produces collision model, carrying out simulated crash
The effect of training data；
S3.3. crash tests data are used to carry out the result of test collisions model in crash tests module；
S3.4. use true sport car data as collision checking data and to bring collision authentication module into, verify collision mould
The accuracy of type；
Step 4 includes:
S4.1. CAE operating mode emulation data are processed by applying working condition detection subsystem, then it is carried out classification generation operating mode instruction
Practice data and working condition measurement data；
S4.2. in operating mode training module, operating mode training data learnt and produce condition model, carrying out simulated condition
The effect of training data；
S4.3. in working condition measurement module, applying working condition test data carry out the result of measurement condition model；
S4.4. use true sport car data as operating mode checking data and to bring operating mode authentication module into, verify operating mode mould
The accuracy of type；
Step 5 includes:
S5.1. use vehicle detection subsystem that CAE vehicle emulation data are processed, then classify to produce vehicle to it
Training data and vehicle test data；
S5.2. in vehicle training module, vehicle training data learnt and produce vehicle model, simulating vehicle
The effect of training data；
S5.3. vehicle test data are used to carry out the result of test carriage pattern type in vehicle test module；
S5.4. use true sport car data as vehicle checking data and to bring vehicle authentication module into, verify vehicle mould
The accuracy of type；
As another kind of embodiment, described K arest neighbors sorting algorithm comprises the following steps:
It is maximum that step 1---initializes distance；
Step 2---calculates unknown sample and distance dist of each training sample；
It is individual closest to ultimate range maxdist in sample that step 3---obtains K up till now；
If step 4---dist is less than maxdist, then using this training sample as K-nearest samples；
Step 5---repeat step 2,3,4, until the distance of unknown sample and all training samples is the completeest；
The number of times that in step 6---statistics K-nearest samples, each class label occurs；
Step 7---selects the class label class label as unknown sample of frequency of occurrences maximum.
Embodiment 3:There is the technical scheme identical with embodiment 1 or 2, more specifically:
Conceptual data collection in such scheme: be entirely CAE emulation data and sport car data；Be divided into three parts as follows
1. training dataset: be used to training pattern or determine model parameter (CAE emulation data and sport car data).
2. checking data set: be used to do Model Selection (model selection), i.e. does the final optimization pass and really of model
Fixed (CAE emulation data and sport car data).
3. test data set: the Generalization Ability of the model then trained for purely test.(CAE emulates data
With sport car data).
In the present embodiment also to the filtering related to during setting loss, weighting choose, feature extraction, normalization, eigentransformation
Have been described.
1. wave filter technology: the filtering method realized includes FIR filtering, FIR Chebyshev approximation, Chebyshev's filter
Ripple, butterworth filter etc., the Filtering.m file in mastery routine realizes.Each wave filter is common wave filter,
Matlab has corresponding function to realize, and specific algorithm refers to signal processing professional book.Provide the interior of FIR filter herein
Hold and the introduction of flow process.
Limited impulse response digital filter (FIR, Finite Impulse Response) be a kind of full zero point be
System, the design of FIR filter is ensureing that amplitude characteristic meets the colleague that technology requires, it is easy to accomplish that strict linear phase is special
Property, so being the outstanding advantages of FIR filter according to there being stable and linear phase characteristic.Chebyshev approximation is the ripples such as one
Approximatioss, it is possible to make error frequency band be uniformly distributed, to same technical specification, this filter order sending out needs shoulder to shoulder is low,
For the wave filter of same exponent number, this approximatioss maximum error is minimum, and the key step of its design is as follows:
Step 1: the setting of filter parameter
The parameter of wave filter includes: cut-off frequecy of passband, stopband cut-off frequency, passband maximum attenuation and stopband minimum decline
Subtract；
Step 2: be arranged on passband and the amplitude-frequency response of stopband coideal
Step 3: be scheduled on the weighting on cut-off frequecy of passband and stopband cut-off frequency point
Step 4: utilize Equation for Calculating Chebyshev approximation filter coefficient
Step 5: preserve coefficient
Step 6: extraction coefficient carries out data filtering
Wherein: the guarantee signal that is disposed to of filter parameter does not haves distortion now during processing
As, the cut-off frequency of filtered signal and sample frequency need to meet Nyquist's theorem, the most after the filtering signal
Highest frequency not can exceed that the 1/2 of original signal sample frequency, otherwise arises that Lou frequency phenomenon.According to the signal in current project
The sample frequency of collection plate is mainly 50Hz and 1KHz, according to formula F as a example by 50HzCut-off< 50/2, therefore select filter cutoff
Frequency is below 25.
2. Feature Extraction Technology: feature extraction is carried out on collision alarm.Judge that the feature that collision uses includes window
Acceleration in difference between acceleration maxima and minima, window in the maximum of acceleration absolute value, window in mouthful
In average energy (in window the quadratic sum of acceleration a little divided by counting), window, the absolute value of each point slope is average
Value.
Judge the feature that part category is used include the average energy between speed, acceleration peak to peak,
Amplitude between maximum and minima/width between the two, acceleration maximum, acceleration minima, maximum place
The width of half-wave, minima place half-wave width, maximum and minima between difference, between peak to peak
Span, the meansigma methods of absolute value of each point slope, signal carry out each of the signal after Fourier transform in 0～38 frequency ranges
The amplitude of frequency component.
3. normalization technology: that causes classification task to eliminate the dimension between feature or order of magnitude difference is unfavorable
Impact, needs to be normalized characteristic so that have comparability between each eigenvalue, it is to avoid the spy that numerical value is bigger
Levy and flood the feature that numerical value is less.Original characteristic is after normalized, and each feature is in identical codomain scope.
Owing to the performance of Z-Score is more preferable, use Z-Score as method for normalizing.
4. feature transform technique: in the case of feature is more, for the dependency eliminated between feature and reduce redundancy
Feature, needs to convert feature, carrys out reflected sample information with the fewest new feature.In the less situation of experiment sample
Under (practical situation of this project) reduce too much intrinsic dimensionality, moreover it is possible to avoid sending out of over-fitting or poor fitting to a certain extent
Raw.According to actual needs, the eigentransformation the most realized is PCA.Being found through experiments, PCA divides for improving this project
Class performance there is no help, has declined, and this is that the feature owing to being used at present is less, does not has redundancy feature, therefore
PCA wouldn't be used, but be as being stepped up of subsequent characteristics, however not excluded that use the probability of PCA later.
In accompanying drawing 1, record: the Truck type choice subsystem that Truck type choice is in the present invention；Data categorization module is
Data classification subsystem in the present invention；The collision detection subsystem that collision judgment module is in the present invention；Operating mode detection mould
Block is the operating mode detection subsystem of the present invention；Vehicle detection module is the vehicle detection subsystem of the present invention；Piece test
Module i.e. piece test subsystem；Module of target detection is the target detection subsystem of the present invention, and region detection module is
The region detection subsystem of the present invention.
The above, only the invention preferably detailed description of the invention, but the protection domain of the invention is not
Being confined to this, any those familiar with the art is in the technical scope that the invention discloses, according to the present invention
The technical scheme created and inventive concept thereof in addition equivalent or change, all should contain the invention protection domain it
In.
Claims (6)
1. set up point long-range loss assessment system of vehicle based on artificial intelligence's KNN learning method for one kind, it is characterised in that including:
Truck type choice subsystem, selects the model data corresponding to vehicle as total data set；
Data classification subsystem, reads CAE emulation data and real vehicle data, and classifies data accordingly；
Collision detection subsystem, it is judged that the most whether vehicle collides；Described collision detection subsystem is to collision
Training data carries out learning thus generates collision model, and described collision model is set up and used K arest neighbors sorting algorithm；
Operating mode detection subsystem, it is judged that all work informations that collision occurs；Operating mode is trained number by described operating mode detection subsystem
Generating condition model according to carrying out learning, described condition model is set up and is used K arest neighbors sorting algorithm；
Vehicle detection subsystem, it is judged that collided type during vehicle collision；Described vehicle detection subsystem, enters vehicle training data
Row learns thus generates vehicle model, and described vehicle model is set up and used K arest neighbors sorting algorithm.
2. setting up point long-range loss assessment system of vehicle based on artificial intelligence's KNN learning method as claimed in claim 1, its feature exists
In,
Described collision detection subsystem includes, collision training module, crash tests module, collision authentication module, described collision is instructed
Practicing module and generate collision model for learning collision training data, crash tests module is for by crash tests number
According to bringing the result detecting collision model in collision model into, collision authentication module uses true sport car data verification collision model
Reliability and accuracy rate；
Described operating mode detection subsystem includes, operating mode training module, working condition measurement module, operating mode authentication module, and described operating mode is instructed
Practicing module and generate condition model for learning operating mode training data, described working condition measurement module is for surveying operating mode
Examination data bring the result detecting condition model in model into, and operating mode authentication module uses true sport car data verification condition model
Reliability and accuracy rate；
Described vehicle detection subsystem includes, vehicle training module, vehicle test module, vehicle authentication module, described vehicle is instructed
Practicing module and generate vehicle model for carrying out learning by vehicle training data, vehicle test module is for testing number by vehicle
According to bringing the result detecting vehicle model in model into, vehicle authentication module uses the reliable of true sport car data verification vehicle model
Property and accuracy rate.
3. set up point long-range loss assessment system of vehicle such as claim 1 or 2 based on artificial intelligence's KNN learning method, it is characterised in that
Described K arest neighbors sorting algorithm comprises the following steps:
It is maximum that step 1---initializes distance；
Step 2---calculates unknown sample and distance dist of each training sample；
It is individual closest to ultimate range maxdist in sample that step 3---obtains K up till now；
If step 4---dist is less than maxdist, then using this training sample as K-nearest samples；
Step 5---repeat step 2,3,4, until the distance of unknown sample and all training samples is the completeest；
The number of times that in step 6---statistics K-nearest samples, each class label occurs；
Step 7---selects the class label class label as unknown sample of frequency of occurrences maximum.
4. set up point long-range damage identification method of vehicle based on artificial intelligence's KNN learning method for one kind, it is characterised in that include following step
Rapid:
Step one. select the model data corresponding to vehicle as total data set；
Step 2. read CAE emulation data and real vehicle data, and accordingly data are classified；
Step 3. judge the most whether vehicle collides；Collision training data is learnt thus generates and touch
Hitting model, described collision model is set up and is used K arest neighbors sorting algorithm；
Step 4. judge all work informations that collision occurs；Operating mode training data is learnt thus generates condition model,
Described condition model is set up and is used K arest neighbors sorting algorithm；
Step 5. judge collided type during vehicle collision；Vehicle training data is learnt thus generates vehicle model, described
Vehicle model is set up and is used K arest neighbors sorting algorithm.
5. setting up point long-range damage identification method of vehicle based on artificial intelligence's KNN learning method as claimed in claim 4, its feature exists
In, comprise the concrete steps that:
Step 3 includes:
S3.1. use collision detection subsystem that CAE collision simulation data are processed, then classify to produce collision training to it
Data and crash tests data；
S3.2. in collision training module, collision training data learnt and produces collision model, carrying out simulated crash training
The effect of data；
S3.3. crash tests data are used to carry out the result of test collisions model in crash tests module；
S3.4. use true sport car data as collision checking data and to bring collision authentication module into, verify collision model
Accuracy；
Step 4 includes:
S4.1. CAE operating mode emulation data are processed by applying working condition detection subsystem, then it is carried out classification generation operating mode training number
According to working condition measurement data；
S4.2. in operating mode training module, operating mode training data learnt and produce condition model, carrying out simulated condition training
The effect of data；
S4.3. in working condition measurement module, applying working condition test data carry out the result of measurement condition model；
S4.4. use true sport car data as operating mode checking data and to bring operating mode authentication module into, verify condition model
Accuracy；
Step 5 includes:
S5.1. use vehicle detection subsystem that CAE vehicle emulation data are processed, then classify to produce vehicle training to it
Data and vehicle test data；
S5.2. in vehicle training module, vehicle training data learnt and produce vehicle model, simulating vehicle training
The effect of data；
S5.3. vehicle test data are used to carry out the result of test carriage pattern type in vehicle test module；
S5.4. use true sport car data as vehicle checking data and to bring vehicle authentication module into, verify vehicle model
Accuracy.
6. setting up based on artificial intelligence's KNN learning method as described in claim 4 or 5 divides the long-range damage identification method of vehicle, and it is special
Levying and be, described K arest neighbors sorting algorithm comprises the following steps:
It is maximum that step 1---initializes distance；
Step 2---calculates unknown sample and distance dist of each training sample；
It is individual closest to ultimate range maxdist in sample that step 3---obtains K up till now；
If step 4---dist is less than maxdist, then using this training sample as K-nearest samples；
Step 5---repeat step 2,3,4, until the distance of unknown sample and all training samples is the completeest；
The number of times that in step 6---statistics K-nearest samples, each class label occurs；
Step 7---selects the class label class label as unknown sample of frequency of occurrences maximum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610364185.3A CN106096625A (en) | 2016-05-27 | 2016-05-27 | Point long-range loss assessment system of vehicle and a method is set up based on artificial intelligence's KNN learning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610364185.3A CN106096625A (en) | 2016-05-27 | 2016-05-27 | Point long-range loss assessment system of vehicle and a method is set up based on artificial intelligence's KNN learning method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106096625A true CN106096625A (en) | 2016-11-09 |
Family
ID=57229407
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610364185.3A Pending CN106096625A (en) | 2016-05-27 | 2016-05-27 | Point long-range loss assessment system of vehicle and a method is set up based on artificial intelligence's KNN learning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106096625A (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103303237A (en) * | 2013-06-21 | 2013-09-18 | 湖南大学 | Air bag detonation control method based on genetic neural network |
| CN104932359A (en) * | 2015-05-29 | 2015-09-23 | 大连楼兰科技股份有限公司 | Vehicle remote unattended loss assessment system based on CAE technology and loss assessment method thereof |
-
2016
- 2016-05-27 CN CN201610364185.3A patent/CN106096625A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103303237A (en) * | 2013-06-21 | 2013-09-18 | 湖南大学 | Air bag detonation control method based on genetic neural network |
| CN104932359A (en) * | 2015-05-29 | 2015-09-23 | 大连楼兰科技股份有限公司 | Vehicle remote unattended loss assessment system based on CAE technology and loss assessment method thereof |
Non-Patent Citations (1)
| Title |
|---|
| XLM289348: "KNN(K-nearest Neighbor)最邻近规则分类", 《HTTP://BLOG.CSDN.NET/XLM289348/ARTICLE/DETAILS/8876353》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106127747B (en) | Car surface damage classifying method and device based on deep learning | |
| CN102147866B (en) | Target identification method based on training Adaboost and support vector machine | |
| CN105320966A (en) | Vehicle driving state recognition method and apparatus | |
| CN110378869A (en) | A kind of rail fastening method for detecting abnormality of sample automatic marking | |
| CN106980113A (en) | Article detection device and object detecting method | |
| CN106056147A (en) | System and method for establishing target division remote damage assessment of different vehicle types based artificial intelligence radial basis function neural network method | |
| Pawlus et al. | Investigation of vehicle crash modeling techniques: theory and application | |
| CN109164450B (en) | Downburst prediction method based on Doppler radar data | |
| CN102087790B (en) | Method and system for low-altitude ground vehicle detection and motion analysis | |
| CN106778908A (en) | A kind of novelty detection method and apparatus | |
| CN103345842A (en) | Road vehicle classification system and method | |
| Cong et al. | Applying wavelet packet decomposition and one-class support vector machine on vehicle acceleration traces for road anomaly detection | |
| CN105740914A (en) | Vehicle license plate identification method and system based on neighboring multi-classifier combination | |
| CN106056150A (en) | System and method for establishing part division remote damage assessment of different vehicle types based on artificial intelligence random forest method | |
| CN106055776A (en) | Regional and remote damage-assessment system and method established based on artificial-intelligence supervised learning linear regression method for different types of vehicles | |
| Pyo et al. | Front collision warning based on vehicle detection using CNN | |
| CN105046259A (en) | Coronal mass ejection (CME) detection method based on multi-feature fusion | |
| CN106066907A (en) | The setting loss grading method judged based on many parts multi-model | |
| CN113807314A (en) | Millimeter wave radar video fusion method based on micro-Doppler effect | |
| CN106067035A (en) | The long-range loss assessment system of different automobile types partial objectives for and method is set up based on artificial intelligence's supervised learning traditional decision-tree | |
| CN106055779A (en) | Remote damage-assessment system and method established based on artificial intelligence semi-supervised learning logistic-regression method for different types of vehicles | |
| CN117173913A (en) | Traffic control method and system based on traffic flow analysis at different time periods | |
| Sabour et al. | Deepflow: Abnormal traffic flow detection using siamese networks | |
| CN106056453A (en) | System and method for establishing working condition division remote damage assessment of different vehicle types based on artificial intelligence semi-supervised learning clustering hypothesis method | |
| CN106067036A (en) | Set up different automobile types based on artificial intelligence's unsupervised learning K means method and divide the long-range loss assessment system of part and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20161109 |
|
| RJ01 | Rejection of invention patent application after publication |