Kinematic semantics is often an important content of a CAD model(it refers to a single part/solid model in this work)in many applications,but it is usually not the belonging of the model,especially for the one retriev...Kinematic semantics is often an important content of a CAD model(it refers to a single part/solid model in this work)in many applications,but it is usually not the belonging of the model,especially for the one retrieved from a common database.Especially,the effective and automatic method to reconstruct the above information for a CAD model is still rare.To address this issue,this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics.First,as the geometry of an assembly interface is formed by one or more adjacent faces on each model,a face-attributed adjacency graph integrated with face structure fingerprint is proposed.This can describe each CAD model as well as its assembly interfaces uniformly.After that,aided by the above descriptor,an improved graph attention network is developed based on a new dual-level anti-interference filtering mechanism,which makes it have the great potential to identify all representative kinds of assembly interface faces with high accuracy that have various geometric shapes but consistent kinematic semantics.Moreover,based on the abovementioned graph and face-adjacent relationships,each assembly interface on a model can be identified.Finally,experiments on representative CAD models are implemented to verify the effectiveness and characteristics of the proposed approach.The results show that the average assembly-interface-face-identification accuracy of the proposed approach can reach 91.75%,which is about 2%–5%higher than those of the recent-representative graph neural networks.Besides,compared with the state-of-the-art methods,our approach is more suitable to identify the assembly interfaces(with various shapes)for each individual CAD model that has typical kinematic pairs.展开更多
The theory and method of wavelet packet decomposition and its energy spectrum dealing with the coal rock Interface Identification are presented in the paper. The characteristic frequency band of the coal rock signal c...The theory and method of wavelet packet decomposition and its energy spectrum dealing with the coal rock Interface Identification are presented in the paper. The characteristic frequency band of the coal rock signal could be identified by wavelet packet decomposition and its energy spectrum conveniently, at the same time, quantification analysis were performed. The result demonstrates that this method is more advantageous and of practical value than traditional Fourier analysis method.展开更多
An identification technique for sharp interface and penetrated isolated particles is developed for simulating two-dimensional,incompressible and immiscible two-phase flows using meshless particle methods in this paper...An identification technique for sharp interface and penetrated isolated particles is developed for simulating two-dimensional,incompressible and immiscible two-phase flows using meshless particle methods in this paper.This technique is based on the numerically computed density gradient of fluid particles and is suitable for capturing large interface deformation and even topological changes such as merging and breaking up of phases.A number of assumed particle configurations will be examined using the technique,including these with different level of randomness of particle distribution.The tests will show that the new technique can correctly identify almost all the interface and isolated particles,and also show that it is better than other existing popular methods tested.展开更多
Scholte waves at the seafloor interface are generally identified by their velocity features and seismic fields,which are measured using ocean bottom seismometers and geophones.These methods are effective in cases wher...Scholte waves at the seafloor interface are generally identified by their velocity features and seismic fields,which are measured using ocean bottom seismometers and geophones.These methods are effective in cases where there is a considerable difference between the velocities of Scholte and acoustic waves in water.However,they are ineffective when the velocities of these two types of waves are close to each other.Thus,in this paper,a method based on acoustic pressure field measurement for identifying Scholte waves is proposed according to their excitation and propagation characteristics.The proposed method can overcome the limitations on the velocities of two types of waves.A tank experiment is designed and conducted according to the proposed method,and an ocean environment is scaled down to the laboratory size.Acoustic measurements are obtained along virtual arrays in the water column using a robotic apparatus.Experiments show that changes in Scholte wave amplitudes,depending on different source depths and propagation distances,are consistent with the theoretical results.This means that Scholte waves generated at the seafloor interface are successfully measured and identified in the acoustic pressure field.展开更多
The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data...The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.展开更多
Machine Learning has evolved with a variety of algorithms to enable state-of-the-art computer vision applications.In particular the need for automating the process of real-time food item identification,there is a huge...Machine Learning has evolved with a variety of algorithms to enable state-of-the-art computer vision applications.In particular the need for automating the process of real-time food item identification,there is a huge surge of research so as to make smarter refrigerators.According to a survey by the Food and Agriculture Organization of the United Nations(FAO),it has been found that 1.3 billion tons of food is wasted by consumers around the world due to either food spoilage or expiry and a large amount of food is wasted from homes and restaurants itself.Smart refrigerators have been very successful in playing a pivotal role in mitigating this problem of food wastage.But a major issue is the high cost of available smart refrigerators and the lack of accurate design algorithms which can help achieve computer vision in any ordinary refrigerator.To address these issues,this work proposes an automated identification algorithm for computer vision in smart refrigerators using InceptionV3 and MobileNet Convolutional Neural Network(CNN)architectures.The designed module and algorithm have been elaborated in detail and are considerably evaluated for its accuracy using test images on standard fruits and vegetable datasets.A total of eight test cases are considered with accuracy and training time as the performance metric.In the end,real-time testing results are also presented which validates the system’s performance.展开更多
In underground coal mines,hydrofracture can cause the increase of breathability in the fractured coal bed.When the hydrofracture crack propagates to the interface between the coal bed and the roof-floor stratum,the cr...In underground coal mines,hydrofracture can cause the increase of breathability in the fractured coal bed.When the hydrofracture crack propagates to the interface between the coal bed and the roof-floor stratum,the crack may enter roof-floor lithology,thus posing a limit on the scope of breathability increase and making it difficult to support the roof and floor board for subsequent coal mining.In this work,a two-dimensional model of coal rock bed that contains hydrofracture crack was constructed.Then an investigation that combines the fracture mechanics and the system of flow and solid in rock failure process analysis(RFPA2D-Flow)were carried out to study the failure mechanism at the interface between rocks and coals,and critical water pressure that hydrofracture crack propagates.The results indicated that the main factors that affect the direction of hydrofracture crack propagation are the angle of intersection between coal-rock interface and horizontal section,horizontal crustal stress difference,tension-shear mixed crack fracture toughness in coal-rock interface and differences in elasticity modulus of coal-rock bed.The possibility of crack directly entering coal-rock interface would increase with the increase in angle of intersection or horizontal crustal stress difference.The trend that crack propagates along the coal-rock interface will become stronger with the decrease of the fracture toughness at the coal-rock interface and the increase of the elasticity modulus difference between the coal bed and the roof strata.The results of this study was to put forward a method of controlling hydrofracture crack,optimize the fracturing well location provides a certain theoretical basis.展开更多
基金supported by the National Natural Science Foundation of China[61702147]the Zhejiang Provincial Science and Technology Program in China[2021C03137].
文摘Kinematic semantics is often an important content of a CAD model(it refers to a single part/solid model in this work)in many applications,but it is usually not the belonging of the model,especially for the one retrieved from a common database.Especially,the effective and automatic method to reconstruct the above information for a CAD model is still rare.To address this issue,this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics.First,as the geometry of an assembly interface is formed by one or more adjacent faces on each model,a face-attributed adjacency graph integrated with face structure fingerprint is proposed.This can describe each CAD model as well as its assembly interfaces uniformly.After that,aided by the above descriptor,an improved graph attention network is developed based on a new dual-level anti-interference filtering mechanism,which makes it have the great potential to identify all representative kinds of assembly interface faces with high accuracy that have various geometric shapes but consistent kinematic semantics.Moreover,based on the abovementioned graph and face-adjacent relationships,each assembly interface on a model can be identified.Finally,experiments on representative CAD models are implemented to verify the effectiveness and characteristics of the proposed approach.The results show that the average assembly-interface-face-identification accuracy of the proposed approach can reach 91.75%,which is about 2%–5%higher than those of the recent-representative graph neural networks.Besides,compared with the state-of-the-art methods,our approach is more suitable to identify the assembly interfaces(with various shapes)for each individual CAD model that has typical kinematic pairs.
文摘The theory and method of wavelet packet decomposition and its energy spectrum dealing with the coal rock Interface Identification are presented in the paper. The characteristic frequency band of the coal rock signal could be identified by wavelet packet decomposition and its energy spectrum conveniently, at the same time, quantification analysis were performed. The result demonstrates that this method is more advantageous and of practical value than traditional Fourier analysis method.
文摘An identification technique for sharp interface and penetrated isolated particles is developed for simulating two-dimensional,incompressible and immiscible two-phase flows using meshless particle methods in this paper.This technique is based on the numerically computed density gradient of fluid particles and is suitable for capturing large interface deformation and even topological changes such as merging and breaking up of phases.A number of assumed particle configurations will be examined using the technique,including these with different level of randomness of particle distribution.The tests will show that the new technique can correctly identify almost all the interface and isolated particles,and also show that it is better than other existing popular methods tested.
基金the National Natural Science Foundation of China(No.11474258)the State Key Laboratory of Acoustics(No.SKLA202206)。
文摘Scholte waves at the seafloor interface are generally identified by their velocity features and seismic fields,which are measured using ocean bottom seismometers and geophones.These methods are effective in cases where there is a considerable difference between the velocities of Scholte and acoustic waves in water.However,they are ineffective when the velocities of these two types of waves are close to each other.Thus,in this paper,a method based on acoustic pressure field measurement for identifying Scholte waves is proposed according to their excitation and propagation characteristics.The proposed method can overcome the limitations on the velocities of two types of waves.A tank experiment is designed and conducted according to the proposed method,and an ocean environment is scaled down to the laboratory size.Acoustic measurements are obtained along virtual arrays in the water column using a robotic apparatus.Experiments show that changes in Scholte wave amplitudes,depending on different source depths and propagation distances,are consistent with the theoretical results.This means that Scholte waves generated at the seafloor interface are successfully measured and identified in the acoustic pressure field.
基金This project is supported by Provincial Youth Science Foundation of Shanxi China (No.20011020)National Natural Science Foundation of China (No.59975064).
文摘The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.
基金This work was supported by Taif University Researchers Supporting Project(TURSP)under number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘Machine Learning has evolved with a variety of algorithms to enable state-of-the-art computer vision applications.In particular the need for automating the process of real-time food item identification,there is a huge surge of research so as to make smarter refrigerators.According to a survey by the Food and Agriculture Organization of the United Nations(FAO),it has been found that 1.3 billion tons of food is wasted by consumers around the world due to either food spoilage or expiry and a large amount of food is wasted from homes and restaurants itself.Smart refrigerators have been very successful in playing a pivotal role in mitigating this problem of food wastage.But a major issue is the high cost of available smart refrigerators and the lack of accurate design algorithms which can help achieve computer vision in any ordinary refrigerator.To address these issues,this work proposes an automated identification algorithm for computer vision in smart refrigerators using InceptionV3 and MobileNet Convolutional Neural Network(CNN)architectures.The designed module and algorithm have been elaborated in detail and are considerably evaluated for its accuracy using test images on standard fruits and vegetable datasets.A total of eight test cases are considered with accuracy and training time as the performance metric.In the end,real-time testing results are also presented which validates the system’s performance.
基金jointly supported by 973 Program(NO.2014 CB239206)PCSIRT(NO.IRT13043)+1 种基金the National Science Foundation of China(NO.51374258,NO.51474158)the Open Projects of State Key Laboratory of Coal Mine Disaster Dynamics and Control(Chongqing University 2011DA105287-FW201412).
文摘In underground coal mines,hydrofracture can cause the increase of breathability in the fractured coal bed.When the hydrofracture crack propagates to the interface between the coal bed and the roof-floor stratum,the crack may enter roof-floor lithology,thus posing a limit on the scope of breathability increase and making it difficult to support the roof and floor board for subsequent coal mining.In this work,a two-dimensional model of coal rock bed that contains hydrofracture crack was constructed.Then an investigation that combines the fracture mechanics and the system of flow and solid in rock failure process analysis(RFPA2D-Flow)were carried out to study the failure mechanism at the interface between rocks and coals,and critical water pressure that hydrofracture crack propagates.The results indicated that the main factors that affect the direction of hydrofracture crack propagation are the angle of intersection between coal-rock interface and horizontal section,horizontal crustal stress difference,tension-shear mixed crack fracture toughness in coal-rock interface and differences in elasticity modulus of coal-rock bed.The possibility of crack directly entering coal-rock interface would increase with the increase in angle of intersection or horizontal crustal stress difference.The trend that crack propagates along the coal-rock interface will become stronger with the decrease of the fracture toughness at the coal-rock interface and the increase of the elasticity modulus difference between the coal bed and the roof strata.The results of this study was to put forward a method of controlling hydrofracture crack,optimize the fracturing well location provides a certain theoretical basis.
