In this study,unsupervised and supervised pattern recognition were implemented in combination to achieve real-time health monitoring.Unsupervised recognition(k-means++)was used to label the spectral characteristics of...In this study,unsupervised and supervised pattern recognition were implemented in combination to achieve real-time health monitoring.Unsupervised recognition(k-means++)was used to label the spectral characteristics of acoustic emission(AE)signals after completing the tensile tests at ambient temperature.Using in-plane tensile at 800 and 1000°C as implementing examples,supervised recognition(K-nearest neighbor(KNN))was used to identify damage mode in real time.According to the damage identification results,four main tensile damage modes of 2D C/SiC composites were identified:matrix cracking(122.6–201 kHz),interfacial debonding(201–294.4 kHz),interfacial sliding(20.6–122.6 kHz)and fiber breaking(294.4–1000 kHz).Additionally,the damage evolution mechanisms for the 2D C/SiC composites were analyzed based on the characteristics of AE energy accumulation curve during the in-plane tensile loading at ambient and elevated temperature with oxidation.Meanwhile,the energy of various damage modes was accurately calculated by harmonic wavelet packet and the damage degree of modes could be analyzed.The identification results show that compared with previous studies,using the AE analysis method,the method has higher sensitivity and accuracy.展开更多
The main aim for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane.This problem is of critical importance since it incorporates temporal ch...The main aim for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane.This problem is of critical importance since it incorporates temporal characteristics often found in real-time applications.Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks.This paper presents a max-density covering learning algorithm based on constructive neural networks which is efficient in terms of the recognition rate and the speed of recognition.The results show that it is possible to solve the spiral problem instantaneously(up to 100% correct classification on the test set).展开更多
Pattern recognition method is used for the investigation of stability,region of filled Ti_2Ni phases in multi-dimensional bond-parameter space.The filling of C,N and O atoms into T_6 octahedra consisting of atoms of e...Pattern recognition method is used for the investigation of stability,region of filled Ti_2Ni phases in multi-dimensional bond-parameter space.The filling of C,N and O atoms into T_6 octahedra consisting of atoms of earhy-transition elements makes the expansion of the stability region of Ti_2Ni phase,and the relative stability of AI_2Cu and MoSi_2 type com- pounds decreases after the introduction of non-metallic elements such as C,N and O.展开更多
基金the National Natural Science Foundation of China(Grant No.12172304)the 111 Project(Grant No.BP0719007).
文摘In this study,unsupervised and supervised pattern recognition were implemented in combination to achieve real-time health monitoring.Unsupervised recognition(k-means++)was used to label the spectral characteristics of acoustic emission(AE)signals after completing the tensile tests at ambient temperature.Using in-plane tensile at 800 and 1000°C as implementing examples,supervised recognition(K-nearest neighbor(KNN))was used to identify damage mode in real time.According to the damage identification results,four main tensile damage modes of 2D C/SiC composites were identified:matrix cracking(122.6–201 kHz),interfacial debonding(201–294.4 kHz),interfacial sliding(20.6–122.6 kHz)and fiber breaking(294.4–1000 kHz).Additionally,the damage evolution mechanisms for the 2D C/SiC composites were analyzed based on the characteristics of AE energy accumulation curve during the in-plane tensile loading at ambient and elevated temperature with oxidation.Meanwhile,the energy of various damage modes was accurately calculated by harmonic wavelet packet and the damage degree of modes could be analyzed.The identification results show that compared with previous studies,using the AE analysis method,the method has higher sensitivity and accuracy.
基金Sponsored by the National High Technology Research Development Program of China(Grant No.2001AA413130).
文摘The main aim for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane.This problem is of critical importance since it incorporates temporal characteristics often found in real-time applications.Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks.This paper presents a max-density covering learning algorithm based on constructive neural networks which is efficient in terms of the recognition rate and the speed of recognition.The results show that it is possible to solve the spiral problem instantaneously(up to 100% correct classification on the test set).
文摘Pattern recognition method is used for the investigation of stability,region of filled Ti_2Ni phases in multi-dimensional bond-parameter space.The filling of C,N and O atoms into T_6 octahedra consisting of atoms of earhy-transition elements makes the expansion of the stability region of Ti_2Ni phase,and the relative stability of AI_2Cu and MoSi_2 type com- pounds decreases after the introduction of non-metallic elements such as C,N and O.
基金河北省自然科学基金(the Natural Science Foundation of Hebei Province of China under Grant No.A2006000941)河北省教育厅2006年科研计划(No.2006408)河北省科学技术研究与发展指导计划(No.072135142)。
