In order to shorten the design cycle of the excavator working device, we have proposed a rapid modeling method for the excavator working device which uses parameters. Based on the Pro/toolkit, which is secondary devel...In order to shorten the design cycle of the excavator working device, we have proposed a rapid modeling method for the excavator working device which uses parameters. Based on the Pro/toolkit, which is secondary development tool of Pro/E4.0,and combined with Vs C＋＋2005 programming software. It developed a flexible set of MFC visualization-friendly interfaces. Users can enter data in the visual interface according to their needs and it will generate a new part model quickly. So it improves the design quality, shortens the design cycle, and makes the cost lower significantly.展开更多
Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization.Thus,designing a round-the-clock intelligent surveillance system has become crucial and urgent.In this study,we...Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization.Thus,designing a round-the-clock intelligent surveillance system has become crucial and urgent.In this study,we develop an acoustic signal-based excavation device recognition system for underground pipeline protection.The front-end hardware system is equipped with an acoustic sensor array,an Analog-to-Digital Converter(ADC)module(ADS1274),and an industrial processor Advanced RISC Machine(ARM)cortex-A8 for signal collection and algorithm implementation.Then,a novel Statistical Time-Frequency acoustic Feature(STFF)is proposed,and a fast Extreme Learning Machine(ELM)is adopted as the classifier.Experiments on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum features.In addition,the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.展开更多
文摘In order to shorten the design cycle of the excavator working device, we have proposed a rapid modeling method for the excavator working device which uses parameters. Based on the Pro/toolkit, which is secondary development tool of Pro/E4.0,and combined with Vs C＋＋2005 programming software. It developed a flexible set of MFC visualization-friendly interfaces. Users can enter data in the visual interface according to their needs and it will generate a new part model quickly. So it improves the design quality, shortens the design cycle, and makes the cost lower significantly.
基金supported by the National Natural Science Foundation of China(Nos.U1909209 and 61503104)。
文摘Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization.Thus,designing a round-the-clock intelligent surveillance system has become crucial and urgent.In this study,we develop an acoustic signal-based excavation device recognition system for underground pipeline protection.The front-end hardware system is equipped with an acoustic sensor array,an Analog-to-Digital Converter(ADC)module(ADS1274),and an industrial processor Advanced RISC Machine(ARM)cortex-A8 for signal collection and algorithm implementation.Then,a novel Statistical Time-Frequency acoustic Feature(STFF)is proposed,and a fast Extreme Learning Machine(ELM)is adopted as the classifier.Experiments on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum features.In addition,the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.
