In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung so...In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung sounds(sounds with wheezes or rales). The proposed method includes two main steps: Firstly, the wavelet packet transform(WPT) is used to extract the original features of lung sounds; then the genetic algorithm(GA) is used to select the best feature set. The obtained optimal feature set is sent to four different classifiers to evaluate the performance of the proposed method. Experimental results show that the feature set obtained by the proposed method provides a higher classification accuracy of 94.6% in comparison with the best wavelet packet basis approach and multi-scale principal component analysis(PCA) approach. Meanwhile, the proposed method has effective generalization performance and can obtain the best feature set without priori knowledge of lung sounds.展开更多
Monitoring techniques are a key technology for examining the conditions in various scenarios, e.g., structural conditions, weather conditions, and disasters. In order to understand such scenarios, the appropriate extr...Monitoring techniques are a key technology for examining the conditions in various scenarios, e.g., structural conditions, weather conditions, and disasters. In order to understand such scenarios, the appropriate extraction of their features from observation data is important. This paper proposes a monitoring method that allows sound environments to be expressed as a sound pattern. To this end, the concept of synesthesia is exploited. That is, the keys, tones, and pitches of the monitored sound are expressed using the three elements of color, that is, the hue, saturation, and brightness, respectively. In this paper, it is assumed that the hue, saturation, and brightness can be detected from the chromagram, sonogram, and sound spectrogram, respectively, based on a previous synesthesia experiment. Then, the sound pattern can be drawn using color, yielding a “painted sound map.” The usefulness of the proposed monitoring technique is verified using environmental sound data observed at a galleria.展开更多
为探究水平管内气液两相流流型变化激励流动噪声特性,结合VOF(volume of fluid)多相流模型与Lighthill声类比理论开展数值计算研究。结果表明,随着气相和液相流速增加,气液两相流呈现弹状流–层状流–泡状流的演变规律,进而导致噪声呈...为探究水平管内气液两相流流型变化激励流动噪声特性,结合VOF(volume of fluid)多相流模型与Lighthill声类比理论开展数值计算研究。结果表明,随着气相和液相流速增加,气液两相流呈现弹状流–层状流–泡状流的演变规律,进而导致噪声呈现不同变化趋势。随着频率增加,弹状流噪声呈现先减小后增大的趋势,而层状流和泡状流噪声分别呈现逐渐减小和增大的趋势。此外,在不同工况下,泡状流诱导噪声最大,弹状流噪声次之,层状流噪声最小。研究成果对于气液两相流噪声控制技术研究具有重要指导作用。展开更多
基金Funded by the International Science and Technology Cooperation Foundation of Chongqing Science and Technology Commission(Grant No.cstc2012gg-gjhz0023)the 2013 Innovative Team Construction Project of Chongqing Universities
文摘In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung sounds(sounds with wheezes or rales). The proposed method includes two main steps: Firstly, the wavelet packet transform(WPT) is used to extract the original features of lung sounds; then the genetic algorithm(GA) is used to select the best feature set. The obtained optimal feature set is sent to four different classifiers to evaluate the performance of the proposed method. Experimental results show that the feature set obtained by the proposed method provides a higher classification accuracy of 94.6% in comparison with the best wavelet packet basis approach and multi-scale principal component analysis(PCA) approach. Meanwhile, the proposed method has effective generalization performance and can obtain the best feature set without priori knowledge of lung sounds.
文摘Monitoring techniques are a key technology for examining the conditions in various scenarios, e.g., structural conditions, weather conditions, and disasters. In order to understand such scenarios, the appropriate extraction of their features from observation data is important. This paper proposes a monitoring method that allows sound environments to be expressed as a sound pattern. To this end, the concept of synesthesia is exploited. That is, the keys, tones, and pitches of the monitored sound are expressed using the three elements of color, that is, the hue, saturation, and brightness, respectively. In this paper, it is assumed that the hue, saturation, and brightness can be detected from the chromagram, sonogram, and sound spectrogram, respectively, based on a previous synesthesia experiment. Then, the sound pattern can be drawn using color, yielding a “painted sound map.” The usefulness of the proposed monitoring technique is verified using environmental sound data observed at a galleria.
文摘为探究水平管内气液两相流流型变化激励流动噪声特性,结合VOF(volume of fluid)多相流模型与Lighthill声类比理论开展数值计算研究。结果表明,随着气相和液相流速增加,气液两相流呈现弹状流–层状流–泡状流的演变规律,进而导致噪声呈现不同变化趋势。随着频率增加,弹状流噪声呈现先减小后增大的趋势,而层状流和泡状流噪声分别呈现逐渐减小和增大的趋势。此外,在不同工况下,泡状流诱导噪声最大,弹状流噪声次之,层状流噪声最小。研究成果对于气液两相流噪声控制技术研究具有重要指导作用。
