中国地质灾害点多面广,目前通过人工排查已发现近30万处隐患点,但近年来发生的多起重大地质灾害并不在已发现的隐患点范围内,应该还有大量的灾害隐患没被发现,尽可能全面识别和发现灾害隐患仍是中国防灾减灾最重要的工作内容之一。就如...中国地质灾害点多面广,目前通过人工排查已发现近30万处隐患点,但近年来发生的多起重大地质灾害并不在已发现的隐患点范围内,应该还有大量的灾害隐患没被发现,尽可能全面识别和发现灾害隐患仍是中国防灾减灾最重要的工作内容之一。就如何进一步推动地质灾害隐患早期识别工作提出了自己的认识和建议:(1)近年来,各种遥感技术在地质灾害隐患识别中发挥了重要作用,但每种技术都有各自的长处和短处,所能识别的隐患类型和特征也不尽相同,只有将各种技术手段综合应用,相互补充和校验,才能最大限度地识别已存在的地质灾害隐患,有效破解隐患识别难题。(2)对于识别难度最大的不稳定斜坡,需要将传统地质勘测与现代技术激光雷达(light detection and ranging,LiDAR)、航空或半航空物探等有机结合,才能提升识别效率和准确性。(3)利用深度机器学习可望实现地质灾害隐患的智能化自动识别,但目前其仅对光谱和纹理特性显著的新生地质灾害具有较好的自动识别能力,而对其他类型如古老滑坡体、一般地质灾害隐患点而言,自动识别的正确率还不高,应加大力度开展相关方面的深入研究。展开更多
Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the...Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.展开更多
Artificial intelligence(AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of com...Artificial intelligence(AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.展开更多
文摘中国地质灾害点多面广,目前通过人工排查已发现近30万处隐患点,但近年来发生的多起重大地质灾害并不在已发现的隐患点范围内,应该还有大量的灾害隐患没被发现,尽可能全面识别和发现灾害隐患仍是中国防灾减灾最重要的工作内容之一。就如何进一步推动地质灾害隐患早期识别工作提出了自己的认识和建议:(1)近年来,各种遥感技术在地质灾害隐患识别中发挥了重要作用,但每种技术都有各自的长处和短处,所能识别的隐患类型和特征也不尽相同,只有将各种技术手段综合应用,相互补充和校验,才能最大限度地识别已存在的地质灾害隐患,有效破解隐患识别难题。(2)对于识别难度最大的不稳定斜坡,需要将传统地质勘测与现代技术激光雷达(light detection and ranging,LiDAR)、航空或半航空物探等有机结合,才能提升识别效率和准确性。(3)利用深度机器学习可望实现地质灾害隐患的智能化自动识别,但目前其仅对光谱和纹理特性显著的新生地质灾害具有较好的自动识别能力,而对其他类型如古老滑坡体、一般地质灾害隐患点而言,自动识别的正确率还不高,应加大力度开展相关方面的深入研究。
基金the National Natural Science Foundation of China (51638007, 51478149, 51678203,and 51678204).
文摘Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.
文摘Artificial intelligence(AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.
