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
Unmanned Aerial Vehicles(UAVs)play a vital role in military warfare.In a variety of battlefield mission scenarios,UAVs are required to safely fly to designated locations without human intervention.Therefore,finding a ...Unmanned Aerial Vehicles(UAVs)play a vital role in military warfare.In a variety of battlefield mission scenarios,UAVs are required to safely fly to designated locations without human intervention.Therefore,finding a suitable method to solve the UAV Autonomous Motion Planning(AMP)problem can improve the success rate of UAV missions to a certain extent.In recent years,many studies have used Deep Reinforcement Learning(DRL)methods to address the AMP problem and have achieved good results.From the perspective of sampling,this paper designs a sampling method with double-screening,combines it with the Deep Deterministic Policy Gradient(DDPG)algorithm,and proposes the Relevant Experience Learning-DDPG(REL-DDPG)algorithm.The REL-DDPG algorithm uses a Prioritized Experience Replay(PER)mechanism to break the correlation of continuous experiences in the experience pool,finds the experiences most similar to the current state to learn according to the theory in human education,and expands the influence of the learning process on action selection at the current state.All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV.The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm,while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.展开更多
近年来,以微博、微信、Facebook为代表的社交网络不断发展,网络表示学习引起了学术界和工业界的广泛关注.传统的网络表示学习模型利用图矩阵表示的谱特性,由于其效率低下、效果不佳,难以应用到真实网络中.近几年,基于神经网络的表示学...近年来,以微博、微信、Facebook为代表的社交网络不断发展,网络表示学习引起了学术界和工业界的广泛关注.传统的网络表示学习模型利用图矩阵表示的谱特性,由于其效率低下、效果不佳,难以应用到真实网络中.近几年,基于神经网络的表示学习方法因算法效率高、较好地保存了网络结构信息,逐渐成为网络表示学习的主流算法.网络中的节点因为不同类型的关系而相互连接,这些关系里隐藏了非常丰富的信息(如兴趣、家人),但所有现存方法都没有区分节点之间边的关系类型.提出一种能够编码这种关系信息的无监督网络表示学习模型NEES(network embedding via edge sampling).首先,通过边采样得到能够反映边关系类型信息的边向量;其次,利用边向量为图中每个节点学习到一个低维表示.分别在几个真实网络数据上进行了多标签分类、边预测等任务,实验结果表明:在绝大多数情况下,该方法都表现最优.展开更多
The paper presents GEneral ReadOut (GERO), a general readout ASIC based on a switched capacitor array for micro-pattern gas detectors. It aims at providing general readout electronics for low-to-medium event-rate gas ...The paper presents GEneral ReadOut (GERO), a general readout ASIC based on a switched capacitor array for micro-pattern gas detectors. It aims at providing general readout electronics for low-to-medium event-rate gas detectors with high sampling frequency, configurable storage depth, and data digitalization. The first prototype GERO chip integrates 16 channels and was fabricated using a 0.18-lm CMOS process. Each channel consists of a sampling array working in a ping-pong mode, a storage array with a 1024-cell depth, and 32 Wilkinson analog-todigital converters. The detailed design and test results are presented in the paper.展开更多
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.
基金Supported by the National Natural Science Foundation of China under Grant No.60573091 (国家自然科学基金)the National High-Tech Research and Development Plan of China under Grant No.2007AA01Z155 (国家高技术研究发展计划(863))+1 种基金the Beijing Natural Science Foundation of China under Grant No.4073035 (北京市自然科学基金)the Program for New Century Excellent Talents in University of China (新世纪优秀人才支持计划)
基金co-supported by the National Natural Science Foundation of China(Nos.62003267,61573285)the Aeronautical Science Foundation of China(ASFC)(No.20175553027)Natural Science Basic Research Plan in Shaanxi Province of China(No.2020JQ-220)。
文摘Unmanned Aerial Vehicles(UAVs)play a vital role in military warfare.In a variety of battlefield mission scenarios,UAVs are required to safely fly to designated locations without human intervention.Therefore,finding a suitable method to solve the UAV Autonomous Motion Planning(AMP)problem can improve the success rate of UAV missions to a certain extent.In recent years,many studies have used Deep Reinforcement Learning(DRL)methods to address the AMP problem and have achieved good results.From the perspective of sampling,this paper designs a sampling method with double-screening,combines it with the Deep Deterministic Policy Gradient(DDPG)algorithm,and proposes the Relevant Experience Learning-DDPG(REL-DDPG)algorithm.The REL-DDPG algorithm uses a Prioritized Experience Replay(PER)mechanism to break the correlation of continuous experiences in the experience pool,finds the experiences most similar to the current state to learn according to the theory in human education,and expands the influence of the learning process on action selection at the current state.All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV.The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm,while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.
文摘近年来,以微博、微信、Facebook为代表的社交网络不断发展,网络表示学习引起了学术界和工业界的广泛关注.传统的网络表示学习模型利用图矩阵表示的谱特性,由于其效率低下、效果不佳,难以应用到真实网络中.近几年,基于神经网络的表示学习方法因算法效率高、较好地保存了网络结构信息,逐渐成为网络表示学习的主流算法.网络中的节点因为不同类型的关系而相互连接,这些关系里隐藏了非常丰富的信息(如兴趣、家人),但所有现存方法都没有区分节点之间边的关系类型.提出一种能够编码这种关系信息的无监督网络表示学习模型NEES(network embedding via edge sampling).首先,通过边采样得到能够反映边关系类型信息的边向量;其次,利用边向量为图中每个节点学习到一个低维表示.分别在几个真实网络数据上进行了多标签分类、边预测等任务,实验结果表明:在绝大多数情况下,该方法都表现最优.
基金supported by the National Natural Science Foundation of China(Nos.11675197 and 11775242)
文摘The paper presents GEneral ReadOut (GERO), a general readout ASIC based on a switched capacitor array for micro-pattern gas detectors. It aims at providing general readout electronics for low-to-medium event-rate gas detectors with high sampling frequency, configurable storage depth, and data digitalization. The first prototype GERO chip integrates 16 channels and was fabricated using a 0.18-lm CMOS process. Each channel consists of a sampling array working in a ping-pong mode, a storage array with a 1024-cell depth, and 32 Wilkinson analog-todigital converters. The detailed design and test results are presented in the paper.
