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Yonghong Zhang
School of Automation, Nanjing University of Information Science and Technology, Nanjing, China

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Research article
Published: 11 February 2021 in International Journal of Remote Sensing
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Cloud detection is one of the important tasks for remote sensing image preprocessing. In this work, multiple infrared observation channels with high time resolution are utilized to extract cloud from geostationary satellite imagery. Compared with detected cloud mask from polar satellite data with the high spatial resolution, the detection from geostationary satellite data has high timeliness and practicability, but it is challenging for the detailed segmentation ability of the network. Due to the fact that some small clouds only occupy a few pixels in geostationary satellite images with low spatial resolution, the existing network has poor segmentation ability for these small targets. To tackle this problem, a new neural network named U-High Resolution Network (U-HRNet) is proposed for cloud detection. Combing both multi-scale feature extraction using a High Resolution Network (HRNet) architecture and merging shallow information and deep information via the skip connection (SC) of a U-Net structure, the proposed U-HRNet produces strong high-resolution representations for accurate detection of details on cloud segmentation. The accuracy of the method is evaluated by manually labelled ground truth against different methods using objective evaluation indices. The results proved the proposed U-HRNet performs well on FengYun-4A (FY-4A) images and can effectively detect incorrect areas of the cloud mask products of the National Satellite Meteorological Centre (NSMC), depicting outperformance over existing methods.

ACS Style

Runzhe Tao; Yonghong Zhang; Lihua Wang; Qi Liu; Jiangeng Wang. U-High resolution network (U-HRNet): cloud detection with high-resolution representations for geostationary satellite imagery. International Journal of Remote Sensing 2021, 42, 3511 -3533.

AMA Style

Runzhe Tao, Yonghong Zhang, Lihua Wang, Qi Liu, Jiangeng Wang. U-High resolution network (U-HRNet): cloud detection with high-resolution representations for geostationary satellite imagery. International Journal of Remote Sensing. 2021; 42 (9):3511-3533.

Chicago/Turabian Style

Runzhe Tao; Yonghong Zhang; Lihua Wang; Qi Liu; Jiangeng Wang. 2021. "U-High resolution network (U-HRNet): cloud detection with high-resolution representations for geostationary satellite imagery." International Journal of Remote Sensing 42, no. 9: 3511-3533.

Journal article
Published: 07 February 2021 in Remote Sensing
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Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have large errors and uncertainty, and actual spatiotemporal heterogeneity of snow depth cannot be effectively detected. This paper proposed a deep learning approach based on downscaling snow depth retrieval by fusion of satellite remote-sensing data with multiple spatial scales and diverse characteristics. The (Fengyun-3 Microwave Radiation Imager) FY-3 MWRI data were downscaled to 500 m resolution to match Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover, meteorological and geographic data. A deep neural network was constructed to capture detailed spectral and radiation signals and trained to retrieve the higher spatial resolution snow depth from the aforementioned input data and ground observation. Verified by in situ measurements, downscaled snow depth has the lowest root mean square error (RMSE) and mean absolute error (MAE) (8.16 cm, 4.73 cm respectively) among Environmental and Ecological Science Data Center for West China Snow Depth (WESTDC_SD, 9.38 cm and 5.36 cm), the Microwave Radiation Imager (MWRI) Ascend Snow Depth (MWRI_A_SD, 9.45 cm and 5.49 cm) and MWRI Descend Snow Depth (MWRI_D_SD, 10.55 cm and 6.13 cm) in the study area. Meanwhile, downscaled snow depth could provide more detailed information in spatial distribution, which has been used to analyze the decrease of retrieval accuracy by various topography factors.

ACS Style

Linglong Zhu; Yonghong Zhang; Jiangeng Wang; Wei Tian; Qi Liu; Guangyi Ma; Xi Kan; Ya Chu. Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning. Remote Sensing 2021, 13, 584 .

AMA Style

Linglong Zhu, Yonghong Zhang, Jiangeng Wang, Wei Tian, Qi Liu, Guangyi Ma, Xi Kan, Ya Chu. Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning. Remote Sensing. 2021; 13 (4):584.

Chicago/Turabian Style

Linglong Zhu; Yonghong Zhang; Jiangeng Wang; Wei Tian; Qi Liu; Guangyi Ma; Xi Kan; Ya Chu. 2021. "Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning." Remote Sensing 13, no. 4: 584.

Journal article
Published: 02 September 2020 in Computer Communications
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Compared to Intrusive Load Monitoring which uses smart power meters at each level to be monitored, Non-Intrusive Load Monitoring (NILM) is an ingenious way that relies on signal readings at a single point to deduce the share of the devices that have contributed to the overall load. This reliable technique that guarantees the safety and privacy of individual users has recently become an increasingly popular topic, as it turns out to be a major solution to assist household users in the process of obtaining details of their electricity consumption. The detailed consumption promotes better management of the electrical power on the consumer side by helping to eliminate any waste of energy. In this paper, an edge gateway has been implemented to safely monitor the overall load in a smart energy system. A load separation method has been introduced based on events detected on a low-frequency power signal, which allows the consumption profile of On/Off and multi-state devices to be generated without relying on the knowledge of the cardinality of these devices Following the extraction of significant features contained in the aggregate signal, an appliance profile recognition approach is presented based on the non-parametric Mean Shift algorithm. The ability of the proposed method to learn and deduce devices profile is validated using the Reference Energy Disaggregation Dataset (REDD). The experimental results show that the proposed approach is efficient in detecting events of binary state and finite state appliances.

ACS Style

Qi Liu; Francis Mawuli Nakoty; Xueyan Wu; Raphael Anaadumba; Xiaodong Liu; Yonghong Zhang; Lianyong Qi. A secure edge monitoring approach to unsupervised energy disaggregation using mean shift algorithm in residential buildings. Computer Communications 2020, 162, 187 -195.

AMA Style

Qi Liu, Francis Mawuli Nakoty, Xueyan Wu, Raphael Anaadumba, Xiaodong Liu, Yonghong Zhang, Lianyong Qi. A secure edge monitoring approach to unsupervised energy disaggregation using mean shift algorithm in residential buildings. Computer Communications. 2020; 162 ():187-195.

Chicago/Turabian Style

Qi Liu; Francis Mawuli Nakoty; Xueyan Wu; Raphael Anaadumba; Xiaodong Liu; Yonghong Zhang; Lianyong Qi. 2020. "A secure edge monitoring approach to unsupervised energy disaggregation using mean shift algorithm in residential buildings." Computer Communications 162, no. : 187-195.

Journal article
Published: 19 August 2020 in Remote Sensing
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Change detection is a very important technique for remote sensing data analysis. Its mainstream solutions are either supervised or unsupervised. In supervised methods, most of the existing change detection methods using deep learning are related to semantic segmentation. However, these methods only use deep learning models to process the global information of an image but do not carry out specific trainings on changed and unchanged areas. As a result, many details of local changes could not be detected. In this work, a trilateral change detection network is proposed. The proposed network has three branches (a main module and two auxiliary modules, all of them are composed of convolutional neural networks (CNNs)), which focus on the overall information of bitemporal Google Earth image pairs, the changed areas and the unchanged areas, respectively. The proposed method is end-to-end trainable, and each component in the network does not need to be trained separately.

ACS Style

Junhao Qian; Min Xia; Yonghong Zhang; Jia Liu; Yiqing Xu. TCDNet: Trilateral Change Detection Network for Google Earth Image. Remote Sensing 2020, 12, 2669 .

AMA Style

Junhao Qian, Min Xia, Yonghong Zhang, Jia Liu, Yiqing Xu. TCDNet: Trilateral Change Detection Network for Google Earth Image. Remote Sensing. 2020; 12 (17):2669.

Chicago/Turabian Style

Junhao Qian; Min Xia; Yonghong Zhang; Jia Liu; Yiqing Xu. 2020. "TCDNet: Trilateral Change Detection Network for Google Earth Image." Remote Sensing 12, no. 17: 2669.

Journal article
Published: 18 April 2020 in ISPRS International Journal of Geo-Information
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Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.

ACS Style

Liguo Weng; Yiming Xu; Min Xia; Yonghong Zhang; Jia Liu; Yiqing Xu. Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network. ISPRS International Journal of Geo-Information 2020, 9, 256 .

AMA Style

Liguo Weng, Yiming Xu, Min Xia, Yonghong Zhang, Jia Liu, Yiqing Xu. Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network. ISPRS International Journal of Geo-Information. 2020; 9 (4):256.

Chicago/Turabian Style

Liguo Weng; Yiming Xu; Min Xia; Yonghong Zhang; Jia Liu; Yiqing Xu. 2020. "Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network." ISPRS International Journal of Geo-Information 9, no. 4: 256.

Journal article
Published: 27 November 2019 in Remote Sensing
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Debris flows have been always a serious problem in the mountain areas. Research on the assessment of debris flows susceptibility (DFS) is useful for preventing and mitigating debris flow risks. The main purpose of this work is to study the DFS in the Shigatse area of Tibet, by using machine learning methods, after assessing the main triggering factors of debris flows. Remote sensing and geographic information system (GIS) are used to obtain datasets of topography, vegetation, human activities and soil factors for local debris flows. The problem of debris flow susceptibility level imbalances in datasets is addressed by the Borderline-SMOTE method. Five machine learning methods, i.e., back propagation neural network (BPNN), one-dimensional convolutional neural network (1D-CNN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) have been used to analyze and fit the relationship between debris flow triggering factors and occurrence, and to evaluate the weight of each triggering factor. The ANOVA and Tukey HSD tests have revealed that the XGBoost model exhibited the best mean accuracy (0.924) on ten-fold cross-validation and the performance was significantly better than that of the BPNN (0.871), DT (0.816), and RF (0.901). However, the performance of the XGBoost did not significantly differ from that of the 1D-CNN (0.914). This is also the first comparison experiment between XGBoost and 1D-CNN methods in the DFS study. The DFS maps have been verified by five evaluation methods: Precision, Recall, F1 score, Accuracy and area under the curve (AUC). Experiments show that the XGBoost has the best score, and the factors that have a greater impact on debris flows are aspect, annual average rainfall, profile curvature, and elevation.

ACS Style

Yonghong Zhang; Taotao Ge; Wei Tian; Yuei-An Liou. Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China. Remote Sensing 2019, 11, 2801 .

AMA Style

Yonghong Zhang, Taotao Ge, Wei Tian, Yuei-An Liou. Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China. Remote Sensing. 2019; 11 (23):2801.

Chicago/Turabian Style

Yonghong Zhang; Taotao Ge; Wei Tian; Yuei-An Liou. 2019. "Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China." Remote Sensing 11, no. 23: 2801.

Case history
Published: 12 August 2019 in Bulletin of Engineering Geology and the Environment
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The 2015 Gorkha earthquake (Mw = 7.8) caused significant earthquake triggered landslides (ETL) in a landscape that is heavily intervened by rainfall triggered landslides (RTL). China’s Belt and Road Initiative plan to boost South-Asian regional trade and mobility through two key highway corridors, i.e. 1) Longmu–Rasuwa–Kathmandu (LRK) and 2) Nyalam–Tatopani–Kathmandu (NTK) route, that dissect the Himalayas through this geologically unstable region. To understand the spatial characteristics and susceptibility of these ETL and RTL, we delineate the landslides by means of time variant satellite imageries, assess their spatial distribution and model their susceptibilities along the highway slopes. We use a coupled frequency ratio (FR) – analytical hierarchy process (AHP) model by considering nine landslide determinants, e.g. geomorphic type (slope, aspect, curvature, elevation), hydrologic type (erosive potential of gullies, i.e. stream power index and distance to streams), normalized difference vegetation index, lithology and civil structure type (i.e. distance to roads). The results demonstrate that elevation and slope predominantly control both these landslide occurrences. The model predicts locations of ETL with higher accuracy than RTL. On comparison, NTK was safer with 133.5 km2 of high RTL or ETL (or both) landslide susceptible areas, whereas LRK has 216.04 km2. For mapping the extent of these landslides, we constricted it to the slope units of highways to reduce the computational effort, but this technique successfully achieved an acceptable threefold average model prediction rate of 82.75% in ETL and 77.9% in RTL. These landslide susceptibility maps and route comparisons would provide guidance towards further planning, monitoring, and implementing landslide risk mitigation measures for the governments.

ACS Style

Kaushal Raj Gnyawali; Yonghong Zhang; Guojie Wang; Lijuan Miao; Ananta Man Singh Pradhan; Basanta Raj Adhikari; Liming Xiao. Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways. Bulletin of Engineering Geology and the Environment 2019, 79, 587 -601.

AMA Style

Kaushal Raj Gnyawali, Yonghong Zhang, Guojie Wang, Lijuan Miao, Ananta Man Singh Pradhan, Basanta Raj Adhikari, Liming Xiao. Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways. Bulletin of Engineering Geology and the Environment. 2019; 79 (2):587-601.

Chicago/Turabian Style

Kaushal Raj Gnyawali; Yonghong Zhang; Guojie Wang; Lijuan Miao; Ananta Man Singh Pradhan; Basanta Raj Adhikari; Liming Xiao. 2019. "Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways." Bulletin of Engineering Geology and the Environment 79, no. 2: 587-601.

Journal article
Published: 17 December 2018 in Atmosphere
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Besides local emissions, long-range transportation of polluted air masses also has a huge impact on haze pollution. In this study, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to determine the transport paths and potential sources of haze pollution in the Yangtze River Delta Urban Agglomeration. Haze days were determined by setting the threshold of meteorological elements. Shanghai, Hangzhou, Nanjing and Hefei were selected as four representative cities to calculate the −72 h backward transport trajectory of haze air mass; thus, the main transport path was obtained after clustering. A potential source contribution function and concentration weighted field were used to identify potential pollution sources of the study. The results showed that the number of haze days in the northern Yangtze River Delta Urban Agglomeration is much higher than that in the south. Haze days and Fine particulate matter (PM2.5) concentration showed a downward trend. The transport paths could be summarized as long-range transports from the northwest and coastal direction during the dry season and short-distance transports from all directions. −72 h air flow trajectories come from the higher altitudes in dry season than these in wet season. The main sources of potential pollution are Hebei, Shandong, Anhui and northern Jiangsu.

ACS Style

Linglong Zhu; Yonghong Zhang; Xi Kan; Jiangeng Wang. Transport Paths and Identification for Potential Sources of Haze Pollution in the Yangtze River Delta Urban Agglomeration from 2014 to 2017. Atmosphere 2018, 9, 502 .

AMA Style

Linglong Zhu, Yonghong Zhang, Xi Kan, Jiangeng Wang. Transport Paths and Identification for Potential Sources of Haze Pollution in the Yangtze River Delta Urban Agglomeration from 2014 to 2017. Atmosphere. 2018; 9 (12):502.

Chicago/Turabian Style

Linglong Zhu; Yonghong Zhang; Xi Kan; Jiangeng Wang. 2018. "Transport Paths and Identification for Potential Sources of Haze Pollution in the Yangtze River Delta Urban Agglomeration from 2014 to 2017." Atmosphere 9, no. 12: 502.

Journal article
Published: 14 December 2018 in Sensors
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The China-Nepal Highway is a vital land route in the Kush-Himalayan region. The occurrence of mountain hazards in this area is a matter of serious concern. Thus, it is of great importance to perform hazard assessments in a more accurate and real-time way. Based on temporal and spatial sensor data, this study tries to use data-driven algorithms to predict landslide susceptibility. Ten landslide instability factors were prepared, including elevation, slope angle, slope aspect, plan curvature, vegetation index, built-up index, stream power, lithology, precipitation intensity, and cumulative precipitation index. Four machine learning algorithms, namely decision tree (DT), support vector machines (SVM), Back Propagation neural network (BPNN), and Long Short Term Memory (LSTM) are implemented, and their final prediction accuracies are compared. The experimental results showed that the prediction accuracies of BPNN, SVM, DT, and LSTM in the test areas are 62.0%, 72.9%, 60.4%, and 81.2%, respectively. LSTM outperformed the other three models due to its capability to learn time series with long temporal dependencies. It indicates that the dynamic change course of geological and geographic parameters is an important indicator in reflecting landslide susceptibility.

ACS Style

Liming Xiao; Yonghong Zhang; Gongzhuang Peng. Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway. Sensors 2018, 18, 4436 .

AMA Style

Liming Xiao, Yonghong Zhang, Gongzhuang Peng. Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway. Sensors. 2018; 18 (12):4436.

Chicago/Turabian Style

Liming Xiao; Yonghong Zhang; Gongzhuang Peng. 2018. "Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway." Sensors 18, no. 12: 4436.

Journal article
Published: 25 October 2018 in Water
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Snowfall over mountainous areas not only has important implications on the water cycle and the Earth’s radiation balance, but also causes potentially hazardous weather. However, snowfall detection remains one of the most difficult problems in modern hydrometeorology. We present a method for detecting snowfall events from optical satellite data for seasonal snow in mountainous areas. The proposed methodology is based on identifying expanded snow cover or suddenly declined snow grain size using time series images, from which it is possible to detect the location and time of snowfall events. The methodology was tested with Moderate Resolution Imaging Spectroradiometer (MODIS) daily radiance data for an entire hydrologic year from July 2014 to June 2015 in the mountainous area of the Manas River Basin, Northwest China. The study evaluated the recordings of precipitation events at eighteen meteorological stations in the study area prove the effectiveness of the proposed method, showing that there was more liquid precipitation in the second and third quarter, and more solid precipitation in the first and fourth quarter.

ACS Style

Jiangeng Wang; Yonghong Zhang; Yinyi Cheng; Xueliang Zhang; Xuezhi Feng; Wei Huang; Hao Zhou. Detecting Snowfall Events over Mountainous Areas Using Optical Imagery. Water 2018, 10, 1514 .

AMA Style

Jiangeng Wang, Yonghong Zhang, Yinyi Cheng, Xueliang Zhang, Xuezhi Feng, Wei Huang, Hao Zhou. Detecting Snowfall Events over Mountainous Areas Using Optical Imagery. Water. 2018; 10 (11):1514.

Chicago/Turabian Style

Jiangeng Wang; Yonghong Zhang; Yinyi Cheng; Xueliang Zhang; Xuezhi Feng; Wei Huang; Hao Zhou. 2018. "Detecting Snowfall Events over Mountainous Areas Using Optical Imagery." Water 10, no. 11: 1514.

Conference paper
Published: 21 September 2018 in Privacy Enhancing Technologies
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As a direct machining tool, the tool will inevitably wear out during production and processing. In order to grasp the wear state of cutting tools accurately and realize the accurate diagnosis in the cutting process, the CEMMD-WPT feature extraction method is proposed, which is based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Wavelet Package Transform (WPT). Firstly, the CEEMD is used to decompose the Acoustic Emission (AE) signal that acquired by cutting tool. The AE signal is adaptively decomposed into several Intrinsic Mode Functions (IMFs) among with each IMF contains different time scale characteristic. Then, for less IMFs that still have mode mixing, is corrected with good local processing ability by WPT. The CEEMD-WPT combination algorithm not only can effectively solve the problem of the mode mixing after CEEMD, but also eliminate the influence of frequency mixing and illusive component after WPT treatment. Finally, this work select the first few IMFs component with large energy values, calculate the proportion of the total energy as feature vectors, and input them into the Support Vector Machine (SVM) for training and testing, to establish the tool state recognition system. Compared with CEEMD feature extraction method, the feature extracted by CEEMD-WPT method is more accurate and more representative, which lays a good foundation for later recognition.

ACS Style

Runzhe Tao; Yonghong Zhang; Lihua Wang; Xiaoping Zhao. Research of Tool State Recognition Based on CEEMD-WPT. Privacy Enhancing Technologies 2018, 59 -70.

AMA Style

Runzhe Tao, Yonghong Zhang, Lihua Wang, Xiaoping Zhao. Research of Tool State Recognition Based on CEEMD-WPT. Privacy Enhancing Technologies. 2018; ():59-70.

Chicago/Turabian Style

Runzhe Tao; Yonghong Zhang; Lihua Wang; Xiaoping Zhao. 2018. "Research of Tool State Recognition Based on CEEMD-WPT." Privacy Enhancing Technologies , no. : 59-70.

Journal article
Published: 01 January 2018 in Computers, Materials & Continua
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ACS Style

Xiaoping Zhao; Jiaxin Wu; Yonghong Zhang; Yunqing Shi; Lihua Wang. Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder. Computers, Materials & Continua 2018, 57, 223 -242.

AMA Style

Xiaoping Zhao, Jiaxin Wu, Yonghong Zhang, Yunqing Shi, Lihua Wang. Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder. Computers, Materials & Continua. 2018; 57 (2):223-242.

Chicago/Turabian Style

Xiaoping Zhao; Jiaxin Wu; Yonghong Zhang; Yunqing Shi; Lihua Wang. 2018. "Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder." Computers, Materials & Continua 57, no. 2: 223-242.

Research article
Published: 02 November 2017 in International Journal of Aerospace Engineering
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Among current detection methods of the atmospheric boundary layer, sounding balloon has disadvantages such as low recovery and low reuse rate, anemometer tower has disadvantages such as fixed location and high cost, and remote sensing detection has disadvantages such as low data accuracy. In this paper, a meteorological element sensor was carried on a six-rotor UAV platform to achieve detection of meteorological elements of the atmospheric boundary layer, and the influence of different installation positions of the meteorological element sensor on the detection accuracy of the meteorological element sensor was analyzed through many experiments. Firstly, a six-rotor UAV platform was built through mechanical structure design and control system design. Secondly, data such as temperature, relative humidity, pressure, elevation, and latitude and longitude were collected by designing a meteorological element detection system. Thirdly, data management of the collected data was conducted, including local storage and real-time display on ground host computer. Finally, combined with the comprehensive analysis of the data of automatic weather station, the validity of the data was verified. This six-rotor UAV platform carrying a meteorological element sensor can effectively realize the direct measurement of the atmospheric boundary layer and in some cases can make up for the deficiency of sounding balloon, anemometer tower, and remote sensing detection.

ACS Style

Yonghong Zhang; Tiantian Dong; Yunping Liu. Design of Meteorological Element Detection Platform for Atmospheric Boundary Layer Based on UAV. International Journal of Aerospace Engineering 2017, 2017, 1 -14.

AMA Style

Yonghong Zhang, Tiantian Dong, Yunping Liu. Design of Meteorological Element Detection Platform for Atmospheric Boundary Layer Based on UAV. International Journal of Aerospace Engineering. 2017; 2017 ():1-14.

Chicago/Turabian Style

Yonghong Zhang; Tiantian Dong; Yunping Liu. 2017. "Design of Meteorological Element Detection Platform for Atmospheric Boundary Layer Based on UAV." International Journal of Aerospace Engineering 2017, no. : 1-14.

Original paper
Published: 25 November 2016 in Nonlinear Dynamics
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This article studies the quantitative stability of quadrotor unmanned aerial vehicles, by analyzing the dynamics model and dynamics stability at the stage of takeoff, landing and yawing, respectively. The dynamics stability problems, such as shaking, losing the tracking accuracy of command and out of control, and the design of structural parameters were investigated in detail. Dynamics stability reflects the dynamics characteristics of the whole systems, which is mainly affected by the structural parameters and control moment. The stability of system can be improved by optimizing structural parameters. The quantitative relationship between structural parameters and dynamics stability is based on the theory of Lyapunov exponent from the designing viewpoint of structural parameter, which aims at improving the reliability and stability of systems. The results indicate that the dynamics stability of systems can be promoted by optimizing the structural parameters of systems, which demonstrates the feasibility and effectiveness of this method.

ACS Style

Yunping Liu; Xianying Li; Tianmiao Wang; Yonghong Zhang; Ping Mei. Quantitative stability of quadrotor unmanned aerial vehicles. Nonlinear Dynamics 2016, 87, 1819 -1833.

AMA Style

Yunping Liu, Xianying Li, Tianmiao Wang, Yonghong Zhang, Ping Mei. Quantitative stability of quadrotor unmanned aerial vehicles. Nonlinear Dynamics. 2016; 87 (3):1819-1833.

Chicago/Turabian Style

Yunping Liu; Xianying Li; Tianmiao Wang; Yonghong Zhang; Ping Mei. 2016. "Quantitative stability of quadrotor unmanned aerial vehicles." Nonlinear Dynamics 87, no. 3: 1819-1833.

Research article
Published: 16 November 2016 in International Journal of Advanced Robotic Systems
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Two-wheeled self-balancing vehicle system is a kind of naturally unstable underactuated system with high-rank unstable multivariable strongly coupling complicated dynamic nonlinear property. Nonlinear dynamics modeling and simulation, as a basis of two-wheeled self-balancing vehicle dynamics research, has the guiding effect for system design of the project demonstration and design phase. Dynamics model of the two-wheeled self-balancing vehicle is established by importing a TSi ProPac package to the Mathematica software (version 8.0), which analyzes the stability and calculates the Lyapunov exponents of the system. The relationship between external force and stability of the system is analyzed by the phase trajectory. Proportional–integral–derivative control is added to the system in order to improve the stability of the two-wheeled self-balancing vehicle. From the research, Lyapunov exponent can be used to research the stability of hyperchaos system. The stability of the two-wheeled self-balancing vehicle is better by inputting the proportional–integral–derivative control. The Lyapunov exponent and phase trajectory can help us analyze the stability of a system better and lay the foundation for the analysis and control of the two-wheeled self-balancing vehicle system.

ACS Style

Yunping Liu; Xijie Huang; Tianmiao Wang; Yonghong Zhang; Xianying Li. Nonlinear dynamics modeling and simulation of two-wheeled self-balancing vehicle. International Journal of Advanced Robotic Systems 2016, 13, 1 .

AMA Style

Yunping Liu, Xijie Huang, Tianmiao Wang, Yonghong Zhang, Xianying Li. Nonlinear dynamics modeling and simulation of two-wheeled self-balancing vehicle. International Journal of Advanced Robotic Systems. 2016; 13 (6):1.

Chicago/Turabian Style

Yunping Liu; Xijie Huang; Tianmiao Wang; Yonghong Zhang; Xianying Li. 2016. "Nonlinear dynamics modeling and simulation of two-wheeled self-balancing vehicle." International Journal of Advanced Robotic Systems 13, no. 6: 1.

Book chapter
Published: 01 October 2016 in Lecture Notes in Electrical Engineering
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The problems of dynamic stability of the quadrotor Unmanned Aerial Vehicles (UAV), such as: cornering, wear, and explosion of oar take place due to the aerodynamic force and gyroscopic effect during takeoff and landing process; the vibration; reduction of instruction tracking accuracy; and out of control are prone to take place due to the influence of atmospheric turbulence and motion coupling during yawing. However, the optimized structural parameters of the aircraft is very important for improving the stability of the motion control and the energy saving. Therefore, the relationship of quantification between structural parameter of quadrotor UAV and dynamic stability is built with the method of Lyapunov exponent starting from structure design of mechanical, which guides the mechanical-structural design and provides important basis for optimizing the control system. This relationship lays a basic foundation for enhancing the reliability and stability for the flight mission. Compared with the direct method of Lyapunov, the method of Lyapunov exponent is easier to build, and the calculation process is simpler.

ACS Style

Yun-Ping Liu; Xian-Ying Li; Tian-Miao Wang; Yonghong Zhang; Ping Mei. The Stability Analysis of Quadrotor Unmanned Aerial Vechicles. Lecture Notes in Electrical Engineering 2016, 383 -394.

AMA Style

Yun-Ping Liu, Xian-Ying Li, Tian-Miao Wang, Yonghong Zhang, Ping Mei. The Stability Analysis of Quadrotor Unmanned Aerial Vechicles. Lecture Notes in Electrical Engineering. 2016; ():383-394.

Chicago/Turabian Style

Yun-Ping Liu; Xian-Ying Li; Tian-Miao Wang; Yonghong Zhang; Ping Mei. 2016. "The Stability Analysis of Quadrotor Unmanned Aerial Vechicles." Lecture Notes in Electrical Engineering , no. : 383-394.

Original article
Published: 17 September 2016 in The International Journal of Advanced Manufacturing Technology
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The aim of this study is to increase the dynamic stability of the quadrotor unmanned aerial vehicles in varying structural parameters. The qualitative analysis is considered the main method for analyzing the dynamic stability, while the index of qualitative analysis of the structural stability and the dynamic stability is still hard to establish. Therefore, the process during takeoff, pitching, or yawing is selected for investigating in the present papers, the method of Lyapunov exponent is adopted for establishing the quantification relationship between structural parameters of the quadrotor unmanned aerial vehicles and dynamic stability, which provides for guiding the design of the vehicle’s mechanical structure and the optimization of its stability control by using the relationship. As compared to its counterpart of Lyapunov’s second method, the main advantage of the concept of Lyapunov exponents is that the methods for calculating the exponent process are constructive which makes the stability analysis of complex nonlinear systems possible.

ACS Style

Yunping Liu; Cheng Chen; Hongtao Wu; Yonghong Zhang; Ping Mei. Structural stability analysis and optimization of the quadrotor unmanned aerial vehicles via the concept of Lyapunov exponents. The International Journal of Advanced Manufacturing Technology 2016, 94, 3217 -3227.

AMA Style

Yunping Liu, Cheng Chen, Hongtao Wu, Yonghong Zhang, Ping Mei. Structural stability analysis and optimization of the quadrotor unmanned aerial vehicles via the concept of Lyapunov exponents. The International Journal of Advanced Manufacturing Technology. 2016; 94 (9-12):3217-3227.

Chicago/Turabian Style

Yunping Liu; Cheng Chen; Hongtao Wu; Yonghong Zhang; Ping Mei. 2016. "Structural stability analysis and optimization of the quadrotor unmanned aerial vehicles via the concept of Lyapunov exponents." The International Journal of Advanced Manufacturing Technology 94, no. 9-12: 3217-3227.

Research article
Published: 22 August 2016 in Journal of the Indian Society of Remote Sensing
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The Qinghai-Tibetan Plateau (QTP) snow cover information acquisition of the high precision spatial and temporal characteristics is of great significance for the research on its land surface atmosphere coupled system and global climate change effects. The Moderate Resolution Imaging Spectro-radiometer (MODIS) daily snow cover products (MOD10A1 and MYD10A1) have been widely used in long time series of spatial and temporal variation analysis, but they are limited to be used because of high cloud cover ratio. In this paper, a 7-day rolling combination algorithm was presented to eliminate cloud obscuration, and the whole cloud amount falls below 7 %. The ground station in situ measurements verify that the overall precision is more than 90 %. The presented algorithm guaranteed the same spatial resolution and temporal resolution, and has higher precision than products MOD10A1 and MYD10A1. The MODIS 7-day rolling combination snow cover datasets products were obtained between 2003 and 2014 in the QTP, and the snow cover area of spatial and temporal variation was analyzed. The change characteristics of snow cover duration was also studied combining with the Digital Elevation Model data. Results show that the snow cover area of the whole QTP has a slowly decreased trend, but increases in autumn. Thus, the snow cover proportion of annual periodic and unstable in different elevations has the highest correlation with area of the elevation.

ACS Style

Yonghong Zhang; Ting Cao; Xi Kan; Jiangeng Wang; Wei Tian. Spatial and Temporal Variation Analysis of Snow Cover Using MODIS over Qinghai-Tibetan Plateau during 2003–2014. Journal of the Indian Society of Remote Sensing 2016, 45, 887 -897.

AMA Style

Yonghong Zhang, Ting Cao, Xi Kan, Jiangeng Wang, Wei Tian. Spatial and Temporal Variation Analysis of Snow Cover Using MODIS over Qinghai-Tibetan Plateau during 2003–2014. Journal of the Indian Society of Remote Sensing. 2016; 45 (5):887-897.

Chicago/Turabian Style

Yonghong Zhang; Ting Cao; Xi Kan; Jiangeng Wang; Wei Tian. 2016. "Spatial and Temporal Variation Analysis of Snow Cover Using MODIS over Qinghai-Tibetan Plateau during 2003–2014." Journal of the Indian Society of Remote Sensing 45, no. 5: 887-897.

Journal article
Published: 10 May 2016 in Journal of the Indian Society of Remote Sensing
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ACS Style

Yonghong Zhang; Xi Kan; Wei Ren; Ting Cao; Wei Tian; Jiangeng Wang. Snow Cover Monitoring in Qinghai-Tibetan Plateau Based on Chinese Fengyun-3/VIRR Data. Journal of the Indian Society of Remote Sensing 2016, 45, 271 -283.

AMA Style

Yonghong Zhang, Xi Kan, Wei Ren, Ting Cao, Wei Tian, Jiangeng Wang. Snow Cover Monitoring in Qinghai-Tibetan Plateau Based on Chinese Fengyun-3/VIRR Data. Journal of the Indian Society of Remote Sensing. 2016; 45 (2):271-283.

Chicago/Turabian Style

Yonghong Zhang; Xi Kan; Wei Ren; Ting Cao; Wei Tian; Jiangeng Wang. 2016. "Snow Cover Monitoring in Qinghai-Tibetan Plateau Based on Chinese Fengyun-3/VIRR Data." Journal of the Indian Society of Remote Sensing 45, no. 2: 271-283.

Journal article
Published: 05 December 2014 in Journal of Modern Power Systems and Clean Energy
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In the last two decades, the wind power generation has been rapidly and widely developed in many regions and countries for tackling the problems of environmental pollution and sustainability of energy supply. However, the high share of intermittent and fluctuating wind power production has also increased the burden of system operator for securing power system reliability during the operational phase. Moreover, the power system restructuring and deregulation have not only introduced the competition for reducing cost but also changed the strategy of reliability evaluation and management of power systems. The conventional long-term reliability evaluation techniques have been well developed, which have been more focused on planning and expansion rather than operation of power systems. This paper proposes a new technique for evaluating operational reliabilities of restructured power systems with high wind power penetration. The proposed technique is based on the combination of the reliability network equivalent and time-sequential simulation approaches. The operational reliability network equivalents are developed to represent reliability models of wind farms, conventional generation and reserve provides, fast reserve providers and transmission network in restructured power systems. A contingency management schema for real time operation considering its coupling with the day-ahead market is proposed. The time-sequential Monte Carlo simulation is used to model the chronological characteristics of corresponding reliability network equivalents. A simplified method is also developed in the simulation procedures for improving the computational efficiency. The proposed technique can be used to evaluate customers’ reliabilities considering high penetration of wind power during the power system operation in the deregulated environment.

ACS Style

Yi Ding; Lin Cheng; Yonghong Zhang; Yusheng Xue. Operational reliability evaluation of restructured power systems with wind power penetration utilizing reliability network equivalent and time-sequential simulation approaches. Journal of Modern Power Systems and Clean Energy 2014, 2, 329 -340.

AMA Style

Yi Ding, Lin Cheng, Yonghong Zhang, Yusheng Xue. Operational reliability evaluation of restructured power systems with wind power penetration utilizing reliability network equivalent and time-sequential simulation approaches. Journal of Modern Power Systems and Clean Energy. 2014; 2 (4):329-340.

Chicago/Turabian Style

Yi Ding; Lin Cheng; Yonghong Zhang; Yusheng Xue. 2014. "Operational reliability evaluation of restructured power systems with wind power penetration utilizing reliability network equivalent and time-sequential simulation approaches." Journal of Modern Power Systems and Clean Energy 2, no. 4: 329-340.