An important objective of the Global Precipitation Measurement (GPM) mission is the detection of falling snow, since it accounts for a significant fraction of precipitation in the mid-high latitudes. The GPM Core Obse...
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An important objective of the Global Precipitation Measurement (GPM) mission is the detection of falling snow, since it accounts for a significant fraction of precipitation in the mid-high latitudes. The GPM Core Observatory carries the first spaceborne Dual-frequency Precipitation Radar (DPR), designed with enhanced sensitivity to detect lighter liquid and solid precipitation. The primary goal of this study is to assess the DPR's ability to identify snowfall using near-coincident CloudSat Cloud Profiling Radar (CPR) observations and products as an independent reference dataset. CloudSat near global coverage and high sensitivity of the W-band CPR make it very suitable for snowfall-related research. While DPR/CPR radar sensitivity disparities contribute substantially to snowfall detection differences, this study also analyzes other factors such as precipitation phase discriminators that produce snowfall identification discrepancies. Results show that even if the occurrence of snowfall events correctly detected by DPR products is quite small compared to CPR (around 5-7%), the fraction of snowfall mass is not negligible (29-34%). A direct comparison of CPR and DPR reflectivities illustrates that DPR misdetection is worsened by a noise-reducing DPR algorithm component that corrects the measured reflectivity. This procedure eliminates the receiver noise and side lobe clutter effects, but also removes radar signals related to snowfall events that are associated with relatively low reflectivity values. In an effort to increase DPR signal fidelity associated with snowfall, this paper proposes a simple algorithm using matched DPR Ku/Ka radar reflectivities producing an increase of the fraction of snowfall mass detected by DPR up to 59%.
The sweep-line method allows explicit state model checkers to delete states from memory on-the-fly during state space exploration, thereby lowering the memory demands of the verification procedure. The sweep-line meth...
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The sweep-line method allows explicit state model checkers to delete states from memory on-the-fly during state space exploration, thereby lowering the memory demands of the verification procedure. The sweep-line method is based on a least-progress-first search order that prohibits the immediate use of standard on-the-fly Buchi automata-based model checking algorithms that rely on a depth-first search order in the search for an acceptance cycle. This paper proposes and experimentally evaluates an algorithm for Buchi automata-based model checking compatible with the search order and deletion of states prescribed by the sweep-line method.
Wireless sensor network (WSN) has been already widely used in many fields in terms of industry, agriculture, and military, and so forth. The basic composition is WSN nodes that are capable of performing processing, ga...
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Wireless sensor network (WSN) has been already widely used in many fields in terms of industry, agriculture, and military, and so forth. The basic composition is WSN nodes that are capable of performing processing, gathering information, and communicating with other connected nodes in the network. The main components of a WSN node are microcontroller, transceiver, and some sensors. Undoubtedly, it also can be added with some actuators to form a tiny mechanical system. Under this case, the existence of task preemption while executing operating system will not only cost more energy for WSN nodes themselves, but also bring unacceptable system states caused by vibrations. However for these nodes, task I/O delays are inevitable due to the existence of task preemption, which will bring extra overhead for the whole system, and even bring unacceptable system states caused by vibrations. This paper mainly considers the earliest deadline first (EDF) task preemption algorithm executed in WSN OS and proposes an improved task preemption algorithm so as to lower the preemption overhead and I/O delay and then improve the system performance. The experimental results show that the improved task preemption algorithm can reduce the I/O delay effectively, so the real-time processing ability of the system is enhanced.
Recently, fire detection is a hot research topic. Although many detection methods have been proposed, there exist high false alarms because of the interference of fire-colored moving object in the complex environments...
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Recently, fire detection is a hot research topic. Although many detection methods have been proposed, there exist high false alarms because of the interference of fire-colored moving object in the complex environments. In this paper, a hybrid method is proposed. First, we get the set of candidate fire regions. Then these candidate fire regions are analyzed to exclude the fire-colored moving object. Our contributions are using the hidden Markov model (HMM) based on spatio-temporal feature and the variance of luminance map motivated by visual attention, and combining both for fire detection. The wrong detection can be reduced greatly. Experiment results show our proposed method has a good performance and it is robust to be used in complex environment compared with previous algorithms. (C) 2011 Elsevier Ltd. All rights reserved.
To meet the real-time and low power consumption demands in MEMS navigation and guidance field, an improved Kalman filter algorithm for GNSS/INS was proposed in this paper named as one-step prediction of P matrix. Quan...
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To meet the real-time and low power consumption demands in MEMS navigation and guidance field, an improved Kalman filter algorithm for GNSS/INS was proposed in this paper named as one-step prediction of P matrix. Quantitative analysis of field test datasets was made to compare the navigation accuracy with the standard algorithm, which indicated that the degradation caused by the simplified algorithm is small enough compared to the navigation errors of the GNSS/INS system itself. Meanwhile, the computation load and time consumption of the algorithm decreased over 50% by the improved algorithm. The work has special significance for navigation applications that request low power consumption and strict real-time response, such as cellphone, wearable devices, and deeply coupled GNSS/INS systems.
In this paper, an implementable algorithm that enables to robustly control batch processes is proposed. The resulting state feedback algorithm is based on repeated on-line solution of constrained open loop min-max pro...
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In this paper, an implementable algorithm that enables to robustly control batch processes is proposed. The resulting state feedback algorithm is based on repeated on-line solution of constrained open loop min-max problems associated to the worst-case perturbations and/or uncertainties. These solutions are then used in a receding horizon scheme in order to yield a robust state feedback controller. A novel algorithm is proposed for the solution of the open loop constrained minmax problems that is based on chattering control combined with the variable stabilizing penalty approach. Simulations are provided in order to illustrate the effectiveness of the proposed control algorithm.
The problem of the previous researches on personalized ranking is that they focused on either explicit feedback data or implicit feedback data rather than making full use of the information in the dataset. Until now, ...
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The problem of the previous researches on personalized ranking is that they focused on either explicit feedback data or implicit feedback data rather than making full use of the information in the dataset. Until now, nobody has studied personalized ranking algorithmby exploiting both explicit and implicit feedback. In order to overcome the defects of prior researches, a new personalized ranking algorithm (MERR_SVD++) based on the newest xCLiMF model and SVD++ algorithm was proposed, which exploited both explicit and implicit feedback simultaneously and optimized the well-known evaluation metric Expected Reciprocal Rank (ERR). Experimental results on practical datasets showed that our proposed algorithm outperformed existing personalized ranking algorithms over different evaluation metrics and that the running time of MERR SVD++ showed a linear correlation with the number of rating. Because of its high precision and the good expansibility, MERR SVD++ is suitable for processing big data and has wide application prospect in the field of internet information recommendation.
The design and implementation of a multiprocessor-based fully digital control architecture for permanent magnet synchronous motor drives is presented. The design part is concerned with the formulation of control algor...
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The design and implementation of a multiprocessor-based fully digital control architecture for permanent magnet synchronous motor drives is presented. The design part is concerned with the formulation of control algorithms for current-regulated pulsewidth modulated inverter and advanced vector control strategies for speed-and position-loop. Under the vector control framework, some recently developed robustness control results are applied to the design of speed-loop controllers. The implementational part integrates the control of current-, speed-, and position-loop using the multiprocessor-based controller. Experimental case studies that correlate simulation and measurement results are provided. The experimental results validate the theoretical development. The paper provides a new approach for designing advanced ac servo drives using currently available high performance microprocessors.
To plan a global path in grid map, a hybrid two-dimensional path planning model based on frothing construction algorithm and local fast marching method is proposed. The proposed model consists of three steps. In first...
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To plan a global path in grid map, a hybrid two-dimensional path planning model based on frothing construction algorithm and local fast marching method is proposed. The proposed model consists of three steps. In first step, frothing construction algorithm is adopted to describe topology relationship of global passable regions in two-dimensional environment, and a grid topology map is obtained. The second step is path planning in topology map. Adopting generalized level-set algorithm, global shortest topology path can be obtained. In order to optimize the global path and real-time capability, global path replanning in grid map is proposed in third step. Local fast matching method is proposed by limiting effective region of cost potential function in global fast matching method. Quantitative analysis and comparisons prove that the proposed model is more suitable to the increasingly demanding path planning requirements than most of state-of-the-art algorithms. (C) 2012 Elsevier Ltd. All rights reserved.
Hurricanes and tropical storms are severe threats to coastal properties, settlements, and infrastructure. Airborne light detection and ranging (lidar) surveys conducted before and after storm events allow detailed ana...
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Hurricanes and tropical storms are severe threats to coastal properties, settlements, and infrastructure. Airborne light detection and ranging (lidar) surveys conducted before and after storm events allow detailed analysis of coastal geomorphologic and sediment volumetric changes and have been proved very useful in the study of coastal changes. Traditionally, most studies use the pixel-based differencing method to quantify the spatial extent and magnitude of coastal changes based on sequential lidar surveys. This research presents a graph theory-based approach and associated software tools for representing and quantifying storm-induced damages to buildings, beaches and sand dunes, coastal vegetation canopy, and infrastructure. Generation of elevation difference grids, construction of local contour trees, and derivation of semantic properties are key components of the new algorithm for change object detection and extraction. An ontology and taxonomy are proposed to classify change objects into different types of coastal damages in terms of their semantic properties. This method has been successfully applied to assess damages of Hurricane Ike to the Bolivar Peninsula on the Texas Gulf Coast based on pre- and post-storm airborne lidar data and colour infrared aerial photographs.
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