One of challenges in GIS Web service is mass GIS data storage and processing. There are at least three obstacles that oppose dealing with this challenge: the mismatch between the frequent requirement of concurrent tas...
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One of challenges in GIS Web service is mass GIS data storage and processing. There are at least three obstacles that oppose dealing with this challenge: the mismatch between the frequent requirement of concurrent tasks for accessing data in GIS Web service and the poor performance of conventional storage mechanism such as relational databases;the insufficiency of relational databases to manage complex data types in GIS Web service;the horizontal scalability of storage for non-stop GIS Web service evolution. This paper proposes a distributed cached storage solution, as the mixture of MongoDB storage and Memcached cache, for GIS Web service. Both techniques are closely related to NoSQL that is a class of database management system identified by its non-adherence to the widely used relational database management system (RDBMS) model. A concise case that facilitates a better understanding of this work indicates its feasibility.
This study proposes a virtual-metrology-based control system for conjecturing machining states and suggesting controller operating modes. The two machining state conjecture models were integrated to validate the virtu...
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This study proposes a virtual-metrology-based control system for conjecturing machining states and suggesting controller operating modes. The two machining state conjecture models were integrated to validate the virtual metrology results using a dual-stage featuring scheme and a two-phase modeling procedure. The featuring scheme is to extract significant features from collecting data for modeling and to minimize the modeling features by using a genetic algorithm. The modeling procedure adopts two quality indicators to evaluate model effectiveness. Two case studies indicate that the system achieves a mean MAPE of precision estimation less than 8.5% and suggests the controller with operating modes in 1 s.
Every year the fire disaster always causes a lot of casualties and property damage. Many researchers are involved in the study of related disaster prevention. Early warning systems and stable fire can significantly re...
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Every year the fire disaster always causes a lot of casualties and property damage. Many researchers are involved in the study of related disaster prevention. Early warning systems and stable fire can significantly reduce the damage caused by fire. Many existing image-based early warning systems can perform well in a particular field. In this paper, we propose a general framework that can be applied in most realistic environments. The proposed system is based on a block-based feature extraction method, which analyses local information in separate regions leading to a reduction in computing data. Local features of fire block are extracted from the detailed characteristics of fire objects, which include fire color, fire source immobility, and disorder. Each local feature has high detection rate and filter out different false-positive cases. Global analysis with fire texture and non-moving properties are applied to further reduce false alarm rate. The proposed system is composed of algorithms with low computation. Through a series of experiments, it can be observed that Experimental results show that the proposed system has higher detection rate and low false alarm rate under various environment.
Automatic generation control (AGC) is one of the most profitable ancillary services of power systems. The main goal of AGC is to maintain zero steady state errors for frequency deviation and good tracking of load dema...
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Automatic generation control (AGC) is one of the most profitable ancillary services of power systems. The main goal of AGC is to maintain zero steady state errors for frequency deviation and good tracking of load demands in a power system. However, the system performance is often constrained by governor dead band nonlinearity. This paper addresses a sliding mode controller for a single area power system with governor dead band. Two RBF neural networks are employed in this presented method, where one network is designed to compensate the dead band and the other network is designed to approximate the output of the dead band. The weight update formulas of the two RBF networks are derived from Lyapunov direct method. Finally, simulation results show the feasibility of the presented method for the AGC problem of a single area power system.
Using the plasma wave characteristics and remote sensing technology, the k-vector direction of plasma waves can provide important information for understanding the global features of space plasma. In this study, we pr...
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Using the plasma wave characteristics and remote sensing technology, the k-vector direction of plasma waves can provide important information for understanding the global features of space plasma. In this study, we proposed a Bayesian k-vector estimation method in magnetized cold plasma based on the wave distribution function method. The proposed method can be applied to various types of sensors with easy visualization and calculation of the estimation accuracy. We verified the effectiveness of the proposed method through simulations.
A grey box model-based method for fault diagnosis is proposed in this paper. The method is based on a first principle model of the process unit: a heat exchanger, and on a grey box model of the faults: the deteriorati...
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A grey box model-based method for fault diagnosis is proposed in this paper. The method is based on a first principle model of the process unit: a heat exchanger, and on a grey box model of the faults: the deterioration of the heat transfer surface by aging and the leaking of the outer container. The deterioration of the heat transfer surface is due to material settling, and in old heat exchangers pieces of this material can break off and cause damage in the equipment. A recursive least squares estimator with forgetting factor is used to track both the heat transfer coefficients and the cold side volume. The heat transfer coefficients are estimated both from the hot and cold side equations. The settled material breakage fault is detected via detection of abrupt positive jumps in the estimated heat transfer coefficients using a detector based on a cumulative sum (CUSUM) test. The proposed method enables simultaneous detection of the two fault types considered. Fault localization along the equipment length is also possible when temperature measurements are available along the length.
Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applicati...
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ISBN:
(数字)9781728169262
ISBN:
(纸本)9781728169279
Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.
Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a novel Two-Step fault diagnosis framework is proposed to diagnose the status o...
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ISBN:
(纸本)9781509008223
Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a novel Two-Step fault diagnosis framework is proposed to diagnose the status of rolling element bearings with imbalanced data. The Wavelet Packet Transform (WPT) is used to determine the feature vectors. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vectors prepared to input to the classifiers. Next, our proposed framework consists of two steps for the fault diagnosis, where Step One makes use of Weighted Extreme Learning Machine (weighted ELM) in an effort to classify the normal or abnormal categories, and Step Two further diagnoses the underlying anomaly in details. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our institute, and the empirical results showed that our approach is really fast and can achieve the diagnosis accuracies more than 95%.
This research proposes a method to eliminate friction effect and reduced tracking error on CNC milling machines. A CNC milling machine has many connected mechanical components which have friction effects such as ball ...
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ISBN:
(纸本)9789746724913
This research proposes a method to eliminate friction effect and reduced tracking error on CNC milling machines. A CNC milling machine has many connected mechanical components which have friction effects such as ball screws and rails. To control servo motors which are used to drive X-Y table in CNC milling machine, the conventional PID controller is widely used. In this research, Panasonic motor driver is set to the torque control mode. To compensate the friction effects, the friction feedforward compensation method is proposed. Moreover, to reduce the position tracking error, position feedforward controller is used. The simulation and implementation of position control in one axis of PID with Friction Feedforward Compensation and Position Feedforward controller (FFC-PFC) are presented.
We propose a distributed event-triggered control law to solve the consensus problem for multi-agent systems with nonlinear output. Under the condition that the underlying digraph is strongly connected, we propose some...
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