This paper presents a statistical approach for estimating the moments by using higher-order cumulants when the additive noise is Gaussian colored noise. It is shown that the kth-order moments of the noise samples are ...
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This paper presents a statistical approach for estimating the moments by using higher-order cumulants when the additive noise is Gaussian colored noise. It is shown that the kth-order moments of the noise samples are only related to the variance of the noise and that the estimators have the properties of unbiasedness and congruence. A numerical simulation is performed for constructing a Markov chain of continuous time parameters. The results show the validity of this algorithm.
This paper discusses the problem of array calibration. Using cyclic statistics, the sensor gain and phase can be different for each signal. Our method is applied to both narrowband and broadband signals.
This paper discusses the problem of array calibration. Using cyclic statistics, the sensor gain and phase can be different for each signal. Our method is applied to both narrowband and broadband signals.
In the recognition of Chinese handwritten characters,it is a pattern matching process with large number of standard *** is the bottleneck of the recognition *** this paper,a multi-layered pipeline architecture is devi...
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In the recognition of Chinese handwritten characters,it is a pattern matching process with large number of standard *** is the bottleneck of the recognition *** this paper,a multi-layered pipeline architecture is devised to solve this bottleneck. The technology of multi-bank storage,parallel computing,*** also implemented to optimize the ***,a high recognition speed is *** experimental system is implemented on a Xilinx XC4013E FPGA *** will be migrated to a custom VLSI chip in the future.
Simulation under Virtual Reality is the front edge of simulation technology. And also, it provide a new method for integrated multisensor simulation under a united environment. In the past, most simulation and animati...
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ISBN:
(纸本)0780336763
Simulation under Virtual Reality is the front edge of simulation technology. And also, it provide a new method for integrated multisensor simulation under a united environment. In the past, most simulation and animation systems utilized in robotics, which are concerned with simulation of the robot and its environment without simulation of sensors, have difficulty in handling robots that utilize sensory feedback in their operation. Currently, navigation and planning heavily depended on perception has already been mainstream in robotics. Sensor fusion plays a important role in navigation. So, it is important to do research on simulators which deal with multisensor, integrated robot simulation. In this paper, we present a system, which is integrated multisensor feedback under virtual reality, and describe the system architecture and dynamic behavior simulation model. Meanwhile, we also give the difference of simulation between VR system and general 2D system. We choice the mobile robot THMRIII as original source, and give it dynamic simulation model. In order to simulate the uncertainty of ultrasonic sensor, we identify three kinds of uncertainty, and give a ultrasonic sensor model based on fuzzy theory. The sensor simulation algorithm is presented. At the end of this paper, we conclude with discussion of sensor fusion under 3D visualized integrated environment.
An efficient Q-learning paradigm implemented on a fuzzy CMAC network is proposed. The fuzzy CMAC network topological architecture is described. First, the continuous states of the system are partitioned into a number ...
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An efficient Q-learning paradigm implemented on a fuzzy CMAC network is proposed. The fuzzy CMAC network topological architecture is described. First, the continuous states of the system are partitioned into a number of fuzzy boxes. Second, the proposed fuzzy CMAC evaluates the Q-values of agents in the fired fuzzy boxes and chooses control actions with maximum Q-values. Then a critic generates an external reinforcement signal according to the outcome or the effect of the control at every time-step, which is used later for further improving the estimation of these Q-values. To speed up the convergence of reinforcement learning, the traditional PID controller with several groups of different parameters is adopted so as to collect a number of taught-lessons. These taught-lessons together with the experienced lessons generated automatically, are sequentially replayed and learned, respectively, under the guidance of different reinforcement mechanisms. The hybrid adaptive and learning control system is applied to the control of a pH-neutralization process. Simulation investigations show that the fuzzy connectionist Q-learning control system has more adaptive, higher intelligence, and stronger generalization ability compared to neural network or fuzzy neural network control techniques using supervised learning.
This paper discusses a rough set approach for evaluating solutions of scheduling problems. Algorithms for solving scheduling problems are planners and the scheduling problems are modelled as constraint satisfaction pr...
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This paper discusses a rough set approach for evaluating solutions of scheduling problems. Algorithms for solving scheduling problems are planners and the scheduling problems are modelled as constraint satisfaction problems. Conventional approach for the analysis of algorithms often focuses on the time and representational complexities, and assumes an identical cost on all operations. The proposed rough set approach augments conventional approaches for the analysis of algorithms in two ways: 1) it permits the consideration of different costs arising from different operations; and 2) it allows one to define a new utility for a complexity analysis.
The rapid advancements in big data and the Internet of Things (IoT) have significantly accelerated the digital transformation of medical institutions, leading to the widespread adoption of Digital Twin Healthcare (DTH...
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The rapid advancements in big data and the Internet of Things (IoT) have significantly accelerated the digital transformation of medical institutions, leading to the widespread adoption of Digital Twin Healthcare (DTH). The Cloud DTH Platform (CDTH) serves as a cloud-based framework that integrates DTH models, healthcare resources, patient data, and medical services. By leveraging real-time data from medical devices, the CDTH platform enables intelligent healthcare services such as disease prediction and medical resource optimization. However, the platform functions as a system of systems (SoS), comprising interconnected yet independent healthcare services. This complexity is further compounded by the integration of both black-box AI models and domain-specific mechanistic models, which pose challenges in ensuring the interpretability and trustworthiness of DTH models. To address these challenges, we propose a Model-Based systems Engineering (MBSE)-driven DTH modeling methodology derived from systematic requirement and functional analyses. To implement this methodology effectively, we introduce a DTH model development approach using the X language, along with a comprehensive toolchain designed to streamline the development process. Together, this methodology and toolchain form a robust framework that enables engineers to efficiently develop interpretable and trustworthy DTH models for the CDTH platform. By integrating domain-specific mechanistic models with AI algorithms, the framework enhances model transparency and reliability. Finally, we validate our approach through a case study involving elderly patient care, demonstrating its effectiveness in supporting the development of DTH models that meet healthcare and interpretability requirements.
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