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.
Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving soft...
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Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs.
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