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International Conference on Robots & Intelligent System (ICRIS)

作者： Liu Tingting Kang Kai Chou Li China Satellite Maritime Tracking & Control Dept Wuxi 214431 Peoples R China

ISBN: (纸本)9781509041558

Generally, the performance of endpoint detection is affected by the noise. In this paper, we propose a novel two layer decision model based on noise classification to detect the activity voice robustly. The training processing mainly contains two steps: firstly, we employ the noisex-92 database, which consists of different types of pure noise, to train a BP neural network in order to classify the noise type precisely;secondly, we train BP neural networks for each noise type covering large range of signal noise ratio (SNR). In the testing phase, we assume that the short period of silence at the beginning of the signal contains features for noise and utilize them to get the noise type. Then, we use the classifier corresponding to the noise type to detect activity voice. We conduct experiments on TIMIT corpus for 5 noise types under 7 SNR conditions. And experimental results show that our method outperforms global classifier, especially in low SNR condition.

关键词： Endpoint Detection two-layer decision model Noise Classification

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Distributed Dynamic Scheduling for Cyber-Physical Production Systems Based on a Multi-Agent System

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IEEE ACCESS 2018年 6卷 1855-1869页

作者： Jiang, Zengqiang Jin, Yang Mingcheng, E. Li, Qi Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China

The world is experiencing a new industrial revolution characterized by intelligent manufacturing. Cyber-physical production systems (CPPSs) have become a research focus due to their proposed use as a solution to the development of flexible and reactive systems. The application of current centralized scheduling methods is difficult because of the enhanced precision control mode of a CPPS. Therefore, this paper focuses on distributed optimal scheduling based on multi-agent systems. First, the goals and constraints of the system are set, a two-layer decision model and the required indicators are designed to ensure the overall optimization effect, and the roles and functions of different agents are then set. Second, the dynamic decision cycle and the multistage negotiation mechanism based on the contract net protocol are studied to ensure the quality of negotiation. A rescheduling algorithm is designed to guarantee adaptability in the case of disturbance in the system. Finally, the applicability and superiority of the strategies are demonstrated via experiments and case studies.

关键词： Cyber-physical production system multi-agent system distributed scheduling two-layer decision model contract net protocol

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Study of Robot Soccer decision-Making System with two-layer

Study of Robot Soccer Decision-Making System with Two-layer

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2nd International Symposium on Test Automation and Instrumentation

作者： Yang Guangying School of Physical and Electronics Engineering Taizhou University Zhejiang 318000 China

ISBN: (纸本)9787506292207

Robot soccer tournament is in dynamic, unpredictable, real-time environments. It is a typical Multi-Agent system. This paper implements a robot soccer system based on the behavior theory, which is mainly composed of the basic behaviors device, the aggression model and the defense model. We choose the two-layer decision making system which have a detailed analyze of advantages of the framework system. In this model, behavior-based system is accepted as robot controller, which makes robot become a unit with certain ability of self-command. decision-making subsystem is the pivotal part of whole system. Its characters, picking up information and deciding cooperative role intentionally, indicate that the whole system has some characters of agent. With the help of computer simulating technology, the robot team is provided with the capability of study. We established the models and empoldered the strategies of the game on the computer. And we transplanted the optimized strategy to the robots team after thousands of tests.

关键词： Soccer robot Multi-Agent system decision-making system Self-command two-layer decision model

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