Browsing by Author "Jhang, Jyun-Yu"

Now showing 1 - 11 of 11

A hybrid of fuzzy theory and quadratic function for estimating and refining transmission map
(2019-01-01)
Jhang, Jyun-Yu
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Lin, Cheng-Jian
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Young, Kuu-Young
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Lin, Chin-Teng
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電控工程研究所
In photographs captured in outdoor environments, particles in the air cause light attenuation and degrade image quality. This effect is especially obvious in hazy environments. In this study, a fuzzy theory is proposed to estimate the transmission map of a single image. To overcome the problem of oversaturation in dehazed images, a quadratic-function-based method is proposed to refine the transmission map. In addition, the color vector of the atmospheric light is estimated using the top 1% of the brightest light area. Finally, the dehazed image is reconstructed using the transmission map and the estimated atmospheric light. Experimental results demonstrate that the proposed hybrid method performs better than the other existing methods in terms of color oversaturation, visibility, and quantitative evaluation.
An Improved Method of Constructing a Data Aggregation Tree in Wireless Sensor Networks
(2012)
Liu, Bing-Hong
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Jhuang, Ying-Hong
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Tung, Li-Ping
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Jhang, Jyun-Yu
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資訊工程學系
With the rapid application development for wireless sensor networks, effective information collection methods are becoming increasingly emphasized. The effectiveness of data aggregation is mainly determined by the routing structure while tree is an important structure for data aggregation. Many researchers have studied the problem of how to construct a routing tree in a wireless sensor network with relay nodes so that the total transmission and reception energy cost consumed by all sensors is minimized, the so-called Minimum Energy-Cost Aggregation Tree with Relay Nodes problem. In this paper, we propose an improved method to reconstruct a routing tree so that the total energy cost can be reduced. Using simulation, we demonstrate that a shortest path routing tree applied with our proposed Reconstruction algorithm could have lower total energy cost than the original one.
Cooperative Carrying Control for Multi-Evolutionary Mobile Robots in Unknown Environments
(2019-03-06)
Jhang, Jyun-Yu
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Lin, Cheng-Jian
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Young, Kuu-Young
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電控工程研究所
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Institute of Electrical and Control Engineering
This study provides an effective cooperative carrying and navigation control method for mobile robots in an unknown environment. The manager mode switches between two behavioral control modes-wall-following mode (WFM) and toward-goal mode (TGM)-based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller (IT2FNC) based on a dynamic group differential evolution (DGDE) is proposed to realize the carrying control and WFM control for mobile robots. The proposed DGDE uses a hybrid method that involves a group concept and an improved differential evolution to overcome the drawbacks of the traditional differential evolution algorithm. A reinforcement learning strategy was adopted to develop an adaptive WFM control and achieve cooperative carrying control for mobile robots. The experimental results demonstrated that the proposed DGDE is superior to other algorithms at using WFM control. Moreover, the experimental results demonstrate that the proposed method can complete the task of cooperative carrying, and can realize navigation control to enable the robot to reach the target location.
FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control
(2018-08-01)
Jhang, Jyun-Yu
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Tang, Kuang-Hui
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Huang, Chuan-Kuei
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Lin, Cheng-Jian
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Young, Kuu-Young
This study used Xilinx Field Programmable Gate Arrays (FPGAs) to implement a functional neuro-fuzzy network (FNFN) for solving nonlinear control problems. A functional link neural network (FLNN) was used as the consequent part of the proposed FNFN model. This study adopted the linear independent functions and the orthogonal polynomials in a functional expansion of the FLNN. Thus, the design of the FNFN model could improve the control accuracy. The learning algorithm of the FNFN model was divided into structure learning and parameter learning. The entropy measurement was adopted in the structure learning to determine the generated new fuzzy rule, whereas the gradient descent method in the parameter learning was used to adjust the parameters of the membership functions and the weights of the FLNN. In order to obtain high speed operation and real-time application, a very high speed integrated circuit hardware description language (VHDL) was used to design the FNFN controller and was implemented on FPGA. Finally, the experimental results demonstrated that the proposed hardware implementation of the FNFN model confirmed the viability in the temperature control of a water bath and the backing control of a car.
Navigation Control of Mobile Robots Using an Interval Type-2 Fuzzy Controller Based on Dynamic-group Particle Swarm Optimization
(2018-10-01)
Jhang, Jyun-Yu
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Lin, Cheng-Jian
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Lin, Chin-Teng
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Young, Kuu-Young
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電機工程學系
This paper presents an effective navigation control method for mobile robots in an unknown environment. The proposed behavior manager (BM) switches between two behavioral control patterns, wall-following behavior (WFB) and toward-goal behavior (TGB), based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller with a dynamic-group particle swarm optimization (DGPSO) algorithm is proposed to provide WFB control and obstacle avoidance for mobile robots. In the WFB learning process, the input signal of a controller is the distance between the wall and the sonar sensors, and its output signal is the speed of two wheels of a mobile robot. A fitness function, which operates on the total distance traveled by the mobile robot, distance from the side wall, angle to the side wall, and moving speed, evaluates the WFB performance of the mobile robot. In addition, an escape mechanism is proposed to avoid a dead cycle. Experimental results reveal that the proposed DGPSO is superior to other methods in WFB and navigation control.
Supervised and Reinforcement Group-Based Hybrid Learning Algorithms for TSK-type Fuzzy Cerebellar Model Articulation Controller
(2019-01-01)
Jhang, Jyun-Yu
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Lin, Cheng-Jian
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Li, Lingling
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電控工程研究所
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Institute of Electrical and Control Engineering
In this study, a Takagi-Sugeno-Kang (TSK)-type fuzzy cerebellar model articulation controller (T-FCMAC) based on a group-based hybrid learning algorithm (GHLA) was proposed for solving various problems. The proposed T-FCMAC model was mainly derived from a traditional cerebellar model articulation controller and the TSK-type fuzzy model. For supervised learning, the proposed GHLA was developed by combining an improved quantum particle swarm optimization algorithm and the Nelder-Mead method for adjusting the parameters of a T-FCMAC. The fuzzy C-mean clustering technique was adopted to improve the performance of quantum particle swarm optimization. A fitness threshold was used to determine the number of clusters in fuzzy C-mean clustering. The grouping concept was also used to improve the search ability and increase the convergence rate. Moreover, because exact training data may be expensive or even impossible to obtain in some real-world applications, a reinforcement GHLA (R-GHLA) was proposed. Experimental results revealed the performance and applicability of the proposed GHLA and R-GHLA.
Using a Reinforcement Q-Learning-Based Deep Neural Network for Playing Video Games
(2019-10-01)
Lin, Cheng-Jian
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Jhang, Jyun-Yu
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Lin, Hsueh-Yi
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Lee, Chin-Ling
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Young, Kuu-Young
This study proposed a reinforcement Q-learning-based deep neural network (RQDNN) that combined a deep principal component analysis network (DPCANet) and Q-learning to determine a playing strategy for video games. Video game images were used as the inputs. The proposed DPCANet was used to initialize the parameters of the convolution kernel and capture the image features automatically. It performs as a deep neural network and requires less computational complexity than traditional convolution neural networks. A reinforcement Q-learning method was used to implement a strategy for playing the video game. Both Flappy Bird and Atari Breakout games were implemented to verify the proposed method in this study. Experimental results showed that the scores of our proposed RQDNN were better than those of human players and other methods. In addition, the training time of the proposed RQDNN was also far less than other methods.
Using a Self-Clustering Algorithm and Type-2 Fuzzy Controller for Multi-robot Deployment and Navigation in Dynamic Environments
(1970-01-01)
Jhang, Jyun-Yu
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Lee, Chin-Ling
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Lin, Cheng-Jian
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Young, Kuu-Young
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電控工程研究所
This study proposes a novel method for multi-robot deployment and navigation under dynamic environments. To automatically determine the location deployment of multiple robots, a grid-based method and self-clustering algorithm (SCA) were used to simplify the environmental information and automatically deploy robot locations. In the navigation process, a behavior selector automatically turns on towards goal mode or wall-following mode (WFM) depending on environmental conditions. WFM control adopts an interval type-2 fuzzy controller (IT2FC). The parameters of the IT2FC are adjusted by using the dynamic group whale optimization algorithm (DGWOA). The proposed DGWOA uses a dynamic group and Levy flight strategy to overcome the problem of falling into a local minimum solution. Experimental results reveal that the proposed method can successfully complete navigation tasks under dynamic environments.
Using a Type-2 Neural Fuzzy Controller for Navigation Control of Evolutionary Robots
(2019-01-01)
Lin, Cheng-Jian
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Jhang, Jyun-Yu
;
Young, Kuu-Young
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, we present an effective navigation control method for mobile robots in an unknown environment. The proposed behavior manager (BM) switches between two behavioral control patterns, wall-following behavior (WFB) and toward-goal behavior (TGB), on the basis of the relationship between the mobile robot and the unknown environment. A type-2 neural fuzzy controller (T2NFC) with an improved whale optimization algorithm (IWOA) is proposed to provide WFB control and obstacle avoidance for mobile robots. In the WFB learning process, the input signal of a controller is the distance between the wall and the sonar sensors, and its output signal is the speed of two wheels of a mobile robot. A fitness function, which operates on the total distance traveled by the mobile robot, distance from the side wall, angle to the side wall, and moving speed, evaluates the WFB performance of the mobile robot. Experimental results reveal that the proposed IWOA is superior to other methods of WFB and navigation control.
Using an Interval Type-2 Fuzzy Neural Network and Tool Chips for Flank Wear Prediction
(2020-01-01)
Lin, Cheng-Jian
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Jhang, Jyun-Yu
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Chen, Shao-Hsien
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Young, Kuu-Young
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電控工程研究所
The precision of part machining is influenced by the tool life. Tools gradually wear out during the cutting process, which reduces the machining accuracy. Many studies have used machining parameters and sensor signals to predict flank wear; however, these methods have many limitations related to sensor installation, which is not only time-consuming and costly but also impractical in industry. This paper proposes an interval type-2 fuzzy neural network (IT2FNN) based on the dynamic-group cooperative differential evolution algorithm for flank wear prediction. Moreover, the Taguchi method is used to design cutting experiments for collecting experimental data and reducing the number of experiments. The CIE-xy color chromaticity values, spindle speed, feed per tooth, cutting depth, and cutting time are used as inputs of the IT2FNN, and the output is the flank wear value. The experimental results indicate that the proposed method can effectively predict flank wear with higher efficiency than other algorithms.
Using Interval Type-2 Recurrent Fuzzy Cerebellar Model Articulation Controller Based on Improved Differential Evolution for Cooperative Carrying Control of Mobile Robots
(2018-01-01)
Jhang, Jyun-Yu
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Lin, Cheng-Jian
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Lin, Tzu-Chao
;
Chen, Chao-Chun
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Young, Kuu-Young
In this study, we propose an effective cooperative carrying method for mobile robots in an unknown environment. During the carrying process, the state manager (SM) switches between wall-following carrying (WFC) and toward-goal carrying (TGC) to avoid obstacles and prevent the objects from dropping. An interval type-2 recurrent fuzzy cerebellar model articulation controller (IT2RFCMAC) based on dynamic group differential evolution (DGDE) is proposed for implementing the WFC and TGC of mobile robots. The adaptive wall-following control is developed using the reinforcement learning strategy to realize cooperative carrying control for mobile robots. The experimental results indicated that the proposed DGDE is superior to other algorithms and can complete the cooperative carrying of mobile robots to reach the goal location.