KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 8 of 8

Some of the metrics are blocked by your
consent settings
Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
(2024-09-01)
Meepokgit, Teerapun
;
Wisayataksin, Sumek
Traffic lights are the most commonly used tool to manage urban traffic to reduce congestion and accidents. However, the poor management of traffic lights can result in further problems. Consequently, many studies on traffic light control have been conducted using deep reinforcement learning in the past few years. In this study, we propose a traffic light control method in which a Deep Q-network with fuzzy logic is used to reduce waiting time while enhancing the efficiency of the method. Nevertheless, existing studies using the Deep Q-network may yield suboptimal results because of the reward function, leading to the system favoring straight vehicles, which results in left-turning vehicles waiting too long. Therefore, we modified the reward function to consider the waiting time in each lane. For the experiment, Simulation of Urban Mobility (SUMO) software version 1.18.0 was used for various environments and vehicle types. The results show that, when using the proposed method in a prototype environment, the average total waiting time could be reduced by 18.46% compared with the traffic light control method using a conventional Deep Q-network with fuzzy logic. Additionally, an ambulance prioritization system was implemented that significantly reduced the ambulance waiting time. In summary, the proposed method yielded better results in all environments.
Some of the metrics are blocked by your
consent settings
A Comparison of the Response and Voltage Regulation Performance of the Single-Channel DC/DC Boost Converter Circuit with Artificial Neural Networks, Fuzzy Logic and PID Controllers
(2023-02-01)
Chaithanakulwat, Arckarakit
;
Thungsuk, Nuttee
;
Savangboon, Teerawut
;
Ngao-Ngam, Somchai
;
Kanharin, Phatcharaphong
Fixed-speed wind turbines for generating electricity are also important because they are clean energy and do not pollute the environment. Developing maximum wind energy tracking and increasing DC voltage to optimal values is also an important factor designers must consider so that the power from generators connected to wind turbines can function efficiently. Therefore, in this researcher paper proposed a single-channel dc/dc boost converter control, three forms of algorithms consisting of neural networks, fuzzy algorithms and PID algorithms. The purpose of bringing these algorithms controlled because they wanted to compare the response and voltage control performance of the single-channel DC/DC boost converter to be associated with a three-phase inverter that controls PWM signal modulation with space vector technique. However, the principles and methodologies in this article are presented to simulate the algorithmic response using the MATLAB/Simulink program and compare it with the prototype mechanism. A comparison of the response performance and voltage regulation of the single-channel DC/DC boost converter showed that the three algorithms have different advantages and disadvantages but can be used together to achieve high efficiency.
Some of the metrics are blocked by your
consent settings
Hybrid Multi-Model Fuzzy Ensemble Approach for Cardiovascular Diseases Detection
(2023-01-01)
Chugh, Manop
;
Anantavrasilp, Isara
;
Thiemjarus, Surapa
Timely detection of cardiovascular diseases (CVDs) is crucial to reducing mortality rates. Recent advances in artificial intelligence (AI) and machine learning (ML) models for CVD detection often suffer from low model performance and hence lower accuracy and practicality of early CVD detection. In this study, we propose a novel hybrid ensemble learning framework that combines multiple ML algorithms and a fuzzy expert system to improve CVD diagnosis and prediction accuracy. We evaluate our proposed method on two standard datasets, namely the UCI Cleveland and Framingham, and compare it with four popular ensemble algorithms, namely Random Forest, Gradient Boosting, eXtreme Gradient Boosting, and Adaptive Boosting. Our results demonstrate that the proposed ensemble learning framework achieves higher accuracies of 91.2% (UCI Cleveland) and 91.7% (Framingham), surpassing existing algorithms by 3.3% and 8.8%, respectively.
Some of the metrics are blocked by your
consent settings
MACD Indicator with the Modified Signal Line and Trading Weight Inference in Fuzzy Environment
(2022-01-01)
Boonprasurt, Prachya
;
Sampaokit, Pipat
;
Witayakiattilerd, Wichai
This research proposes improvements to moving average convergence divergence (MACD) by modifying the signal line. MACD with the modified signal line is called MACDP. It can signal trading faster than traditional MACD. The confidence level defined from a fuzzy logic process is applied to interpret the MACDP in each period. Furthermore, the research presents trading weight inference using a fuzzy logic process to increase the efficiency of trading. We analyze the recent stock movement of the SET100 group in the stock exchange of Thailand as a case study.
Some of the metrics are blocked by your
consent settings
E-framework for tourism product scoring
(2019-07-01)
Visadsoontornsakul, Punyavee
A tourism product score (or rating) biased by beneficial factors impacts other tourists' expectations and product satisfaction. Receiving unbiased scores of tourism products is the challenge in the tourism research field. Our research work, an E-framework for a tourism product scoring system, overcomes the issue of bias. The framework encourages multidisciplinary collaboration between social scientists and engineers to deliver unbiased indicators for tourism products. The system is online and auditable. Fuzzy logic is applied to translate satisfaction feedback into numerical data, which is then used as a product score. This paper presents a design framework and implementation detail called the Tourism Product Scoring System (TPSS). The framework was built based on cloud technology and tested in preproduction. TPSS will benefit the tourism industry by providing a reliable score on tourism products and supporting the tourism standard implementation.
Some of the metrics are blocked by your
consent settings
Fuzzy logic-based traffic incident detection system with discrete wavelet transform
(2014-01-01)
La-Inchua, Jaraspat
;
Chivapreecha, Sorawat
;
Thajchayapong, Suttipong
This paper presents a fuzzy logic-based traffic incident detection system to detect a lane-blocking traffic incident that usually causes of traffic congestion. The proposed system uses fuzzy logic to identify traffic status as normal and abnormal. Macroscopic and microscopic traffic variables, namely, mean speed and standard deviation of inter-arrival time are used as inputs to the fuzzy inference system (FIS). As traffic variables have many fluctuations which are considered as noisy signals, discrete wavelet transform (DWT) as used for de-noising and also extracting features from noisy signals. It is found that the proposed system that uses DWT can give higher detection rate when compared with the system without DWT. Furthermore, the majority voting is also applied to the outputs of FIS in order to increase detection rate. Finally, based on simulation results, the performance of the proposed detection system for lane-blocking traffic incidents will be shown. © 2014 IEEE.
Some of the metrics are blocked by your
consent settings
A new system for traffic incident detection using fuzzy logic and majority voting
(2013-09-02)
La-Inchua, Jaraspat
;
Chivapreecha, Sorawat
;
Thajchayapong, Suttipong
This paper presents a system to detect lane-blocking traffic incidents which are amongst major causes of traffic jam. The proposed system uses fuzzy logic to identify traffic status as normal and abnormal. Mean speed and standard deviation of inter-arrival time are used as inputs to the fuzzy inference system (FIS), and then, the majority voting is applied to the outputs of FIS to improve detection rate and mean time to detection. Furthermore, based on simulation results, we show that the proposed lane-blocking detection system is very suitable for real-time implementation. © 2013 IEEE.
Some of the metrics are blocked by your
consent settings
Application of DWT and fuzzy logic algorithm for classifying simultaneous fault types
(2011-12-01)
Pothisarn, C.
;
Ngaopitakkul, A.
This paper proposes a technique using discrete wavelet transform (DWT) and fuzzy logic for identifying types of simultaneous fault along the transmission systems. The PSCAD/EMTDC is used to simulate fault signals. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The coefficients detail (phase A, B, C and zero sequence of post-fault current signals) of DWT at the first peak time that positive sequence current can detect fault, is performed as input variables for the proposed algorithm. The result shows that the proposed technique gives satisfactory accuracy, and will be very useful in the development of a power system protection scheme. © 2011 IEEE.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results