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Implementation of Dynamic Fuzzy Logic Control of Traffic Light with Accident Detection and Action System using iTraffic Simulation
No full textTraffic is one of the most common issues in the big cities around the world. Thus, develop and improve the traffic light control systems became the focus of recent studies. To solve the problem, we proposed a dynamic hybrid fuzzy logic control system that is further branched into two separate systems: An Accident Detection system and an Action system that is intended to solve the congestion related to the vehicular traffic. The primary target of this paper is to discuss the Action system, which depends on the Accident Detection system. This paper explained the two parts of the Action system. It showed the improvement of the Action system with %9.32 in total car crossed. It also presented different scenarios using iTraffic simulation and description of each scenario is displayed with details about the road variables and the simulation results with and without the action system
Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasting at PT. PLN Gresik Indonesia
No full textThe prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side
Cuckoo Search Based DTC of PMSM
No full textHysteresis controllers (HC) are used to limit the torque and flux in the control band in conventional configuration of direct torque control (DTC) while in the space vector pulse width modulated (SVPWM) DTC, the HC are switched to PI or PID controllers. This paper presents a modern approach for the speed control applied on a DTC of a permanent magnet synchronous motor (PMSM) using the Cuckoo Search Optimization (CSO) algorithm in order to optimize the PI speed controller parameters of the outer loop and PID flux and torque controllers of the inner loop. The system is tested at no load and with a step change in load.The performance of the controllers is presented and the results of simulation indicate a very rapid dynamic response and the system achieves the steady state (SS.) in a very short time. Also it shows that both the SS. and dynamic performances are improved by applying of the CSO algorithm. The proposed DTC simulation model of the PMSM is presented using MATLAB / SIMULINK and capable of simulating both the steady-state and dynamic response. The CSO results are compared with another control strategy that incorporates fuzzy logic controller (FLC) with DTC
Experimental Analysis of Simplified Rules Fuzzy Logic Speed Controller for Wide Speed Range Operations
No full textThis paper presents the experimental analysis of simplified rules Fuzzy Logic Speed Controller (FLSC) of Induction Motor drive. The maximum gain of input scaling factor, FLSC is generally limited by the coverage of universe of discoursed (UoD). Thus, to further increase the input gain scaling factor, the outer membership function need to be increased. This analysis covers various values in the range of UoD values from [-1,1] to [-5,5] for the wide speed range operations from low to rated speed ranges. The FLSC is employed to the indirect Field Oriented Control method fed by a voltage source inverter. Simulation and experimental verification is done by using Matlab/Simulink and dSPACE 1103 controller experimental rigs respectively. Based on the results, speed performance behaviours are improved over the wide speed range operations in term of rise time and setting time. The tuning approached is simple without additional algorithm for faster and more accurate response
Assessing the User Satisfaction Perspectives of Information System: A Library Case Study in Indonesia
No full textUser satisfaction is one of the system use variables which is affected by the system creation variables in the information system (IS) success measurements, especially in the mandatory use of IS. This paper reports the relational variable assessments between three variables of the system creation dimension towards the user satisfaction variable in the library information system (LIS) implementation of a sampled university in Indonesia. Practically, the measurement has never been done since the early system implementation in the institution. The study focused on the status of the user satisfaction construct and what are the factors which influenced the construct. The used measurement model was adopted and adapted from the DeLone and McLean’s IS success model. A total of 185 respondents were selected in this study using multi-stage purposeful random sampling. The researchers used the partial least squares structural equation modeling (PLS-SEM) with the SmartPLS version 2.0 for analyzing the collected data. Findings of the study showed that users of the LIS were sufficiently satisfied and the proposed hypotheses were accepted. In terms of the adopted model, besides the findings theoretically proved that the user satisfaction construct has affected by the system creation constructs; the findings may also have proposed the practical recommendations to the sampled institution for the next LIS improvements in particular.
System for Prediction of Non Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model
No full textThis paper proposes and examines the performance of a hybrid model called the wavelet radial bases function NN (WRBFNN). This model will be compared its performance with the WFFNN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in both RBFNN and FFNN models. The performance of both WRBFNN and WFFNN models is evaluated using MAPE and MSE value indicators, while the computation process of the two models is compared using two indicators, many epoch, and length of training. In stationary benchmark data, all models have a performance with very high accuracy. The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal elements. In terms of speed in computing, the WRBFNN model is superior with a much smaller number of epochs and much shorter training time
Analysing Mobile Random Early Detection for Congestion Control in Mobile Ad-hoc Network
No full textThis research paper suggests and analyse a technique for congestion control in mobile ad hoc networks. The technique is based on a new hybrid approach that uses clustering and queuing techniques. In clustering, in general cluster head transfers the data, following a queuing method based on a RED (Random Early Detection), the mobile environment makes it Mobile RED (or MRED), It majorly depends upon mobility of nodes and mobile environments leads to unpredictable queue size . To simulate this technique, the Network Simulator 2 (or NS2) is used for various scenarios. The simulated results are compared with NRED (Neighbourhood Random Early Detection) queuing technique of congestion control. It has been observed that the results are improved using MRED comparatively
Design of Linear Plasma Position Controllers with Intelligent Feedback Systems for Aditya Tokamak
No full textIn order to increase the performance of Aditya tokamak, it is necessary to determine the feedback coil current for positioning the plasma within the magnetic chamber. In this paper, transfer functions are obtained for the plasma position prediction system. Four different feedback controllers are developed to improve the performance of the prediction system. From the analysis, Neural Network controller does not have overshoot while the PID controller has lesser settling time than the other two controllers.
An Influence of Measurement Scale of Predictor Variable on Logistic Regression Modeling and Learning Vector Quntization Modeling for Object Classification
No full textMuch real world decision making is based on binary categories of information that agree or disagree, accept or reject, succeed or fail and so on. Information of this category is the output of a classification method that is the domain of statistical field studies (eg Logistic Regression method) and machine learning (eg Learning Vector Quantization (LVQ)). The input argument of a classification method has a very crucial role to the resulting output condition. This paper investigated the influence of various types of input data measurement (interval, ratio, and nominal) to the performance of logistic regression method and LVQ in classifying an object. Logistic regression modeling is done in several stages until a model that meets the suitability model test is obtained. Modeling on LVQ was tested on several codebook sizes and selected the most optimal LVQ model. The best model of each method compared to its performance on object classification based on Hit Ratio indicator. In logistic regression model obtained 2 models that meet the model suitability test is a model with predictive variables scaled interval and nominal, while in LVQ modeling obtained 3 pieces of the most optimal model with a different codebook. In the data with interval-scale predictor variable, the performance of both methods is the same. The performance of both models is just as bad when the data have the predictor variables of the nominal scale. In the data with predictor variable has ratio scale, the LVQ method able to produce moderate enough performance, while on logistic regression modeling is not obtained the model that meet model suitability test. Thus if the input dataset has interval or ratio-scale predictor variables than it is preferable to use the LVQ method for modeling the object classification
Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach
No full textDefining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, “Bat-Cluster (BC).” This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values