Approximation of the inverse kinematics of a robotic manipulator using a neural network

A fundamental property of a robotic manipulator system is that it is capable of accurately following complex position trajectories in three-dimensional space. An essential component of the robotic control system is the solution of the inverse kinematics problem which allows determination of the joint angle trajectories from the desired trajectory in the Cartesian space. There are several traditional methods based on the known geometry of robotic manipulators to solve the inverse kinematics problem. These methods can become impractical in a robot-vision control system where the environmental parameters can alter. Artificial neural networks with their inherent learning ability can approximate the inverse kinematics function and do not require any knowledge of the manipulator geometry. This thesis concentrates on developing a practical solution using a radial basis function network to approximate the inverse kinematics of a robot manipulator. This approach is distinct from existing approaches as the centres of the hidden-layer units are regularly distributed in the workspace, constrained training data is used and the training phase is performed using either the strict interpolation or the least mean square algorithms. An online retraining approach is also proposed to modify the network function approximation to cope with the situation where the initial training and application environments are different. Simulation results for two and three-link manipulators verify the approach. A novel real-time visual measurement system, based on a video camera and image processing software, has been developed to measure the position of the robotic manipulator in the three-dimensional workspace. Practical experiments have been performed with a Mitsubishi PA10-6CE manipulator and this visual measurement system. The performance of the radial basis function network is analysed for the manipulator operating in two and three-dimensional space and the practical results are compared to the simulation results. Advantages and disadvantages of the proposed approach are discussed

Environmental Economic Hydrothermal System Dispatch by Using a Novel Differential Evolution

This paper proposes the Novel Differential Evolution (NDE) method for solving the environmental economic hydrothermal system dispatch (EEHTSD) problem with the aim to reduce electricity generation fuel costs and emissions of thermal units. The EEHTSD problem is constrained by limitations on generations, active power balance, and amount of available water. NDE applies two modified techniques. The first one is modified mutation, which is used to balance global and local search. The second one is modified selection, which is used to keep the best solutions. When performing this modified selection, the proposed method completely reduces the impact of crossover by setting it to one. Moreover, the task of tuning this factor can be canceled. Original Differential Evolution (ODE), ODE with the first modification (MMDE), and ODE with the second modification (MSDE), and NDE were tested on two different hydrothermal systems for comparison and evaluation purposes. The performance of NDE was also compared to existing methods. It was indicated that the proposed NDE is a very promising method for solving the EEHTSD problem

TERRORIST ATTACKS AND CORPORATE INVESTMENT IN INDONESIA

Using yearly data from 1997 to 2017, this paper studies the effect of terrorism (number of attacks) on corporate investment in Indonesia. Applying an investment-type model, we show that firms reduce their capital expenditure due to an increase in the number of terrorist attacks. On average, a one standard deviation increase in the number of terrorist attacks reduces corporate investment by 9.23%. We also find heterogenous reactions of firms to terrorism across different sectors and different panels based on firm characteristics. Finally, our main results remain consistent after performing several robustness tests

A STUDY OF INDONESIA’S STOCK MARKET: HOW PREDICTABLE IS IT?

Using monthly data from January 1995 to December 2017, this paper tests whetherIndonesian stock index returns are predictable. In particular, we use eight macrovariables to predict the Indonesian composite and six sectoral index returns using thefeasible generalized least squares estimator. Our results suggest that the Indonesianstock index returns are predictable. However, the predictability depends not only onthe macro predictor used but also on the indexes examined. Second, we find that themost popular predictor is the exchange rate, followed by the interest rate. Finally, ourmain findings hold for a number of robustness tests.Using monthly data from January 1995 to December 2017, this paper tests whetherIndonesian stock index returns are predictable. In particular, we use eight macrovariables to predict the Indonesian composite and six sectoral index returns using thefeasible generalized least squares estimator. Our results suggest that the Indonesianstock index returns are predictable. However, the predictability depends not only onthe macro predictor used but also on the indexes examined. Second, we find that themost popular predictor is the exchange rate, followed by the interest rate. Finally, ourmain findings hold for a number of robustness tests

CAN ECONOMIC POLICY UNCERTAINTY PREDICT EXCHANGE RATE AND ITS VOLATILITY? EVIDENCE FROM ASEAN COUNTRIES

This paper studies whether the global economic policy uncertainty (EPU) predicts the exchange rate and its volatility in 10 ASEAN countries using monthly data from January 1997 to December 2017. Applying the recently developed predictive regression model of Westerlund and Narayan (2012, 2015), we discover that the EPU positively and statistically significantly predicts the exchange rate of six out of ten currencies. One standard deviation increase in the EPU index leads to a depreciation of between 0.050% and 2.047% in these currencies. Moreover, the EPU predicts the exchange rate volatility for all 10 ASEAN countries. Their exchange rate volatilities increase by between 0.107% and 0.645% as a result of a one standard deviation increase in the EPU index. These results are robust to different forecasting horizons, different sub-sample periods, and after controlling for the global financial crisis

A STUDY OF INDONESIA’S STOCK MARKET

Using monthly data from January 1995 to December 2017, this paper tests whether Indonesian stock index returns are predictable. In particular, we use eight macro variables to predict the Indonesian composite and six sectoral index returns using the feasible generalized least squares estimator. Our results suggest that the Indonesian stock index returns are predictable. However, the predictability depends not only on the macro predictor used but also on the indexes examined. Second, we find that the most popular predictor is the exchange rate, followed by the interest rate. Finally, our main findings hold for a number of robustness tests

Benefits of triple-layer remote phosphor structure in improving color quality and luminous flux of white LED

Remote phosphor structure has higher luminous efficiency comparing to that of both conformal phosphor and in-cup phosphor structures. However, it is hard to control the color quality of remote phosphor structure, and this issue has become one of the most researchable objectives to many researchers in recent years. Up to now, there are two remote phosphor structures applied to improve the color quality, including dual-layer phosphor configuration and triple-layer phosphor configuration. The purpose of this research is to select one of those configurations to have multi-chip white LEDs (WLEDs) achieved the highest color rendering index (CRI), color quality scale (CQS), luminous efficacy (LE), and color uniformity. In this research, WLEDs with two correlated color temperatures (CCT) of 6600K and 7700K were applied. The obtained results showed that triple-layer phosphor configuration is more outstanding in CRI, CQS, and LE. Moreover, the color deviation has been significantly reduced, which means the color uniformity has been enhanced with the application of triple-layer phosphor configuration. These results can be proven by scattering properties of phosphor layers based on Mie theory. Thus, the researched results have become a reliable and valuable reference for manufacturing higher-quality WLEDs

A robust diagnosis method for speed sensor fault based on stator currents in the RFOC induction motor drive

A valid diagnosis method for the speed sensor failure (SSF) is an essential requirement to ensure the reliability of Fault-Tolerant Control (FTC) models in induction motor drive (IMD) systems. Most recent researches have focused on directly comparing the measured and estimated rotor speed signal to detect the speed sensor fault. However, using that such estimated value in both the fault diagnosis and the controller reconfiguration phases leads to the insufficient performance of FTC modes. In this paper, a novel diagnosis-technique based on the stator current model combined with a confusion prevention condition is proposed to detect the failure states of the speed sensor in the IMD systems. It helps the FTC mode to separate between the diagnosis and reconfiguration phases against a speed sensor fault. This proposed SSF diagnosis method can also effectively apply for IMs’ applications at the low-speed range where the speed sensor signal often suffers from noise. MATLAB/Simulink software has been used to implement the simulations in various speed ranges. The achieved results have demonstrated the capability and effectiveness of the proposed SSF method against speed sensor faults