Synchronization controller for a 3-RRR parallel manipulator

A 3-RRR parallel manipulator has been well-known as a closed-loop kinematic chain mechanism in which the end-effector generally a moving platform is connected to the base by several independent actuators. Performance of the robot is decided by performances of the component actuators which are independently driven by tracking controllers without acknowledging information from each other. The platform performance is degraded if any actuator could not be driven well. Therefore, this paper aims to develop an advanced synchronization (SYNC) controller for position tracking of a 3-RRR parallel robot using three DC motor-driven actuators. The proposed control scheme consists of three sliding mode controllers (SMC) to drive the actuators and a supervisory controller named PID-neural network controller (PIDNNC) to compensate the synchronization errors due to system nonlinearities, uncertainties and external disturbances. A Lyapunov stability condition is added to the PIDNNC training mechanism to ensure the robust tracking performance of the manipulator. Numerical simulations have been performed under different working conditions to demonstrate the effectiveness of the suggested control approach

Higuchi Fractal Properties of Onset Epilepsy Electroencephalogram

Epilepsy is a medical term which indicates a common neurological disorder characterized by seizures, because of abnormal neuronal activity. This leads to unconsciousness or even a convulsion. The possible etiologies should be evaluated and treated. Therefore, it is necessary to concentrate not only on finding out efficient treatment methods, but also on developing algorithm to support diagnosis. Currently, there are a number of algorithms, especially nonlinear algorithms. However, those algorithms have some difficulties one of which is the impact of noise on the results. In this paper, in addition to the use of fractal dimension as a principal tool to diagnose epilepsy, the combination between ICA algorithm and averaging filter at the preprocessing step leads to some positive results. The combination which improved the fractal algorithm become robust with noise on EEG signals. As a result, we can see clearly fractal properties in preictal and ictal period so as to epileptic diagnosis

Navigating the Existential Crisis from Literature to Real Life: A Text-to-Self Pedagogical Approach and Its Potential for Existential Literature Instruction

Amidst the existential crisis in contemporary society, young individuals worldwide find themselves susceptible to self-inflicted harm or even self-destruction in response to their psychological ruptures and injuries, while lacking the requisite self-awareness and the patience to persevere on the journey of self-understanding. In this article, we draw upon Rosenblatt’s (1995) notion of Literature as Exploration to propound a text-to-self pedagogical approach to reading Haruki Murakami and Can Xue and to teaching contemporary existentialist literature. Comprising three strategies, namely The Art World in My Eyes, The Mind Film, and The Literary Conversation, this approach encourages students to engage deeply with the portrayal of death in the writers’ flagship novels that view death as an unwavering companion to young individuals’ life journey and growth. Based on Rosenblatt (1995), the three strategies, and our survey-based analysis of 2,000 Vietnamese high school students’ existential perceptions, we propose a model for literature instruction through promoting students’ transition from literary interpretations to self-discovery and self-realization of death associated with the existential crisis. This four-stage model, we expect, will get students poised to reconcile with their lost, solitary, and vulnerable ego, actively befriend their ‘inner child’, and develop into individuals with healed hearts and positive worldviews

Extracting Fetal Electrocardiogram from Being Pregnancy Based on Nonlinear Projection

Fetal heart rate extraction from the abdominal ECG is of great importance due to the information that carries in assessing appropriately the fetus well-being during pregnancy. In this paper, we describe a method to suppress the maternal signal and noise contamination to discover the fetal signal in a single-lead fetal ECG recordings. We use a locally linear phase space projection technique which has been used for noise reduction in deterministically chaotic signals. Henceforth, this method is capable of extracting fetal signal even when noise and fetal component are of comparable amplitude. The result is much better if the noise is much smaller (P wave and T wave can be discovered)

Advanced Metering Infrastructure Based on Smart Meters in Smart Grid

Due to lack of situational awareness, automated analysis, poor visibility, and mechanical switches, today\u27s electric power grid has been aging and ill‐suited to the demand for electricity, which has gradually increased, in the twenty‐first century. Besides, the global climate change and the greenhouse gas emissions on the Earth caused by the electricity industries, the growing population, one‐way communication, equipment failures, energy storage problems, the capacity limitations of electricity generation, decrease in fossil fuels, and resilience problems put more stress on the existing power grid. Consequently, the smart grid (SG) has emerged to address these challenges. To realize the SG, an advanced metering infrastructure (AMI) based on smart meters is the most important key

The Nonlinearity of Working Capital and Cross-Sectional Stock Returns: Does Financial Constraints Matter?

This study is the first to examine the impacts of working capital (WC) and financial constraints on cross-sectional stock returns in Taiwan. The findings indicate a non-linear relationship between WC and stock returns. Moreover, the nonlinearity between WC and cross-sectional stock returns is robust after controlling for financial constraints, risk, and growth factors, before the Covid-19 pandemic. In contrast, there is no evidence of nonlinearity between WC and stock returns throughout the Covid-19 outbreak. In addition, the study shows that any deviations from the minimum WC level enhance the stock returns cross-sectionally. It is found that a positive Deviation effect exists in the Taiwan stock exchange before the Covid-19 pandemic by employing portfolio sorting methodologies. The return difference of the long buying highest Deviation and short selling lowest Deviation portfolios earn from 0.6% to 0.9% per month after controlling for financial constraints, risks, and growth factors. Interestingly, it is determined that the deviation effect becomes negative for small stocks during the Covid-19 pandemic, implying that investors prefer small stocks to maintain minimum working capital. The results support the trade-off theory and liquidity preference theory. Finally, the study provides insights into working capital management for managers, and investment strategies for investors during the pandemic

Identify aerodynamic derivatives of the airplane attitude channel using a spiking neural network

The paper proposes a method for identifying aerodynamic coefficient derivatives of aircraft attitude channel using spiking neural network (SNN) and Gauss-Newton algorithm based on data obtained from actual flights. Using SNN combination with Gauss-Newton iterative calculation algorithm allows the identification of aerodynamic coefficient derivatives in a nonlinear model for aerodynamic parameters with higher accuracy and faster calculation time. The paper proposes an algorithm to train the SNN multi-layer network by Normalized Spiking Error Back Propagation (NSEBP), in which, in the forward propagation period, the time of output spikes is calculating by solving quadratic equations instead of detection by traditional methods. The phase of propagation of errors backward uses the step-by-step calculation instead of the conventional gradient calculation method. The identification results are compared with the results when using the RBN network to prove the algorithm efficienc

Analyzing surface EMG signals to determine relationship between jaw imbalance and arm strength loss

BACKGROUND: This study investigated the relationship between dental occlusion and arm strength; in particular, the imbalance in the jaw can cause loss in arm strength phenomenon. One of the goals of this study was to record the maximum forces that the subjects can resist against the pull-down force on their hands while biting a spacer of adjustable height on the right or left side of the jaw. Then EMG measurement was used to determine the EMG-Force relationship of the jaw, neck and arms muscles. This gave us useful insights on the arms strength loss due to the biomechanical effects of the imbalance in the jaw mechanism. METHODS: In this study to determine the effects of the imbalance in the jaw to the strength of the arms, we conducted experiments with a pool of 20 healthy subjects of both genders. The subjects were asked to resist a pull down force applied on the contralateral arm while biting on a firm spacer using one side of the jaw. Four different muscles – masseter muscles, deltoid muscles, bicep muscles and trapezoid muscles – were involved. Integrated EMG (iEMG) and Higuchi fractal dimension (HFD) were used to analyze the EMG signals. RESULTS: The results showed that (1) Imbalance in the jaw causes loss of arm strength contra-laterally; (2) The loss is approximately a linear function of the height of the spacers. Moreover, the iEMG showed the intensity of muscle activities decreased when the degrees of jaw imbalance increased (spacer thickness increased). In addition, the tendency of Higuchi fractal dimension decreased for all muscles. CONCLUSIONS: This finding indicates that muscle fatigue and the decrease in muscle contraction level leads to the loss of arm strength