Research on the Behavior of Gen Z on Using of Green Tourism: The Role of Knowledge Sharing

Purpose: The main objective of this study is to clarify the role of knowledge sharing about green tourism on tourists’decision to use tourism services.   Theoretical framework: This study applies the theory of planned behavior (TPB) as a basis framework to determine the factors that affect tourists’ decisions.   Design/Methodology/Approach: This study used quantitative research methods with data collected through questionnaires with respondents of young people in Hanoi, Vietnam. The results were 288 valid questionnaires processed with structural equation modeling (SEM) through SPSS and Amos software.   Findings: This study confirms the influence of attitude factors, and perceived behavioral control on Gen Z's decision to use green tourism services. Moreover, the study confirms the role of knowledge sharing about green tourism as an antecedent factor that shapes the mechanism influence of green perception of Gen Z in the relationship between attitude and decision to use green tourism services.   Research, practical & social implications: This study helps administrators have a deeper insight into the importance of knowledge sharing activities in training, recruitment and orienting sustainable development policies towards raising awareness about the goal of building sustainable development. building and developing a green economy in general and the tourism industry in particular.   Originality/Value: This study has shown that knowledge sharing builds and leads to positive attitudes about green tourism services. This is a new contribution of the study with providing evidence in identifying the primary factors in the relationship between attitudes and decisions to use green tourism services

Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles

This paper addresses the problem of crack detection which is essential for health monitoring of built infrastructure. Our approach includes two stages, data collection using unmanned aerial vehicles (UAVs) and crack detection using histogram analysis. For the data collection, a 3D model of the structure is first created by using laser scanners. Based on the model, geometric properties are extracted to generate way points necessary for navigating the UAV to take images of the structure. Then, our next step is to stick together those obtained images from the overlapped field of view. The resulting image is then clustered by histogram analysis and peak detection. Potential cracks are finally identified by using locally adaptive thresholds. The whole process is automatically carried out so that the inspection time is significantly improved while safety hazards can be minimised. A prototypical system has been developed for evaluation and experimental results are included.Comment: In proceeding of The 34th International Symposium on Automation and Robotics in Construction (ISARC), pp. 823-829, Taipei, Taiwan, 201

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

Multilateral teleoperation over communication time delay using the time-domain passivity approach

A general framework to stabilize multilateral teleoperation system over a communication time delay based on the well-known time-domain passivity approach (TDPA) is proposed. The uniqueness of this framework is that it is independent of the amount of communication time delay, the multilateral control architecture, and the number of masters and slaves. The multilateral system was first decoupled into subsystems with respect to each terminal by identifying the input signals' contributions to the output terminals, which was not straightforward due to the coupled nature of the multilateral teleoperation system. The decoupled subsystems were then converted into an electrical circuit using a mechanical-electrical analogy. Time-delay power network (TDPN) was introduced to clarify active energy sources from the time delay and passivity observer/passivity controller (PO/PC) was utilized to dissipate those active energies. A less-conservative method compared with prior work was proposed to guarantee the stability. Experiments with a trilateral teleoperation system and with a multilateral teleoperation system with a dual master and dual slave were conducted to validate the proposed framework

Arbitrage and asset market equilibrium in infinite dimensional economies with risk-averse expected utilities

We consider a model with an infinite numbers of states of nature, von Neumann - Morgenstern utilities and where agents have different prob- ability beliefs. We show that no-arbitrage conditions, defined for finite dimensional asset markets models, are not sufficient to ensure existence of equilibrium in presence of an infinite number of states of nature. How- ever, if the individually rational utility set U is compact, we obtain an equilibrium. We give conditions which imply the compactness of U. We give examples of non-existence of equilibrium when these conditions do not hold

A Robust Mobile Robot Navigation System using Neuro-Fuzzy Kalman Filtering and Optimal Fusion of Behavior-based Fuzzy Controllers

This study proposes a control system model for mobile robots navigating in unknown environments. The proposed model includes a neuro-fuzzy Extended Kalman Filter for localization task and a behaviorbased fuzzy multi-controller navigation module. The neuro-fuzzy EKF, used for estimating the robot’s position from sensor readings, is an enhanced EKF whose noise covariance matrix is progressively adjusted by a fuzzy neural network. The navigation module features a series of independently-executed fuzzy controllers, each deals with a specific navigation sub-task, or behavior, and a multi-objective optimizer to coordinate all behaviors. The membership functions of all fuzzy controllers play the roles of objective functions for the optimizer, which produces an overall Pareto-optimal control signal to drive the robot. A number of simulations and real-world experiments were conducted to evaluate the performance of this model

Model predictive direct power control of three-level T-type inverter-fed doubly-fed induction generator for wind energy system

The paper proposes a simplified direct power control strategy of a doubly-fed induction generator fed by a three-level T-type inverter based on finite control set model predictive control. A mathematical model based on grid voltage orientation was employed to determine the predictive values of the stator flux, rotor current, and capacitor voltages for all feasible rotor-side inverter output voltages. The active and reactive powers were calculated by using the grid voltage and the rotor current. A cost function was applied to track the active and reactive powers, maintain the balance of capacitor voltages, and reduce the common-mode voltage. The best switching control input was chosen by minimizing the cost function and implemented to the inverter. Different operating conditions of wind turbine systems were studied with Matlab/Simulink environment. The simulation results validate the improved performance of the proposed method compared with the classical control in terms of transient response and steady-state conditions

Experiment-based Comparative Analysis of Nonlinear Speed Control Methods for Induction Motors

Field-oriented control (FOC) for induction motors is widely used in industrial applications. By using a fast and accurate torque controller based on a stator current controller it is possible to flexibly implement advanced speed control methods to achieve proper performance both in transient and steady-state states. In this study, a deadbeat controller was used for the current loop. The nonlinear methods used for the outer loop controller were backstepping, flatness-based control, and exact feedback linearization with state derivative. The dynamic responses of these three controls were compared through various experimental results. The advantages and disadvantages of the different control structures were analyzed and evaluated in detail. Based on this evaluation, an appropriate scheme can be specified when deployed in practice