Exploring an Objective Weighting System for Travel & Tourism Pillars

AbstractThe World Economic Forum employs Travel & Tourism Competitiveness Indexes (TTCI) to measure the travel & tourism (T&T) global competitiveness of a country. The TTCI overall scores are calculated with an arithmetic mean aggregation from the scores of the fourteen composite pillars with a subjective assumption of all the pillars having the same weights. This paper attempts to release such a subjective assumption by proposing a new solution framework to explore an objective weighting system for the pillars. The proposed solution framework employs the Expectation Maximization (EM) clustering algorithm to group the 139 ranked countries into three classes and then performs the Artificial Neural Network (ANN) analysis to explore the objective weighting system for the fourteen pillars. The results show that tourism infrastructure, ground transport infrastructure, air transport infrastructure, cultural resources, health and hygiene, and ICT infrastructure are the six most critical pillars contributing to the TTCI overall scores. Accordingly, the policy makers should allocate limited resources with priority to improve these six pillars to frog leap the T&T global competitiveness

ARCHITECTURE OPTIMIZATION, TRAINING CONVERGENCE AND NETWORK ESTIMATION ROBUSTNESS OF A FULLY CONNECTED RECURRENT NEURAL NETWORK

Recurrent neural networks (RNN) have been rapidly developed in recent years. Applications of RNN can be found in system identification, optimization, image processing, pattern reorganization, classification, clustering, memory association, etc. In this study, an optimized RNN is proposed to model nonlinear dynamical systems. A fully connected RNN is developed first which is modified from a fully forward connected neural network (FFCNN) by accommodating recurrent connections among its hidden neurons. In addition, a destructive structure optimization algorithm is applied and the extended Kalman filter (EKF) is adopted as a network\u27s training algorithm. These two algorithms can seamlessly work together to generate the optimized RNN. The enhancement of the modeling performance of the optimized network comes from three parts: 1) its prototype - the FFCNN has advantages over multilayer perceptron network (MLP), the most widely used network, in terms of modeling accuracy and generalization ability; 2) the recurrency in RNN network make it more capable of modeling non-linear dynamical systems; and 3) the structure optimization algorithm further improves RNN\u27s modeling performance in generalization ability and robustness. Performance studies of the proposed network are highlighted in training convergence and robustness. For the training convergence study, the Lyapunov method is used to adapt some training parameters to guarantee the training convergence, while the maximum likelihood method is used to estimate some other parameters to accelerate the training process. In addition, robustness analysis is conducted to develop a robustness measure considering uncertainties propagation through RNN via unscented transform. Two case studies, the modeling of a benchmark non-linear dynamical system and a tool wear progression in hard turning, are carried out to testify the development in this dissertation. The work detailed in this dissertation focuses on the creation of: (1) a new method to prove/guarantee the training convergence of RNN, and (2) a new method to quantify the robustness of RNN using uncertainty propagation analysis. With the proposed study, RNN and related algorithms are developed to model nonlinear dynamical system which can benefit modeling applications such as the condition monitoring studies in terms of robustness and accuracy in the future

Indoor air Quality and Its Effects on Health in Urban Houses of Indonesia: A case study of Surabaya

There is a possibility that the sick building syndrome has already spread widely among the newly constructed apartments in major cities of Indonesia. This study investigates the current conditions of indoor air quality, focusing especially on formaldehyde and TVOC, and their effects on health among occupants in the urban houses located in the city of Surabaya. A total of 471 respondents were interviewed and 82 rooms were measured from September 2017 to January 2018. The results indicated that around 50% of the respondents in the apartments showed some degrees of chemical sensitivity risk. More than 60% of the measured formaldehyde levels in the apartments exceeded the WHO standard, 0.08 ppm. The respondents living in rooms with higher mean formaldehyde values tended to have higher multiple chemical sensitivity risk scores. KEYWORDS: Indoor air quality, Sick building syndrome, QEESI, Formaldehyde, Developing countrie

Robustness of Radial Basis Functions

Eickhoff R, Rückert U. Robustness of Radial Basis Functions. In: Cabestany J, Prieto A, Sandoval DF, eds. Proceedings of the 8th International Work-Conference on Artificial Neural Networks (IWANN). Barcelona, Spain; 2005: 264-271.Neural networks are intended to be used in future nanoelectronic technology since these architectures seem to be robust to malfunctioning elements and noise in its inputs and parameters. In this work, the robustness of radial basis function networks is analyzed in order to operate in noisy and unreliable environment. Furthermore, upper bounds on the mean square error under noise contaminated parameters and inputs are determined if the network parameters are constrained. To achieve robuster neural network architectures fundamental methods are introduced to identify sensitive parameters and neurons

Robustness of radial basis functions

Eickhoff R, Rückert U. Robustness of radial basis functions. Neurocomputing. 2007;70(16-18):2758-2767.Neural networks are intended to be used in future nanoelectronic technology since these architectures seem to be robust to malfunctioning elements and noise in its inputs and parameters. In this work, the robustness of radial basis function networks is analyzed in order to operate in noisy and unreliable environment. Furthermore, upper bounds on the mean square error under noise contaminated parameters and inputs are determined if the network parameters are constrained. To achieve robuster neural network architectures fundamental methods are introduced to identify sensitive parameters and neurons