Alternative methodological approaches in understanding tourist spatial behavior in urban heritage destination

The complexities of cities as spaces lead to the difficulties in understanding the spatial behavior of tourists within cities. Mapping and modelling of tourists’ spatial activity is viewed by many researchers as an under researched field in which much progress is still needed. The advancement of tracking technologies development offers an opportunity to further and expand the nature of understanding the tourist particularly in urban destination. Understanding of this phenomenon may also allow planners and tourism managers to make decisions and to address tourism development in a well-versed manner. The rapid advancement and availability of small, cheap and reliable tracking devices drawing on GPS technology is assisting researchers to develop new methods of spatial research. From this point of view, tourism is mainly a geographic activity. Most of the information needed in tourism planning is spatial, indicating where and how extensive the tourism resources are, how intensively the resources are used and so on. Basically, different methods can be employed to utilize the collected spatial data. However, the most basic method is to present the collected information on a map. The tracks collected from tourists using advanced tracking technologies can be further used to calculate many different variables that describe the spatial activity of tourist particularly in urban destination. This paper aims to capture the new methodological approaches which can help to explain different modes of behavior in urban destination versus traditional approaches. In this context, advanced tracking technologies were seen as the best tool in order to give both actual and detailed insight into people’s individual and collective travel behavior

Conceptualizing tourist typologies: traditional approaches versus advanced tracking technologies

Essentially, there is an attempt to categorize populations into broad behavioral groupings derived mainly from quantitative data which highlighted the causes and effects specifically factors encouraging one to take a trip. These causes and effects are the predictions of expressed tourist behavior such as ‘what’ and ‘why’. Why people choose to visit a certain tourism attraction and what impact result from this visit. Recently, there is an increasing recognition amongst both academics and marketers that an understanding of tourism as a social phenomenon requires the construction of tourist typologies. These typologies are important in order to represent an attempt to increase the knowledge of tourist behavior. Thus, it is essential to analyze the role of tourist behavior and tourist typologies in order to optimize the effectiveness and efficiency of the marketing activities especially in the urban area which seems to be more complex to be defined and understood. However, tourist typologies have also been claimed as too simplified in details about how the tourist actually behave (Hall, 2005). Thus, there is a need to develop the tourist typologies in a more complex manner in order to understand more clearly how the tourist behave and how they incline to use the space in an urban destination. This including moving beyond simplistic typologies from traditional methodology towards a more analytically flexible conceptualization that allows exploration of the assumptions implicit in the ‘tourist gaze’, the tourist ‘destination’, the marketing ‘image’, the ‘visit’ (Wearing and Wearing, 2001). Therefore, a suggested new model in terms of new methodological approached may provide better account for the significant range and diversity of tourist experiences. At this point of view, tourism is mainly a geographic activity. Most of the information needed in tourism planning is spatial, indicating where and how extensive the tourism resources are, how intensively the resources are used and so on. Hence, the advancement of tracking technologies development offers an opportunity to further and expand the nature of understanding the tourist particularly in urban destination. Apart from that, deeper understanding of tourists’ behavior may also help the researcher the ability to create typologies of tourists based on their spatial behavior and enhance non-spatial typologies by characterizing types of tourists’ spatial activity

Total design of polymer composite automotive bumper fascia

An automobile bumper fascia is a component, which contributes to vehicle crashworthiness during front or rear collisions. In the past, the fascia was made of plastic materials. The weight reduction in the bumper fascia without sacrificing the safety of the car was extensively studied. In this paper, the bumper fascia made of polymeric based composite material was designed with solid modelling software. The polymeric based composite material was selected because of low weight, high specific stiffness, high specific strength, high-energy absorption and easy to produce in complex shapes. Four conceptual designs of a bumper fascia were developed with a 3-D solid model. To decide the final design of bumper fascia, the matrix evaluation method was used. The weight of the bumper fascia was obtained through weight analysis that had been carried out using Pro/Engineer software. The fascia was successfully designed with less weight compared to the current fasci

INPAINTING OF DENTAL �PANORAMIC TOMOGRAPHY �VIA DEEP LEARNING METHOD

The tradition of image inpainting has existed for a long time; it is used to correct old and corrupted images. In recent times, progress in deep learning allows artificial neural networks to perform inpainting on clinical images to reduce image artifacts. In this paper, we demonstrated how various neural network models could perform inpainting on a dental panoramic tomography that was taken by using cone-beam computed tomography (CBCT). Experiments were done to compare the output of three different artificial neural network models: shallow convolutional autoencoder, deep convolutional autoencoder, and U-Net architecture. The dataset was taken from an open online dataset provided by Noor Medical Imaging Center. Qualitative assessment of the output shows that the U-net model reproduces the best output images with minimal blurriness. This result is also supported by the quantitative measurement, which shows that the U-net model has the smallest mean squared root error and the highest structural similarity index measure. The experiment results give an early indication that it is feasible to use U-Net to fix and reduce any image artifact that occurs in dental panoramic tomography

Product and Vendor Development Programme in Encouraging Supply Chain Management: A Case Study

This paper reviews the methods used by an automotive manufacturer in enhancing the Supply Chain Management (SCM) system through a setup termed as Product and Vendor Development Programme (PVD). PVD was developed to eliminate problems faced due to late delivery and poor quality of supplies and availability of supplies at the lowest possible costs. The paper explores the methodologies that have been employed by the PVD. Results overtime show that the PVD has improved the SCM system especially in the areas of quality and delivery services, other services and as well as cutting costs that manufacturers had to face due to problems that arose in the shortcomings of the supply services. The PVD has managed to promote the Localization Programme and has also been able to establish qualified vendors. Findings also establish that the PVD team is the key to the success for development of the PVD programme. The paper presents an original discussion about viewing PVD programme from a successful automotive manufacturer

Product and Vendor Development Programme in Encouraging Supply Chain Management: A Case Study

This paper reviews the methods used by an automotive manufacturer in enhancing the Supply Chain Management (SCM) system through a setup termed as Product and Vendor Development Programme (PVD). PVD was developed to eliminate problems faced due to late delivery and poor quality of supplies and availability of supplies at the lowest possible costs. The paper explores the methodologies that have been employed by the PVD. Results overtime show that the PVD has improved the SCM system especially in the areas of quality and delivery services, other services and as well as cutting costs that manufacturers had to face due to problems that arose in the shortcomings of the supply services. The PVD has managed to promote the Localization Programme and has also been able to establish qualified vendors. Findings also establish that the PVD team is the key to the success for development of the PVD programme. The paper presents an original discussion about viewing PVD programme from a successful automotive manufacturer

Tourists' real-time destination image of Kuala Lumpur

Purpose: This paper aims to capture real-time images of tourists during their visitation. This effort is to clarify a debate among scholars that there is a lack of current effort to genuinely represent an accurate image of the tourist experience during their visit. Previous studies on destination image focused on measuring and successfully capturing the tourists' perceived image using the perspective of “before and after” visitation. Design/methodology/approach: The paper applies volunteer-employed photography and questionnaire methods to capture real-time tourist images. The paper was conducted in Kuala Lumpur, involving 384 international tourists. The data are analysed by supplemental photo analysis, was categorised into manifest and latent content. Findings: The paper provides empirical insights into the changes in tourists' image when visiting an urban destination. The insights suggest that a city's image during visitation continuously changes based on the tourists' movement and preferences. Practical implications: The findings of this paper are critical in assisting tourism agencies and authorities in portraying an accurate image to achieve greater tourism satisfaction. Originality/value: This paper contributes to the interpretation and portrayal of the real-time image of Kuala Lumpur based on the manifest and latent content of the photos taken

Experimental Simplified Rule Of Self Tuning Fuzzy Logic-Model Reference Adaptive Speed Controller For Induction Motor Drive

Fuzzy logic controller (FLC) has shown excellent performance in dealing with the non-linearity and complex dynamic model of the induction motor. However, a conventional constant parameter FLC (CPFL) will not be able to provide-good coverage performance for a wide speed range operation with a single tuning parameter. Therefore, this paper proposed a self tuning mechanism FLC approach by model reference adaptive controller (ST-MRAC) to continuously allow to adjust the parameters. Due to real time hardware application, the dominant rules selection method for simplified rules has been implemented as part of the reducing computational burden. Experiment results validate a good performance of the ST-MRAC compared to the CPFL for the speed performance in terms of the wide range of operations and disturbance showed remarkable performance

Experimental investigations into the behavior of scaling factors in a fuzzy logic speed control induction motor with model reference adaptive control

This paper presents a self-tuning fuzzy logic speed controller (FLSC) with model reference adaptive control (MRAC) for an induction motor (IM) drive system. The MRAC is examined by output scaling the factor tuner for optimum motor speed performance. A detailed investigation is carried out on the scaling factor control of the input change error and main FLSC output increment. This proposed method utilizes seven simplified rules of the 5 × 5 matrix membership functions to minimize the computational burden and memory space limitations. All simulation work is conducted using Simulink and Fuzzy Tools in the MATLAB software and the experimental testing with the aid of a digital signal controller board, dSPACE DS1103. Based on the results, the output scaling factor makes a more significant impact on the performance effect compared to the input error scaling factor. The input change error and output SF also exhibit similar behavior, indicating that a large range of UoD tuners works well in terms of capability load rejection while a small range of UoD tuners performs well in terms of rise time. The analysis includes no-load and load tests to ascertain the overshoot percentage, rise time, and settling time for transient and steady-state conditions

Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle

This paper presents modelling techniques for Lithium Iron Phosphate (LiFePO4) battery in an electric vehicle. Artificial intelligence techniques namely multi-layered perceptron neural network (MLPNN) and Elman recurrent neural network are devised to estimate the energy remained in the battery bank which referred to state of charge (SOC). The New European Driving Cycle (NEDC) test data is used to excite the cells in driving cycle-based conditions under varied temperature range [0-55]°C. Accurate SOC prediction is a key function for satisfactory implementation of Battery Supervisory System (BSS). It is demonstrated that artificial intelligence methods can be effectively used with highly accurate results. The accuracy of the modeling results is demonstrated through validation and correlation tests