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Infrared simulation plays an important role in small and affordable unmanned aerial vehicles. Its key and main goal is to get the infrared image of a specific target. Infrared physical model is established through a theoretical research, thus the temperature field is available. Then infrared image of a specific target can be simulated properly while taking atmosphere state and effect of infrared imaging system into account. For recent years, some research has been done in this field. Among them, the infrared simulation for large scale is still a key problem to be solved. In this passage, a method of classification based on texture blending is proposed and this method effectively solves the problem of classification of large number of images and increase the frame rate of large infrared scene rendering. The paper aims to discuss these issues.

Mosart Atmospheric Tool (MAT) is used first to calculate data of sun radiance, skyshine radiance, path radiance, temperatures of different material which is an offline process. Then, shader in OGRE does final calculation to get simulation result and keeps a high frame rate. Considering this, the authors convert data in MAT file into textures which can be easily handled by shader. In shader responding, radiance can be indexed by information of material, vertex normal, eye and sun. Adding the effect of infrared imaging system, the final radiance distribution is obtained. At last, the authors get infrared scene by converting radiance to grayscale.

In the fragment shader, fake infrared textures are used to look up temperature which can calculate radiance of itself and related radiance.

The radiance is transferred into grayscale image while considering effect of infrared imaging system.

Simulation results show that a high frame rate can be reached while guaranteeing the fidelity.

The purpose of this paper is to study the dynamical group consensus of heterogeneous multi-agent systems with fixed topologies.

The tool used in this paper to model the topologies of multi-agent systems is algebraic graph theory. The matrix theory and stability theory are applied to research the group consensus of heterogeneous multi-agent systems with fixed topologies. The Laplace transform and Routh criterion are utilized to analyze the convergence properties of heterogeneous multi-agent systems.

It is discovered that the dynamical group consensus for heterogeneous multi-agent systems with first-order and second-order agents can be achieved under the reasonable hypothesizes. The group consensus condition is only relied on the nonzero eigenvalues of the graph Laplacian matrix.

The novelty of this paper is to investigate the dynamical group consensus of heterogeneous multi-agent systems with first-order and second-order agents and fixed topologies and obtain a sufficient group consensus condition.

Light Detection and Ranging (LiDAR) offers a fast and effective way to acquire DSM and extract ground objects such as building, trees and so on. However, it is difficult to extract sharp and precise building boundary from LiDAR data, because its ground sample distance (GSD) is often worse than that of high resolution image. Recently, fusion of LiDAR and high resolution image becomes a promising approach to extract precise boundary. To find the correct and precise boundary, the aim of this paper is to present a series of novel algorithms to improve the quality.

To find the correct and precise boundary, this paper presents a series of novel algorithms to improve the quality. At first, a progressive algorithm is presented to register LiDAR data and images; second, a modified adaptive TIN algorithm is presented to filter ground point, where a region growth method is applied in the adaptive TIN algorithm; third, a novel criterion based on the density, connectivity and distribution of point cluster is developed to distinguish trees point; fourth, a novel method based on the height difference between neighbor points is employed to extract coarse boundaries; at last, a knowledge based rule is put forward to identify correct building boundary from parallel edges.

Thorough experiments, it is conducted that: the registration results are accurate and reliable; filtered ground points has good quality, without missing or redundancy; all tree clusters bigger than one grid are detected, and points of walls and edges are eliminated with the new criterion; detected edges exactly locate at real building boundaries, and statistics show the detection correctness is 98 percent, and the detection completeness is 95 percent.

All results prove that precise boundary can be extracted with fusion of LiDAR and high resolution image.

The paper aims to identify the emergent themes of hotel guests’ satisfaction, to compare the distribution of the attributes of the themes between Chinese and North American guests and to compare the importance of the themes for different satisfaction levels between Chinese and North American guests from a cross-cultural perspective.

By adopting Python (a computer language), the word-frequency method was used to identify emergent themes of hotel guests’ satisfaction. Topic modeling was adopted to compare the attributes distribution of each theme and the features of satisfaction between Chinese and North American guests.

First, three themes were identified including functionality, staff and price. Functionality can be further categorized into five subthemes, namely, room, travel, food, environment and hotel facility. Second, the distribution of the attributes of the themes between Chinese and North American guests was compared from a cross-cultural perspective. Chinese guests tend to mention both lifestyles- and social norms–related attributes and expect personalized service, while North American guests mainly prefer to describe lifestyle-related attributes and prefer standardized service. Third, the study compared the changing importance of the themes (functionality, staff and price) for different satisfaction levels between Chinese and North American guests. As the satisfaction level decreases, the importance of functionality decreases, that of staff increases and that of price remain stable for Chinese guests. In contrast, the importance of each theme has fluctuated mildly from the high to the low satisfaction level for North American guests.

Proposed managerial implications are to highlight lifestyle- and social norms-related attributes, as well as personalized service for Chinese guests. However, lifestyle-related attributes and standardized service should be facilitated for North American guests. Specific suggestions were made to help improve hotel performance such as the good performance of functional-related attributes, which could enhance satisfaction and better staff performance, which would reduce dissatisfaction.

By mining big data, this study investigated hotel guests’ satisfaction from a dynamic instead of a static perspective. This study provides some rare insights into differences in key attributes influencing satisfaction levels of Chinese versus North American guests staying in luxury hotels in China. This study also takes a novel approach to examine the dynamics of the importance of the various themes at different satisfaction levels, and contrast these dynamics between Chinese and North American guests. The findings offer valuable insight into market segmentation and management in the hospitality industry.

The purpose of this paper is to explore a semantics representation framework for narrative images, conforming to the image-interpretation process.

This paper explores the essential features of semantics evolution in the process of narrative images interpretation. It proposes a novel semantics representation framework, ESImage (evolution semantics of image) for narrative images. ESImage adopts a hierarchical architecture to progressively organize the semantic information in images, enabling the evolutionary interpretation under the support of a graph-based semantics data model. Also, the study shows the feasibility of this framework by addressing the issues of typical semantics representation with the scenario of the Dunhuang fresco.

The process of image interpretation mainly concerns three issues: bottom-up description, the multi-faceted semantics representation and the top-down semantics complementation. ESImage can provide a comprehensive solution for narrative image semantics representation by addressing the major issues based on the semantics evolution mechanisms of the graph-based semantics data model.

ESImage needs to be combined with machine learning to meet the requirements of automatic annotation and semantics interpretation of large-scale image resources.

This paper sorts out the characteristics of the gradual interpretation of narrative images and has discussed the major issues in its semantics representation. Also, it proposes the semantic framework ESImage which deploys a flexible and sound mechanism to represent the semantic information of narrative images.

To meet the emerging demand for fine-grained annotation and semantic enrichment of cultural heritage images, this paper proposes a new approach that can transcend the boundary of information organization theory and Panofsky's iconography theory.

After a systematic review of semantic data models for organizing cultural heritage images and a comparative analysis of the concept and characteristics of deep semantic annotation (DSA) and indexing, an integrated DSA framework for cultural heritage images as well as its principles and process was designed. Two experiments were conducted on two mural images from the Mogao Caves to evaluate the DSA framework's validity based on four criteria: depth, breadth, granularity and relation.

Results showed the proposed DSA framework included not only image metadata but also represented the storyline contained in the images by integrating domain terminology, ontology, thesaurus, taxonomy and natural language description into a multilevel structure.

DSA can reveal the aboutness, ofness and isness information contained within images, which can thus meet the demand for semantic enrichment and retrieval of cultural heritage images at a fine-grained level. This method can also help contribute to building a novel infrastructure for the increasing scholarship of digital humanities.

The aim of this paper is threefold: first, to review the technological state of the art on tire sensor systems; second, to summarize basic methodologies and explore the potential of tire sensing for intelligent vehicle developments and third, to address challenges in the development of tire sensing systems and inspire future research in this field.

Nowadays, automotive industry is moving toward an intelligent and autonomous driving era with the assistance of sensing technology development, whereas tire-road conditions sensing and utilization are of great interest from the point of view of vehicle dynamics control, vehicle safety and vehicle performance evaluation.

Tire sensing is an emerging technology whereby sensor systems are installed on the tire to provide fundamental insights into tire-road interactions for ground vehicles and wheel robots. In the past two decades, tire sensing systems based on various sensor types have been proposed to offer the possibility to investigate tire-road interactions.

Instrumenting the tire with sensors, especially accelerometers and optical sensors, can sense the tire-road interactions and enhance the vehicle performance. The harsh environment inside tire cavity requires reliable, accurate, low weight, modularized and inexpensive sensors. Challenges, such as the data transmission, power management, lack of physics-based tire models need to be solved before the tire sensor becomes commercially viable for production vehicles.

The purpose of this paper is to determine the corrosion fatigue behavior and mechanism of 6005A aluminum alloy and welded joint.

Electron back-scattered diffraction (EBSD) were adopted to characterize the microstructure of 6005A aluminum alloy and welded joint. Through potentiodynamic polarization, electrochemical impedance spectroscopy (EIS) and corrosion fatigue experiments, the corrosion fatigue behavior and mechanism of 6005A aluminum alloy base metal and welded joint were studied.

The results show that the corrosion fatigue crack initiation of 6005A aluminum alloy base metal and welded joint is mainly caused by the preferential anodic dissolution and hydrogen concentration in the areas with inclusions and welding defects.

The research is an originality study on the corrosion fatigue behavior and mechanism of 6005A aluminum alloy and welded joint.

This study aims to propose a comprehensive causal model to examine the relationships between customer experience and four key factors in brand building, i.e., brand loyalty, brand trust, brand affect and brand involvement. The dimensionality of customer experience in full-service hotel is also particularly examined in relation to brand building.

Three steps of data collection were used: interviews of 50 customers on their experiences of staying full-service hotels, a small survey of 176 hotel guests to establish the measurement scale of customer experience and a major survey of 732 hotel customers in ten major Chinese cities to test the model of brand loyalty.

Customers’ experiences with full-service hotels are proposed to be categorized into functional, affective and social. There is a chain effect from customer experience to brand trust and to brand affect and then to brand loyalty. The brand involvement does moderate relationships between customer experience and brand trust and brand affect but not brand loyalty.

For full-service hotels, social and functional experiences are critical in building brand loyalty, and therefore, they need to be the focal points in the enhancement of customer experience. Also, hoteliers are advised to develop emotional connections between the customers and the hotel brand – an effective way of building trust and affection.

According to the authors’ knowledge, this paper is one of the first few studies to link customer experience to brand loyalty with comprehensive causal effect analysis. This study also contributes to the knowledge of customer experience in the context of the full-service hotel sector.

Innovation through tradition (ITT) is an increasingly important area of research particularly in the creative and cultural industries. The purpose of this study is to develop a process framework of ITT for rural heritage bed and breakfast (B&B) sector and investigate the antecedents and challenges of ITT implementation.

Nine heritage B&Bs displaying successful ITT in Songyang county in China were selected as the research site. Multiple cases of B&B were interviewed, and some observations were conducted. This study adopted the process-oriented reflexive critical incident technique to collect qualitative data and analysed it thematically.

Based on the findings, a five-phase innovation framework is proposed to demonstrate how ITT could be achieved in practice. These phases are idea generation, idea evaluation, initial implementation, continuing implementation and sustaining improvement. Three key antecedents (experience corridors, networks and institutional pressures) of ITT implementation were also identified.

This study has showed that rural heritage B&Bs can differentiate themselves from competition by the means of ITT. This study proposes a process framework for this kind of innovation bringing to light the required steps, the antecedents and key activities which the practitioners should pay great attention. This study highlights the needs for continuing and sustaining innovations in the long term.

This study proposes a novel five-phase process framework of ITT to encompass the innovation activities in heritage setting. For practitioners, this study recommends enhancing the sensing capability of local entrepreneurs through personal travel experience and establishing business networks as the key antecedents of a successful ITT under rural heritage setting.