Search results

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

The purpose of this paper is to research and design a feasible automatic identification and data capture (AIDC) system for a manufacturing small to medium enterprise (SME) that is able to facilitate the flow of accurate and real‐time data throughout the manufacturing process.

The processes and operations conducted at a manufacturing SME were critically analysed in order to identify areas, where the use of an AIDC system could be used to improve the efficiency and visibility of the processes throughout manufacture. The areas for improvement could then be identified and solved through specific applications and/or systems of which a cost benefit analysis could be conducted.

Significant cost savings are found through the implementation of a radio frequency identification (RFID) system based on the reduction of safety stock, the elimination of manual job tracking and the reduction of the manual input and written data throughout the process.

The read range of the technology outlined in this project was found to be limited due to the metal interference of the products, which should be aimed to be improved through the detection of other RFID transponders or a better adhesive medium used.

The most common limitations were found to be the lack of IT infrastructure, limited knowledge on the benefits of the system and also cultural resistance to change. However, appropriate training is to be provided to overcome any problems.

AIDC systems utilising data carrier technologies have been successfully implemented within many large multinational organisations but research into the implementation of AIDC systems within SMEs is far more limited.

Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.

The emergence of radio frequency identification (RFID) technology promises enormous opportunities to shift business process automation up to the wire level. The purpose of this paper is to explore the methodology of incorporating business logics into RFID edge systems, and thereby facilitate the business process automation in the RFID‐applied environment.

Following the object‐oriented modelling perspective, concepts of classes, instances are deployed to characterise the runtime context of RFID business scenarios; event patterns are used to aggregate RFID tag read events into business meaningful events; and business rules are established to automate business transactions according to the elicited events.

The paper has emphasised the synergy between business process automation and automatic data acquisition, and has identified the inter‐relations between RFID tag read events, application‐level events, business rules, and business operations. The reported research has demonstrated a feasible scheme of incorporating business process control and automation into RFID‐enabled applications.

The paper analyses the characteristics of RFID data and event handling in relation to business rule modelling and process automation. The features of event‐relied awareness, context containment and overlapping, etc. are all captured and described by the proposed object‐oriented business model. The given data‐driven RFID middleware architecture can serve as one reference architecture for system design and development. Hence, the paper plays an important role in connecting automatic data acquisition and existing business processes, and thereby bridges the physical world and the digital world.

The pervasiveness of mobile devices has led to the permanent use of their new features by the crowd to perform different tasks. The purpose of this paper is to exploit this massive consumption of new information technologies supported by the concept of crowdsourcing in a governmental context to access citizens as a source of ideas and support. The aim is to find out how crowdsourcing combined with the new technologies can constitute a great force to enhance the performance of the suspect investigation process.

This paper provides a structured view of a suspect investigation framework, especially based on the image processing techniques, including the automatic face analysis. This crowdsourcing framework is mainly based on the personal description as an identification technique to facilitate the suspect investigation and the use of MongoDB as a document-oriented database to store the information.

The case study demonstrates that the proposed framework provides satisfying results in each step of the identification process. The experimental results show how the combination between the crowdsourcing concept and the mobile devices pervasiveness has fruitfully strengthened the identification process with the use of automatic face analysis techniques.

A review of the literature has shown that previous work has focused mainly on the presentation of forensic techniques that can be used in the investigation process steps. However, this paper implements a complete framework whose whole strength is based on the crowdsourcing concept as a new paradigm used by institutions to solve many organizational problems.

This work analyzes a pharmaceutical supply chain (PSC) in terms of supply chain visibility (SCV). The current good distribution practice (GDP) guideline demands increased visibility from firms. The purpose of this paper is to propose a solution for SCV enhancements based on automatic identification (Auto-ID) technologies.

The authors qualitatively analyze data from ten case studies of actors in a PSC. A review of Auto-ID technologies supports the derivation of solutions to enhance SCV.

This work shows that the functionalities of Auto-ID technologies offered by current practical monitoring solutions and challenges created by the GDP guideline necessitate further SCV enhancements. To enhance SCV, the authors propose three solutions: securPharm with passive radio frequency identification tags, transport containers with sensor nodes, and an SCV dashboard.

This study is limited to a PSC in Germany and is therefore not intended to be exhaustive. Thus, the results serve as a foundation for further analyses.

This study provides an overview of the functionality of Auto-ID technologies. In juxtaposition with the influence of the GDP guideline, the use of our Auto-ID-based solutions can help to enhance SCV.

This work analyzes a PSC in Germany, with consideration given to the influence of current legislation. Based on a multiple-case-study design, the authors derive three Auto-ID-based solutions for enhancing SCV.

This paper investigates the semantic association mechanisms of weak demand signals that facilitate innovative product development in terms of conceptual and temporal precedence, despite their inherent ambiguity and uncertainty.

To address this challenge, a domain ontology approach is proposed to construct a customer demand scenario-based framework that eliminates the blind spots in weak demand signal identification. The framework provides a basis for identifying such signals and introduces evaluation indices, such as depth, novelty and association, which are integrated to propose a three-dimensional weak signal recognition model based on domain ontology that outperforms existing research.

Empirical analysis is carried out based on customer comments of new energy vehicles on car platform such as “Auto Home” and “Bitauto”. Results demonstrate that in terms of recognition quantity, the three-dimensional weak demand signal recognition model, based on domain ontology, can accurately identify six demand weak signals. Conversely, the keyword analysis method exhibits a recognition quantity of four weak signals; in terms of recognition quality, the three-dimensional weak demand signal recognition model based on domain ontology can exclude non-demand signals such as “charging technology”, while keyword analysis methods cannot. Overall, the model proposed in this paper has higher sensitivity.

This paper proposes a novel method for identifying weak demand signals that considers the frequency of the signal's novelty, depth and relevance to the target demand. To verify its effectiveness, customer review data for new energy vehicles is used. The results provide a theoretical reference for formulating government policies and identifying weak demand signals for businesses.

Internet has changed radically in the way people interact in the virtual world, in their careers or social relationships. IoT technology has added a new vision to this process by enabling connections between smart objects and humans, and also between smart objects themselves, which leads to anything, anytime, anywhere, and any media communications. IoT allows objects to physically see, hear, think, and perform tasks by making them talk to each other, share information and coordinate decisions. To enable the vision of IoT, it utilizes technologies such as ubiquitous computing, context awareness, RFID, WSN, embedded devices, CPS, communication technologies, and internet protocols. IoT is considered to be the future internet, which is significantly different from the Internet we use today. The purpose of this paper is to provide up-to-date literature on trends of IoT research which is driven by the need for convergence of several interdisciplinary technologies and new applications.

A comprehensive IoT literature review has been performed in this paper as a survey. The survey starts by providing an overview of IoT concepts, visions and evolutions. IoT architectures are also explored. Then, the most important components of IoT are discussed including a thorough discussion of IoT operating systems such as Tiny OS, Contiki OS, FreeRTOS, and RIOT. A review of IoT applications is also presented in this paper and finally, IoT challenges that can be recently encountered by researchers are introduced.

Studies of IoT literature and projects show the disproportionate importance of technology in IoT projects, which are often driven by technological interventions rather than innovation in the business model. There are a number of serious concerns about the dangers of IoT growth, particularly in the areas of privacy and security; hence, industry and government began addressing these concerns. At the end, what makes IoT exciting is that we do not yet know the exact use cases which would have the ability to significantly influence our lives.

This survey provides a comprehensive literature review on IoT techniques, operating systems and trends.

The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.

The purpose of this paper is to present an exploratory study which aims to assess the potential use of 3D mapping of buildings and construction sites using unmanned aerial system (UAS) imagery for supporting the construction management tasks.

The case studies were performed in two different residential construction projects. The equipment used was a quadcopter equipped with digital camera and GPS that allow for the registry of geo-referenced images. The Pix4D Mapper and PhotoScan software were used to generate the 3D models. The study sought to examine three main constructs related to the 3D mapping developed: the easiness of development, the quality of the models in accordance with the proposed use and the usefulness and limitations of the mapping for construction management purposes.

The main contributions of this study include a better understanding of the development process of 3D mapping from UAS imagery, the potential uses of this mapping for construction management and the identification of barriers and benefits related to the application of these emerging technologies for the construction industry.

The importance of the study is related to the initiative to identify and evaluate the potential use of 3D mapping from UAS imagery, which can provide a 3D view of the construction site from different perspectives, for construction management tasks applications, trying to bring positive contributions to this knowledge area.