Search results

The purpose of this paper is to improve the online monitoring level of low-frequency oscillation in the power system. A modal identification method of discrete Fourier transform (DFT) curve fitting based on ambient data is proposed in this study.

An autoregressive moving average mathematical model of ambient data was established, parameters of low-frequency oscillation were designed and parameters of low-frequency oscillation were estimated via DFT curve fitting. The variational modal decomposition method is used to filter direct current components in ambient data signals to improve the accuracy of identification. Simulation phasor measurement unit data and measured data of the power grid proved the correctness of this method.

Compared with the modified extended Yule-Walker method, the proposed approach demonstrates the advantages of fast calculation speed and high accuracy.

Modal identification method of low-frequency oscillation based on ambient data demonstrated high precision and short running time for small interference patterns. This study provides a new research idea for low-frequency oscillation analysis and early warning of power systems.

Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain.

In the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS.

The real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities.

The cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management.

The purpose of this paper is to capture the actual structural behavior of the longest timber footbridge in Spain by means of a multi-scale model updating approach in conjunction with ambient vibration tests.

In a first stage, a numerical pre-test analysis of the full bridge is performed, using standard beam-type finite elements with isotropic material properties. This approach offers a first structural model in which optimal sensor placement (OSP) methodologies are applied to improve the system identification process. In particular, the effective independence (EFI) method is used to determine the optimal locations of a set of sensors. Ambient vibration tests are conducted to determine experimentally the modal characteristics of the structure. The identified modal parameters are compared with those values obtained from this preliminary model. To improve the accuracy of the numerical predictions, the material response is modeled by means of a homogenization-based multi-scale computational approach. In a second stage, the structure is modeled by means of three-dimensional solid elements with the above material definition, capturing realistically the full orthotropic mechanical properties of wood. A genetic algorithm (GA) technique is adopted to calibrate the micromechanical parameters which are either not well-known or susceptible to considerable variations when measured experimentally.

An overall good agreement is found between the results of the updated numerical simulations and the corresponding experimental measurements. The longitudinal and transverse Young's moduli, sliding and rolling shear moduli, density and natural frequencies are computed by the present approach. The obtained results reveal the potential predictive capabilities of the present GA/multi-scale/experimental approach to capture accurately the actual behavior of complex materials and structures.

The uniqueness and importance of this structure leads to an intensive study of its structural behavior. Ambient vibration tests are carried out under environmental excitation. Extraction of modal parameters is obtained from output-only experimental data. The EFI methodology is applied for the OSP on a large-scale structure. Information coming from several length scales, from sub-micrometer dimensions to macroscopic scales, is included in the material definition. The strong differences found between the stiffness along the longitudinal and transverse directions of wood lumbers are incorporated in the structural model. A multi-scale model updating approach is carried out by means of a GA technique to calibrate the micromechanical parameters which are either not well-known or susceptible to considerable variations when measured experimentally.

Building on recent smart hospitality systematic reviews and extensive literature analyses, this paper aims to explore recent developments, themes and issues within smart hospitality. It synthesises existing knowledge, extrapolating forward and contributes to the future development of smart hospitality by serving as a reference to enrich academic/industry discussions and stimulate future research.

The research examined 8 recent review articles on smart hospitality and tourism and extracted 145 articles in peer-reviewed sources from Web of Science focussed on smart hospitality. These publications supported in-depth analysis to explore the body of knowledge and develop foresight for the future of smart hospitality within business ecosystems at tourism destinations. It synthesises knowledge and provides the basis for the development of a comprehensive in-depth research agenda in smart hospitality innovations as well as the formulation of agile hospitality ecosystems.

This paper illustrates that smart hospitality introduces disruptive innovations that affect the entire hospitality ecosystem. Smart hospitality takes advantage of smart cities and smart tourism towards establishing agile business ecosystems in networked destinations. Having reviewed the existing literature, the study developed a conceptual framework and introduced a comprehensive future research agenda. This includes the drivers of smart hospitality, namely, customer-centricity, personalisation, individualisation and contextualisation; marketing-driven hospitality excellence and metaverse; as well as operation agility, asset strategy, talent management and supplier interoperation. It also identified the foundations that provide the infostructure for smart hospitality, including ambient intelligence, big data, processes and sustainability, providing the capability blocks to co-create value for all stakeholders in the hospitality ecosystem.

This study conceptualises smart hospitality as a disruptive and innovative power that will affect the competitiveness of hospitality and tourism organisations as part of a comprehensive ecosystem. It identifies the key stakeholders and explores how they can take advantage of emerging developments. This paper proposes the drivers and foundation for future research on smart hospitality. The research provides a conceptual synthesis of the literature and the concepts that have been elaborated. The foundations are effectively the infostructure that enables the drivers to add value to different stakeholders. Key issues are identified to stimulate further research on the area to support smart hospitality development and adoption.

Market demands, especially within the automotive sector, are pushing towards increased product complexity and performance with zero ship parts per million (PPM) requirements. To achieve both quality and performance goals very stringent requirements are being placed on the test manufacturing solution. These requirements lead to conflicts between cost, performance and quality. The purpose of this paper is twofold: first, to investigate the conflicts that exist between quality, performance, and cost, and second, to review current practices and techniques being used in tests to minimise ship PPM.

In the paper a test process development flow chart is presented, along with a review of current methods being used for both defect screening and performance testing. The relationship between test coverage and ship PPM is investigated using established yield models. The cost in terms of gross margin degradation of yield loss at final test to extensive screening and aggressive limits is modelled.

The paper finds that to maintain ship PPM very high levels of test coverage are required – typically test coverage needs to be > 98 per cent. The cost of yield loss to this testing typically matches on a percentage point basis gross margin degradation. Reviewing current test methods shows the need both for extensive defect‐screening techniques for the defective portion of the population, and for optimised guardbanding techniques for performance testing. Weaknesses that exist are the absence of a model to predict outgoing PPM, and the conservative nature of existing guardband techniques for performance testing.

This is a review paper and it serves to highlight both the weaknesses in current practices, and areas where improved models are required.

The purpose of this paper is to estimate total emission during idling of vehicles and validate emission results from real-world data.

Motor Vehicle Emission Simulator (MOVES)2010b emission model is customised for developing country like India and a case study of the Ashram intersection in Delhi has been selected in order to measure the emissions of vehicles during idling.

Results show that 3.997 mg/m3 of hydrocarbon, 1.82 mg/m3 of NOx and 17.688 mg/m3 of carbon monoxide is emitted from the cars, trucks and buses, respectively, at Ashram intersection in one day. As there are 600 intersections throughout Delhi, a total of 2,398.055 mg/m3 of hydrocarbon, 1,087.068 mg/m3 of NOx and 10,612.612 mg/m3 of carbon monoxide is emitted from cars, trucks and buses in a day in all of Delhi.

Knowledge of idling emission and fuel loss is very little for Indian traffic condition during delays.

Earlier most of the research groups have designed and developed hybrid renewable energy system models with technological, scientific and industrial advancement for the energy systems, but slight attention has been paid towards the grid-connected sustainable urban residential energy systems (SUR_eS) for metropolitan cities. The current research wishes to design, model and analyze grid-connected energy system for residential applications for sustainable urban residential energy system. The works aims to explore the potential of the augmented energy system for grid-connected energy system.

The proposed grid-connected SUR_eS are validated for a sample location at New Delhi (India) with a hybrid optimization model for electric renewable (HOMER) software to define and understand the various load profile. It presents the sensitivity analysis approach to validate the design of the proposed energy system.

The obtained results reports the key barriers, proposed model and scenarios for sustainable urban energy system development.

Similar approaches can be replicated to design and develop an independent, self-sustainable cleaner and environmental-friendly energy system in the future scenario for the extension of complex grid infrastructures.

It will assist the stakeholder in solving the complex urban sustainability issues raised due to the shortage of energy.

It will offer a clean and environment friendly sustainable energy resources with reduced carbon emissions. It will benefit sustainable energy resources with a mix of challenges and opportunities, to suggest an approach for implementation of efficient energy policies to optimize the existing and forthcoming energy systems.

The current research offers a design and model to analyze grid-connected energy system sustainable urban residential applications. It explores the potential of the augmented energy system. The proposed model are validated for a sample location with HOMER simulation software to define and understand various scenarios of the multiple load profile. The work presents the sensitivity analysis approach to validate the proposed energy system.

This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation.

A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/optimisation of energy efficiency.

An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add-on to currently used energy management systems.

The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described).

A decision support system has been created as an innovative add-on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved.

For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge-based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.

The purpose of this study was to build and develop mathematical models correlating ambient conditions and electrical energy to the coefficient of performance (COP) of an air-source heat pump (ASHP) water heater. This study also aimed to design a simulation application to compute the COP under different heating up scenarios, and to calculate the mean significant difference under the specified scenarios by using a statistical method.

A data acquisition system was designed with respect to the required sensors and data loggers on the basis of the experimental setup. The two critical scenarios (with hot water draws and without hot water draws) during the heating up cycles were analyzed. Both mathematical models and the simulation application were developed using the analyzed data.

The predictors showed a direct linear relationship to the COP under the no successive hot water draws scenario, while they exhibited a linear relationship with a negative gradient to the COP under the simultaneous draws scenario. Both scenarios showed the ambient conditions to be the primary factor, and the weight of importance of the contribution to the COP was five times more in the scenario of simultaneous hot water draws than in the other scenario. The average COP of the ASHP water heater was better during a heating cycle with simultaneous hot water draws but demonstrated no mean significant difference from the other scenario.

There was a need to include other prediction parameters such as air speed, difference in condenser temperature and difference in compressor temperature, which could help improve model accuracy. However, these were excluded because of insufficient funding for the purchase of additional temperature sensors and an air speed transducer.

The research was conducted in a normal middle-income family home, and all the results were obtained from the collected data from the data acquisition system. Moreover, the experiment was very feasible because the conduction of the study did not interfere with the activities of the house, as occupants were able to carry out their activities as usual.

This paper attempts to justify the system efficiency under different heating up scenarios. Based on the mathematical model, the performance of the system could be determined all year round and the payback period could be easily evaluated. Finally, from the study, homeowners could see the value of the efficiency of the technology, as they could easily compute its performance on the basis of the ambient conditions at their location.

This is the first research on the mathematical modeling of the COP of an ASHP water heater using ambient conditions and electrical energy as the predictors and by using surface fitting multi-linear regression. Further, the novelty is the design of the simulation application for a Simulink environment to compute the performance from real-time data.

This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems.

Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems.

The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support.

Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems.

Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support.

This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making.

This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.