3,146 research outputs found
Developing A Road Freight Transport Performance Measurement System To Drive Sustainability:An Empirical Study Of Egyptian Road Freight Transport Companies
While several road freight performance measurement systems have been developed, only a limited number of quantified performance measurement frameworks encompassing diverse sets of performance metrics from multiple sustainable perspectives are available on a technological platform. These sets of metrics could be integrated as crucial performance indicators for assessing the operational performance of various road freight transport companies. These indicators include fuel efficiency, trip duration, vehicle loading, and cargo capacity. The objective of this research is to construct a conceptual road freight performance measurement framework that comprehensively incorporates performance elements from sustainable viewpoints (economic, environmental, and social), leveraging technology to measure the performance of road freight transport companies. This proposed framework aims to aid these companies in gauging their performance using technology, thus enhancing their operations towards sustainability.Within the road freight transport sector, several challenges exist, with congestion, road infrastructure maintenance, and driver training and qualifications being particularly pressing issues. The developed performance measurement framework offers the means for companies to evaluate the effects of technology integration on vehicles and overall performance. This allows companies to measure their performance from an operational standpoint rather than solely a strategic one, thereby identifying areas requiring improvement. Egypt was chosen as the empirical study location due to its relatively low level of technological integration within its road freight sector.This thesis employs an explanatory mixed methods approach, encompassing four distinct phases. The first phase entails a review to formulate the proposed theoretical performance measurement framework. Subsequently, the second phase involves conducting semi-structured interviews using a Delphi method to both develop a conceptual performance measurement framework and explore the present state of Egypt's road freight transport sector. Following this, the third phase encompasses surveys based on the results derived from Delphi analysis, involving diverse participants from the road freight transport industry. The aim is to validate the developed performance measurement framework through an empirical study conducted in Egypt. Lastly, the fourth phase centres around organizing focus groups involving stakeholders within road freight transport companies. The goal here is to propose a roadmap for implementing the developed road freight transport performance measurement framework within the Egyptian context.The primary theoretical contribution of this research is the development of a road freight transport performance measurement framework that integrates the three sustainability dimensions with technology. Additionally, this study offers practical guidance for the application of the developed framework in various countries and contexts. From a practical standpoint, this research aids road freight transport managers in evaluating their operational performance, thereby identifying challenges, devising action plans, and making informed decisions to mitigate these issues and enhance sustainability-oriented performance. Ultimately, the developed road freight transport performance measurement framework is poised to promote performance measurement aligned with technology, fostering progress towards achieving the sustainable development goals by 2030
Probabilistic design of support structures for offshore wind turbines by means of non-Gaussian spectral analysis
Offshore wind energy is of special importance in order to meet the ambitious goals to produce climate-neutral energy. Therefore, an accelerated installation of offshore wind turbines is required. The design is to be achieved with respect to standards and guidelines. Especially probabilistic design methods allow an accurate and economic structural design.
Not only the environmental conditions vary during the lifetime, but the short-term loads are also subject of random scattering. For the design of offshore wind turbines, the required load simulations are usually carried out in time domain. In comparison, it is less time-consuming to obtain loads by means of frequency-domain analysis. This is very beneficial for the probabilistic design which requires significantly more load simulations in time domain. However, non-linearities and time-variant behaviour of offshore wind turbines cannot be represented well during the load simulation in frequency domain. Extreme loads and fatigue loads can be calculated by means of frequency-domain analysis.
The determination of the distribution functions of extreme values is well established on a theoretical background. As for the fatigue design, different empirical models exist which describe the distribution function of fatigue loads on the basis of frequency-domain analysis. In this thesis, a new model is introduced which leads to more accurate results.
Since frequency-domain analysis is not always suitable, the transformation of signals given in frequency domain is required to generate respective random time series. As for the design of offshore wind turbines, only limited recommendations are stated in standards on how to carry out this transformation. Detailed analysis shows that accurate results with respect to wave-induced loads are also obtained for coarser discretisation of spectra. The resulting loads and their statistical properties are still accurate, while the numerical effort can be reduced in comparison to the stated recommendations.
On the basis of theoretical findings, time series from load simulations of offshore wind turbine are analysed regarding their spectral properties. Investigations are carried out to evaluate the agreement between the extreme load and fatigue loads which are either simulated or calculated on the basis of the spectral properties. It is also shown that currents within sea states lead to increased fatigue loads
Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems
This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems
Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality
As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel.
In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach.
Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties.
Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel
Human Activity Recognition and Fall Detection Using Unobtrusive Technologies
As the population ages, health issues like injurious falls demand more attention. Wearable devices can be used to detect falls. However, despite their commercial success, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this thesis, a monitoring system consisting of two 24×32 thermal array sensors and a millimetre-wave (mmWave) radar sensor was developed to unobtrusively detect locations and recognise human activities such as sitting, standing, walking, lying, and falling. Data were collected by observing healthy young volunteers simulate ten different scenarios. The optimal installation position of the sensors was initially unknown. Therefore, the sensors were mounted on a side wall, a corner, and on the ceiling of the experimental room to allow performance comparison between these sensor placements. Every thermal frame was converted into an image and a set of features was manually extracted or convolutional neural networks (CNNs) were used to automatically extract features. Applying a CNN model on the infrared stereo dataset to recognise five activities (falling plus lying on the floor, lying in bed, sitting on chair, sitting in bed, standing plus walking), overall average accuracy and F1-score were 97.6%, and 0.935, respectively. The scores for detecting falling plus lying on the floor from the remaining activities were 97.9%, and 0.945, respectively. When using radar technology, the generated point clouds were converted into an occupancy grid and a CNN model was used to automatically extract features, or a set of features was manually extracted. Applying several classifiers on the manually extracted features to detect falling plus lying on the floor from the remaining activities, Random Forest (RF) classifier achieved the best results in overhead position (an accuracy of 92.2%, a recall of 0.881, a precision of 0.805, and an F1-score of 0.841). Additionally, the CNN model achieved the best results (an accuracy of 92.3%, a recall of 0.891, a precision of 0.801, and an F1-score of 0.844), in overhead position and slightly outperformed the RF method. Data fusion was performed at a feature level, combining both infrared and radar technologies, however the benefit was not significant. The proposed system was cost, processing time, and space efficient. The system with further development can be utilised as a real-time fall detection system in aged care facilities or at homes of older people
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
- …