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Quote of the day
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You don’t have to apologise if the answer is no! Be upfront & honest when you say it. Do it for self-care. Many can’t say no because they don’t want to appear rude or ill-mannered; & they’re concerned about the opinion of others. This is harmful behaviour. You’ve got to stop it!
Are you guilty of this? Always criticizing others because you’re impatient? You don’t tolerate those who behave differently. You expect people to behave like you. When we focus on someone else’s flaw, we stop seeing the person. We just see their mistakes. Stop this bad habit!
No matter how much you regret it or worry about what happened, the past is over. You can’t change a thing. But you can come to terms with the fact that everything happens for a reason. Live in the present where the Almighty has given you the options to restore calm to your heart!
The Complete Nonverbal Communication Course – Body Language
Nonverbal Communication for Business, Public Speaking and Life – Display Confidence and High Self Esteem
Nonverbal communication can be the most memorable and most powerful way of communicating with other human beings. Whether you are meeting one-on-one with a friend, sitting in a business meeting, or giving a presentation, you need to know ho to use nonverbal communication to yuor benefit.Who this course is for:
Rapid changes in the marine industry due to the introduction and advancements within telecommunications are predicted, and it has been suggested that the vast majority of commercial vessels will be broadband capable within the very near future. Increased data transfer rates are expected to lead to a change in the industry’s approach to, and desired for, optimised Prognostics and Health Management (PHM) systems for fault detection, isolation and prediction of marine diesel engines.
An optimised stand-alone PHM solution for small to medium-sized marine diesel engines could provide improved energy efficiency, which could result in cost and environmental benefits. If the state of health or condition of a system, subsystem or component is known, condition-based maintenance can be carried out, and system design optimisation can be achieved thereby reducing the total cost of ownership and efficiency of the system.
The focus of the proposed project is to design and develop IoT solutions for machine learning-based predictive maintenance with medium-sized marine diesel engines. The design of such systems will rely on vibration and acoustic sensors as primary data sources, and machine learning models for predictive maintenance and fault. These systems’ aim is to detect future engine failures and help scheduling maintenance in advance.
Furthermore, this project will also explore the utilisation of energy harvesting solutions which can potentially lead to the development of a battery-free predictive maintenance system.
The supervision team for the PhD is Dr Domenico Balsamo and Dr Rishad Shafik, experts in hardware and software design for embedded systems and IoT. Royston Diesel Power Company.
You should have, or expect to achieve, at least a 2:1 Honours degree, or international equivalent in Electrical and Electronic Engineering or closely related discipline.
– attach a covering letter and CV. The covering letter must state the title of the studentship, quote reference code ENG069 and state how your interests and experience relate to the project
Rapid changes in the marine industry due to the introduction and advancements within telecommunications are predicted, and it has been suggested that the vast majority of commercial vessels will be broadband capable within the very near future. Increased data transfer rates are expected to lead to a change in the industry’s approach to, and desired for, optimised Prognostics and Health Management (PHM) systems for fault detection, isolation and prediction of marine diesel engines.
An optimised stand-alone PHM solution for small to medium-sized marine diesel engines could provide improved energy efficiency, which could result in cost and environmental benefits. If the state of health or condition of a system, subsystem or component is known, condition-based maintenance can be carried out, and system design optimisation can be achieved thereby reducing the total cost of ownership and efficiency of the system.
The focus of the proposed project is to design and develop IoT solutions for machine learning-based predictive maintenance with medium-sized marine diesel engines. The design of such systems will rely on vibration and acoustic sensors as primary data sources, and machine learning models for predictive maintenance and fault. These systems’ aim is to detect future engine failures and help scheduling maintenance in advance.
Furthermore, this project will also explore the utilisation of energy harvesting solutions which can potentially lead to the development of a battery-free predictive maintenance system.
The supervision team for the PhD is Dr Domenico Balsamo and Dr Rishad Shafik, experts in hardware and software design for embedded systems and IoT. Royston Diesel Power Company.
You should have, or expect to achieve, at least a 2:1 Honours degree, or international equivalent in Electrical and Electronic Engineering or closely related discipline.
– attach a covering letter and CV. The covering letter must state the title of the studentship, quote reference code ENG069 and state how your interests and experience relate to the project