search.noResults

search.searching

dataCollection.invalidEmail
note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
PROFESSOR GAOYONG LUO - R&D MANAGER, SHJ MEDICAL GAS SPECIALISTS, UK SYSTEM OPERATIONS


Machine learning for medical gas systems


SHJ Medical Gas Specialists has developed a real-time machine learning system for optimal operations of medical gas systems, which uses big data analytics with edge and cloud computing to save energy costs.


Compressed air is essential to a wide range of industries and highly specialised applications such as medical gas systems (MGS), where it is a particularly critical resource. Hospitals rely on air compressors and dryers as well as traditional gas alarm panels for a host of functions ranging from facility operation to patient care. However, compressed air is the most expensive service on site, costing many times the price of electricity, and is often generated inefficiently and then subsequently wasted.1 This may be attributed to the fact that


there is no available technology or method that can be applied to allow real-time monitoring and intelligent control of MGS, particularly the energy-efficient and reliable operation of medical air plant. The current control systems available, which mostly use fixed speed compressors, tend to operate inefficiently and without optimisation. Research has indicated that significant


energy costs can be made through optimisation of both the production facility and system level.2


However, that can


only be achieved by considering the compressed air system as a whole and deciding whether current alarm and monitoring methods can be upgraded to an intelligent system to suit next- generation needs. Enhanced energy efficiency will depend on system automation, which is in the process of undergoing a major transformation.3


Advanced computation and


communications technologies are reaching such a level of maturity that designers can make dramatic changes in the way they design their automatic control systems. A major shift from dedicated mechanisms to cyber-physical systems means that we are no longer constrained by the mechanical and electrical design of a machine. Instead, machines in which the mechanism’s motions are defined by sensors/actuators and control software provide significant opportunities for flexible manufacturing, adaptive throughput, energy management and an increase in machine lifetime value. The resulting cost savings and


competitive advantages are essential as the evolution and convergence of many new technologies - mechatronic systems,


Gaoyong Luo Gaoyong Luo is research and development manager at SHJ


Medical Gas Specialists. He is a professor and has been the Field Chair of Electronics Information and Communication Engineering at Guangzhou University. He has a Ph.D. in


electrical engineering from Brunel University, UK. He has taught thousands of students and supervised the work of masters and Ph.D. students. His main research interests are in the field of


wavelets, artificial intelligence, spread spectrum communications, wireless positioning, remote sensing and audio coding, with expertise in control and modulation theory and applications to automation and communication systems. Dr. Luo has published over


100 referred journal/conference papers and many patents. He is the author of Wavelets in Engineering Applications.


40


controllers, edge and cloud computing, big data, machine learning and the Internet of Things (IoT) – develop. They provide the basis for increasing the self- awareness of the machine, allowing it to optimise its own performance for given duty cycles, diagnose and compensate for non-catastrophic faults, and coordinate operation with other machines with minimal input from the operator. The need to modernise MGS through


a greater focus on technological innovation to improve reliability and security, to maximise operating profits and productivity, and to minimise energy consumption, has now been established. To meet the emerging challenges and opportunities, developing expertise in data storage, communications, IoT, energy efficiency and healthcare will help to supercharge a compound technological breakthrough to build a new design method. Its implementation is being supported by pioneering work in the prototyping and production of real-time machine learning systems over IoT networks. These machines are enabled to


understand and learn from the data so that, by real-time processing of measurement data provided by dedicated sensors installed in the machine, the system can enable autonomous decision making based on online diagnosis. This leads to ever- increasing machine reliability, with the goal of achieving zero defects, together with higher productivity and efficiency.


IFHE DIGEST 2021


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100  |  Page 101  |  Page 102  |  Page 103  |  Page 104  |  Page 105  |  Page 106  |  Page 107  |  Page 108  |  Page 109  |  Page 110  |  Page 111  |  Page 112  |  Page 113  |  Page 114  |  Page 115  |  Page 116