Hypothesis / aims of study Pelvic floor disorders, such as stress urinary incontinence (SUI) and pelvic organ prolapse (POP), are costly in both psychosocial and economic terms and affect more than 25% of the female population [1]. Although the exact mechanisms of POP and SUI are poorly understood, the development of such conditions are likely to alter the pressures acting on the vaginal wall, and thus change the vaginal pressure profile. There is little research on the change in the vaginal pressure profile in response to either surgical correction of POP or as a consequence of pelvic floor muscle training. Recent reviews suggest that pelvic floor muscle training (PFMT) is an effective first line treatment for women with stress urinary incontinence, and mild POP [2]. However, more than 30 % of women are unable to effectively contract their pelvic floor muscles and PFMT is contingent on the exercises being performed correctly [3]. This means increasing the pressure in the region of the pelvic floor muscles (PFM), while keeping abdominal pressure low. This study aimed to develop and test a novel compliant and wireless intra-vaginal pressure sensing device (IVPSD) which is able to record the vaginal pressure profile at rest, during PFMT and everyday activities. Vaginal pressure profiles could be used as biofeedback for PFMT, being able to measure both abdominal and PFM pressure simultaneously, in addition to assessing the vaginal pressure profile preand post-surgery.

Beat the Leak: A new device to measure the vaginal pressure profile

Electricity demand is rising in lockstep with global population growth. The present power system, which is almost a century old, faces numerous issues in maintaining a steady supply of electricity from huge power plants to customers. To meet these issues, the electricity industry has enthusiastically embraced the new smart grid concept proposed by engineers. If we can provide a secure smart grid, this movement will be more useful and sustainable. Machine learning, which is a relatively recent era of information technology, has the potential to make smart grids extremely safe. This paper is a literature survey of the application of machine learning in different areas of smart grids. This paper concludes by mentioning the best machine learning algorithms that can be used in different aspects of the smart grid.

Data-Driven Methods of Machine Learning in modeling the Smart Grids

Wireless communication indoor positioning method in 5G sub-station using deep neural network and location fingerprint algorithm

Wireless networks are becoming ubiquitous and as the cost of equipment decreases and performance increases, it becomes both economically and technologically feasible to deploy wireless networks in power systems and industrial environments for a wide range of applications. They have advantage of providing diverse controlling features through a unified communication platform. Application of such networks in the smart grid/industrial environments is under active research and expected to become an integral part of the power system. This research propose novel technique smart grid communication in wireless 5G networks for monitoring and controlling management. Here the smart grid designing has been done based on wireless communication networks. The smart grid network for renewable energy has been controlled using Stackelberg equilibrium based SCADA (supervisory control and data acquisition) method. The control method based collected data has been monitored for detection of malicious activities in the network using supervised radial basis fuzzy systems. The experimental analysis has been carried out based on control system and network malicious activities. Here the control system based parameters analysed are Scalability of 65%, QoS of 71%, Power consumption of 41%, Network Efficiency of 92%. Then machine learning based malicious activities detection in terms of accuarcy of 96%, network security of 88%, throughput of 94%, Network delay of 41%. Proposed method supports interoperability of multiple types of inverters, is scalable and flexible, and transmits data over a secure communication channel.

/pdf/smart-grid-based-wireless-communication-in-5g-network-for-3kk08bt9.pdf

Smart Grid based Wireless Communication in 5G Network for Monitoring and Control Systems in Renewable Energy Management

The phased array antenna is one of the most significant applications in fifth-generation mobile networks. It is one of the most important applications in fifth-generation networks. An electric power source that powers the whole application, including the antenna's root, is required. Even with the most outstanding design, if the programme does not have a sound power supply system with minimal packet loss and can't Path find performance, it will be rendered ineffective. When seen from the perspective of the multiplex information, Machine Learning comprises a communication network based on the Internet that transmits information to the control centre via the objects (IOT). To put it another way, the proposed communication infrastructure, via the provision of, and the chance micro-grid state to collect, analyze, and two-way communication link control information, offers the chance to resolve the voltage regulation issues. This cutting-edge communication infrastructure, as well as a suggested state estimation filter focused on improving speed and performance in renewable energy production, are both examples of creative communication infrastructure. Current research is focused on analyzing and enumerating a range of energy abundances in the context of smart grids, which are now in their fifth generation. Rather than concentrating on the future development plan, which should be a problem of illusion, it should concentrate on the composition of the future potential of smart grid communications framework. An in-depth investigation to give evidence to the Machine learning will help intelligent networks in the future conduct a thorough evaluation.

https://ijnaa.semnan.ac.ir/article_5285_eb0ba24df57ecca97391c4ab2bf9f7e3.pdf

G Johncy

Papers

An intellectual procurement innovation of smart grid power system with wireless communication networks based on machine learning