High Voltage DC Lithium Battery
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This is the second collaboration between Deep Mind and NHS. Prior to this, Deep Mind worked with the Royal Free Hospital in northern London to monitor the patient's kidney function with a smartphone. Deep Mind co-founder Mustafa Suleyman said the collaboration was the first pure medical research conducted by the company. At the same time, this is the first time the company has applied machine learning to medical projects.
At the heart of the study is the sharing of 1 million eye scans, and Deep Mind researchers will train a machine learning system to better identify early symptoms of visual illness.
"This study is very important, especially the diagnosis of diabetic retinopathy. If you have diabetes, then your chances of becoming blind will increase by 25 times. If we can detect this condition and start treatment as soon as possible, then, 98% of severe visual loss can be avoided," Suleyman said.
Moorfields' collaboration with Deep Mind is due to Pearse Keane, a consulting ophthalmologist at the hospital. On Deep Mind's official website, Pearse Keane explored how to do an eye scan, and then he contacted Suleyman to start the collaborative project. 
The last collaboration between Deep Mind and NHS has led to concerns about privacy. Relatively speaking, the controversy of this cooperation will be smaller because the information shared is anonymous. “This means that no patients can be identified from these scanned images. At the same time, these scans are historical, that is, our findings can be used to improve future medical care, but will not affect the current patient acceptance. Nursing. When the data used in the study, that is, the researchers could not identify the anonymous data of individual patients, then the patient's explicit permission is no longer necessary."
Peng Tee Khaw, director of the Moorfields Eye Disease Research Center, said that the key to this collaboration is that accurate eye scans are rapidly increasing. “The level of sophistication of these scans is incredible and more detailed than the scans of other body parts: we can see very microscopic aspects, the question is how do we deal with such a large amount of data.â€
“To track the history of a patient, I need to use my lifelong experience. When predicting the future of the patient, they depend on my experience. If we can use machine-assisted deep learning, we can do our job better. Because I will have 10,000 life experiences."
High Voltage DC Lithium Battery System
384V High Voltage DC Lithium battery system is manufactured by UFO POWER, designing for telecommunication room, Internet Data Center (IDC), UPS replacement and other core communication areas as an ideal backup power supply. It is equipped with reliable lithium iron phosphate (LiFePO4) battery cell to provide standby power. The lithium battery system consists of 48V LiFePO4 battery modules to form a 384V system in series. The high voltage Lithium battery system uses 19'' Standard Cabinet and the modular design of battery cabinets makes it useful to meet higher energy storage capacities. It can be expanded in parallel according to the need.
The High Voltage DC Lithium Battery System is equipped with smart management system. The unit BMU built in the 48V battery module collects battery parameters, including cell voltage, total voltage, charge/discharge current, capacity, temperature, etc.
Features of High Voltage DC Lithium Battery System
â— Safe LiFePO4 Battery Cells
â— 19'' Standard Cabinet Installation
â— Real-time Monitoring
â— Flexible capacity scalability
â— Wide Application
Google Deep Mind begins to enter the medical field
Google Deep Mind has beaten the human Go championship with Alpha Go, and now it is beginning to enter the medical field. According to reports, Deep Mind and NHS (National Health Service System) have once again collaborated with Moorfields Eye Hospital to develop a machine learning system for identifying visual diseases. Through an eye scan, the system recognizes early symptoms of visual illness and achieves the goal of preventing visual illness in advance.