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Smart fabric keeps you warm and cool

16.02.2019

The concept of smart clothes that can automatically cool or heat their wearer is still in development and is constantly being tested by many developers in many formats. However, material and chemical engineering specialists at the University of Maryland in the US have their own answer to the question of how such smart clothes should be created. Today, the research team has presented a material with unique properties and structure, capable of automatically detecting body temperature and other related features, and then effectively cool or, conversely, heat its wearer according to the situation.

Despite the fact that scientists are still in no hurry to reveal all the most interesting details regarding their new development, it can be understood that the material has really interesting properties - firstly, it is made up of a double fabric system, and secondly, it has the ability to very quickly read the level of thermal radiation emanating from the skin of a person at the moment when his body emits heat or vice versa, seeks to delay it.

As for the double fabric system, it is a test sample in which one part of the fabric has water-repellent properties, and the second, on the contrary, absorbs it - this allows the fabric to be used both for heating and for cooling. Most likely, the finished working concept will be similar to what was previously presented by scientists from the University of California, who developed a jacket that is warm on one side and cool on the other. In addition, it is known that the new material also uses a special artificial intelligence system, thus determining the exact level of heat or cold.

It is worth noting the fact that work on this type of fundamental technology has been carried out by specialists from the University of Maryland over the past five years, and they have very carefully observed what their fellow developers offer. Based on this observation and their own success, they have a very good chance of soon introducing an absolutely complete type of such an automatic and intelligent material for use in the textile industry.

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One method for scaling up deep learning is to distribute a single neural network model across multiple machines and run the computations in parallel. But the large amount of data that must be transferred between computers creates traffic jams, significantly reducing the speed of tasks, Fujitsu explained. In order to take advantage of the full potential of GPUs for high-speed computing, data must be stored in the internal memory of the processors themselves. However, this amount is usually less than the memory capacity of conventional computers, which limits the ability to scale up neural networks.

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The company used the new technology as part of the Caffe open source deep learning platform, measuring the consumption of internal GPU memory. After starting training, the technology analyzes the structure of the neural network and optimizes the order of execution of calculations and the location of data in memory so that the free memory area can be reused. When used in conjunction with AlexNet and VGGNet, image recognition neural networks used for scientific research, Fujitsu's new technology has increased the scale of the neural network by up to 2 times compared to previous counterparts. As such, the amount of internal GPU memory used has been reduced by more than 40%, according to the company.

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