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IRM-01/02 - miniature PCB power supplies from Mean Well

22.08.2016

Mean Well has expanded its line of compact PCB-mounted AC-DC converters with the new IRM-01 and IRM-02 series with output power of 1W and 2W, respectively.

With the release of new power supplies, the IRM family already has ten series in the most popular power range of 1, 2, 3, 5, 10, 15, 20, 30, 45, 60 W. Power supplies are made in a plastic case, filled with a compound and are available in two versions: DIP-mounting and SMD-mounting. Modules for SMD mounting have an S at the end of the name, for example IRM-01-12S.

The IRM-01 and IRM-02 power supplies are characterized by their miniature size and ultra-low power consumption at idle (<75 mW). The products have an input/output insulation dielectric strength of 3 kV and are operable in a wide input voltage range of 85-305 V (AC)/120-430 V (DC) at a temperature of -30?C...+85?C. In terms of electromagnetic compatibility, the power supplies meet the requirements of EN55022 for class B (without additional filter elements).

The new series of power supplies IRM-01 and IRM-02 are suitable for use in various industrial devices, automation equipment, IT equipment and other devices and systems where low power and compact size are required.

Main technical parameters of IRM-01 and IRM-02 series:

Output power 1 and 2 W
Output voltage from a range: 3,3; 5; 9; 12; fifteen; 15 V
Board Mount Type: DIP/SMD
Input voltage range 85-305V (AC) / 120-430V (DC)
Operating temperature range: -30°С...+85°С (with dependence above +70°С)
Short circuit, overload and overvoltage protection
Dimensions (LxWxH): 33,7 x 22,2 x 15 mm (DIP); 33,7 x 22,2 x 16mm (SMD)

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Predicting color is not that difficult: for example, if a light wave reaches a wavelength of 510 nm, then most people will say that it is green. But figuring out how a particular molecule smells is much more difficult. 22 teams of scientists have created a set of algorithms that can predict the smell of various molecules based on their chemical structure. The full range of practical applications of the program remains to be seen, but the developers hope that, first of all, it will help perfumers, pharmacists and food workers develop new, unique combinations of smells.

The work began with a recent study by Leslie Vosshull and colleagues at the Rockefeller University in New York, in which 49 volunteers had to guess the smell of 467 odorants. For each of them, a comparison system of 19 basic patterns was developed: the subjects said whether the smell was similar to fish or garlic, assessed the intensity and individual pleasantness of the aroma. As a result, a catalog was created with more than a million cells characterizing certain odorous molecules.

When computational biologist Pablo Meyer found out about this, he immediately saw the study as an opportunity to test whether a computer system could predict how people would evaluate smells. Despite the fact that researchers have discovered about 400 odor receptors in the human body, it remains a mystery to scientists how exactly they work together so that a person can distinguish even subtle shades of odors.

In 2015, Meyer and colleagues launched the DREAM Olfaction Prediction Challenge. The participants of the competition received at their disposal the same rating tables of volunteers that describe odors, along with the chemical structure of the molecules that produce them. In addition, the participants were provided with a database of 4800 descriptions for each individual molecule - its atoms, their relative position, general geometry, which eventually amounted to about 2 million data points. Ultimately, the data should be used to train computer programs to recognize odors based on structural information.

Twenty-two teams from all over the world entered the competition, and while many did a good job, two teams stand out in particular. The Michigan State team, led by Yang Fang Guang, was the best at coming up with an algorithm for predicting the smell of individual things. Another team from the University of Arizona, led by Richard Gerkin, was the best at training the program to average odor ratings across the entire sample. Meyer reports this in an article published in the journal Science.

Of course, many scientists are skeptical about the developments, saying that the work done, although it makes a significant contribution to science, is still a rather primitive selection, and 19 descriptive elements for the entire spectrum of smells in nature are clearly very, very few.

Alternative studies with volunteers used 80 or more of these criteria to verbally evaluate different odors. It is not clear whether the existing algorithm will be able to correctly predict the odor score if it has to face such an array of information. So, to date, the perception of smells remains a mystery for both physicians and engineers.

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