1
General Chat / Re: Can you guess the lyrics of this song?
« Last post by Art on Today at 03:43:45 am »OK, I'll answer your question.
YES
YES
Recent Posts
2
General Chat / Re: Why doesn't nature's cells consume all of Earth?
« Last post by LOCKSUIT on Today at 12:26:52 am »No competing for limited territory! :P
Maybe individuals populated lots before a multi-celled blob saturated all soil as an immortal jellyfish blob pumpkin. They were like, eh!, attackkk!! Nibble nibble, then the pumpkin disappeared and left were the mites.
Maybe individuals populated lots before a multi-celled blob saturated all soil as an immortal jellyfish blob pumpkin. They were like, eh!, attackkk!! Nibble nibble, then the pumpkin disappeared and left were the mites.
3
General Chat / Re: Why doesn't nature's cells consume all of Earth?
« Last post by WriterOfMinds on June 17, 2018, 08:48:24 pm »No type of organism consumes infinite resources because there is always some limiting factor on its ability to grow in numbers. Here are some examples:
*When a species' population grows, it is easier for that species' predators to find and catch a member.
*Disease spreads more easily through a large population in a small space.
*Eventually the population starts to run out of food. The lack of food slows reproduction and/or causes outright starvation.
*Members of the same species compete for limited territory, and may harm or kill each other, or limit one another's reproductive opportunities.
*Some species have relatively benign, socially enforced limits on reproduction (e.g. in a pack of wolves, there is typically only one pair that breeds at a time).
Not all of these considerations apply to microbes, but some do. If any species of microbes started to get close to consuming all the available resources in an area, they would either begin to starve and die back, or be eaten in large numbers by predators attracted to their surplus, or simply stop growing because there is not enough room -- no more suitable habitat in the form of soil-grain surface area.
*When a species' population grows, it is easier for that species' predators to find and catch a member.
*Disease spreads more easily through a large population in a small space.
*Eventually the population starts to run out of food. The lack of food slows reproduction and/or causes outright starvation.
*Members of the same species compete for limited territory, and may harm or kill each other, or limit one another's reproductive opportunities.
*Some species have relatively benign, socially enforced limits on reproduction (e.g. in a pack of wolves, there is typically only one pair that breeds at a time).
Not all of these considerations apply to microbes, but some do. If any species of microbes started to get close to consuming all the available resources in an area, they would either begin to starve and die back, or be eaten in large numbers by predators attracted to their surplus, or simply stop growing because there is not enough room -- no more suitable habitat in the form of soil-grain surface area.
4
General Chat / Re: Can you guess the lyrics of this song?
« Last post by LOCKSUIT on June 17, 2018, 06:17:59 pm »Oh I know why no one is posting....
Crap....
Gotta wait a few days then. :D
Quote
Once you're !done!, say so, but don't post until everyone else is done too, i.e. in a few days.
Crap....
Gotta wait a few days then. :D
5
General Chat / Re: Why doesn't nature's cells consume all of Earth?
« Last post by ivan.moony on June 17, 2018, 04:03:31 pm »Life on earth uses Sun heat as a power source to produce organic compounds from compounds found on the Earth (Stephen Hawking).
6
General Chat / Re: Why doesn't nature's cells consume all of Earth?
« Last post by Art on June 17, 2018, 02:21:52 pm »If you just stop for a minute and look around you...
Everything you see and touch came from the Earth. Cars, houses, computers, silverware, glass, plastic...everything in your life came from the Earth.
And if left alone long enough, without humans, it would go back to the Earth once more!
Everything you see and touch came from the Earth. Cars, houses, computers, silverware, glass, plastic...everything in your life came from the Earth.
And if left alone long enough, without humans, it would go back to the Earth once more!
7
Robotics News / Re: Festo
« Last post by 8pla.net on June 17, 2018, 12:13:28 pm »Wonder what the springs are for?
8
AI in Film and Literature. / Re: Automata, the series
« Last post by 8pla.net on June 17, 2018, 12:03:52 pm »I most enjoyed the rotary analog.
9
Robotics News / MIT engineers build smart power outlet
« Last post by Tyler on June 17, 2018, 12:00:26 pm »MIT engineers build smart power outlet
15 June 2018, 2:30 pm
Have you ever plugged in a vacuum cleaner, only to have it turn off without warning before the job is done? Or perhaps your desk lamp works fine, until you turn on the air conditioner that’s plugged into the same power strip.
These interruptions are likely “nuisance trips,” in which a detector installed behind the wall trips an outlet’s electrical circuit when it senses something that could be an arc-fault — a potentially dangerous spark in the electric line.
The problem with today’s arc-fault detectors, according to a team of MIT engineers, is that they often err on the side of being overly sensitive, shutting off an outlet’s power in response to electrical signals that are actually harmless.
Now the team has developed a solution that they are calling a “smart power outlet,” in the form of a device that can analyze electrical current usage from a single or multiple outlets, and can distinguish between benign arcs — harmless electrical spikes such as those caused by common household appliances — and dangerous arcs, such as sparking that results from faulty wiring and could lead to a fire. The device can also be trained to identify what might be plugged into a particular outlet, such as a fan versus a desktop computer.
The team’s design comprises custom hardware that processes electrical current data in real-time, and software that analyzes the data via a neural network — a set of machine learning algorithms that are inspired by the workings of the human brain.
In this case, the team’s machine-learning algorithm is programmed to determine whether a signal is harmful or not by comparing a captured signal to others that the researchers previously used to train the system. The more data the network is exposed to, the more accurately it can learn characteristic “fingerprints” used to differentiate good from bad, or even to distinguish one appliance from another.
Joshua Siegel, a research scientist in MIT’s Department of Mechanical Engineering, says the smart power outlet is able to connect to other devices wirelessly, as part of the “internet of things” (IoT). He ultimately envisions a pervasive network in which customers can install not only a smart power outlet in their homes, but also an app on their phone, through which they can analyze and share data on their electrical usage. These data, such as what appliances are plugged in where, and when an outlet has actually tripped and why, would be securely and anonymously shared with the team to further refine their machine-learning algorithm, making it easier to identify a machine and to distinguish a dangerous event from a benign one.
“By making IoT capable of learning, you’re able to constantly update the system, so that your vacuum cleaner may trigger the circuit breaker once or twice the first week, but it’ll get smarter over time,” Siegel says. “By the time that you have 1,000 or 10,000 users contributing to the model, very few people will experience these nuisance trips because there’s so much data aggregated from so many different houses.”
Siegel and his colleagues have published their results in the journal Engineering Applications of Artificial Intelligence. His co-authors are Shane Pratt, Yongbin Sun, and Sanjay Sarma, the Fred Fort Flowers and Daniel Fort Flowers Professor of Mechanical Engineering and vice president of open learning at MIT.
Electrical fingerprints
To reduce the risk of fire, modern homes may make use of an arc fault circuit interrupter (AFCI), a device that interrupts faulty circuits when it senses certain potentially dangerous electrical patterns.
“All the AFCI models we took apart had little microprocessors in them, and they were running a regular algorithm that looked for fairly primitive, simple signatures of an arc,” Pratt says.
Pratt and Siegel set out to design a more discerning detector that can discriminate between a multitude of signals to tell a benign electrical pattern from a potentially harmful one.
Their hardware setup consists of a Raspberry Pi Model 3 microcomputer, a low-cost, power-efficient processor which records incoming electrical current data; and an inductive current clamp that fixes around an outlet’s wire without actually touching it, which senses the passing current as a changing magnetic field.
Between the current clamp and the microcomputer, the team connected a USB sound card, commodity hardware similar to what is found in conventional computers, which they used to read the incoming current data. The team found such sound cards are ideally suited to capturing the type of data that is produced by electronic circuits, as they are designed to pick up very small signals at high data rates, similar to what would be given off by an electrical wire.
The sound card also came with other advantages, including a built-in analog-to-digital converter which samples signals at 48 kiloherz, meaning that it takes measurements 48,000 times a second, and an integrated memory buffer, enabling the team’s device to monitor electrical activity continuously, in real-time.
In addition to recording incoming data, much of the microcomputer’s processing power is devoted to running a neural network. For their study, they trained the network to establish “definitions,” or recognize associated electrical patterns, produced by four device configurations: a fan, an iMac computer, a stovetop burner, and an ozone generator — a type of air purifier that produces ozone by electrically charging oxygen in the air, which can produce a reaction similar to a dangerous arc-fault.
The team ran each device numerous times over a range of conditions, gathering data which they fed into the neural network.
“We create fingerprints of current data, and we’re labeling them as good or bad, or what individual device they are,” Siegel says. “There are the good fingerprints, and then the fingerprints of the things that burn your house down. Our job in the near-term is to figure out what’s going to burn down your house and what won’t, and in the long-term, figure out exactly what’s plugged in where.”
“Shifting intelligence”
After training the network, they ran their whole setup — hardware and software — on new data from the same four devices, and found it was able to discern between the four types of devices (for example, a fan versus a computer) with 95.61 percent accuracy. In identifying good from bad signals, the system achieved 99.95 percent accuracy — slightly higher than existing AFCIs. The system was also able to react quickly and trip a circuit in under 250 milliseconds, matching the performance of contemporary, certified arc detectors.
Siegel says their smart power outlet design will only get more intelligent with increasing data. He envisions running a neural network over the internet, where other users can connect to it and report on their electrical usage, providing additional data to the network that helps it to learn new definitions and associate new electrical patterns with new appliances and devices. These new definitions would then get shared wirelessly to users’ outlets, improving their performance,and reducing the risk of nuisance trips without compromising safety.
“The challenge is, if we’re trying to detect a million different devices that get plugged in, you have to incentivize people to share that information with you,” Siegel says. “But there are enough people like us who will see this device and install it in their house and will want to train it.”
Beyond electrical outlets, Siegel sees the team’s results as a proof of concept for “pervasive intelligence,” and a world made up of everyday devices and appliances that are intelligent, self-diagnostic, and responsive to people’s needs.
“This is all shifting intelligence to the edge, as opposed to on a server or a data center or a desktop computer,” Siegel says. “I think the larger goal is to have everything connected, all of the time, for a smarter, more interconnected world. That’s the vision I want to see.”
Source: MIT News - CSAIL - Robotics - Computer Science and Artificial Intelligence Laboratory (CSAIL) - Robots - Artificial intelligence
Reprinted with permission of MIT News : MIT News homepage
Use the link at the top of the story to get to the original article.
15 June 2018, 2:30 pm
Have you ever plugged in a vacuum cleaner, only to have it turn off without warning before the job is done? Or perhaps your desk lamp works fine, until you turn on the air conditioner that’s plugged into the same power strip.
These interruptions are likely “nuisance trips,” in which a detector installed behind the wall trips an outlet’s electrical circuit when it senses something that could be an arc-fault — a potentially dangerous spark in the electric line.
The problem with today’s arc-fault detectors, according to a team of MIT engineers, is that they often err on the side of being overly sensitive, shutting off an outlet’s power in response to electrical signals that are actually harmless.
Now the team has developed a solution that they are calling a “smart power outlet,” in the form of a device that can analyze electrical current usage from a single or multiple outlets, and can distinguish between benign arcs — harmless electrical spikes such as those caused by common household appliances — and dangerous arcs, such as sparking that results from faulty wiring and could lead to a fire. The device can also be trained to identify what might be plugged into a particular outlet, such as a fan versus a desktop computer.
The team’s design comprises custom hardware that processes electrical current data in real-time, and software that analyzes the data via a neural network — a set of machine learning algorithms that are inspired by the workings of the human brain.
In this case, the team’s machine-learning algorithm is programmed to determine whether a signal is harmful or not by comparing a captured signal to others that the researchers previously used to train the system. The more data the network is exposed to, the more accurately it can learn characteristic “fingerprints” used to differentiate good from bad, or even to distinguish one appliance from another.
Joshua Siegel, a research scientist in MIT’s Department of Mechanical Engineering, says the smart power outlet is able to connect to other devices wirelessly, as part of the “internet of things” (IoT). He ultimately envisions a pervasive network in which customers can install not only a smart power outlet in their homes, but also an app on their phone, through which they can analyze and share data on their electrical usage. These data, such as what appliances are plugged in where, and when an outlet has actually tripped and why, would be securely and anonymously shared with the team to further refine their machine-learning algorithm, making it easier to identify a machine and to distinguish a dangerous event from a benign one.
“By making IoT capable of learning, you’re able to constantly update the system, so that your vacuum cleaner may trigger the circuit breaker once or twice the first week, but it’ll get smarter over time,” Siegel says. “By the time that you have 1,000 or 10,000 users contributing to the model, very few people will experience these nuisance trips because there’s so much data aggregated from so many different houses.”
Siegel and his colleagues have published their results in the journal Engineering Applications of Artificial Intelligence. His co-authors are Shane Pratt, Yongbin Sun, and Sanjay Sarma, the Fred Fort Flowers and Daniel Fort Flowers Professor of Mechanical Engineering and vice president of open learning at MIT.
Electrical fingerprints
To reduce the risk of fire, modern homes may make use of an arc fault circuit interrupter (AFCI), a device that interrupts faulty circuits when it senses certain potentially dangerous electrical patterns.
“All the AFCI models we took apart had little microprocessors in them, and they were running a regular algorithm that looked for fairly primitive, simple signatures of an arc,” Pratt says.
Pratt and Siegel set out to design a more discerning detector that can discriminate between a multitude of signals to tell a benign electrical pattern from a potentially harmful one.
Their hardware setup consists of a Raspberry Pi Model 3 microcomputer, a low-cost, power-efficient processor which records incoming electrical current data; and an inductive current clamp that fixes around an outlet’s wire without actually touching it, which senses the passing current as a changing magnetic field.
Between the current clamp and the microcomputer, the team connected a USB sound card, commodity hardware similar to what is found in conventional computers, which they used to read the incoming current data. The team found such sound cards are ideally suited to capturing the type of data that is produced by electronic circuits, as they are designed to pick up very small signals at high data rates, similar to what would be given off by an electrical wire.
The sound card also came with other advantages, including a built-in analog-to-digital converter which samples signals at 48 kiloherz, meaning that it takes measurements 48,000 times a second, and an integrated memory buffer, enabling the team’s device to monitor electrical activity continuously, in real-time.
In addition to recording incoming data, much of the microcomputer’s processing power is devoted to running a neural network. For their study, they trained the network to establish “definitions,” or recognize associated electrical patterns, produced by four device configurations: a fan, an iMac computer, a stovetop burner, and an ozone generator — a type of air purifier that produces ozone by electrically charging oxygen in the air, which can produce a reaction similar to a dangerous arc-fault.
The team ran each device numerous times over a range of conditions, gathering data which they fed into the neural network.
“We create fingerprints of current data, and we’re labeling them as good or bad, or what individual device they are,” Siegel says. “There are the good fingerprints, and then the fingerprints of the things that burn your house down. Our job in the near-term is to figure out what’s going to burn down your house and what won’t, and in the long-term, figure out exactly what’s plugged in where.”
“Shifting intelligence”
After training the network, they ran their whole setup — hardware and software — on new data from the same four devices, and found it was able to discern between the four types of devices (for example, a fan versus a computer) with 95.61 percent accuracy. In identifying good from bad signals, the system achieved 99.95 percent accuracy — slightly higher than existing AFCIs. The system was also able to react quickly and trip a circuit in under 250 milliseconds, matching the performance of contemporary, certified arc detectors.
Siegel says their smart power outlet design will only get more intelligent with increasing data. He envisions running a neural network over the internet, where other users can connect to it and report on their electrical usage, providing additional data to the network that helps it to learn new definitions and associate new electrical patterns with new appliances and devices. These new definitions would then get shared wirelessly to users’ outlets, improving their performance,and reducing the risk of nuisance trips without compromising safety.
“The challenge is, if we’re trying to detect a million different devices that get plugged in, you have to incentivize people to share that information with you,” Siegel says. “But there are enough people like us who will see this device and install it in their house and will want to train it.”
Beyond electrical outlets, Siegel sees the team’s results as a proof of concept for “pervasive intelligence,” and a world made up of everyday devices and appliances that are intelligent, self-diagnostic, and responsive to people’s needs.
“This is all shifting intelligence to the edge, as opposed to on a server or a data center or a desktop computer,” Siegel says. “I think the larger goal is to have everything connected, all of the time, for a smarter, more interconnected world. That’s the vision I want to see.”
Source: MIT News - CSAIL - Robotics - Computer Science and Artificial Intelligence Laboratory (CSAIL) - Robots - Artificial intelligence
Reprinted with permission of MIT News : MIT News homepage
Use the link at the top of the story to get to the original article.
10
Home Made Robots / Re: absolutely nothing motor controller
« Last post by 8pla.net on June 17, 2018, 11:47:29 am »Ranch said, "For the simplicity of doing it that way"
That way being the Engineering Design Process.
Good success is my prediction. Thank you for sharing.
Just adding, the material (other than hand-drawn)
posted to this thread is quite interesting as well.
That way being the Engineering Design Process.
Good success is my prediction. Thank you for sharing.
Just adding, the material (other than hand-drawn)
posted to this thread is quite interesting as well.
| Can you guess the lyrics of this song? by Art (General Chat) |
| Why doesn't nature's cells consume all of Earth? by LOCKSUIT (General Chat) |
| Automata, the series by 8pla.net (AI in Film and Literature.) |
| absolutely nothing motor controller by 8pla.net (Home Made Robots) |
| XKCD Comic : Brookhaven RHIC by Tyler (XKCD Comic) |
| Expert AI/ML practitioner opinions required for research by Researcher (General AI Discussion) |
| Still alive, well still breathing ;P by Freddy (General Chat) |
| XKCD Comic : Customer Rewards by ranch vermin (XKCD Comic) |
| Festo by 8pla.net (Robotics News) |
| MIT engineers build smart power outlet by Tyler (Robotics News) |
| Magnetic 3-D-printed structures crawl, roll, jump, and play catch by Tyler (Robotics News) |
| Teaching robots how to move objects by Tyler (Robotics News) |
| Artificial intelligence senses people through walls by Tyler (Robotics News) |
| On a mission to build the uncrashable car by Tyler (Robotics News) |
| Pizza on the way! by ranch vermin (Robotics News) |
| I think I'll have a Salad! by Art (Robotics News) |
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