#Computer Vision Technology

[thirdeye-ai]

PPE Monitoring Solution for Leading Automotive Component Manufacturers

Implementing a PPE monitoring solution for an automotive component manufacturer addresses safety challenges such as unsafe incidents and compliance issues. The solution includes CCTV surveillance for real-time monitoring, integration with SAP and Industry 4.0 for data synchronization, and IoT integration to enhance safety measures. It ensures comprehensive checks for helmets, eyeglasses, shoes, and other PPE, effectively mitigating accidents and promoting workplace safety.

[helmerichpayne]

A BETTER SET OF EYES: COMPUTER VISION TECHNOLOGY TO MONITOR SAFETY ZONES ANDAUTOMATE DRILL PIPE TALLY

An indispensable item for every roughneck is the tally book, used to measure and count the drill pipe entering and exiting the wellbore. The current practice is for a crew member to measure the pipe with a pipe strap and enter the information, each time while tripping, into their tally book. This manual entry is prone to error, leading to potential mistakes in the calculated drilling depth and poorly sequenced lithologies, which in turn may contribute to an unsafe environment and drill bit damage due to inaccurate drillstring length.

These mistakes often require an additional trip out of hole and increase the amount of nonproductive time. Computer vision technology has shown promise in other industries with its ability to automate similar recognition and counting tasks. A dual-use system has been developed where the same cameras for pipe counting can be used for red zone entry detection, holding the potential to enhance the overall safety of the drilling process.

A pilot application has been created serving dual applications: both counting and measuring the pipe entering the wellbore and detecting personnel movement in the red zone during pipe delivery operations. Each stage of the design process was intently developed, considering requirements for both functionalities of the system. This computer vision technology is the first of its kind on a drilling rig. No other system has been developed that accomplishes not only one of the functions, but also two.

Just as we have seen rapid improvements each year in driver assistance technology, the time has come to apply recent advancements in computer vision capabilities to increase the efficiency and overall safety of the rig. To learn more about computer vision technology, visit us here: https://www.helmerichpayne.com/resources/technical-publications/a-better-set-of-eyes-computer-vision-technology-to-monitor-safety-zones-andautomate-drill-pipe-tally or you can also reach out to us directly here: https://www.helmerichpayne.com/contact.

[f-o-and-selfship-club]

I bring out a design for my CTCD TV sona. I named her Vision, which is reference to her TV head

Vision is a TV that was brought to the attic by Muriel Bagge and she acts a friendly and humble ally to Courage. She is also a computer kisser hehe

[d0nutzgg]

Analyzing An Ataxic Dysarthria Patient's Speech with Computer Vision and Audio Processing

Hey everyone, so as you know I have been doing research on patients like myself who have Ataxic Dysarthria and other neurological speech disorders related to diseases and conditions that affect the brain. I was analyzing this file

with a few programs that I have written.

The findings are very informative and I am excited that I am able to explain this to my Tumblr following as I feel it not only promotes awareness but provides an understanding of what we go through with Ataxic Dysarthria.

Analysis of the audio file with an Intonation Visualizer I built

As you can tell this uses a heatmap to visualize loudness and softness of a speaker's voice. I used it to analyze the file and I found some really interesting and telling signs of Ataxic Dysarthria

At 0-1 seconds it is mostly pretty quiet (which is normal because it is harder for patients with AD to start their speaking off. You can notice that around 1-3 seconds it gets louder, and then when she speaks its clearer and louder than the patients voice. However the AD makes the patients speech constantly rise and fall in loudness from around -3 to 0 decibels most of the audio when the patient is speaking. The variation though between 0 and -3 varies quickly though which is a common characteristic in AD

The combination of the constant rising and falling in loudness and intonation as well as problems getting sentences started is one of the things that makes it so hard for people to understand those with Ataxic Dysarthria.

The second method I used is using a line graph (plotted) that gives an example of the rate of speech and elongated syllables of the patient.

As you can see I primarily used the Google Speech Recognition library to transcribe and count the syllables using Pyphen via "hyphenated" (elongated) words in the speech of the patient. This isn't the most effective method but it worked well for this example and here is the results plotted out using Matplotlib:

As you can see when they started talking at first there was a rise from the softer speech, as the voice of the patient got louder, they were speaking faster (common for those with AD / and HD) my hypothesis (and personal experience) is that this is how we try to get our words out where we can be understood by "forcing" out words resulting in a rise and fall of syllables / rate of speech that we see at the first part. The other spikes typically happen when she speaks but there is another spike at the end which you can see as well when the patient tries to force more words out.

This research already indicates a pretty clear pattern what is going on in the patients speech. As they try to force out words, their speech gets faster and thus gets louder as they try to communicate.

I hope this has been informative for those who don't know much about speech pathology or neurological diseases. I know it's already showing a lot of exciting progress and I am continuing to develop scripts to further research on this subject so maybe we can all understand neurological speech disorders better.

As I said, I will be posting my research and findings as I go. Thank you for following me and keeping up with my posts!

[technonomad-blog]

The new Apple Vision Pro ad got me hyped

[theneondreaming]

Apple Vision Pro

[gameofpolthrones]

2024

[naya-mishra]

This article highlights the key difference between Machine Learning and Artificial Intelligence based on approach, learning, application, output, complexity, etc.

[thirdeye-ai]

Quality Inspection Software with Computer Vision Technology

ThirdEye AI's Quality Inspection Software leverages computer vision technology to ensure product excellence across industries. With features like anomaly detection, object presence validation, segmentation, classification, and visual defect detection, it delivers high-quality, reliable, and safe production outcomes. Ideal for industries such as automotive, electronics, pharmaceuticals, food and beverage, and packaging, ThirdEye AI ensures perfection in every inspection.

[u3core]

Shaping a Sustainable Future by Advancing ESG Goals

U3Core application of DigitalU3 redefines how organizations approach Environmental, Social, and Governance (ESG) strategies by combining real-time data, advanced analytics, and AI-powered automation

Email us at: info@digitalu3.com

[jcmarchi]

New training approach could help AI agents perform better in uncertain conditions

New Post has been published on https://thedigitalinsider.com/new-training-approach-could-help-ai-agents-perform-better-in-uncertain-conditions/

New training approach could help AI agents perform better in uncertain conditions

A home robot trained to perform household tasks in a factory may fail to effectively scrub the sink or take out the trash when deployed in a user’s kitchen, since this new environment differs from its training space.

To avoid this, engineers often try to match the simulated training environment as closely as possible with the real world where the agent will be deployed.

However, researchers from MIT and elsewhere have now found that, despite this conventional wisdom, sometimes training in a completely different environment yields a better-performing artificial intelligence agent.

Their results indicate that, in some situations, training a simulated AI agent in a world with less uncertainty, or “noise,” enabled it to perform better than a competing AI agent trained in the same, noisy world they used to test both agents.

The researchers call this unexpected phenomenon the indoor training effect.

“If we learn to play tennis in an indoor environment where there is no noise, we might be able to more easily master different shots. Then, if we move to a noisier environment, like a windy tennis court, we could have a higher probability of playing tennis well than if we started learning in the windy environment,” explains Serena Bono, a research assistant in the MIT Media Lab and lead author of a paper on the indoor training effect.

The researchers studied this phenomenon by training AI agents to play Atari games, which they modified by adding some unpredictability. They were surprised to find that the indoor training effect consistently occurred across Atari games and game variations.

They hope these results fuel additional research toward developing better training methods for AI agents.

“This is an entirely new axis to think about. Rather than trying to match the training and testing environments, we may be able to construct simulated environments where an AI agent learns even better,” adds co-author Spandan Madan, a graduate student at Harvard University.

Bono and Madan are joined on the paper by Ishaan Grover, an MIT graduate student; Mao Yasueda, a graduate student at Yale University; Cynthia Breazeal, professor of media arts and sciences and leader of the Personal Robotics Group in the MIT Media Lab; Hanspeter Pfister, the An Wang Professor of Computer Science at Harvard; and Gabriel Kreiman, a professor at Harvard Medical School. The research will be presented at the Association for the Advancement of Artificial Intelligence Conference.

Training troubles

The researchers set out to explore why reinforcement learning agents tend to have such dismal performance when tested on environments that differ from their training space.

Reinforcement learning is a trial-and-error method in which the agent explores a training space and learns to take actions that maximize its reward.

The team developed a technique to explicitly add a certain amount of noise to one element of the reinforcement learning problem called the transition function. The transition function defines the probability an agent will move from one state to another, based on the action it chooses.

If the agent is playing Pac-Man, a transition function might define the probability that ghosts on the game board will move up, down, left, or right. In standard reinforcement learning, the AI would be trained and tested using the same transition function.

The researchers added noise to the transition function with this conventional approach and, as expected, it hurt the agent’s Pac-Man performance.

But when the researchers trained the agent with a noise-free Pac-Man game, then tested it in an environment where they injected noise into the transition function, it performed better than an agent trained on the noisy game.

“The rule of thumb is that you should try to capture the deployment condition’s transition function as well as you can during training to get the most bang for your buck. We really tested this insight to death because we couldn’t believe it ourselves,” Madan says.

Injecting varying amounts of noise into the transition function let the researchers test many environments, but it didn’t create realistic games. The more noise they injected into Pac-Man, the more likely ghosts would randomly teleport to different squares.

To see if the indoor training effect occurred in normal Pac-Man games, they adjusted underlying probabilities so ghosts moved normally but were more likely to move up and down, rather than left and right. AI agents trained in noise-free environments still performed better in these realistic games.

“It was not only due to the way we added noise to create ad hoc environments. This seems to be a property of the reinforcement learning problem. And that was even more surprising to see,” Bono says.

Exploration explanations

When the researchers dug deeper in search of an explanation, they saw some correlations in how the AI agents explore the training space.

When both AI agents explore mostly the same areas, the agent trained in the non-noisy environment performs better, perhaps because it is easier for the agent to learn the rules of the game without the interference of noise.

If their exploration patterns are different, then the agent trained in the noisy environment tends to perform better. This might occur because the agent needs to understand patterns it can’t learn in the noise-free environment.

“If I only learn to play tennis with my forehand in the non-noisy environment, but then in the noisy one I have to also play with my backhand, I won’t play as well in the non-noisy environment,” Bono explains.

In the future, the researchers hope to explore how the indoor training effect might occur in more complex reinforcement learning environments, or with other techniques like computer vision and natural language processing. They also want to build training environments designed to leverage the indoor training effect, which could help AI agents perform better in uncertain environments.

[thetatechnolabsusa]

Enhancing Travel Safety with Computer Vision - Real-Time Monitoring and Threat Detection

In today's rapidly evolving world, ensuring the safety and security of travelers has become a top priority for governments, transportation authorities, and travel organizations worldwide. With the advent of advanced technologies like Computer Vision, a new era of travel safety is dawning. By harnessing the power of Computer Vision for real-time monitoring and threat detection, travel stakeholders can proactively identify and mitigate potential risks, enhancing the overall safety and security of travelers.

Computer Vision, a branch of artificial intelligence, enables machines to interpret and understand the visual world through digital images or videos. In the context of travel safety, Computer Vision systems utilize cameras and sensors to continuously monitor various aspects of the travel environment, ranging from airports and train stations to public transportation systems and tourist attractions.

One of the key advantages of Computer Vision in enhancing travel safety is its ability to provide real-time monitoring of crowded areas. By deploying high-resolution cameras equipped with advanced image processing algorithms, travel authorities can closely monitor crowd dynamics, detect unusual behavior patterns, and identify potential security threats in crowded spaces such as airport terminals or train stations. This proactive approach allows authorities to swiftly respond to security incidents, thereby minimizing the risk to travelers.

Furthermore, Computer Vision technology can augment existing security measures by automating the detection of prohibited items or suspicious activities. Through the use of object recognition algorithms, Computer Vision systems can analyze video streams in real-time to identify items such as weapons, explosives, or contraband materials. By flagging suspicious objects or activities for further inspection, these systems enable security personnel to take preemptive action, preventing potential security breaches before they escalate.

In addition to threat detection, Computer Vision can also play a crucial role in enhancing traveler authentication and verification processes. By integrating facial recognition technology into security checkpoints and boarding gates, travel authorities can streamline the passenger verification process while ensuring a high level of accuracy and security. This not only improves the overall efficiency of travel operations but also enhances the traveler experience by reducing wait times and eliminating the need for physical identification documents.

Moreover, Computer Vision systems can be leveraged to enhance traffic management and pedestrian safety in transportation hubs and tourist destinations. By analyzing traffic flow patterns and pedestrian behavior in real-time, these systems can identify potential congestion points or safety hazards, allowing authorities to implement proactive measures to mitigate risks and ensure smooth traffic flow. This not only improves the safety of travelers but also enhances the overall efficiency of transportation operations.

Furthermore, Computer Vision technology can be integrated with existing surveillance systems to provide comprehensive situational awareness and incident management capabilities. By aggregating data from multiple sources, including CCTV cameras, sensors, and IoT devices, Computer Vision systems can provide travel authorities with a holistic view of the travel environment, enabling them to quickly assess and respond to security incidents in real-time.

In conclusion, Computer Vision development services in Dallas, offered by companies are poised to revolutionize travel safety by enabling real-time monitoring and threat detection in various travel environments. By harnessing the power of advanced image processing algorithms and machine learning techniques, travel stakeholders can proactively identify and mitigate potential risks, ensuring the safety and security of travelers worldwide. As technology continues to evolve, the collaboration between Computer Vision experts and travel authorities will play an increasingly vital role in shaping the future of travel safety and security. With the expertise and innovative solutions provided by companies like us, the travel industry can look forward to a safer and more secure future for all travelers.

[ricisidro]

Who needs an expensive iOS Apple VisionPro spatial computer that combines digital content with physical space costing $3,499 (Php 202,726)?

Mine only cost $121 (Php 7,000).

#Andoid #Tecno #Spark30Pro #JBL #JBLGo3 #smartphones #computer #technology #tech #CarryOn #Netflix

[techdriveplay]

What Is the Future of Robotics in Everyday Life?

As technology continues to evolve at a rapid pace, many are asking, what is the future of robotics in everyday life? From automated vacuum cleaners to advanced AI assistants, robotics is steadily becoming an integral part of our daily routines. The blending of artificial intelligence with mechanical engineering is opening doors to possibilities that seemed like science fiction just a decade…