Typical reinforcement learning (RL) methods show limited applicability for real-world industrial control problems because industrial systems involve various constraints and simultaneously require continuous and discrete control. To overcome these challenges, we devise a novel RL algorithm that enables an agent to handle a highly constrained action space. This algorithm has two main features. First, we devise two distance-based Q-value update schemes, incentive update and penalty update, in a distance-based incentive/penalty update technique to enable the agent to decide discrete and continuous actions in the feasible region and to update the value of these types of actions. Second, we propose a method for defining the penalty cost as a shadow price-weighted penalty. This approach affords two advantages compared to previous methods to efficiently induce the agent to not select an infeasible action. We apply our algorithm to an industrial control problem, microgrid system operation, and the experimental results demonstrate its superiority.

MIT CSAIL's flexible sensors can be applied as skin to the bodies of soft robots. When you picture a robot, you likely envision one large and rigid, with limited movement and an outer shell that is hard to the touch. Several projects currently underway seek to change that, with the use of soft, more human-like artificial skin. Artificial skins include any surface-based device or distributed network of sensors that enable an agent to perceive mechanical deformations, touch, temperature, vibration, and/or pain, according to Ryan Truby, a post-doctoral fellow in the Massachusetts Institute of Technology (MIT) Computer Science & Artificial Intelligence Lab (CSAIL). Engineers are working to create skins that include as many of these sensations as possible, while also possessing high sensitivity and spatial resolution in sensing, he adds.

The Arlo Pro 4 is a small but mighty outdoor home security camera. The Arlo Pro 4 Spotlight camera has higher video quality and better field of view than almost any camera we've tested--including the popular Nest Cam Outdoor. Other Arlo Pro 4 features include color night vision output, two-way talk capabilities, timely smart alerts, and easy integration with Amazon Alexa and Google Assistant. The Pro 4 is entirely wire-free and runs on a rechargeable battery that can last up to six months per charge. It also has a built-in spotlight that illuminates when motion is detected, and a smart siren that can be triggered automatically or remotely via the Arlo app.

Greenwaves reveal their latest AI chip, GAP9. Just like the previous generation, it is aimed at AI inferencing in systems at the very edge of the network. Edge computing will increasingly become an integral part of the digital transformation phenomenon. The main benefits deriving from the use of these technologies are the reduction of processing latency, which allows real-time responses, and the saving of bandwidth, sending already processed and, therefore, smaller information to the data center. Compared to GreenWaves Technologies' currently shipping product, GAP8, the latest GAP9 reduces energy consumption by 5 times while enabling inference on neural networks 10 times larger.

Intel's new Tiger Lake processors for ultra-thin laptops are packed with new silicon building blocks for AI, graphics and other technologies to serve a "spectrum of needs" in the mobile computing space, according to top Intel engineer Boyd Phelps. In an interview with CRN, Phelps said the Santa Clara, Calif.-based company has taken a holistic and balanced approach to the engineering and design of the new processors, which are the first CPUs in the 11th-generation Intel Core family. This means that the company has devised ways, for instance, to offload certain AI workloads, like blurring the background in a Zoom video call, to new accelerators within the chip to make the workloads run faster while also saving on power. "For us, we thought about it in the context of how the different workloads have evolved and emerged. They all have kind of a different sweet spot, so for us, we geared Tiger Lake to meet that spectrum of needs," said Phelps, who is vice president of the Client Engineering Group and general manager of Client and Core Development Group at Intel.

The primary appeal of motorized top-down/bottom-up shades is their ability to open and close in two directions: They can open by dropping the top of the shade down from the window's head to the sill, and by lifting the bottom of the shade up from the sill to the head. But Hunter Douglas couldn't justify the lofty price tag of its Duette with PowerView Automation shades unless they were also the most luxurious and innovative shades we've reviewed to date. Top-down/bottom-up shades are a fantastic option because they enhance privacy without completely blocking light from entering the room. If your window faces a busy street, you can lower the shade down from the top to admit light without exposing your room to a view from the street. Or you can drop the top of the shade down in the early morning, so the room is bathed in morning sunlight without impeding your ability to move about the room freely--anyone looking toward your window will only be able to as much of you as you wish to expose. And since these are motorized smart shades, you can create automated schedules to reposition the shades as many times each day and night that you'd care to program, including at sunrise and sunset.

This project focuses on segmenting different objects such as animals, plants, plastic, and ROV(Remotely Operated Vehicle) using a low code wrapper Monk [2]toolkit via Unet[1]. It is essential to understand the sea garbage collection. For employing an automatic river or sea trash cleaner system should have a proper understanding of different objects present in the water. This project helps to develop such a system on small scale. Through this blog, I will share some insights about MonkAI, and how it can be used to simplify the process of object segmentation and build other computer vision applications.

We propose a new approach for power control in wireless networks using self-supervised learning. We partition a multi-layer perceptron that takes as input the channel matrix and outputs the power control decisions into a backbone and a head, and we show how we can use contrastive learning to pre-train the backbone so that it produces similar embeddings at its output for similar channel matrices and vice versa, where similarity is defined in an information-theoretic sense by identifying the interference links that can be optimally treated as noise. The backbone and the head are then fine-tuned using a limited number of labeled samples. Simulation results show the effectiveness of the proposed approach, demonstrating significant gains over pure supervised learning methods in both sum-throughput and sample efficiency.

In Milan, Italy, STIIMA, the National Research Council's Institute for Smart Industrial Technology Systems for Advanced Manufacturing, and the Polytechnic University of Milan have set up a joint experimental "re-manufacturing" and "de-manufacturing" facility. While still at a pilot experimental level, this is an excellent example of the enormous potential of artificial intelligence in the circular economy. This is because there are no similar plants in the world capable of managing electronic waste, understanding what the items are, dismantling them and recovering their useful or valuable components. For this reason, millions of tonnes of old TVs, monitors, broken PCs, telephones, and electrical appliances of every type, are piling up at waste sites, from where they are often taken to fuel an illegal and extremely polluting market. Its real size is difficult to estimate, but according to UNEP, the United Nations Environmental Protection agency, the global market for electronic waste is worth more than 62 billion dollars and only 20% of it is officially recycled.

Inside a lab at Stanford University's Precourt Institute for Energy, there are a half dozen refrigerator-sized cabinets designed to kill batteries as fast as they can. Each holds around 100 lithium-ion cells secured in trays that can charge and discharge the batteries dozens of times per day. Ordinarily, the batteries that go into these electrochemical torture chambers would be found inside gadgets or electric vehicles, but when they're put in these hulking machines, they aren't powering anything at all. Instead, energy is dumped in and out of these cells as fast as possible to generate reams of performance data that will teach artificial intelligence how to build a better battery. In 2019, a team of researchers from Stanford, MIT, and the Toyota Research Institute used AI trained on data generated from these machines to predict the performance of lithium-ion batteries over the lifetime of the cells before their performance had started to slip.