Accurate and reliable state of charge (SoC) estimation becomes increasingly important to provide a stable and efficient environment for Lithium-ion batteries (LiBs) powered devices. Most data-driven SoC models are built for a fixed ambient temperature, which neglect the high sensitivity of LiBs to temperature and may cause severe prediction errors. Nevertheless, a systematic evaluation of the impact of temperature on SoC estimation and ways for a prompt adjustment of the estimation model to new temperatures using limited data have been hardly discussed. To solve these challenges, a novel SoC estimation method is proposed by exploiting temporal dynamics of measurements and transferring consistent estimation ability among different temperatures. First, temporal dynamics, which is presented by correlations between the past fluctuation and the future motion, is extracted using canonical variate analysis. Next, two models, including a reference SoC estimation model and an estimation ability monitoring model, are developed with temporal dynamics. The monitoring model provides a path to quantitatively evaluate the influences of temperature on SoC estimation ability. After that, once the inability of the reference SoC estimation model is detected, consistent temporal dynamics between temperatures are selected for transfer learning. Finally, the efficacy of the proposed method is verified through a benchmark. Our proposed method not only reduces prediction errors at fixed temperatures (e.g., reduced by 24.35% at -20{\deg}C, 49.82% at 25{\deg}C) but also improves prediction accuracies at new temperatures.

For health prognostic task, ever-increasing efforts have been focused on machine learning-based methods, which are capable of yielding accurate remaining useful life (RUL) estimation for industrial equipment or components without exploring the degradation mechanism. A prerequisite ensuring the success of these methods depends on a wealth of run-to-failure data, however, run-to-failure data may be insufficient in practice. That is, conducting a substantial amount of destructive experiments not only is high costs, but also may cause catastrophic consequences. Out of this consideration, an enhanced RUL framework focusing on data self-generation is put forward for both non-cyclic and cyclic degradation patterns for the first time. It is designed to enrich data from a data-driven way, generating realistic-like time-series to enhance current RUL methods. First, high-quality data generation is ensured through the proposed convolutional recurrent generative adversarial network (CR-GAN), which adopts a two-channel fusion convolutional recurrent neural network. Next, a hierarchical framework is proposed to combine generated data into current RUL estimation methods. Finally, the efficacy of the proposed method is verified through both non-cyclic and cyclic degradation systems. With the enhanced RUL framework, an aero-engine system following non-cyclic degradation has been tested using three typical RUL models. State-of-art RUL estimation results are achieved by enhancing capsule network with generated time-series. Specifically, estimation errors evaluated by the index score function have been reduced by 21.77%, and 32.67% for the two employed operating conditions, respectively. Besides, the estimation error is reduced to zero for the Lithium-ion battery system, which presents cyclic degradation.

Here are the Wyze Outdoor Cam's specs: The Wyze Outdoor Starter Bundle includes one Outdoor Cam and one base station required for use. Running on two 2,600 mAh integrated rechargeable batteries, Wyze's Outdoor Cam is completely wire-free and claims a battery life of three to six months for normal use (about 10-20 events per day). A base station is required to use the camera, but it's included in the Wyze Outdoor Cam Starter Bundle, so there are no additional products to buy. Up to four total cameras can be added to the base station, allowing you to outfit the exterior of your home with multiple cameras for less than the cost of one Arlo Pro 4, our No. 1 pick for outdoor security cameras. Wyze's outdoor camera delivers 1080p video and night vision that are easy on the eyes, as well as two-way talk functionality that's clear and easy to understand.

Project Loon is using balloons such as this to set up an aerial wireless network for telecommunications.Credit: Loon The goal of an autonomous machine is to achieve an objective by making decisions while negotiating a dynamic environment. Given complete knowledge of a system's current state, artificial intelligence and machine learning can excel at this, and even outperform humans at certain tasks -- for example, when playing arcade and turn-based board games1. But beyond the idealized world of games, real-world deployment of automated machines is hampered by environments that can be noisy and chaotic, and which are not adequately observed. The difficulty of devising long-term strategies from incomplete data can also hinder the operation of independent AI agents in real-world challenges. Writing in Nature, Bellemare et al.2 describe a way forward by demonstrating that stratospheric balloons, guided by AI, can pursue a long-term strategy for positioning themselves about a location on the Equator, even when precise knowledge of buffeting winds is not known.

Reolink's Argus cameras have ably filled the need for an essentials-only wireless security camera. The Argus 2E is the latest in the family, but where it sits in the lineage is a little confusing. Given its name, you'd be forgiven for thinking it's an update on the Argus 2, but that model evolved into the completely redesigned Argus 3, The 2E actually replaces the Argus Pro, which, contrary to its name was not a premium version of the Argus, and even lacked a few of the main model's features. It makes sense, then, that the 2E doesn't sport the new design of the Argus 3 but looks like a slightly modified version of the Argus Pro. Most of the specs are the same, too: 1080p video, two-way audio, and passive infrared motion detection. And like the Argus Pro, the 2E is powered by a 5200mAh rechargeable battery that can be continually charged with an optional solar panel ($25) should you deploy this indoor/outdoor camera outside.

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.