Accurate prediction of battery health is essential for real-world system management and lab-based experiment design. However, building a life-prediction model from different cycling conditions is still a challenge. Large lifetime variability results from both cycling conditions and initial manufacturing variability, and this -- along with the limited experimental resources usually available for each cycling condition -- makes data-driven lifetime prediction challenging. Here, a hierarchical Bayesian linear model is proposed for battery life prediction, combining both individual cell features (reflecting manufacturing variability) with population-wide features (reflecting the impact of cycling conditions on the population average). The individual features were collected from the first 100 cycles of data, which is around 5-10% of lifetime. The model is able to predict end of life with a root mean square error of 3.2 days and mean absolute percentage error of 8.6%, measured through 5-fold cross-validation, overperforming the baseline (non-hierarchical) model by around 12-13%.

As is well known, the number of robots will increase during the next ten years. The Boston Consulting Group expects that by 2025, robots will perform 25% of all labor-intensive tasks. This is due to cost-related and performance-related enhancements. The United States, Canada, Japan, South Korea, and the United Kingdom will push robot adoption. The four leading industries are computer and electronic products, electrical equipment and appliances, transportation equipment, and machinery.

Mathematical modeling of lithium-ion batteries (LiBs) is a primary challenge in advanced battery management. This paper proposes two new frameworks to integrate physics-based models with machine learning to achieve high-precision modeling for LiBs. The frameworks are characterized by informing the machine learning model of the state information of the physical model, enabling a deep integration between physics and machine learning. Based on the frameworks, a series of hybrid models are constructed, through combining an electrochemical model and an equivalent circuit model, respectively, with a feedforward neural network. The hybrid models are relatively parsimonious in structure and can provide considerable voltage predictive accuracy under a broad range of C-rates, as shown by extensive simulations and experiments. The study further expands to conduct aging-aware hybrid modeling, leading to the design of a hybrid model conscious of the state-of-health to make prediction. The experiments show that the model has high voltage predictive accuracy throughout a LiB's cycle life.

Experiments using large numbers of miniature swarm robots are desirable to teach, study, and test multi-robot and swarm intelligence algorithms and their applications. To realize the full potential of a swarm robot, it should be capable of not only motion but also sensing, computing, communication, and power management modules with multiple options. Current swarm robot platforms developed for commercial and academic research purposes lack several of these critical attributes by focusing only on a few of these aspects. Therefore, in this paper, we propose the HeRoSwarm, a fully-capable swarm robot platform with open-source hardware and software support. The proposed robot hardware is a low-cost design with commercial off-the-shelf components that uniquely integrates multiple sensing, communication, and computing modalities with various power management capabilities into a tiny footprint. Moreover, our swarm robot with odometry capability with Robot Operating Systems (ROS) support is unique in its kind. This simple yet powerful swarm robot design has been extensively verified with different prototyping variants and multi-robot experimental demonstrations.

The use of AI (artificial intelligence) technologies is transforming industries from manufacturing to healthcare, retail, agriculture, transportation, and beyond. Precedence Research estimates the global market for AI will reach nearly $1.6 trillion by 2030, up from about $87 billion in 2021. A new AI and machine learning-powered project funded by the NSF (National Science Foundation) will leverage these powerful technologies to transform the way scientists analyze ocean imagery, adding yet one more way AI is changing the way humans interact with everything--from other humans to machines and even data from the depths of the sea. Every day, new information from Earth's oceans is being collected by research crews and ROVs (remotely operated vehicles) equipped with cameras, video cameras, and instruments that measure parameters from the ROV's surroundings, such as water temperature. This equipment allows research vehicles to collect massive amounts of imagery and other data about the ocean.

Robotic engineers have unveiled what they claim are the world's fastest shoes - footwear designed with eight wheels that increase walking speeds by 250 percent. Called Moonwalkers, they strap around your shoes and propel you forward using tiny electric motors that power weight wheels, mimicking that of roller skates. The shoes are the brainchild of a team of robotics engineers at Shift Robotics, which thought of the idea when the founder started walking to work and realized powered shoes would dramatically cut his commute by more than half. This is because it increases walking speeds from the average 3mph up to 7mph. The team markets the tech, which retails for $1,399, for those'who have the need for speed' and see's Moonwalkers as the future of walking.

Robotic engineers have unveiled what they claim are the world's fastest shoes - footwear designed with eight wheels that increase walking speeds by 250 percent. Called Moonwalkers, they strap around your shoes and propel you forward using tiny electric motors that power weight wheels, mimicking that of roller skates. The shoes are the brainchild of a team of robotics engineers at Shift Robotics, which thought of the idea when the founder started walking to work and realized powered shoes would dramatically cut his commute by more than half. This is because it increases walking speeds from the average three miles per hour up to seven miles per hour. The team markets the tech, which retails for $1,399, as for those'who have the need for speed' and see's Moonwalkers as the future of walking.

Accurate co-estimations of battery states, such as state-of-charge (SOC), state-of-health (SOH,) and remaining useful life (RUL), are crucial to the battery management systems to assure safe and reliable management. Although the external properties of the battery charge with the aging degree, batteries' degradation mechanism shares similar evolving patterns. Since batteries are complicated chemical systems, these states are highly coupled with intricate electrochemical processes. A state-coupled co-estimation method named Deep Inter and Intra-Cycle Attention Network (DIICAN) is proposed in this paper to estimate SOC, SOH, and RUL, which organizes battery measurement data into the intra-cycle and inter-cycle time scales. And to extract degradation-related features automatically and adapt to practical working conditions, the convolutional neural network is applied. The state degradation attention unit is utilized to extract the battery state evolution pattern and evaluate the battery degradation degree. To account for the influence of battery aging on the SOC estimation, the battery degradation-related state is incorporated in the SOC estimation for capacity calibration. The DIICAN method is validated on the Oxford battery dataset. The experimental results show that the proposed method can achieve SOH and RUL co-estimation with high accuracy and effectively improve SOC estimation accuracy for the whole lifespan.

Created by Jamy Herrmann at ECAL, MEMOGRAM is a (non)camera that prints our images in the form of a written description, inviting users to (re)discover those moments in images. Today, for many, the memories that remain are only those of images taken with digital cameras. This project uses many different techniques since it is both tangible and digital. Both versions are made in 3D printing and then wrapped with a paper explaining the steps of use. The electronics are comprised of a thermal printer (and a paper roll) connected to a custom PCB equipped with an Arduino nano and a bluetooth UART module.

Machine learning has been used to quickly discover some of the most promising materials for fluoride-ion batteries. The work could accelerate development of these batteries, which are tipped by some to rival, or even replace, lithium-based ones. In theory, fluoride-ion systems are ideal for batteries in everything from electric vehicles to consumer electronics. That's because fluoride ions are lightweight, small and highly stable. Fluoride is also cheaper than lithium and cobalt that are required for lithium-ion batteries.