Unmanned aircraft systems (UAS) are being increasingly adopted for various applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit failure and/or battery degradation. The proposed autonomy is modeled as a Markov Decision Process (MDP) whose solution is a contingency management policy that appropriately executes emergency landing, flight termination or continuation of planned flight actions. Motivated by the potential for errors in fault/failure indicators, partial observability of the MDP state space is investigated. The performance of optimal policies is analyzed over varying observability conditions in a high-fidelity simulator. Results indicate that both partially observable MDP (POMDP) and maximum a posteriori MDP policies performed similarly over different state observability criteria, given the nearly deterministic state transition model.

Lithium-ion batteries (LiBs) degrade slightly until the knee onset, after which the deterioration accelerates to end of life (EOL). The knee onset, which marks the initiation of the accelerated degradation rate, is crucial in providing an early warning of the battery's performance changes. However, there is only limited literature on online knee onset identification. Furthermore, it is good to perform such identification using easily collected measurements. To solve these challenges, an online knee onset identification method is developed by exploiting the temporal information within the discharge data. First, the temporal dynamics embedded in the discharge voltage cycles from the slight degradation stage are extracted by the dynamic time warping. Second, the anomaly is exposed by Matrix Profile during subsequence similarity search. The knee onset is detected when the temporal dynamics of the new cycle exceed the control limit and the profile index indicates a change in regime. Finally, the identified knee onset is utilized to categorize the battery into long-range or short-range categories by its strong correlation with the battery's EOL cycles. With the support of the battery categorization and the training data acquired under the same statistic distribution, the proposed SOH estimation model achieves enhanced estimation results with a root mean squared error as low as 0.22%.

We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do. While the error rate is high, generative AI seems to be able to effectively structure vast amounts of scientific knowledge and provide interesting and testable hypotheses. The future scientific enterprise may include synergistic efforts with a swarm of "hypothesis machines", challenged by automated experimentation and adversarial peer reviews. In a university or research institute, a significant portion of fresh ideas arises out of discussions.

State-of-health (SOH) estimation is a key step in ensuring the safe and reliable operation of batteries. Due to issues such as varying data distribution and sequence length in different cycles, most existing methods require health feature extraction technique, which can be time-consuming and labor-intensive. GRU can well solve this problem due to the simple structure and superior performance, receiving widespread attentions. However, redundant information still exists within the network and impacts the accuracy of SOH estimation. To address this issue, a new GRU network based on Hilbert-Schmidt Independence Criterion (GRU-HSIC) is proposed. First, a zero masking network is used to transform all battery data measured with varying lengths every cycle into sequences of the same length, while still retaining information about the original data size in each cycle. Second, the Hilbert-Schmidt Independence Criterion (HSIC) bottleneck, which evolved from Information Bottleneck (IB) theory, is extended to GRU to compress the information from hidden layers. To evaluate the proposed method, we conducted experiments on datasets from the Center for Advanced Life Cycle Engineering (CALCE) of the University of Maryland and NASA Ames Prognostics Center of Excellence. Experimental results demonstrate that our model achieves higher accuracy than other recurrent models.

With the rapid surge in the number of on-road Electric Vehicles (EVs), the amount of spent lithium-ion (Li-ion) batteries is also expected to explosively grow. The spent battery packs contain valuable metal and materials that should be recovered, recycled, and reused. However, only less than 5% of the Li-ion batteries are currently recycled, due to a multitude of challenges in technology, logistics and regulation. Existing battery recycling is performed manually, which can pose a series of risks to the human operator as a consequence of remaining high voltage and chemical hazards. Therefore, there is a critical need to develop an automated battery recycling system. In this paper, we present ABatRe-sim, an open-source robotic battery recycling simulator, to facilitate the research and development in efficient and effective battery recycling au-omation. Specifically, we develop a detailed CAD model of the battery pack (with screws, wires, and battery modules), which is imported into Gazebo to enable robot-object interaction in the robot operating system (ROS) environment. It also allows the simulation of battery packs of various aging conditions. Furthermore, perception, planning, and control algorithms are developed to establish the benchmark to demonstrate the interface and realize the basic functionalities for further user customization. Discussions on the utilization and future extensions of the simulator are also presented.

An Arlo doorbell will work with Alexa, the Google Assistant, Siri, Samsung's SmartThings or IFTTT integrations. Unlike some smart home devices, Arlo plays nice. And right now, you can save $100 on the brand's wire-free version of the Essential Video Doorbell at Amazon. You can also get the same discount through Arlo's site directly. Get half off a no-wires-required video doorbell from a brand that works with whichever smart home assistant you prefer. The rechargeable battery inside makes the unit easy to install, particularly if your front entry isn't already wired for a doorbell.

The optimal design of many engineering processes can be subject to expensive and time-consuming experimentation. For efficiency, we seek to avoid wasting valuable resources in testing sub-optimal designs. One way to achieve this is by obtaining cheaper approximations of the desired system, which allow us to quickly explore new regimes and avoid areas that are clearly sub-optimal. As an example, consider the case diagrammed in Figure 1 from battery materials research with the goal of designing electrode materials for optimal performance in pouch cells. We can use experiments with cheaper coin cells and shorter test procedures to approximate the behaviour of the material in longer stability tests in pouch cells, which is in turn closer to the expected performance in electric car applications [Chen et al., 2019, Dörfler et al., 2020, Liu et al., 2021]. Similarly, design goals regarding battery life such as discharge capacity retention can be approximated using an early prediction model on the first few charge cycles rather than running aging and stability tests to completion [Attia et al., 2020].

Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devices. One major challenge preventing the wide adoption of FL in IoT is the pervasive power supply constraints of IoT devices due to the intensive energy consumption of battery-powered clients for local training and model updates. Low battery levels of clients eventually lead to their early dropouts from edge networks, loss of training data jeopardizing the performance of FL, and their availability to perform other designated tasks. In this paper, we propose FedLE, an energy-efficient client selection framework that enables lifespan extension of edge IoT networks. In FedLE, the clients first run for a minimum epoch to generate their local model update. The models are partially uploaded to the server for calculating similarities between each pair of clients. Clustering is performed against these client pairs to identify those with similar model distributions. In each round, low-powered clients have a lower probability of being selected, delaying the draining of their batteries. Empirical studies show that FedLE outperforms baselines on benchmark datasets and lasts more training rounds than FedAvg with battery power constraints.

Artificial Intelligence (AI) had become more prevalent in our daily life. From virtual assistance to electrical appliances and automated vehicles, the application of AI can be seen everywhere. Now, AI is widely used in every industry and with the rise of AIaaS, its adoption is expected to expand in the future. There are various example of AIaaS platforms that can be adopted based on the need of organisation. Therefore, what are the example of AIaaS platforms available and its contributions?

Given the high power density low discharge rate and decreasing cost rechargeable lithium-ion batteries LiBs have found a wide range of applications such as power grid level storage systems electric vehicles and mobile devices. Developing a framework to accurately model the nonlinear degradation process of LiBs which is indeed a supervised learning problem becomes an important research topic. This paper presents a classical-quantum hybrid machine learning approach to capture the LiB degradation model that assesses battery cell life loss from operating profiles. Our work is motivated by recent advances in quantum computers as well as the similarity between neural networks and quantum circuits. Similar to adjusting weight parameters in conventional neural networks the parameters of the quantum circuit namely the qubits degree of freedom can be tuned to learn a nonlinear function in a supervised learning fashion. As a proof of concept paper our obtained numerical results with the battery dataset provided by NASA demonstrate the ability of the quantum neural networks in modeling the nonlinear relationship between the degraded capacity and the operating cycles. We also discuss the potential advantage of the quantum approach compared to conventional neural networks in classical computers in dealing with massive data especially in the context of future penetration of EVs and energy storage.