Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL) to support connected eco-driving at signalized intersections in mixed traffic. Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency in mixed connected traffic. The HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy; a multi-stream neural network that extracts the hidden features of vision and V2I information; and a deep RL-based policy network that generate both longitudinal and lateral eco-driving actions. In order to evaluate our approach, we developed a Unity-based simulator and designed a mixed-traffic intersection scenario. Moreover, several baselines were implemented to compare with our new design, and numerical experiments were conducted to test the performance of the HRL model. The experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach.

Artificial intelligence (AI) and machine learning (ML) are gaining acceptance quickly in the oil and gas industry. Between 2018 and 2020, the percentage of companies that had deployed these technologies more than doubled--from 13 to 32%. Today, 50% of oil and gas executives say they have already begun using AI to help solve challenges at their organizations and 92% are either currently investing in AI or plan to in the next 2 years. While these technologies have mostly been operationalized for specific use cases or particular processes, they are increasingly being incorporated into a whole host of systems and software to improve efficiency, productivity, and profitability. And the potential impact is significant: International Data Corporation estimates that the benefits enabled by AI and ML can reduce an organization's total costs by up to 20%, improve asset availability by 20%, and extend the lives of machines by years. Technologies Enable Transformation It's not AI and ML on their own that make a difference.

Aircraft detection is notoriously challenging owing to the orientation and size variations of aircraft objects. Existing detection pipelines compromise with efficiency or accuracy to deal with the large visual variations. We present a novel cascaded framework that joins object detection and orientation prediction through multi-task learning. The cascaded framework consists of three stages and operates in a coarse-to-fine manner. Each stage simultaneously rejects false targets, regresses the locations of object candidates, and calibrates the orientations of the candidates to upright gradually.

There are a few different ways that solar farms can deploy AI-powered inspection. The most common way is through the use of an Unmanned Aerial Vehicle (UAV) or drone. UAVs provide a non-contact way for solar farm operators to perform quality control of their solar panels using aerial imagery. Images collected by a UAV over a solar farm can be processed by an algorithm either in the cloud or on-device. The results of the AI algorithm will tell the quality controller which PV panels have visible signs of defective equipment. To speed up the inspection process and improve accuracy, solar farm operators are turning to AI-powered inspection. This involves the use of deep learning algorithms that can automatically detect solar panel defects from images. Deep learning algorithms are a type of machine learning algorithm that uses a neural network to learn how to solve a task. Neural networks are composed of interconnected layers that can learn how to recognize solar panel defects from images.

In this study, we propose task planning framework for multiple robots that builds on a behavior tree (BT). BTs communicate with a data distribution service (DDS) to send and receive data. Since the standard BT derived from one root node with a single tick is unsuitable for multiple robots, a novel type of BT action and improved nodes are proposed to control multiple robots through a DDS asynchronously. To plan tasks for robots efficiently, a single task planning unit is implemented with the proposed task types. The task planning unit assigns tasks to each robot simultaneously through a single coalesced BT. If any robot falls into a fault while performing its assigned task, another BT embedded in the robot is executed; the robot enters the recovery mode in order to overcome the fault. To perform this function, the action in the BT corresponding to the task is defined as a variable, which is shared with the DDS so that any action can be exchanged between the task planning unit and robots. To show the feasibility of our framework in a real-world application, three mobile robots were experimentally coordinated for them to travel alternately to four goal positions by the proposed single task planning unit via a DDS.

Terrestrial vegetation is a critical component of global biogeochemical cycles and provides important ecosystem services to support human life [1]. Given its importance, it is essential to know the spatial-temporal variations of vegetation [2]. These variations are due to several determining factors such as global climate variability, climate gradients, and anthropogenic factors such as Land Use and Land Cover Change (LULCC). The diversity in climatic conditions and vegetation types pose different obstacles to monitoring and classifying land cover using remote sensing. Mexico is considered one of the mega-diverse countries on the planet due to its location in a transition zone between Nearctic and Neotropic regions making it more difficult for land use classification and monitoring. The anthropogenic factors, could be a trigger for deforestation and forest degradation [3] and have a severe impact on the global carbon cycle, soil erosion, hydrological cycles, and in general, affect on the ecosystem services that sustain society [4]. As a result, timely land cover monitoring and classification are of crucial importance for assessing gradual degradation-ecosystem processes. Furthermore, it is important to be in line with the United Nations Sustainable Development Goals (SDGs) specifically SDG 15 concerning "Life on Land" [5].

One of the core envisions of the sixth-generation (6G) wireless networks is to accumulate artificial intelligence (AI) for autonomous controlling of the Internet of Everything (IoE). Particularly, the quality of IoE services delivery must be maintained by analyzing contextual metrics of IoE such as people, data, process, and things. However, the challenges incorporate when the AI model conceives a lake of interpretation and intuition to the network service provider. Therefore, this paper provides an explainable artificial intelligence (XAI) framework for quality-aware IoE service delivery that enables both intelligence and interpretation. First, a problem of quality-aware IoE service delivery is formulated by taking into account network dynamics and contextual metrics of IoE, where the objective is to maximize the channel quality index (CQI) of each IoE service user. Second, a regression problem is devised to solve the formulated problem, where explainable coefficients of the contextual matrices are estimated by Shapley value interpretation. Third, the XAI-enabled quality-aware IoE service delivery algorithm is implemented by employing ensemble-based regression models for ensuring the interpretation of contextual relationships among the matrices to reconfigure network parameters. Finally, the experiment results show that the uplink improvement rate becomes 42.43% and 16.32% for the AdaBoost and Extra Trees, respectively, while the downlink improvement rate reaches up to 28.57% and 14.29%. However, the AdaBoost-based approach cannot maintain the CQI of IoE service users. Therefore, the proposed Extra Trees-based regression model shows significant performance gain for mitigating the trade-off between accuracy and interpretability than other baselines.

ACAS Xu is an air-to-air collision avoidance system designed for unmanned aircraft that issues horizontal turn advisories to avoid an intruder aircraft. Due the use of a large lookup table in the design, a neural network compression of the policy was proposed. Analysis of this system has spurred a significant body of research in the formal methods community on neural network verification. While many powerful methods have been developed, most work focuses on open-loop properties of the networks, rather than the main point of the system -- collision avoidance -- which requires closed-loop analysis. In this work, we develop a technique to verify a closed-loop approximation of ACAS Xu using state quantization and backreachability. We use favorable assumptions for the analysis -- perfect sensor information, instant following of advisories, ideal aircraft maneuvers and an intruder that only flies straight. When the method fails to prove the system is safe, we refine the quantization parameters until generating counterexamples where the original (non-quantized) system also has collisions.

TinyML is a sort of machine learning in which deep learning networks are shrunk to fit on a piece of hardware. Artificial Intelligence and intelligent gadgets are combined in this project. In your pocket lies 45x18mm of Artificial Intelligence. Suddenly, your Arduino board's do-it-yourself weekend project has a little machine learning model implanted in it. New embedded machine learning frameworks will enable the spread of AI-powered IoT devices.

When it comes to manufacturing new lightweight, yet strong components for new passenger jets, scientists are treating the process like trying to brew the most delicious cup of coffee. By using artificial intelligence (AI) and machine learning, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory are intelligently and automatically selecting the perfect settings for a different kind of hot brew -- the process of friction stir welding, a common ingredient needed to manufacture airplane components. In a new collaboration with GE Research, Edison Welding Institute and GKN Aerospace, Argonne computer scientists are putting the power of the laboratory's automated machine learning expertise and supercomputers to use. By reducing the number of costly experiments and time-consuming simulations with a new machine learning approach, they can generate accurate models that provide valuable information about the welding process in much less time and at a fraction of the cost. This approach, called DeepHyper, is a scalable automated machine learning package developed by Argonne computational scientist Prasanna Balaprakash and his colleagues at Argonne.