Optimized High Precision Stacking of Fuel Cell Components for Medium to Large Production Volumes

PEM fuel cells are well established in a number of niche markets. However, due to low production volume and manufacturer-specific designs, the assembly has been carried out manually most of the time. With new fields of application being exploited there is a rising demand for production systems. As there is no standardized design or material, production systems are often custom-made, thus being inflexible to design changes or different products. In combination with a volatile demand the need for flexible and scalable systems arises. In this paper special attention is paid onto pick and place operations of the catalyst coated membrane (CCM). Design criteria of a vacuum gripper are derived from the material properties. To meet the further requirements for a high position accuracy in an automated assembly the impact of process parameters onto the repeatability is investigated to identify optimization trends. The requirements and investigations lead to a conceptual assembly system that is able to cover several steps in fuel cell production.

Fieldswarm technology for tillage and crop care

Today agriculture equipment is characterized by a high level of productivity and automation. Tractor-implement systems establish productivity by increasing working width and higher operational speed supported by larger storage volumes and increased tractor power availability. Dimensions and weight of tractor – implement systems have become the most important limitations for further growing productivity. A trend from Bigger, Faster, Wider towards Smart, Connected and Modular is under discussion. Modular and scalable systems with a high and variable degree of autonomy have potential to deliver configuration capability regarding actual task and field conditions while providing scalability of peak performance, productivity and economy. Possible machine concepts can consist of a tractor with automated and modular process units and / or autonomous self-propelled systems working as single unit or in a fleet (swarm). Swarm operation requires an adaptive and multimodal frontend to manage and lead a heterogeneous swarm of smart process applications. The example Feldschwarm, shown in the paper, is designed to the requirements of tillage and seeding in a first step.

Delivering unemployment assistance in times of crisis

Key Insights●The COVID-19 public health emergency caused widespread economic shutdown. The resulting surge in unemployment and Unemployment Insurance benefits claims threatened to overwhelm the legacy systems state workforce agencies rely on to collect, process, and pay claims.●In the State of Rhode Island, we developed a scalable cloud solution to collect Pandemic Unemployment Assistance claims robustly and securely. These claims are part of a new program created under the CARES Act that extended Unemployment Insurance benefits to independent contractors and gig-economy workers not covered by traditional Unemployment Insurance programs.●Our new system was developed, tested, and deployed within ten days following the passage of the CARES Act, making Rhode Island the first state in the country to collect, validate, and pay Pandemic Unemployment Assistance claims. A cloud-enhanced interactive voice response system was deployed a week later to handle the corresponding surge in weekly certifications for continuing unemployment benefits. ●Cloud solutions can augment legacy systems by offloading processes that are more efficiently handled in modern scalable systems, reserving the limited resources of legacy systems for what they were originally designed for. This agile use of combined technologies allowed Rhode Island to deliver timely Pandemic Unemployment Assistance benefits with an estimated cost savings of $502 thousand (representing a 411% return on investment).

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

The development in Multi-Robot Systems (MRS) has become one of the most exploited fields of research in robotics in recent years. This is due to the robustness and versatility they present to effectively undertake a set of tasks autonomously. One of the essential elements for several vehicles, in this case, Unmanned Aerial Vehicles (UAVs), to perform tasks autonomously and cooperatively is trajectory planning, which is necessary to guarantee the safe and collision-free movement of the different vehicles. This document includes the planning of multiple trajectories for a swarm of UAVs based on 3D Probabilistic Roadmaps (PRM). This swarm is capable of reaching different locations of interest in different cases (labeled and unlabeled), supporting of an Emergency Response Team (ERT) in emergencies in urban environments. In addition, an architecture based on Robot Operating System (ROS) is presented to allow the simulation and integration of the methods developed in a UAV swarm. This architecture allows the communications with the MavLink protocol and control via the Pixhawk autopilot, for a quick and easy implementation in real UAVs. The proposed method was validated by experiments simulating building emergences. Finally, the obtained results show that methods based on probability roadmaps create effective solutions in terms of calculation time in the case of scalable systems in different situations along with their integration into a versatile framework such as ROS.

The Scalable Systems Laboratory: a Platform for Software Innovation for HEP

The Scalable Systems Laboratory (SSL), part of the IRIS-HEP Software Institute, provides Institute participants and HEP software developers generally with a means to transition their R&D from conceptual toys to testbeds to production-scale prototypes. The SSL enables tooling, infrastructure, and services supporting innovation of novel analysis and data architectures, development of software elements and tool-chains, reproducible functional and scalability testing of service components, and foundational systems R&D for accelerated services developed by the Institute. The SSL is constructed with a core team having expertise in scale testing and deployment of services across a wide range of cyberinfrastructure. The core team embeds and partners with other areas in the Institute, and with LHC and other HEP development and operations teams as appropriate, to define investigations and required service deployment patterns. We describe the approach and experiences with early application deployments, including analysis platforms and intelligent data delivery systems.

Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions

We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making.

Big Data Processing and Big Analytics

Development and wide acceptance of data-driven applications in many aspects of our daily lives is generating waste volume of diverse data, which can be collected and analyzed to support various valuable decisions. Management and processing of this big data is a challenge. The development and extensive use of highly distributed and scalable systems to process big data have been widely considered. New data management architectures (e.g., distributed file systems and NoSQL databases) are used in this context. However, features of big data like their complexity and data analytics demands indicate that these concepts solve big data problems only partially. A development of so called NewSQL databases is highly relevant and even special category of big data management systems is considered. In this chapter, the authors discuss these trends and evaluate some current approaches to big data processing and analytics, identify the current challenges, and suggest possible research directions.