Non-noble electrocatalysts for alkaline fuel cells

The doping of solid phase precursors followed by pyrolysis or the copyrolysis of gas phase precursors has allowed us to produce catalysts with good activity toward oxygen reduction. Efforts are currently underway to better understand the reasons for the catalytic activity of the bulk doped catalysts with a view toward further improving their activity

Advanced double layer capacitors

There is a need for large amounts of power to be delivered rapidly in a number of airborne and space systems. Conventional, portable power sources, such as batteries, are not suited to delivering high peak power pulses. The charge stored at the electrode-electrolyte double layer is, however, much more assessible on a short time scale. Devices exploiting this concept were fabricated using carbon and metal oxides (Pinnacle Research) as the electrodes and sulfuric acid as the electrolyte. The approach reported, replaces the liquid sulfuric acid electrolyte with a solid ionomer electrolyte. The challenge is to form a solid electrode-solid ionomer electrolyte composite which has a high capacitance per geometric area. The approach to maximize contact between the electrode particles and the ionomer was to impregnate the electrode particles using a liquid ionomer solution and to bond the solvent-free structure to a solid ionomer membrane. Ruthenium dioxide is the electrode material used. Three strategies are being pursued to provide for a high area electrode-ionomer contact: mixing of the RuOx with a small volume of ionomer solution followed by filtration to remove the solvent, and impregnation of the ionomer into an already formed RuOx electrode. RuOx powder and electrodes were examined by non-electrochemical techniques. X-ray diffraction has shown that the material is almost pure RuO2. The electrode structure depends on the processing technique used to introduce the Nafion. Impregnated electrodes have Nafion concentrated near the surface. Electrodes prepared by the evaporation method show large aggregates of crystals surrounded by Nafion

Optimal Tracking Of Nonlinear Discrete-time Systems Using Zero-Sum Game Formulation And Hybrid Learning

This paper presents a novel hybrid learning-based optimal tracking method to address zero-sum game problems for partially uncertain nonlinear discrete-time systems. An augmented system and its associated discounted cost function are defined to address optimal tracking. Three multi-layer neural networks (NNs) are utilized to approximate the optimal control and the worst-case disturbance inputs, and the value function. The critic weights are tuned using the hybrid technique, whose weights are updated once at the sampling instants and in an iterative manner over finite times within the sampling instants. The proposed hybrid technique helps accelerate the convergence of the approximated value functional to its actual value, which makes the optimal policy attain quicker. A two-layer NN-based actor generates the optimal control input, and its weights are adjusted based on control input errors. Moreover, the concurrent learning method is utilized to ease the requirement of persistent excitation. Further, the Lyapunov method investigates the stability of the closed-loop system. Finally, the proposed method is evaluated on a two-link robot arm and demonstrates promising results

Continual Optimal Adaptive Tracking Of Uncertain Nonlinear Continuous-time Systems Using Multilayer Neural Networks

This study provides a lifelong integral reinforcement learning (LIRL)-based optimal tracking scheme for uncertain nonlinear continuous-time (CT) systems using multilayer neural network (MNN). In this LIRL framework, the optimal control policies are generated by using both the critic neural network (NN) weights and single-layer NN identifier. The critic MNN weight tuning is accomplished using an improved singular value decomposition (SVD) of its activation function gradient. The NN identifier, on the other hand, provides the control coefficient matrix for computing the control policies. An online weight velocity attenuation (WVA)-based consolidation scheme is proposed wherein the significance of weights is derived by using Hamilton-Jacobi-Bellman (HJB) error. This WVA term is incorporated in the critic MNN update law to overcome catastrophic forgetting. Lyapunov stability is employed to demonstrate the uniform ultimate boundedness of the overall closed-loop system. Finally, a numerical example of a two-link robotic manipulator supports the theoretical claims

Lifelong Learning Control of Nonlinear Systems with Constraints using Multilayer Neural Networks with Application to Mobile Robot Tracking

This Paper Presents a Novel Lifelong Multilayer Neural Network (MNN) Tracking Approach for an Uncertain Nonlinear Continuous-Time Strict Feedback System that is Subject to Time-Varying State Constraints. the Proposed Method Uses a Time-Varying Barrier Function to Accommodate the Constraints Leading to the Development of an Efficient Control Scheme. the Unknown Dynamics Are Approximated using a MNN, with Weights Tuned using a Singular Value Decomposition (SVD)-Based Technique. an Online Lifelong Learning (LL) based Elastic Weight Consolidation (EWC) Scheme is Also Incorporated to Alleviate the Issue of Catastrophic Forgetting. the Stability of the overall Closed-Loop System is Analyzed using Lyapunov Analysis. the Effectiveness of the Proposed Method is Demonstrated by using a Quadratic Cost Function through a Numerical Example of Mobile Robot Control Which Demonstrates a 38% Total Cost Reduction When Compared to the Recent Literature and 6% Cost Reduction is Observed When the Proposed Method with LL is Compared to the Proposed Method Without LL

Continual Learning-Based Optimal Output Tracking of Nonlinear Discrete-Time Systems with Constraints: Application to Safe Cargo Transfer

This Paper Addresses a Novel Lifelong Learning (LL)-Based Optimal Output Tracking Control of Uncertain Non-Linear Affine Discrete-Time Systems (DT) with State Constraints. First, to Deal with Optimal Tracking and Reduce the Steady State Error, a Novel Augmented System, Including Tracking Error and its Integral Value and Desired Trajectory, is Proposed. to Guarantee Safety, an Asymmetric Barrier Function (BF) is Incorporated into the Utility Function to Keep the Tracking Error in a Safe Region. Then, an Adaptive Neural Network (NN) Observer is Employed to Estimate the State Vector and the Control Input Matrix of the Uncertain Nonlinear System. Next, an NN-Based Actor-Critic Framework is Utilized to Estimate the Optimal Control Input and the Value Function by using the Estimated State Vector and Control Coefficient Matrix. to Achieve LL for a Multitask Environment in Order to Avoid the Catastrophic Forgetting Issue, the Exponential Weight Velocity Attenuation (EWVA) Scheme is Integrated into the Critic Update Law. Finally, the Proposed Tracker is Applied to a Safe Cargo/ Crew Transfer from a Large Cargo Ship to a Lighter Surface Effect Ship (SES) in Severe Sea Conditions

Advanced double layer capacitors

Work was conducted that could lead to a high energy density electrochemical capacitor, completely free of liquid electrolyte. A three-dimensional RuO sub x-ionomer composite structure has been successfully formed and appears to provide an ionomer ionic linkage throughout the composite structure. Capacitance values of approximately 0.6 F/sq cm were obtained compared with 1 F/sq cm when a liquid electrolyte is used with the same configuration

Lifelong Learning-Based Multilayer Neural Network Control of Nonlinear Continuous-Time Strict-Feedback Systems

In This Paper, We Investigate Lifelong Learning (LL)-Based Tracking Control for Partially Uncertain Strict Feedback Nonlinear Systems with State Constraints, employing a Singular Value Decomposition (SVD) of the Multilayer Neural Networks (MNNs) Activation Function based Weight Tuning Scheme. the Novel SVD-Based Approach Extends the MNN Weight Tuning to (Formula Presented.) Layers. a Unique Online LL Method, based on Tracking Error, is Integrated into the MNN Weight Update Laws to Counteract Catastrophic Forgetting. to Adeptly Address Constraints for Safety Assurances, Taking into Account the Effects Caused by Disturbances, We Utilize a Time-Varying Barrier Lyapunov Function (TBLF) that Ensures a Uniformly Ultimately Bounded Closed-Loop System. the Effectiveness of the Proposed Safe LL MNN Approach is Demonstrated through a Leader-Follower Formation Scenario Involving Unknown Kinematics and Dynamics. Supporting Simulation Results of Mobile Robot Formation Control Are Provided, Confirming the Theoretical Findings

Economic Impact Of India-China Trade War: Future Directions

The growing trade war among India and China is creating imbalance in the among developing countries. Both countries are affecting in their business prospects. India is basically importing raw material for export of good and services. Growing tension leads to unnecessary growth impetus which affects industry growth, loss of employment opportunities and other trade related problems. India has considerable potential for reducing its trade deficit with China, as we can see from Made-in-China products sold on the Indian market. Most of them are low- and mid-range products. India can make these things itself. The value tune to the cores of rupees is loss for the both counties; it will create far reaching impact in Indian business environment. These papers highlight the possible causes and consequences of trade war between to Asian giants and suggest how to promote regional growth prospects for speedy development of economics

Rate-Based End-to-End Congestion Control of Multimedia Traffic in Packet Switched Networks

This paper proposes an explicit rate-based end-to-end congestion control mechanism to alleviate congestion of multimedia traffic in packet switched networks such as the Internet. The congestion is controlled by adjusting the transmission rates of the sources in response to the feedback information from destination such as the buffer occupancy, packet arrival rate and service rate at the outgoing link, so that a desired quality of service (QoS) can be met. The QoS is defined in terms of packet loss ratio, transmission delay, power, and network utilization. Comparison studies demonstrate the effectiveness of the proposed scheme over New-Reno TCP (a variant of AIMD: additive increase multiplicative decrease) technique during simulated congestion. Since it is end-to-end, no router support is necessary, the proposed methodology can be readily applied to today\u27s Internet, as well as for real-time video and voice data transfer in unicast networks