Control Infrastructure for a Pulsed Ion Accelerator

We report on updates to the accelerator controls for the Neutralized Drift Compression Experiment II, a pulsed induction-type accelerator for heavy ions. The control infrastructure is built around a LabVIEW interface combined with an Apache Cassandra backend for data archiving. Recent upgrades added the storing and retrieving of device settings into the database, as well as ZeroMQ as a message broker that replaces LabVIEW's shared variables. Converting to ZeroMQ also allows easy access via other programming languages, such as Python

Manipulation of Grass Growth Through Strategic Distribution of Nitrogen Fertilisation

The objective of this study was to evaluate possibilities and limits of manipulating the grass growth of pastures by different nitrogen (N) application strategies with the aim to better synchronise grass supply and feed demand. In Switzerland, the use of N is strongly restricted by legislation. An efficient and well allocated N fertilisation is therefore important

Has the Ultra Low Emission Zone in London improved air quality?

London introduced the world's most stringent emissions zone, the Ultra Low Emission Zone (ULEZ), in April 2019 to reduce air pollutant emissions from road transport and accelerate compliance with the EU air quality standards. Combining meteorological normalisation, change point detection, and a regression discontinuity design with time as the forcing variable, we provide an ex-post causal analysis of air quality improvements attributable to the London ULEZ. We observe that the ULEZ caused only small improvements in air quality in the context of a longer-term downward trend in London's air pollution levels. Structural changes in nitrogen dioxide (NO2) and ozone (O3) concentrations were detected at 70% and 24% of the (roadside and background) monitoring sites and amongst the sites that showed a response, the relative changes in air pollution ranged from −9% to 6% for NO2, −5% to 4% for O3, and −6% to 4% for particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5). Aggregating the responses across London, we find an average reduction of less than 3% for NO2 concentrations, and insignificant effects on O3 and PM2.5 concentrations. As other cities consider implementing similar schemes, this study implies that the ULEZ on its own is not an effective strategy in the sense that the marginal causal effects were small. On the other hand, the ULEZ is one of many policies implemented to tackle air pollution in London, and in combination these have led to improvements in air quality that are clearly observable. Thus, reducing air pollution requires a multi-faceted set of policies that aim to reduce emissions across sectors with coordination among local, regional and national government

Control System for the LEDA 6.7-MeV Proton Beam Halo Experiment

Measurement of high-power proton beam-halo formation is the ongoing scientific experiment for the Low Energy Demonstration Accelerator (LEDA) facility. To attain this measurement goal, a 52-magnet beam line containing several types of beam diagnostic instrumentation is being installed. The Experimental Physics and Industrial Control System (EPICS) and commercial software applications are presently being integrated to provide a real-time, synchronous data acquisition and control system. This system is comprised of magnet control, vacuum control, motor control, data acquisition, and data analysis. Unique requirements led to the development and integration of customized software and hardware. EPICS real-time databases, Interactive Data Language (IDL) programs, LabVIEW Virtual Instruments (VI), and State Notation Language (SNL) sequences are hosted on VXI, PC, and UNIX-based platforms which interact using the EPICS Channel Access (CA) communication protocol. Acquisition and control hardware technology ranges from DSP-based diagnostic instrumentation to the PLC-controlled vacuum system. This paper describes the control system hardware and software design, and implementation.Comment: LINAC2000 Conference, 4 pg

FISH1D 2.1 User’s Manual

FISH1D is a computer program that solves the one-dimensional Poisson equation for electrostatic Fields In Semiconductor Heterostructures. The program will print or plot the electrostatic potential, electric field, electron and hole densities, dopant density, ionized dopant density, and other quantities of interest versus position at an applied bias voltage (assuming zero current). A capacitance or sheet carrier concentration versus voltage analysis may also be performed. While FISH1D was originally written for the ternary AlxGa1_xAs, it has been modified to simulate CdxHg1_xTe, ZnSe, GexSi1_x, and Si as well, and the program can be readily modified to analyze other semiconductors through the addition of new material subroutines or using the most recent option, the MATDEF card. This card enables the user to enter new material definitions by layers in the input deck without having to recompile, an advantage of FISH1D 2.1 over FISH1D 2.0. The primary purpose of this document is explain how to use FISH1D; for a more thorough discussion of the numerical implementation of FISH1D, the user is directed to the references. A theoretical basis for FISH1D is provided in Appendix I of this manual. The development of FISH1D was supported by the Semiconductor Research Corporation, the National Science Foundation Materials Research Laboratory, and by the Eastman Kodak Company

A reinforcement learning-based adaptive control model for future street planning an algorithm and a case study

With the emerging technologies in Intelligent Transportation System (ITS), the adaptive operation of road space is likely to be realised within decades. An intelligent street can learn and improve its decision-making on the right-of-way (ROW) for road users, liberating more active pedestrian space while maintaining traffic safety and efficiency. However, there is a lack of effective controlling techniques for these adaptive street infrastructures. To fill this gap in existing studies, we formulate this control problem as a Markov Game and develop a solution based on the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street parking areas in real-time. Integrated with the SUMO traffic simulator, this model was evaluated using the road network of the South Kensington District against three cases of divergent traffic conditions: pedestrian flow rates, AVs traffic flow rates and parking demands. Results reveal that our model can achieve an average reduction of 3.87% and 6.26% in street space assigned for on-street parking and vehicular operations. Combined with space gained by limiting the number of driving lanes, the average proportion of sidewalks to total widths of streets can significantly increase by 10.13%

Approximate optimum curbside utilisation for pick-up and drop-off (PUDO) and parking demands using reinforcement learning

With the uptake of automated transport, especially Pick-Up and Drop-Off (PUDO) operations of Shared Autonomous Vehicles (SAVs), the valet parking of passenger vehicles and delivery vans are envisaged to saturate our future streets. These emerging behaviours would join conventional on-street parking activities in an intensive competition for scarce curb resources. Existing curbside management approaches principally focus on those long-term parking demands, neglecting those short-term PUDO or docking events. Feasible solutions that coordinate diverse parking requests given limited curb space are still absent. We propose a Reinforcement Learning (RL) method to dynamically dispatch parking areas to accommodate a hybrid stream of parking behaviours. A partially-learning Deep Deterministic Policy Gradient (DDPG) algorithm is trained to approximate optimum dispatching strategies. Modelling results reveal satisfying convergence guarantees and robust learning patterns. Namely, the proposed model successfully discriminates parking demands of distinctive sorts and prioritises PUDOs and docking requests. Results also identify that when the demand-supply ratio situates at 2:1 to 4:1, the service rate approximates an optimal (83\%), and curbside occupancy surges to 80%. This work provides a novel intelligent dispatching model for diverse and fine-grained parking demands. Furthermore, it sheds light on deploying distinctive administrative strategies to the curbside in different contexts

A reinforcement learning-based adaptive control model for future street planning an algorithm and a case study

With the emerging technologies in Intelligent Transportation System (ITS), the adaptive operation of road space is likely to be realised within decades. An intelligent street can learn and improve its decision-making on the right-of-way (ROW) for road users, liberating more active pedestrian space while maintaining traffic safety and efficiency. However, there is a lack of effective controlling techniques for these adaptive street infrastructures. To fill this gap in existing studies, we formulate this control problem as a Markov Game and develop a solution based on the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street parking areas in real-time. Integrated with the SUMO traffic simulator, this model was evaluated using the road network of the South Kensington District against three cases of divergent traffic conditions: pedestrian flow rates, AVs traffic flow rates and parking demands. Results reveal that our model can achieve an average reduction of 3.87% and 6.26% in street space assigned for on-street parking and vehicular operations. Combined with space gained by limiting the number of driving lanes, the average proportion of sidewalks to total widths of streets can significantly increase by 10.13%