Paper | Title | Other Keywords | Page |
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MOAL01 | Maturity of the MAX IV Laboratory in Operation and Phase II Development | operation, controls, experiment, detector | 1 |
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MAX~IV Laboratory, the first 4th generation synchrotron located in the south of Sweden, entered operation in 2017 with the first three experimental stations. In the past two years the project organisation has been focused on phase II of the MAX IV Laboratory development, aiming to raise the number of beamlines in operation to 16. The KITS group, responsible for the control and computing systems of the entire laboratory, was a major actor in the realisation of this phase as well as in the continuous up-keep of the user operation. The challenge consisted principally of establishing a clear project management plan for the support groups, including KITS, to handle this high load in an efficient and focused way, meanwhile gaining the experience of operating a 4th generation light source. The momentum gained was impacted by the last extensive shutdown due to the pandemic and shifted toward the remote user experiment, taking advantage of web technologies. This article focuses on how KITS has handled this growing phase in term of technology and organisation, to finally describe the new perspective for the MAX IV Laboratory, which will face a bright future. | |||
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Slides MOAL01 [79.837 MB] | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOAL01 | ||
About • | Received ※ 10 October 2021 Revised ※ 22 November 2021 Accepted ※ 13 December 2021 Issue date ※ 22 December 2021 | ||
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MOPV016 | Design and Implement of Web Based SCADA System for HUST Field-Reversed Configuration Device | controls, SCADA, experiment, framework | 153 |
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As a large complex fusion research device for stud-ying field reversed configuration (FRC) plasma, HUST FRC(HFRC) is composed of many subsystems. In order to coordinate all systems and ensure the correct, orderly and stable operation of the whole experimental device, it is very important to have a unified and powerful control system. HFRC SCADA(Supervisory Control And Data Ac-quisition) system has selected the in-house developed CFET’Control system Framework for Experimental Devices Toolkit’as the control framework, with ad-vantages of strong abstraction, simplified framework, transparent protocol and flexible extension due to Web technology. | |||
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Poster MOPV016 [1.062 MB] | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOPV016 | ||
About • | Received ※ 09 October 2021 Revised ※ 16 October 2021 Accepted ※ 09 February 2022 Issue date ※ 23 February 2022 | ||
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TUPV005 | OPC-UA Data Acquisition for the C2MON Framework | monitoring, controls, SCADA, software | 376 |
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The CERN Control and Monitoring Framework(C2MON) is a monitoring platform developed at CERN and since 2016 made available under an LGPL3 open source license. It stands at the heart of the CERN Technical Infrastructure Monitoring (TIM) that supervises the correct functioning of CERN’s technical and safety infrastructure. This diverse technological infrastructure requires a variety of industrial communication protocols. OPC UA [2], an open and platform-independent architecture, can be leveraged as an integration protocol for a large number of existing data sources, and represents a welcome alternative to proprietary protocols. With the increasing relevance of the open communication standard OPC UA in the world of industrial control, adding OPC UA data acquisition capabilities to C2MON provides an opportunity to accommodate modern and industry-standard compatible use cases. This paper describes the design and development process of the C2MON OPC UA data acquisition module, the requirements it fulfills, as well as the opportunities for innovation it yields in the context of industrial controls at CERN. | |||
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Poster TUPV005 [0.548 MB] | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-TUPV005 | ||
About • | Received ※ 07 October 2021 Revised ※ 23 October 2021 Accepted ※ 20 November 2021 Issue date ※ 13 February 2022 | ||
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TUPV013 | Back End Event Builder Software Design for INO Mini-ICAL System | software, detector, network, monitoring | 413 |
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The Indian-based Neutrino Observatory collaboration has proposed to build a 50 KT magnetized Iron Calorimeter (ICAL) detector to study atmospheric neutrinos. The paper describes the design of back-end event builder for Mini-ICAL, which is a first prototype version of ICAL and consists of 20 Resistive Plate Chamber (RPC) detectors. The RPCs push the event and monitoring data using a multi-tier network technology to the event builder which carries out event building, event track display, data quality monitoring and data archival functions. The software has been designed for high performance and scalability using asynchronous data acquisition and lockless concurrent data structures. Data storage mechanisms like ROOT, Berkeley DB, Binary and Protocol Buffers were studied for performance and suitability. Server data push module designed using publish-subscribe pattern allowed transport & remote client implementation technology agnostic. Event Builder has been deployed at mini-ICAL with a throughput of 3MBps. Since the software modules have been designed for scalability, they can be easily adapted for the next prototype E-ICAL with 320 RPCs to have sustained data rate of 200MBps | |||
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Poster TUPV013 [0.760 MB] | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-TUPV013 | ||
About • | Received ※ 09 October 2021 Revised ※ 19 October 2021 Accepted ※ 24 February 2022 Issue date ※ 15 March 2022 | ||
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TUPV046 | Modification of Data Acquisition System in HLS-II Experimental Station | experiment, data-management, controls, synchrotron | 506 |
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With the proposal of the concept of super-facility in recent years, users of experimental stations only need to pay attention to data with scientific significance, and the management of massive experimental data are assisted by the super-facility technical support platform to effectively improve user efficiency. Based on this theory, we modified the data acquisition system of the XMCD experimental station in HLS-II. We continue to use LabVIEW software to reduce development workload. Meanwhile, we have added the interaction program with the high-level application in the original data acquisition process under the principle of keeping the user habits of XMCD experimental station. We have modularized the XMCD experimental software and redesigned the experimental architecture into 4 modules: Swiping Card Module, Experimental Equipment Control Module, Storage System Interaction Module and Data Management System Interaction Module. In this way, we have completed the collection of rawdata and metadata, the docking of the data persistent storage system, and the docking of data centralized management. | |||
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Poster TUPV046 [1.640 MB] | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-TUPV046 | ||
About • | Received ※ 09 October 2021 Revised ※ 06 November 2021 Accepted ※ 15 January 2022 Issue date ※ 15 March 2022 | ||
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WEPV025 | Initial Studies of Cavity Fault Prediction at Jefferson Laboratory | cavity, cryomodule, SRF, electron | 700 |
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Funding: This work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC05-06OR23177. The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach and report on initial results. Recent modifications to the low-level RF control system will provide access to streaming signals and we outline a path forward for leveraging deep learning on streaming data |
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Poster WEPV025 [1.111 MB] | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV025 | ||
About • | Received ※ 08 October 2021 Revised ※ 19 October 2021 Accepted ※ 11 February 2022 Issue date ※ 05 March 2022 | ||
Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||