ICALEPCS2019 - List of Keywords (Windows)

Paper	Title	Other Keywords	Page
MOPHA014	Building and Packaging EPICS Modules With Conda	EPICS, Linux, factory, software	223
	B. Bertrand, A. Harrisson ESS, Lund, Sweden
	Conda is an open source package, dependency and environment management system. It runs on Windows, macOS and Linux and can package and distribute software for any language (Python, R, Ruby, C/C++…). It allows one to build a software in a clean and repeatable way. EPICS is made of many different modules that need to be compiled together. Conda makes it easy to define and track dependencies between EPICS base and the different modules (and their versions). Anaconda’s new compilers allow conda to build binaries that can run on any modern linux distribution (x86₆4). Not relying on any specific OS packages removes issues that can arise when upgrading the OS. At ESS, conda packages are built using gitlab-ci and pushed to a local channel on our Artifactory server. Using conda makes it easy for the users to install the EPICS modules they want, where they want (locally on a machine, in a docker container for testing…). All dependencies and requirements are handled by conda. Conda environments make it possible to work on different versions on the same machine without any conflict.
	Poster MOPHA014 [0.847 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA014
About •	paper received ※ 27 September 2019 paper accepted ※ 08 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPHA134	PyDM - Status Update	controls, framework, EPICS, Linux	536
	H.H. Slepicka, M.L. Gibbs SLAC, Menlo Park, California, USA
	PyDM (Python Display Manager) is a Python and Qt-based framework for building user interfaces for control systems providing a no-code, drag-and-drop system to make simple screens, as well as a straightforward Python framework to build complex applications. In this brief presentation we will talk about the state of PyDM, the new functionality that has been added in the last year of development, including full support for EPICS PVAccess and other structured data sources as well as the features targeted for release in 2020.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA134
About •	paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WECPL05	Migrating to Tiny Core Linux in a Control System	Linux, controls, hardware, embedded	920
	R.A. Washington STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
	The ISIS Accelerator Controls (IAC) group currently uses a version of Microsoft Windows Embedded as its chosen Operating System (OS) for control of front-line hardware. Upgrading to the current version of the Windows Embedded OS is not possible without also upgrading hardware, or changing the way software is delivered to the hardware platform. The memory requirements are simply too large to be considered a viable option. A new alternative was sought and that process led to Tiny Core Linux being selected due to its frugal memory requirements and ability to run from a RAM-disk. This paper describes the process of migrating from Windows Embedded Standard 2009 to Tiny Core Linux as the OS platform for IAC embedded hardware.
	Slides WECPL05 [1.455 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WECPL05
About •	paper received ※ 27 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPHA121	Deep Neural Network for Anomaly Detection in Accelerators	network, synchrotron, operation, controls	1375
	M. Piekarski, W.T. Kitka NSRC SOLARIS, Kraków, Poland J. Jaworek-Korjakowska AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Kraków, Poland
	The main goal of NSRC SOLARIS is to provide scientific community with high quality synchrotron light. In order to do this it is essential to monitor subsystems that are responsible for beam stability. In this paper a deep neural network for anomaly detection in time series data is proposed. Base model is a pre-trained, 19-layer convolutional neural network VGG-19. Its task is to identify abnormal status of sensors in certain time step. Each time window is a square matrix so can be treated as an image. Any kind of anomalies in synchrotron’s subsystems may lead to beam loss, affect experiments and in extreme cases can cause damage of the infrastructure, therefore when anomaly is detected operator should receive a warning about possible instability.
	Poster WEPHA121 [1.368 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA121
About •	paper received ※ 29 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WESH1003	jddd Migration to OpenJDK¹¹⁺: Benefits and Pitfalls	controls, interface, software, FEL	1501
	E. Sombrowski, K. Rehlich, G. Schlesselmann DESY, Hamburg, Germany
	The Java Doocs Data Display (jddd) is a Java-based tool for creating and running graphical user interfaces for accelerator control systems. It is the standard graphical user interface for operating the European XFEL accelerator. Since Java 8 Oracle introduced a number of major changes in the Java ecosystem’s legal and technical contexts that significantly impact Java developers and users. The most impactful changes for our software were the removal of Java Web Start, Oracles new licensing model and shorter release cycles. To keep jddd up to date, the source code had to be refactored and new distribution concepts for the different operating systems had to be developed. In this paper the benefits and pitfalls of the jddd migration from Oracle Java8 to OpenJDK¹¹⁺ will be described.
	Poster WESH1003 [7.285 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WESH1003
About •	paper received ※ 17 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THCPL05	Signal Analysis for Automated Diagnostic Applied to LHC Cryogenics	cryogenics, software, vacuum, controls	1601
	K.O.E. Martensson, B. Bradu, G. Ferlin CERN, Geneva, Switzerland
	The operation of the LHC at CERN is highly dependent on its associated infrastructure to operate properly, such as its cryogenic system where many conditions must be fulfilled for superconducting magnets and RF cavities. In 2018, the LHC cryogenic system caused 172 hours of accelerator downtime (out of 5760 running hours). Since the cryogenics recovery acts as a time amplifier, it is important to identify not optimized processes and malfunctioning systems at an early stage to anticipate losses of availability. The LHC cryogenic control systems embeds about 60,000 I/O whereof more than 20,000 analog signals which have to be monitored by operators. It is therefore crucial to select only the relevant and necessary information to be presented. This paper presents a signal analysis system created to automatically generate adequate daily reports on potential problems in the LHC cryogenic system which are not covered by conventional alarms, and examples of real issues that have been found and treated during the 2018 physics run. The analysis system, which is written in Python, is generic and can be applied to many different systems.
	Slides THCPL05 [1.781 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-THCPL05
About •	paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOPHA014

Building and Packaging EPICS Modules With Conda

EPICS, Linux, factory, software

223

B. Bertrand, A. Harrisson
ESS, Lund, Sweden

Conda is an open source package, dependency and environment management system. It runs on Windows, macOS and Linux and can package and distribute software for any language (Python, R, Ruby, C/C++…). It allows one to build a software in a clean and repeatable way. EPICS is made of many different modules that need to be compiled together. Conda makes it easy to define and track dependencies between EPICS base and the different modules (and their versions). Anaconda’s new compilers allow conda to build binaries that can run on any modern linux distribution (x86₆4). Not relying on any specific OS packages removes issues that can arise when upgrading the OS. At ESS, conda packages are built using gitlab-ci and pushed to a local channel on our Artifactory server. Using conda makes it easy for the users to install the EPICS modules they want, where they want (locally on a machine, in a docker container for testing…). All dependencies and requirements are handled by conda. Conda environments make it possible to work on different versions on the same machine without any conflict.

Poster MOPHA014 [0.847 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA014

About •

paper received ※ 27 September 2019 paper accepted ※ 08 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPHA134

PyDM - Status Update

controls, framework, EPICS, Linux

536

H.H. Slepicka, M.L. Gibbs
SLAC, Menlo Park, California, USA

PyDM (Python Display Manager) is a Python and Qt-based framework for building user interfaces for control systems providing a no-code, drag-and-drop system to make simple screens, as well as a straightforward Python framework to build complex applications. In this brief presentation we will talk about the state of PyDM, the new functionality that has been added in the last year of development, including full support for EPICS PVAccess and other structured data sources as well as the features targeted for release in 2020.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA134

About •

paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WECPL05

Migrating to Tiny Core Linux in a Control System

Linux, controls, hardware, embedded

920

R.A. Washington
STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom

The ISIS Accelerator Controls (IAC) group currently uses a version of Microsoft Windows Embedded as its chosen Operating System (OS) for control of front-line hardware. Upgrading to the current version of the Windows Embedded OS is not possible without also upgrading hardware, or changing the way software is delivered to the hardware platform. The memory requirements are simply too large to be considered a viable option. A new alternative was sought and that process led to Tiny Core Linux being selected due to its frugal memory requirements and ability to run from a RAM-disk. This paper describes the process of migrating from Windows Embedded Standard 2009 to Tiny Core Linux as the OS platform for IAC embedded hardware.

Slides WECPL05 [1.455 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WECPL05

About •

paper received ※ 27 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPHA121

Deep Neural Network for Anomaly Detection in Accelerators

network, synchrotron, operation, controls

1375

M. Piekarski, W.T. Kitka
NSRC SOLARIS, Kraków, Poland
J. Jaworek-Korjakowska
AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Kraków, Poland

The main goal of NSRC SOLARIS is to provide scientific community with high quality synchrotron light. In order to do this it is essential to monitor subsystems that are responsible for beam stability. In this paper a deep neural network for anomaly detection in time series data is proposed. Base model is a pre-trained, 19-layer convolutional neural network VGG-19. Its task is to identify abnormal status of sensors in certain time step. Each time window is a square matrix so can be treated as an image. Any kind of anomalies in synchrotron’s subsystems may lead to beam loss, affect experiments and in extreme cases can cause damage of the infrastructure, therefore when anomaly is detected operator should receive a warning about possible instability.

Poster WEPHA121 [1.368 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA121

About •

paper received ※ 29 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WESH1003

jddd Migration to OpenJDK¹¹⁺: Benefits and Pitfalls

controls, interface, software, FEL

1501

E. Sombrowski, K. Rehlich, G. Schlesselmann
DESY, Hamburg, Germany

The Java Doocs Data Display (jddd) is a Java-based tool for creating and running graphical user interfaces for accelerator control systems. It is the standard graphical user interface for operating the European XFEL accelerator. Since Java 8 Oracle introduced a number of major changes in the Java ecosystem’s legal and technical contexts that significantly impact Java developers and users. The most impactful changes for our software were the removal of Java Web Start, Oracles new licensing model and shorter release cycles. To keep jddd up to date, the source code had to be refactored and new distribution concepts for the different operating systems had to be developed. In this paper the benefits and pitfalls of the jddd migration from Oracle Java8 to OpenJDK¹¹⁺ will be described.

Poster WESH1003 [7.285 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WESH1003

About •

paper received ※ 17 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THCPL05

Signal Analysis for Automated Diagnostic Applied to LHC Cryogenics

cryogenics, software, vacuum, controls

1601

K.O.E. Martensson, B. Bradu, G. Ferlin
CERN, Geneva, Switzerland

The operation of the LHC at CERN is highly dependent on its associated infrastructure to operate properly, such as its cryogenic system where many conditions must be fulfilled for superconducting magnets and RF cavities. In 2018, the LHC cryogenic system caused 172 hours of accelerator downtime (out of 5760 running hours). Since the cryogenics recovery acts as a time amplifier, it is important to identify not optimized processes and malfunctioning systems at an early stage to anticipate losses of availability. The LHC cryogenic control systems embeds about 60,000 I/O whereof more than 20,000 analog signals which have to be monitored by operators. It is therefore crucial to select only the relevant and necessary information to be presented. This paper presents a signal analysis system created to automatically generate adequate daily reports on potential problems in the LHC cryogenic system which are not covered by conventional alarms, and examples of real issues that have been found and treated during the 2018 physics run. The analysis system, which is written in Python, is generic and can be applied to many different systems.

Slides THCPL05 [1.781 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-THCPL05

About •

paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)