1. General
- Project Status Reports
- Control System Upgrades
- Device Control and Integrating Diverse Systems
- Experiment Control
2. Software
- Software Technology Evolution
- User Interfaces and User eXperience (UX)
- Data Management
- Data Analytics
3. Hardware
- Hardware Technology
- Timing Systems, Synchronization and Real-Time Applications
- Control System Infrastructure
4. Subsystems
- Human Aspects, Collaborations, Management
- Functional Safety Systems for Machine Protection, Personnel Safety
- Feedback Control, Machine Tuning and Optimization
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1.1 Project Status Reports
This track presents an overview of new or upgraded experimental physics facilities from a control system perspective. Project Status Reports typically cover the stages of a project from the conceptual design through commissioning. Presentations should include descriptions of the most challenging controls issues facing the facility. Projects with novel, complex and/or very demanding control system requirements are strongly encouraged.
Topics covered in this track include:
- Reports on control system design and development for facilities such as particle accelerators and detectors, fusion devices, light and photon sources, neutron sources, telescopes, gravitational wave detectors among others.
Keywords: facility, project, design, installation, commissioning, operation, decommissioning, status
1.2 Control System Upgrades
This track focuses on upgrades to existing control systems for the purpose of improving sustainability and/or the provision of additional capabilities. Upgrades can include extensions or improvements of control system hardware, software, infrastructure or frameworks. Submissions are encouraged to include management of the change control process and transition of upgraded systems to operations.
Topics covered in this track include:
- Upgrades to existing control systems
- Technologies and tools used to facilitate the upgrade process
- Approach to selection and/or adoption of new technologies, and negotiations with stakeholders
- Design approach to optimize system flexibility, maintainability and sustainability
- Strategies for upgrade transitions (e.g. change management, testing, new operational models, new maintenance paradigms, operator training)
- Risk analysis and mitigation, strategy for minimizing downtime during the transition
- Lessons learned during the upgrade process
- Return On Investment (ROI) analysis, with considerations on Total Cost of Ownership (TCO) models
Keywords: legacy systems, upgrade, obsolete, maintainability, long-term support, shutdown periods, operations, risk analysis and mitigation, strategy, change management
1.3 Device Control and Integrating Diverse Systems
Large-scale experimental control systems are frequently built from the aggregation of many heterogeneous components comprised of in-house and commercial off-the-shelf systems. Component selection is driven by decisions of technical requirements, industrial standards, institutional policy, community best practices, and financial or resource considerations.
This track aims to present the experiences, issues and lessons learned related to the design, construction and evolution of diverse control system elements and their integration, covering architectures, technologies and methods.
Topics covered in this track include:
- Design paradigms and technology evolution (e.g. Industrial Internet of Things, interoperability)
- Control system coupling
- Low-level control component integration
- Integrating subsystems (e.g. vacuum, LLRF, power supplies)
- Customization levels of commercial off-the-shelf components
- Scalability and real-time performance
- Remote commissioning & control
Keywords: drivers, scalability, customization, integration, process control, SCADA, PLC, PAC, IPC, industrial communications, fieldbus, smart sensors, alarms, motion control, robotics, digital twin, dynamic simulation, virtual commissioning, fault detection, wireless, system-on-chip, IIoT, Remote Commissioning & control
1.4 Experiment Control
This track focuses on the domain specific control systems and data acquisition for user facility experiments. Experiment control systems must interact with a variety of instrument hardware, sample environment equipment, detectors, data acquisition electronics, and external systems such as accelerators. These control systems must be flexible and easy to use for experiments with quick turnaround and a heterogeneous user community.
Topics covered in this track include:
- Intelligent systems, automation applications and techniques
- Sample environment control including robotic sample changers
- High sample throughput capabilities, AI/ML supported decision making
- Interactive and scripting/macro environments for scanning, sequencing and run control
- Tools to support remote experiment participation, monitoring and access, given Covid 19 restrictions
- User interfaces, live feedback and on-line data reduction and visualization
- Cutting edge detector and data acquisition
- User information systems and databases
Keywords: intelligent systems, automation, macro, macro environment, scan, metadata, remote operation, data acquisition, sequencer, image acquisition, data reduction, data visualization, detectors, pixel array detector, CCD
2.1 Software Technology Evolution
This track covers new and/or innovative software technologies used to build control systems. Of particular interest is experience gained and lessons learned from applying new approaches in practical software development.
Topics covered in this track include:
- New control system frameworks and evolution of existing control system toolkits (e.g. EPICS, TANGO, DOOCS, ACS)
- Reports on performance and scalability of middleware technology and the usage of web services, microservices and service-oriented architecture (SOA). Example areas include:
- Messaging: e.g. ZeroMQ, ActiveMQ,DiagDetStat
- Stream processing: e.g. Kafka, ReactiveX
- Service mesh: e.g. Open Service Mesh, Istio, NATS
- Advances in software development techniques including new programming languages, design and code for easy debugging, refactoring in practice, model-driven development, test-driven development, domain-specific languages and code generation, and/or new operating systems or extensions
- Approaches to integrate control system frameworks and machine learning toolkits (e.g. TensorFlow, Spark, H2O, Singa)
Note: GUI toolkits, web tools and integration of low and high-level components are covered in other tracks.
Keywords: middleware, control system frameworks, web-services, SCADA
2.2 User Interfaces and User eXperience (UX)
This track focuses on how human beings interact with computer-based systems. This includes the user perspective, what humans expect from their experience, how humans control hardware as well as how humans interact with user interfaces.
Topics covered in this track include:
- User perspective and UX (User eXperience)
- User-oriented design
- Style guides, look and feel
- Application development strategies (e.g. native apps, cross-platform apps, Web-apps)
- Emerging human-machine-interface trends (e.g. voice / gesture / tactile control)
- Robot / Avatar control interfaces (e.g. virtual / augmented / mixed reality, smart glasses)
- Interface building toolkits (e.g. CSS, JDDD, Taurus, ATK) and programming languages
- Data visualization tools (e.g. archive viewers, plotting)
- Reporting tools (e.g. dashboards, electronic logbooks, dashboards, alarm handlers)
Keywords: data visualization tools, reporting tools, GUI building toolkits, GUI application development, human-machine-interface, robot / avatar control interfaces
2.3 Data Management
The contributions to this track focus on the lifecycle and the management of scientific, operations, and engineering data. Data and MetaData policies, guidelines and processes related to samples, experiments, processes and publications are of a particular interest as well as the constraint arose from infrastructure like storage and processing, indexing, search, and retrieval of datasets as well as metadata.
Topics covered in this track include:
- Data Governance
- Data formats, metadata systems and ontologies
- Data catalogue and portal
- Laboratory Information Management Systems
- Data center for scientific data management
- Public and private clouds
- High performance data storage systems
- Distributed database management
- Data Architecture (standards, data model)
- Data Quality (integrity, quality, QA)
- Master Data (integration, reference, compression, replication, criticality, reduction)
- Data Warehousing (mining, extraction, transform, deploy)
- Best Practices
Note: Technology should be cited that supports the data management application and its benefit to this field. Installation and management of IT infrastructure (not related to data management) is covered in ‘Control System Infrastructure’. Data interpretation and detailed descriptions of algorithms is covered in ‘Data Analytics’.
Keywords: open data, FAIR principles, meta data, curation, raw data, processed data, long term storage, data security, catalogues, data standards, data models, data quality, data tiers, restrictions, data transfer, edge, replication.
2.4 Data Analytics
This track focuses on interpreting and examining datasets. This includes scientific data analysis, uncovering hidden patterns, correlations and other insights from different data sources. Use cases can cover experiment types, the plants under control, or the control systems themselves. Data can be analyzed on-line, off-line or in a combination of both. Analytics also favors data visualization to communicate insights, inferring conclusions and representing the results in a comprehensible form.
This track is linked to the Tracks ‘Experiment Control’ and ‘Feedback Control’ (in case of On-line Analysis), ‘Data Management’ (for data formats and metadata), and ‘Integrating Diverse Systems’ (on experiment simulation)
Topics covered in this track include:
- Scientific Data Modeling
- Analytics ecosystems (e.g. Hadoop), frameworks (e.g. Spark)
- Data mining (e.g. ElasticSearch)
- NoSQL and time series databases (e.g. Cassandra, MongoDB)
- Predictive analytics
- Machine learning
- Statistics, and statistical models
- Dependability
- Risk, Bayes analysis
- Real time analytics
- Searching and retrieval
Keywords: data analysis, analytics, real-time data, acquisition, visualization, NoSQL, SQL, big data, anomaly detection, notebooks
3.1 Hardware Technology
This track focuses on hardware and gateware (FPGA/HDL) design as applied to the operation of large physics facilities, with an emphasis on collaborative efforts among laboratories and companies using Open Source Hardware practices.
Topics covered in this track include:
- Hardware platforms: microTCA.4, xTCA, FMC, VME, VXS, VPX, PCI/PCIe, PXI/PXIe, Network Attached Devices (NAD)
- Innovative printed circuit board (PCB) design
- Designing for programmable logic, such as Field Programmable Gate Arrays (FPGA), and System-on-Chip (SoC) platforms
- Best practices and hardware/software co-design
- Hardware and gateware modelling, simulation, verification and testing
- Data links for distributed controls and data acquisition
- Radiation-hardened design
- Collaborative design tools
- Reliability and Electromagnetic Compatibility (EMC), availability and redundancy
- Integrated self-diagnostics
- Upgrade and maintenance strategies and life cycle management
- Continuous Integration/Continous Deployment practices for hardware platforms
- Code generation tools for HDL
Keywords: xTCA, FMC, VME, PCI, PCIe, PCB, FPGA, HDL, SoC, EMC, Open Source
3.2 Timing Systems, Synchronization and Real-Time Applications
This track focuses on custom-fit solutions developed to accomplish that time precision, stability or jitter are managed to fulfill system requirements.
Topics covered in this track include:
- Timing system architectures and technologies. Overview of new projects and upgrades at facility level.
- Solutions for extremely high precision timing requirements, from the development of specific components to advanced system architectures.
- Hardware-based systems for beam diagnostic and experiment synchronization, including beam synchronous trigger techniques for data acquisition and pump and probe applications.
- Real-time systems, including the experiment control systems with tight time constraints.
Keywords: timing, synchronization, timing protocol, event systems, distributed clock systems, pump and probe, timestamp, continuous scan, real-time control system
3.3 Control System Infrastructure
This track addresses the IT infrastructure of networks, processing nodes, data storage systems and database architecture used in Controls Systems. This includes cloud computing solutions as well as a special focus on cyber security as it is becoming a major issue in our facilities.
Topics covered in this track include:
- Ethernet networks, VLAN, switches, topology and administration
- Data center design and architectures including power management and cooling
- Storage systems architectures, files systems, hierarchical storage management
- Database engines
- High performance computing and cloud computing
- Application hosting: virtualization, container management, orchestration
- Configuration management and code deployment, patch management and security, software installation methods and tools for distributed processors
- Disaster recovery strategy, vulnerability management
- Cybersecurity, remote access, related security measures, intrusion detection and prevention
- Infrastructure monitoring and technical solution (Security Operations Center, log analysis)
Keywords: operating systems and proprietary operating systems, hypervisors, containers, orchestration, virtual machines, HPC, network, multi-core processor, VPN, firewall, VLAN, backup and recovery, storage, cybersecurity, infrastructure monitoring
4.1 Human Aspects, Collaborations, Management
This track focuses on human aspects, leadership, collaboration environments as well as operations, maintenance and project management topics. It covers tools, processes and analytics used to effectively support control systems over their full life cycle.
Topics covered in this track include:
- Human aspects such as diversity at the workplace and work-life balance
- Educating, attracting and mentoring the next generation of controls engineers
- Leadership in a local, shared and distributed environment
- Team building in diverse and/or multicultural environments
- Virtual work environment and challenges
- Collaborative processes and tools (e.g. teleconferencing, social media, joint document creation and sharing) enabling effective interactions between diverse institutes and countries
- Risk management (identification, evaluation, prioritization, and tracking) in design, construction and commissioning processes
- Processes and analytics to efficiently support the operations & maintenance of an existing facility (e.g. data driven maintenance, ...)
- Requirements and interface management (defining, controlling, and communicating the information)
- Lessons learned through project lifecycle phases and their application in future projects
- Scope, schedule, budget, life cycle cost & project cost estimation, and quality assurance management techniques
- System engineering and project management tools and approaches
Keywords: systems engineering, project management, operational & maintenance management, risk management, collaborative tools & processes, remote work, human aspects, diversity & multicultural environments, education, mentoring & coaching, succession planning
4.2 Functional Safety Systems for Machine Protection, Personnel Safety
This track presents the role and implications of functional safety systems. This includes machine protection systems used for the protection of equipment, and personnel safety systems used for the protection of people.
Topics covered in this track include:
- Standards, methods and processes used to develop, commission, and maintain safety systems
- Use and qualification of commercial/complex devices such as FPGAs, ASICS, and general-purpose computers/software for functional safety applications
- Use and qualification of novel beam instrumentation such as current monitors, loss monitors, and radiation monitors in functional safety systems
- Model based functional safety system engineering approaches such as system/software modeling, automated code generation, automated Verification & Validation activities
- Aspects of new/upgraded functional safety systems such as specification, architecture, reliability, availability, and maintainability
- Operational experience and lessons learned such as assessment of successes and failures
- Human factors as it pertains to how the human/machine interface contributes positively to successful operation and ease of use
- Cyber security as a reliability/risk factor in functional safety systems
Keywords: PPS, PSS, EPS, MPS, PLC, SIL, radiation protection, risk analysis, interlock, human factors, safety inspection
4.3 Feedback Control, Machine Tuning and Optimization
Modern experimental physics facilities are very complex machines that cannot be operated without the use of sophisticated systems to automate tasks where manual management by physicists or operators is not reasonable or possible.
Examples are optimization tools for tuning and improving machine performance, as well as feedback and feed-forward systems assuring the stability of critical parameters during operation.
Of particular interest are systems featuring human-like capabilities, able to learn and adapt to different situations. These systems can benefit from the acquired knowledge and gained experience to understand behaviors and recognize phenomena, and eventually support humans in solving complex problems.
Topics covered in this track include:
- Software or hardware feedback and feed-forward systems
- System identification, modelling and simulation
- Advanced controls: Predictive, adaptive techniques
- Predictive analysis and anomaly detection
- Automatic tuning and optimization techniques
- Artificial intelligence: machine learning, neural networks, expert systems.
Keywords: feedback, feed-forward, system identification, process modeling, dynamic simulation, predictive analysis, adaptive control, intelligent automation, artificial intelligence, expert systems, machine learning, automatic tuning, artificial neural networks, multi objective optimization, anomaly detection
