The purpose of this document is to guide you step by step in exploring the various basic features of Xcos tool included in Scilab for a user who has never used a hybrid dynamic systems modeler and simulator. This presentation is intentionally limited to the essential to allow easier handling of Xcos.
There is increasing need for large-scale recommendation systems. Typical solutions rely on periodically retrained batch algorithms, but for massive amounts of data, training a new model could take hours. This is a problem when the model needs to be more up-to-date. For example, when recommending TV programs while they are being transmitted the model should take into consideration users who watch a program at that time.
The promise of online recommendation systems is fast adaptation to changes, but methods of online machine learning from streams is commonly believed to be more restricted and hence less accurate than batch trained models. Combining batch and online learning could lead to a quickly adapting recommendation system with increased accuracy. However, designing a scalable data system for uniting batch and online recommendation algorithms is a challenging task. In this talk we present our experiences in creating such a recommendation engine with Apache Flink and Apache Spark.
6 Nines: How Stripe keeps Kafka highly-available across the globe with Donny ...HostedbyConfluent
Availability is a key metric for any Kafka deployment, but when every event is critical the system must be centered around keeping publishers and consumers highly available, even when a Kafka cluster goes down. At Stripe our core business relies on Kafka, and as we outgrew a single Kafka cluster we had to build a multi-cluster system which would fit our needs while supporting a target of 99.9999% availability for our most critical use cases.
In this talk we’ll discuss our solution to this problem: an in-house proxy layer and multi-cluster toplogy which we’ve built and operated over the past 3 years. Our proxy layer enables multiple Kafka clusters to work in coordination across the globe, while hitting our ambitious availability targets and providing clean client abstractions.
In this talk we’ll discuss how our Kafka deployment provides: availability for both publishers and consumers in the face of cluster outages, increased security and observability, simplified cluster maintenance, and global routing for constraints such as data locality. We’ll highlight the benefits & tradeoffs of our approach, the design of our proxy layer, Kafka configuration decisions, and where we’re planning to go from here.
PostgreSQL continuous backup and PITR with BarmanEDB
How can I achieve an RPO of 5 minutes for the backups of my PostgreSQL databases? And what about RPO=0 for zero data loss backups? This talk will give you answers to those questions, by guiding you through an overview of Disaster Recovery of PostgreSQL databases with Barman, covering its key concepts and providing useful patterns and tips.
ABRIS: Avro Bridge for Apache Spark with Felipe Melo and Georgi ChochovDatabricks
Avro has become the standard format for data serialization and data in motion. It provides rich and evolvable data structures and a compact, fast and language agnostic binary data format. Alongside the format itself, a number of instrumental technologies have been created to support schema management, such as Confluent’s Schema Registry. Currently, except for batch oriented jobs, the burden of integrating Avro with Spark is all on user’s shoulders.
Additionally, APIs for integrating with Schema Registry are still missing, making schema evolution in Spark applications that rely on Avro unnecessarily complicated. ABRiS solves those gaps by allowing applications using Spark structured APIs to seamlessly integrate with Avro. It provides conversions between Avro records and Spark Rows in a storage-agnostic manner and integrated with Schema Registry, all through a clean and simple API. In this presentation we cover how ABRiS works and demonstrate through simple examples how to use its features.
This session is aimed at the regular ISPF user who wants to learn about recent features of ISPF that can make life easier, and also at those that want to learn about the new features for ISPF in z/OS V2R2.
KSQL in Practice (Almog Gavra, Confluent) Kafka Summit London 2019confluent
KSQL is a streaming SQL engine for Apache Kafka. The focus of this talk is to educate users on how to build, deploy, operate, and maintain KSQL applications. It is meant for developers and teams looking to leverage KSQL to build production data pipelines. The audience will get an overview of how KSQL works, how to test their KSQL applications in development environments, the deployment options in production, and some common troubleshooting techniques for when things go wrong. The talk will cover the latest best practices for running KSQL in production, as well as look forward to what we plan to do to improve the KSQL operational experience.
There is increasing need for large-scale recommendation systems. Typical solutions rely on periodically retrained batch algorithms, but for massive amounts of data, training a new model could take hours. This is a problem when the model needs to be more up-to-date. For example, when recommending TV programs while they are being transmitted the model should take into consideration users who watch a program at that time.
The promise of online recommendation systems is fast adaptation to changes, but methods of online machine learning from streams is commonly believed to be more restricted and hence less accurate than batch trained models. Combining batch and online learning could lead to a quickly adapting recommendation system with increased accuracy. However, designing a scalable data system for uniting batch and online recommendation algorithms is a challenging task. In this talk we present our experiences in creating such a recommendation engine with Apache Flink and Apache Spark.
6 Nines: How Stripe keeps Kafka highly-available across the globe with Donny ...HostedbyConfluent
Availability is a key metric for any Kafka deployment, but when every event is critical the system must be centered around keeping publishers and consumers highly available, even when a Kafka cluster goes down. At Stripe our core business relies on Kafka, and as we outgrew a single Kafka cluster we had to build a multi-cluster system which would fit our needs while supporting a target of 99.9999% availability for our most critical use cases.
In this talk we’ll discuss our solution to this problem: an in-house proxy layer and multi-cluster toplogy which we’ve built and operated over the past 3 years. Our proxy layer enables multiple Kafka clusters to work in coordination across the globe, while hitting our ambitious availability targets and providing clean client abstractions.
In this talk we’ll discuss how our Kafka deployment provides: availability for both publishers and consumers in the face of cluster outages, increased security and observability, simplified cluster maintenance, and global routing for constraints such as data locality. We’ll highlight the benefits & tradeoffs of our approach, the design of our proxy layer, Kafka configuration decisions, and where we’re planning to go from here.
PostgreSQL continuous backup and PITR with BarmanEDB
How can I achieve an RPO of 5 minutes for the backups of my PostgreSQL databases? And what about RPO=0 for zero data loss backups? This talk will give you answers to those questions, by guiding you through an overview of Disaster Recovery of PostgreSQL databases with Barman, covering its key concepts and providing useful patterns and tips.
ABRIS: Avro Bridge for Apache Spark with Felipe Melo and Georgi ChochovDatabricks
Avro has become the standard format for data serialization and data in motion. It provides rich and evolvable data structures and a compact, fast and language agnostic binary data format. Alongside the format itself, a number of instrumental technologies have been created to support schema management, such as Confluent’s Schema Registry. Currently, except for batch oriented jobs, the burden of integrating Avro with Spark is all on user’s shoulders.
Additionally, APIs for integrating with Schema Registry are still missing, making schema evolution in Spark applications that rely on Avro unnecessarily complicated. ABRiS solves those gaps by allowing applications using Spark structured APIs to seamlessly integrate with Avro. It provides conversions between Avro records and Spark Rows in a storage-agnostic manner and integrated with Schema Registry, all through a clean and simple API. In this presentation we cover how ABRiS works and demonstrate through simple examples how to use its features.
This session is aimed at the regular ISPF user who wants to learn about recent features of ISPF that can make life easier, and also at those that want to learn about the new features for ISPF in z/OS V2R2.
KSQL in Practice (Almog Gavra, Confluent) Kafka Summit London 2019confluent
KSQL is a streaming SQL engine for Apache Kafka. The focus of this talk is to educate users on how to build, deploy, operate, and maintain KSQL applications. It is meant for developers and teams looking to leverage KSQL to build production data pipelines. The audience will get an overview of how KSQL works, how to test their KSQL applications in development environments, the deployment options in production, and some common troubleshooting techniques for when things go wrong. The talk will cover the latest best practices for running KSQL in production, as well as look forward to what we plan to do to improve the KSQL operational experience.
Slides from Talk by Jan Medved on Yang modeling and its support in OpenDaylight meetup
http://www.meetup.com/OpenDaylight-Silicon-Valley/events/212834752
Yang is a data modeling language that is rapidly being adopted to model Netconf, an IETF standardized network management protocol, as well as to model other data interfaces in OpenDaylight. Join us for the talk by expert Jan Medved to learn about Yang and its usage within OpenDaylight.
Maxim Fateev - Beyond the Watermark- On-Demand Backfilling in FlinkFlink Forward
http://flink-forward.org/kb_sessions/beyond-the-watermark-on-demand-backfilling-in-flink/
Flink has consistency guarantees and efficient checkpointing model which make it a good fit for Uber’s money-related use cases, such as driver incentives. However, Flink’s time-progress model is built around a single watermark, which is incompatible with Uber’s business need for generating aggregates retroactively. The talk covers our solution for on-demand backfilling. It also outlines other abstractions and features we expect Flink to support as it matures.
When does InnoDB lock a row? Multiple rows? Why would it lock a gap? How do transactions affect these scenarios? Locking is one of the more opaque features of MySQL, but it’s very important for both developers and DBA’s to understand if they want their applications to work with high performance and concurrency. This is a creative presentation to illustrate the scenarios for locking in InnoDB and make these scenarios easier to visualize. I'll cover: key locks, table locks, gap locks, shared locks, exclusive locks, intention locks, insert locks, auto-inc locks, and also conditions for deadlocks.
Netflix’s architecture involves thousands of microservices built to serve unique business needs. As this architecture grew, it became clear that the data storage and query needs were unique to each area; there is no one silver bullet which fits the data needs for all microservices. CDE (Cloud Database Engineering team) offers polyglot persistence, which promises to offer ideal matches between problem spaces and persistence solutions. In this meetup you will get a deep dive into the Self service platform, our solution to repairing Cassandra data reliably across different datacenters, Memcached Flash and cross region replication and Graph database evolution at Netflix.
Introducing the Apache Flink Kubernetes OperatorFlink Forward
Flink Forward San Francisco 2022.
The Apache Flink Kubernetes Operator provides a consistent approach to manage Flink applications automatically, without any human interaction, by extending the Kubernetes API. Given the increasing adoption of Kubernetes based Flink deployments the community has been working on a Kubernetes native solution as part of Flink that can benefit from the rich experience of community members and ultimately make Flink easier to adopt. In this talk we give a technical introduction to the Flink Kubernetes Operator and demonstrate the core features and use-cases through in-depth examples."
by
Thomas Weise
Slides for my talk event-sourced architectures with Akka. Discusses Akka Persistence as mechanism to do event-sourcing. Presented at Javaone 2014 and Jfokus 2015.
Apache Flink 101 - the rise of stream processing and beyondBowen Li
Apache Flink is the most popular and widely adopted streaming processing framework, powering real time stream event computations at extremely large scale in companies like Uber, Lyft, AWS, Alibaba, Pinterest, Splunk, Yelp, etc.
In this talk, we will go over use cases and basic (yet hard to achieve!) requirements of stream processing, and how Flink fills the gaps and stands out with some of its unique core building blocks, like pipelined execution, native event time support, state support, and fault tolerance.
We will also take a look at how Flink is going beyond stream processing into areas like unified data processing, enterprise intergration, AI/machine learning (especially online ML), and serverless computation, and how Flink fits with its distinct value.
SPEAKER: Bowen Li
SPEAKER BIO: Bowen is a committer of Apache Flink, senior engineer at Alibaba, and host of Seattle Flink Meetup.
“Alexa, be quiet!”: End-to-end near-real time model building and evaluation i...Flink Forward
Flink Forward San Francisco 2022.
To improve Amazon Alexa experiences and support machine learning inference at scale, we built an automated end-to-end solution for incremental model building or fine-tuning machine learning models through continuous learning, continual learning, and/or semi-supervised active learning. Customer privacy is our top concern at Alexa, and as we build solutions, we face unique challenges when operating at scale such as supporting multiple applications with tens of thousands of transactions per second with several dependencies including near-real time inference endpoints at low latencies. Apache Flink helps us transform and discover metrics in near-real time in our solution. In this talk, we will cover the challenges that we faced, how we scale the infrastructure to meet the needs of ML teams across Alexa, and go into how we enable specific use cases that use Apache Flink on Amazon Kinesis Data Analytics to improve Alexa experiences to delight our customers while preserving their privacy.
by
Aansh Shah
Using the New Apache Flink Kubernetes Operator in a Production DeploymentFlink Forward
Flink Forward San Francisco 2022.
Running natively on Kubernetes, using the new Apache Flink Kubernetes Operator is a great way to deploy and manage Flink application and session deployments. In this presentation, we provide: - A brief overview of Kubernetes operators and their benefits. - Introduce the five levels of the operator maturity model. - Introduce the newly released Apache Flink Kubernetes Operator and FlinkDeployment CRs - Dockerfile modifications you can make to swap out UBI images and Java of the underlying Flink Operator container - Enhancements we're making in: - Versioning/Upgradeability/Stability - Security - Demo of the Apache Flink Operator in-action, with a technical preview of an upcoming product using the Flink Kubernetes Operator. - Lessons learned - Q&A
by
James Busche & Ted Chang
Evaluation of TPC-H on Spark and Spark SQL in ALOJADataWorks Summit
The Evaluation of TPC-H on Spark and Spark SQL in ALOJA was conducted at the Big Data Lab to obtain the master degree in Management Information Systems at the Johann-Wolfgang Goethe University in Frankfurt, Germany. Furthermore, the analysis was partially accomplished in collaboration and close coordination with the Barcelona Super Computer Center.
The intention of this research was the integration of a TPC-H on Spark Scala benchmark into ALOJA, an open-source and public platform for automated and cost-efficient benchmarks and to perform an evaluation on the runtime of Spark Scala with or without Hive Metastore compared to Spark SQL. Various alternate file formats with different applied compressions on underlying data and its impact are evaluated. The conducted performance evaluation exposed diverse and captivating outcomes for both benchmarks. Further investigations attempt to detect possible bottlenecks and other irregularities. The aim is to provide an explanation to enhance knowledge of Spark’s engine based on examining the physical plans. Our experiments show, inter alia, that: (1) Spark Scala performs better in case of heavy expression calculation, (2) Spark SQL is the better choice in case of strong data access locality in combination with heavyweight parallel execution. In conclusion, diverse results were observed with the consequence that each API has its advantages and disadvantages.
Surprisingly, our findings are well spread between Spark SQL and Spark Scala and contrary to our expectations Spark Scala did not outperform Spark SQL in all aspects but support the idea that applied optimizations appear to be implemented in a different way by Spark for its core and its extension Spark SQL. The API on top of Spark provides extra information about the underlying structured data, which is probably used to perform additional optimizations.
In conclusion, our research demonstrates that there are differences in the generation of query execution plans that goes hand-in-hand with similar discoveries leading to inefficient joins, and it underlines the importance of our benchmark to identify disparities and bottlenecks.
Speaker
Raphael Radowitz, Quality Specialist, SAP Labs Korea
Exactly-Once Financial Data Processing at Scale with Flink and PinotFlink Forward
Flink Forward San Francisco 2022.
At Stripe we have created a complete end to end exactly-once processing pipeline to process financial data at scale, by combining the exactly-once power from Flink, Kafka, and Pinot together. The pipeline provides exactly-once guarantee, end-to-end latency within a minute, deduplication against hundreds of billions of keys, and sub-second query latency against the whole dataset with trillion level rows. In this session we will discuss the technical challenges of designing, optimizing, and operating the whole pipeline, including Flink, Kafka, and Pinot. We will also share our lessons learned and the benefits gained from exactly-once processing.
by
Xiang Zhang & Pratyush Sharma & Xiaoman Dong
Practical learnings from running thousands of Flink jobsFlink Forward
Flink Forward San Francisco 2022.
Task Managers constantly running out of memory? Flink job keeps restarting from cryptic Akka exceptions? Flink job running but doesn’t seem to be processing any records? We share practical learnings from running thousands of Flink Jobs for different use-cases and take a look at common challenges they have experienced such as out-of-memory errors, timeouts and job stability. We will cover memory tuning, S3 and Akka configurations to address common pitfalls and the approaches that we take on automating health monitoring and management of Flink jobs at scale.
by
Hong Teoh & Usamah Jassat
Customizing Xcos with new Blocks and PaletteScilab
In this tutorial, we show how to create and customize Xcos blocks and palettes. Moreover, we use the "Xcos toolbox skeleton" for a better result. The LHY model in Xcos scheme (already developed in other tutorials) is used as a starting point.
Slides from Talk by Jan Medved on Yang modeling and its support in OpenDaylight meetup
http://www.meetup.com/OpenDaylight-Silicon-Valley/events/212834752
Yang is a data modeling language that is rapidly being adopted to model Netconf, an IETF standardized network management protocol, as well as to model other data interfaces in OpenDaylight. Join us for the talk by expert Jan Medved to learn about Yang and its usage within OpenDaylight.
Maxim Fateev - Beyond the Watermark- On-Demand Backfilling in FlinkFlink Forward
http://flink-forward.org/kb_sessions/beyond-the-watermark-on-demand-backfilling-in-flink/
Flink has consistency guarantees and efficient checkpointing model which make it a good fit for Uber’s money-related use cases, such as driver incentives. However, Flink’s time-progress model is built around a single watermark, which is incompatible with Uber’s business need for generating aggregates retroactively. The talk covers our solution for on-demand backfilling. It also outlines other abstractions and features we expect Flink to support as it matures.
When does InnoDB lock a row? Multiple rows? Why would it lock a gap? How do transactions affect these scenarios? Locking is one of the more opaque features of MySQL, but it’s very important for both developers and DBA’s to understand if they want their applications to work with high performance and concurrency. This is a creative presentation to illustrate the scenarios for locking in InnoDB and make these scenarios easier to visualize. I'll cover: key locks, table locks, gap locks, shared locks, exclusive locks, intention locks, insert locks, auto-inc locks, and also conditions for deadlocks.
Netflix’s architecture involves thousands of microservices built to serve unique business needs. As this architecture grew, it became clear that the data storage and query needs were unique to each area; there is no one silver bullet which fits the data needs for all microservices. CDE (Cloud Database Engineering team) offers polyglot persistence, which promises to offer ideal matches between problem spaces and persistence solutions. In this meetup you will get a deep dive into the Self service platform, our solution to repairing Cassandra data reliably across different datacenters, Memcached Flash and cross region replication and Graph database evolution at Netflix.
Introducing the Apache Flink Kubernetes OperatorFlink Forward
Flink Forward San Francisco 2022.
The Apache Flink Kubernetes Operator provides a consistent approach to manage Flink applications automatically, without any human interaction, by extending the Kubernetes API. Given the increasing adoption of Kubernetes based Flink deployments the community has been working on a Kubernetes native solution as part of Flink that can benefit from the rich experience of community members and ultimately make Flink easier to adopt. In this talk we give a technical introduction to the Flink Kubernetes Operator and demonstrate the core features and use-cases through in-depth examples."
by
Thomas Weise
Slides for my talk event-sourced architectures with Akka. Discusses Akka Persistence as mechanism to do event-sourcing. Presented at Javaone 2014 and Jfokus 2015.
Apache Flink 101 - the rise of stream processing and beyondBowen Li
Apache Flink is the most popular and widely adopted streaming processing framework, powering real time stream event computations at extremely large scale in companies like Uber, Lyft, AWS, Alibaba, Pinterest, Splunk, Yelp, etc.
In this talk, we will go over use cases and basic (yet hard to achieve!) requirements of stream processing, and how Flink fills the gaps and stands out with some of its unique core building blocks, like pipelined execution, native event time support, state support, and fault tolerance.
We will also take a look at how Flink is going beyond stream processing into areas like unified data processing, enterprise intergration, AI/machine learning (especially online ML), and serverless computation, and how Flink fits with its distinct value.
SPEAKER: Bowen Li
SPEAKER BIO: Bowen is a committer of Apache Flink, senior engineer at Alibaba, and host of Seattle Flink Meetup.
“Alexa, be quiet!”: End-to-end near-real time model building and evaluation i...Flink Forward
Flink Forward San Francisco 2022.
To improve Amazon Alexa experiences and support machine learning inference at scale, we built an automated end-to-end solution for incremental model building or fine-tuning machine learning models through continuous learning, continual learning, and/or semi-supervised active learning. Customer privacy is our top concern at Alexa, and as we build solutions, we face unique challenges when operating at scale such as supporting multiple applications with tens of thousands of transactions per second with several dependencies including near-real time inference endpoints at low latencies. Apache Flink helps us transform and discover metrics in near-real time in our solution. In this talk, we will cover the challenges that we faced, how we scale the infrastructure to meet the needs of ML teams across Alexa, and go into how we enable specific use cases that use Apache Flink on Amazon Kinesis Data Analytics to improve Alexa experiences to delight our customers while preserving their privacy.
by
Aansh Shah
Using the New Apache Flink Kubernetes Operator in a Production DeploymentFlink Forward
Flink Forward San Francisco 2022.
Running natively on Kubernetes, using the new Apache Flink Kubernetes Operator is a great way to deploy and manage Flink application and session deployments. In this presentation, we provide: - A brief overview of Kubernetes operators and their benefits. - Introduce the five levels of the operator maturity model. - Introduce the newly released Apache Flink Kubernetes Operator and FlinkDeployment CRs - Dockerfile modifications you can make to swap out UBI images and Java of the underlying Flink Operator container - Enhancements we're making in: - Versioning/Upgradeability/Stability - Security - Demo of the Apache Flink Operator in-action, with a technical preview of an upcoming product using the Flink Kubernetes Operator. - Lessons learned - Q&A
by
James Busche & Ted Chang
Evaluation of TPC-H on Spark and Spark SQL in ALOJADataWorks Summit
The Evaluation of TPC-H on Spark and Spark SQL in ALOJA was conducted at the Big Data Lab to obtain the master degree in Management Information Systems at the Johann-Wolfgang Goethe University in Frankfurt, Germany. Furthermore, the analysis was partially accomplished in collaboration and close coordination with the Barcelona Super Computer Center.
The intention of this research was the integration of a TPC-H on Spark Scala benchmark into ALOJA, an open-source and public platform for automated and cost-efficient benchmarks and to perform an evaluation on the runtime of Spark Scala with or without Hive Metastore compared to Spark SQL. Various alternate file formats with different applied compressions on underlying data and its impact are evaluated. The conducted performance evaluation exposed diverse and captivating outcomes for both benchmarks. Further investigations attempt to detect possible bottlenecks and other irregularities. The aim is to provide an explanation to enhance knowledge of Spark’s engine based on examining the physical plans. Our experiments show, inter alia, that: (1) Spark Scala performs better in case of heavy expression calculation, (2) Spark SQL is the better choice in case of strong data access locality in combination with heavyweight parallel execution. In conclusion, diverse results were observed with the consequence that each API has its advantages and disadvantages.
Surprisingly, our findings are well spread between Spark SQL and Spark Scala and contrary to our expectations Spark Scala did not outperform Spark SQL in all aspects but support the idea that applied optimizations appear to be implemented in a different way by Spark for its core and its extension Spark SQL. The API on top of Spark provides extra information about the underlying structured data, which is probably used to perform additional optimizations.
In conclusion, our research demonstrates that there are differences in the generation of query execution plans that goes hand-in-hand with similar discoveries leading to inefficient joins, and it underlines the importance of our benchmark to identify disparities and bottlenecks.
Speaker
Raphael Radowitz, Quality Specialist, SAP Labs Korea
Exactly-Once Financial Data Processing at Scale with Flink and PinotFlink Forward
Flink Forward San Francisco 2022.
At Stripe we have created a complete end to end exactly-once processing pipeline to process financial data at scale, by combining the exactly-once power from Flink, Kafka, and Pinot together. The pipeline provides exactly-once guarantee, end-to-end latency within a minute, deduplication against hundreds of billions of keys, and sub-second query latency against the whole dataset with trillion level rows. In this session we will discuss the technical challenges of designing, optimizing, and operating the whole pipeline, including Flink, Kafka, and Pinot. We will also share our lessons learned and the benefits gained from exactly-once processing.
by
Xiang Zhang & Pratyush Sharma & Xiaoman Dong
Practical learnings from running thousands of Flink jobsFlink Forward
Flink Forward San Francisco 2022.
Task Managers constantly running out of memory? Flink job keeps restarting from cryptic Akka exceptions? Flink job running but doesn’t seem to be processing any records? We share practical learnings from running thousands of Flink Jobs for different use-cases and take a look at common challenges they have experienced such as out-of-memory errors, timeouts and job stability. We will cover memory tuning, S3 and Akka configurations to address common pitfalls and the approaches that we take on automating health monitoring and management of Flink jobs at scale.
by
Hong Teoh & Usamah Jassat
Customizing Xcos with new Blocks and PaletteScilab
In this tutorial, we show how to create and customize Xcos blocks and palettes. Moreover, we use the "Xcos toolbox skeleton" for a better result. The LHY model in Xcos scheme (already developed in other tutorials) is used as a starting point.
The purpose of this document is to guide you step by step in exploring the various basic features of Scilab for a user who has never used numerical computation software. This presentation is voluntarily limited to the essential to allow easier handling of Scilab.
In this tutorial the reader can learn about data fitting, interpolation and approximation in Scilab. Interpolation is very important in industrial applications for data visualization and metamodeling.
The aim of this paper is to show the possibilities offered by Scilab/Xcos to model and simulate aeraulic and HVAC systems. In particular we develop a new Xcos module combined with the use of Modelica language to show how aeraulic systems can be modeled and studied. In this paper we construct a reduced library composed by few elements: hoods, pipes and ideal junctions. The library can be easily extended to obtain a more complete toolbox. The developed library is tested on a simple aeraulic circuit.
Modeling an ODE: 3 different approaches - Part 3Scilab
In this tutorial we show how to model a physical system described by ODE using the Modelica extensions of the Xcos environment. The same model has been solved also with Scilab and Xcos in two previous tutorials.
Modeling an ODE: 3 different approaches - Part 1Scilab
In this tutorial we show how to model a physical system described by ODE using Scilab standard programming language. The same model solution is also described in Xcos and Xcos + Modelica in two other tutorials.
Modeling an ODE: 3 different approaches - Part 2Scilab
In this tutorial we show how to model a physical system described by ODE using Xcos environment. The same model solution is also described in Scilab and Xcos + Modelica in two other tutorials.
In this Scilab tutorial, we introduce readers to the Control System Toolbox that is available in Scilab/Xcos and known as CACSD. This first tutorial is dedicated to "Linear Time Invariant" (LTI) systems and their representations in Scilab.
This document presents all existing and non-existing optimization features in Scilab (examples of nonlinear optimization, available algorithms to solve quadratic problems, non-linear least squares problems, semidefinite programming, genetic algorithms, simulated annealing and linear matrix inequalities...)
L’objectif de ce document est de vous guider pas à pas dans la découverte des différentes fonctionnalités de base du logiciel Scilab pour un utilisateur n’ayant jamais utilisé un logiciel de calcul. Cette présentation se limite volontairement à l’essentiel pour permettre une prise en main facilitée de Scilab.
Scilab Technical Talk at NTU, TP and HCMUT (Dr Claude Gomez)TBSS Group
A very comprehensive set of slides presented by CEO, Scilab Enterprise, Dr Claude Gomez. TBSS-Scilab Singapore Center is the partner of Scilab Enterprise in Singapore and TBSS Khai Kinh Co. Ltd. is the partner in Vietnam. Both companies are the TBSS Group of Companies.
ScilabTEC 2015 - Bavarian Center for AgricultureScilab
"Development of a diagnostic tool for performance analysis during the testing of agricultural implements"
By Zoltan Gobor, Bavarian State Research Center for Agriculture for ScilabTEC 2015
The article discusses the availability of information about Open Source Software for LMS and digitization. The term open source describes practices in production and development that promote access to the end product's source materials which is freely available throughout the web.
Reproducibility of model-based results: standards, infrastructure, and recogn...FAIRDOM
Written and presented by Dagmar Waltemath (University of Rostock) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
The purpose of this document is to guide you step by step in exploring the various basic features of Xcos for a user who has never used a hybrid dynamic systems modeler and simulator.
Slides from a talk at the Montreal Neurological Institute 10/2016 - Progress and challenges for standardized (pre)processing in functional magnetic resonance imaging
Using Apache Spark with IBM SPSS Modeler with Dr. Steve Poulin.
An introduction to Apache Spark and its relevant integration with IBM SPSS Modeler. Why integrate? What type of benefits?
A review the integration process high level and advise which enhanced features to pay attention to, and common pitfalls to avoid.
XOOPS 2.5.x Debugging with FirePHP/FireBugxoopsproject
FirePHP is a Mozilla Firefox plugin/extension that merges with Firebug and enables you to log to your Firebug Console using a simple PHP method call. All data is sent via response headers and will not interfere with the content on your page, therefore it is ideally suited for AJAX development where clean JSON and XML responses are required.
In this tutorial we'll show you how to use FirePHP/FireBug to debug XOOPS.
Why electric vehicles need model-based design?
Because of the rising complexity in new vehicles, model-based design & systems engineering is needed to cascade the requirements and trace back any modification along the engineering lifecycle. Find out more in this presentation of a customer case about electric motor optimization.
Keynote of the French Space Agency CNES on the Asteroidlander MASCOT boarding the Hayabusa2 mission in collaboration with the Japanese Space Agency JAXA and the German Aerospace Center DLR
Faster Time to Market using Scilab/XCOS/X2C for motor control algorithm devel...Scilab
Rapid Prototyping becomes very popular for faster algorithm development. With a graphical representation of the algorithm and the possibility to simulate complete designs, engineers can help to reduce the time to market. A tight integration with MPLAB-X IDE allows the combination with standard C-coding to easily get mass production code. This solution was used to optimise a sensorless field oriented controlled PMSM motor driven pump efficiency. A model for closed loop simulation was developed using X2C blocks [1][2] for the FOC algorithm based on the existing application note AN1292 [3]. Enhancements to the original version were implemented and verified with simulation. The X2C Communicator was used to generate code of the new algorithm. With the online debugging capabilities and the scope functionality the algorithm was further tuned and optimized to achieve the highest possible efficiency of the pump.
Scilab and Xcos for Very Low Earth Orbits satellites modellingScilab
Very Low Earth Orbits are orbits in altitudes lower than 450 km. The interaction between the atmosphere particles and the surfaces of the spacecraft is responsible for the aerodynamic torques and forces. Simulating several aspects of the performance of a satellite flying in VLEO is very important to make decisions about the design of the spacecraft and the mission.
X2C -a tool for model-based control development and automated code generation...Scilab
Peter Dirnberger, Stefan Fragner
Nowadays, the market demands compact, stable, easy maintain-and customizable embedded systems. To meet these requirements, afast, simple and reliable implementation of control algorithms is crucial. This paper demonstrateshow model-based design with the help of Scilab/Xcosand X2C, developed by LCM,simplifiesand speedsup the development and implementation of controlalgorithms. As an example, acontrol schemefor a bearingless motoris presented.
A Real-Time Interface for Xcos – an illustrative demonstration using a batter...Scilab
As part of an EU-founded research project, the Scilab based development tool LoRra (Low-Cost Rapid Control Prototyping Platform) was created. This allows the realization of the continuously model based and highly automated Rapid Control Prototyping (RCP) design process for embedded software within the Scilab / Xcos environment (cf. Figure 1). Based on the application battery management system (BMS), this paper presents a Real-Time interface for Scilab.
Aircraft Simulation Model and Flight Control Laws Design Using Scilab and XCosScilab
The increasing demand in the aerospace industry for safety and performance has been requiring even more resourceful flight control laws in all market segments, since the airliners until the newest flying cars. The de facto standard for flight control laws design makes extensive use of tools supporting numerical computing and dynamic systems visual modeling, such that Scilab and XCos can nicely suit this kind of development.
Multiobjective optimization and Genetic algorithms in ScilabScilab
In this Scilab tutorial we discuss about the importance of multiobjective optimization and we give an overview of all possible Pareto frontiers. Moreover we show how to use the NSGA-II algorithm available in Scilab.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
3. Xcos
for
very
beginners
-‐
3/15
Table
of
content
Introduction
About
this
document
4
Install
Scilab
4
Mailing
lists
4
Complementary
resources
4
Become
familiar
with
Xcos
General
environment
5
Example
of
a
simple
diagram
design
6
Superblocks
9
Annexes
Menu
bar
11
Available
palettes
13
Install
a
C
compiler
14
4. Xcos
for
very
beginners
-‐
4/15
Introduction
About
this
document
The
purpose
of
this
document
is
to
guide
you
step
by
step
in
exploring
the
various
basic
features
of
Xcos
tool
included
in
Scilab
for
a
user
who
has
never
used
a
hybrid
dynamic
systems
modeler
and
simulator.
This
presentation
is
intentionally
limited
to
the
essential
to
allow
easier
handling
of
Xcos.
The
examples,
diagrams
and
illustrations
are
made
with
Scilab
5.4.1.
You
can
reproduce
all
those
examples
from
this
version.
Install
Scilab
Scilab
is
open
source
software
for
numerical
computation
that
anybody
can
freely
download.
Available
under
Windows,
Linux
and
Mac
OS
X,
Scilab
can
be
downloaded
at
the
following
address:
http://www.scilab.org/
You
can
be
notified
of
new
releases
of
Scilab
software
by
subscribing
to
our
notification
channel
at
the
following
address:
http://lists.scilab.org/mailman/listinfo/release
Mailing
lists
To
ease
the
exchange
between
Scilab
users,
dedicated
mailing
lists
exist
(list
for
the
education
world,
international
list
in
English).
The
principle
is
simple:
registrants
can
communicate
with
each
other
by
e-‐mail
(questions,
answers,
sharing
of
documents,
feedbacks...).
To
browse
the
available
lists
and
to
subscribe,
go
to
the
following
address:
http://www.scilab.org/communities/user_zone/mailing_list
Complementary
resources
Scilab
website
has
a
dedicated
section
on
Scilab
use
(http://www.scilab.org/en/resources/documentation)
with
useful
links
and
documents
that
can
be
freely
downloaded
and
printed.
You
can
also
find
in
the
same
section
a
similar
document
entitled
“Scilab
for
very
beginners”
which
is
available
for
download.
5. Xcos
for
very
beginners
-‐
5/15
Become
familiar
with
Xcos
Numerical
simulation
is
nowadays
essential
in
system
design
process.
Complex
phenomena
simulation
(physical,
mechanical,
electronics,
etc.)
allows
the
study
of
their
behavior
and
results
without
having
to
conduct
costly
real
experiments.
Widely
used
in
the
world
of
industry,
the
future
generation
of
engineers
and
scientists
are
trained
since
secondary
school
to
the
concepts
of
modeling
and
simulation.
Xcos
is
Scilab
tool
dedicated
to
the
modeling
and
simulation
of
hybrid
dynamic
systems
including
both
continuous
and
discrete
models.
It
also
allows
simulating
systems
governed
by
explicit
equations
(causal
simulation)
and
implicit
equations
(acausal
simulation).
Xcos
includes
a
graphical
editor
which
allows
to
easily
represent
models
as
block
diagrams
by
connecting
the
blocks
to
each
other.
Each
block
represents
a
predefined
basic
function
or
a
user-‐defined
one.
General
environment
After
launching
Scilab,
the
environment
by
default
consists
of
the
console,
files
and
variables
browsers
and
a
command
history.
In
the
console
after
the
prompt
“
-->
”,
just
type
a
command
and
press
the
Enter
key
to
obtain
the
corresponding
result.
Xcos
can
be
launched:
• From
the
toolbar,
via
the
icon,
or
• From
the
menu
bar,
in
Applications / Xcos,
or
• From
the
console,
in
typing:
-->xcos
6. Xcos
for
very
beginners
-‐
6/15
Xcos
opens
by
default
with
two
windows:
• A
palette
browser
which
provides
a
set
of
predefined
blocks,
• An
editing
window
which
is
the
working
space
to
design
diagrams.
To
design
a
diagram,
just
select
blocks
in
the
palette
browser
and
position
them
in
the
editing
window
(click,
drag
and
drop).
Blocks
are
then
connected
to
each
other
using
their
different
ports
(input,
output,
event)
in
order
to
simulate
the
created
model.
Example
of
a
simple
diagram
design
We
are
going
to
explain
how
to
design
from
scratch
a
model
of
continuous-‐time
system
represented
by
a
first-‐order
transfer
function.
Launch
Xcos.
As
seen
before,
Xcos
opens
by
default
with
the
palette
browser
and
an
editing
window.
In
the
palette
browser,
we
are
going
to
use
the
following
blocks:
Designation
Representation
Sub-‐palette
Step
Sources / STEP_FUNCTION
Continuous
transfer
function
Continuous
time
systems / CLR
Clock
Sources / CLOCK_c
Visualization
Sinks / CSCOPE
7. Xcos
for
very
beginners
-‐
7/15
Arrange
the
blocks
in
the
editing
window.
To
connect
the
input
and
output
ports
to
each
other,
click
on
the
output
(black
arrow)
of
the
STEP-‐FUNCTION
block
and
in
maintaining
the
mouse
button
pressed,
connect
it
to
the
input
port
of
the
CLR
block
(a
green
highlighted
square
appears
to
indicate
that
the
link
is
correct)
as
described
in
the
images
below:
Release
to
complete
the
link.
Complete
the
connections
between
the
different
blocks
to
achieve
this
result:
It
is
possible
to
improve
the
general
look
of
your
diagram
in
using
the
blocks
alignment
options
(Format
Menu/Align
blocks)
and
the
links
style
(Format
Menu/Link
style).
At
any
time,
blocks
can
be
moved
or
repositioned
by
selecting
them
and
by
maintaining
the
mouse
button
pressed
while
moving
them.
Release
the
blocks
in
the
desired
position.
Simulation
is
launched
by
clicking
on
the
icon
(or
from
the
Simulation
Menu/Start)
and
can
be
stopped
by
clicking
on
the
icon
(or
from
the
Simulation
Menu/Stop).
A
new
window
is
displayed
(scope)
with
the
simulation
running.
At
the
bottom
of
the
diagram
editing
window,
a
statement
indicates
that
the
simulation
is
in
progress:
8. Xcos
for
very
beginners
-‐
8/15
You
can
see
that
the
simulation
time
is
quite
long
(you
may
have
needed
to
stop
the
simulation
while
running)
and
that
the
response
is
flat.
Thus,
we
choose
to
modify
the
parameters
of
CLR
block
and
of
the
simulation.
A
"Context"
containing
Scilab
script
allows
easy
use
of
functions
and
variables
in
Xcos
blocks.
We
are
going
to
use
this
context
to
set
the
blocks
parameters
for
diagram
simulation.
Click
on
Simulation/Set
Context
in
the
menu
bar
and
define
the
following
variables:
• K
=
1
• Tau
=
1
You
can
now
use
these
variables
to
set
up
the
diagram
blocks.
Double-‐click
on
CLR
block.
A
dialog
box
opens
with
the
default
settings
of
the
block.
Modify
these
settings
as
follows:
• Numerator:
K
• Denominator:
1+Tau*s
The
new
transfer
function
is
displayed
on
the
block:
If
necessary,
enlarge
the
block
so
that
the
display
fits
in
it.
9. Xcos
for
very
beginners
-‐
9/15
We
are
now
going
to
set
up
the
simulation
and
the
blocks
to
visualize
the
time
response
of
the
system
to
a
stepe.
For
this,
we
limit
the
simulation
time
to
5
seconds
(Simulation
Menu/Setup)
in
modifying
the
final
integration
time.
Double-‐click
on
CSCOPE
block
to
set
up
the
display
of
values
between
0
and
1.2,
then
the
scope
refresh
period
to
5
seconds.
To
do
it,
change
the
following
settings:
• Ymin:
0
• Ymax:
1.2
• Refresh
period:
5
Restart
the
simulation
and
view
the
result:
Superblocks
To
ease
the
understanding
of
certain
diagrams,
it
is
often
useful
to
use
superblocks
or
composite
blocks.
A
superblock
contains
a
part
of
a
diagram
and
blocks
representing
its
inputs
and
outputs.
The
superblock
can
be
handled
as
a
single
block
within
the
parent
diagram.
After
designing
a
diagram
and
selecting
the
part
of
the
diagram
(or
sub-‐diagram)
that
we
want
to
gather
into
a
block,
the
creation
of
the
superblock
is
done
from
the
menu
Edit/Region
to
superblock.
The
selection
is
now
a
block
which
content
can
be
displayed
by
double-‐click.
A
new
editing
window
opens
with
the
initial
selected
blocks.
10. Xcos
for
very
beginners
-‐
10/15
It
is
also
possible
to
hide
the
created
superblock
to
disable
access
to
the
subdiagram.
To
do
so,
right-‐click
on
the
superblock,
then
on
Superblock
Mask/Create.
We
can
also
make
some
sub-‐diagram
configuration
settings
available
in
a
single
setup
interface
by
a
right-‐click
on
the
superblock,
then
Superblock
Mask/Customize.
Then
just
add
the
parameters
you
want
to
make
available.
This
presentation
was
intentionally
short
and
many
other
possibilities
for
simulating
systems
exist
with
many
available
blocks.
To
continue
to
handle
easily
Xcos,
we
invite
you
to
visit
the
many
diagrams
examples
available
in
Xcos
demonstrations
by
clicking
on
the
menu
?/Xcos
Demos.
11. Xcos
for
very
beginners
-‐
11/15
Annexes
Menu
bar
The
useful
menu
bar
of
Xcos
is
the
one
of
the
editing
window.
File
Menu
• New
diagram
(Ctrl+N
under
Windows
and
Linux / Cmd+N
under
Mac
OS
X)
Open
a
new
Xcos
editing
window.
• Open
(Ctrl+O
under
Windows
and
Linux
/ Cmd+O
under
Mac
OS
X)
Load
a
Xcos
file
including
a
diagram
or
a
palette
in
.zcos
or
.xcos
format.
• Open
file
in
Scilab
current
directory
Load
a
Xcos
file
including
a
diagram
or
a
palette
from
Scilab
working
directory
in
.zcos
or
.xcos
format.
• Recent
files
Provide
the
recently
opened
files.
• Close
(Ctrl+W
under
Windows
and
Linux
/ Cmd+W
under
Mac
OS
X)
Close
the
current
diagram
if
several
diagrams
are
opened.
Quit
Xcos
if
only
one
diagram
is
opened.
The
auxiliary
windows
such
as
the
palette
browser
are
also
closed
in
closing
the
last
diagram.
• Save
(Ctrl+S
under
Windows
and
Linux
/ Cmd+S
under
Mac
OS
X)
Save
the
changes
to
the
diagram.
If
the
diagram
has
not
been
previously
saved
in
a
file,
it
will
be
proposed
to
be
saved
(see
Save
As).
• Save
as
(Ctrl+Shift+S
under
Windows
and
Linux
/ Cmd+
Shift
+S
under
Mac
OS
X)
Save
the
diagram
or
the
palette
with
a
new
name.
The
diagram
takes
the
name
of
the
file
(without
the
extension).
• Export
(Ctrl+E
under
Windows
and
Linux
/ Cmd+E
under
Mac
OS
X)
Export
an
image
of
the
current
Xcos
diagram
in
standard
formats
(PNG,
SVG,
etc.).
• Export
all
diagrams
Export
images
of
the
diagram
and
the
content
of
its
superblocks.
• Print
(Ctrl+P
under
Windows
and
Linux
/ Cmd+P
under
Mac
OS
X)
Print
the
current
diagram.
• Quit
Xcos
(Ctrl+Q
under
Windows
and
Linux
/ Cmd+Q
under
Mac
OS
X)
Quit
Xcos.
Edit
Menu
• Undo
(Ctrl+Z
under
Windows
and
Linux
/ Cmd+Z
under
Mac
OS
X)
Cancel
the
last
operation.
• Redo
(Ctrl+Y
under
Windows
and
Linux
/ Cmd+Y
under
Mac
OS
X)
Restore
the
last
operation
canceled.
• Cut
(Ctrl+X
under
Windows
and
Linux
/ Cmd+X
under
Mac
OS
X)
Remove
the
selected
objects
of
a
diagram
and
keep
a
copy
in
the
clipboard.
• Copy
(Ctrl+C
under
Windows
and
Linux
/ Cmd+C
under
Mac
OS
X)
Put
a
copy
of
the
selected
objects
in
the
clipboard.
• Paste
(Ctrl+V
under
Windows
and
Linux
/ Cmd+V
under
Mac
OS
X)
Add
the
content
of
the
clipboard
in
the
current
diagram.
12. Xcos
for
very
beginners
-‐
12/15
• Delete
(Delete)
Erase
the
selected
blocks
or
links.
When
a
block
is
erased,
all
its
connected
links
are
also
erased.
• Select
all
(Ctrl+A
under
Windows
and
Linux
/ Cmd+A
under
Mac
OS
X)
Select
all
the
elements
of
the
current
diagram.
• Inverse
selection
Reverse
the
current
selection.
• Block
Parameters
(Ctrl+B
under
Windows
and
Linux
/ Cmd+B
under
Mac
OS
X)
Set
the
selected
block
(see
the
block
help
to
obtain
more
information
on
its
setup).
• Region
to
superblock
Convert
a
selection
of
blocks
into
a
superblock.
View
Menu
• Zoom
In
(Ctrl+Plus
numeric
keypad
under
Windows
and
Linux
/ Cmd+Plus
numeric
keypad
under
Mac
OS
X)
Enlarge
the
view
of
10 %.
• Zoom
Out
(Ctrl+Minus
numeric
keypad
under
Windows
and
Linux
/ Cmd+Minus
numeric
keypad
under
Mac
OS
X)
Reduce
the
view
of
10 %.
• Fit
diagram
or
blocks
to
view
Adjust
the
view
to
the
window
size.
• Normal
100 %
Scale
the
view
to
its
default
size.
• Palette
browser
Show
/
Hide
the
palette
browser.
• Diagram
browser
Display
a
window
which
lists
the
global
properties
of
the
diagram
and
of
all
the
objects
it
contains
(blocks
and
links).
• Viewport
Show
/
Hide
a
complete
overview
of
the
current
diagram.
With
viewport,
the
user
can
move
the
working
area
on
a
particular
part
of
the
diagram.
Simulation
Menu
• Setup
Modify
the
simulation
parameters.
• Execution
trace
and
Debug
Set
the
simulation
in
a
debug
mode.
• Set
Context
Enable
to
enter
Scilab
instructions
to
define
variables
or
functions
that
can
be
used
in
setting
up
diagram
blocks.
• Compile
Compile
the
diagram.
• Modelica
initialize
Enable
the
initialization
of
the
variables
in
the
acausal
diagram
subsystem.
• Start
Launch
the
simulation.
• Stop
Stop
the
simulation.
13. Xcos
for
very
beginners
-‐
13/15
Format
Menu
• Rotate
(Ctrl+R
under
Windows
and
Linux / Cmd+R
under
Mac
OS
X)
Rotate
the
selected
block(s)
90°
counterclockwise.
• Flip
(Ctrl+F
under
Windows
and
Linux / Cmd+F
under
Mac
OS
X)
Reverse
the
positions
of
input
and
output
events
positioned
above
and
below
of
a
selected
block.
• Mirror
(Ctrl+M
under
Windows
and
Linux / Cmd+M
under
Mac
OS
X)
Reverse
the
positions
of
regular
input
and
output
positioned
on
the
left
and
on
the
right
of
a
selected
block.
• Show
/
Hide
shadow
Show
/
Hide
the
shadow
of
the
selected
block.
• Align
blocks
By
selecting
several
blocks,
it
is
possible
to
align
them
on
the
horizontal
axis
(left,
right
and
center)
or
on
the
vertical
axis
(top,
bottom
and
middle).
• Border
color
Change
the
border
color
of
the
selected
blocks.
• Fill
color
Change
the
fill
color
of
the
selected
blocks.
• Link
style
Modify
a
link
style
• Diagram
background
Change
the
background
color
of
the
diagram.
• Grid
Enable
/
Disable
the
grid.
Thanks
to
the
grid,
blocks
and
links
positioning
is
easier.
Tools
Menu
• Code
generation
Allow
the
generation
of
the
simulation
code
of
the
selected
superblock.
?
Menu
• Xcos
Help
Open
the
help
on
Xcos
functioning,
palettes,
blocks
and
examples.
• Block
Help
Open
the
help
on
a
selected
block
• Xcos
Demos
Open
examples
of
diagrams
and
simulate
them.
The
user
can,
if
he
wants
to,
modify
those
diagrams
and
save
them
for
a
future
use.
(Be
careful,
the
execution
of
some
demonstration
diagrams
needs
to
have
a
C
compiler
installed
on
your
machine.
Please
refer
to
the
page
15).
Available
palettes
• Commonly
Used
Blocks
More
used
blocks.
• Continuous
time
systems
Continuous
blocks
(integration,
derivative,
PID).
• Discontinuities
14. Xcos
for
very
beginners
-‐
14/15
Blocks
whose
outputs
are
discontinuous
functions
of
their
inputs
(hysteresis).
• Discrete
time
systems
Blocks
for
modeling
in
discrete
time
(derivative,
sampled,
blocked).
• Lookup
Tables
Blocks
computing
output
approximations
from
the
inputs.
• Event
handling
Blocs
to
manage
events
in
the
diagram
(clock,
multiplication,
frequency
division).
• Mathematical
Operations
Blocks
for
modeling
general
mathematical
functions
(cosine,
sine,
division,
multiplication).
• Matrix
Blocks
for
simple
and
complex
matrix
operations.
• Electrical
Blocks
representing
basic
electrical
components
(voltage
source,
resistor,
diode,
capacitor).
• Integer
Blocks
allowing
the
manipulation
of
integers
(logical
operators,
logic
gates).
• Port
&
Subsystem
Blocks
to
create
subsystems.
• Zero
crossing
detection
Blocks
used
to
detect
zero
crossings
during
simulation.
These
blocks
use
the
solvers
capabilities
(ODE
or
DAE)
to
perform
this
operation.
• Signal
Routing
Blocks
for
signal
routing,
multiplexing,
sample/blocked.
• Signal
Processing
Blocks
for
signal
processing
applications.
• Implicit
Blocks
for
implicit
systems
modeling.
• Annotations
Blocks
used
for
annotations.
• Sinks
Output
blocks
used
for
graphical
display
(scope)
and
for
data
export
(file
or
Scilab).
• Sources
Blocks
of
data
sources
(pulse,
ramp,
sine
wave)
and
for
reading
data
from
Scilab
files
or
variables.
• Thermo-‐Hydraulics
Blocks
representing
basic
thermo-‐hydraulic
components
(pressure
source,
pipes,
valves).
• Demonstrations
blocks
Blocks
used
in
demonstration
diagrams.
• User-‐Defined
Functions
User-‐blocks
to
model
a
behavior
(C,
Scilab
or
Modelica
simulation
function).
Install
a
C
compiler
For
some
systems
simulation
(acausal
systems
containing
for
example
hydraulic
or
electrical
blocks),
it
is
necessary
to
have
a
C
compiler
installed
in
the
machine.
Under
Windows
Install
MinGW
module
from
Scilab,
Applications
Menu
/
Module
manager
–
ATOMS
/
Windows
Tools
category.
MinGW
module
will
make
the
link
between
Scilab
and
GCC
compiler
(which
you
15. Xcos
for
very
beginners
-‐
15/15
also
have
to
install
separately).
Follow
the
procedure
detailed
in
the
module
install
window
which
will
guide
you
step
by
step
in
the
install
of
MinGW
and
GCC
compiler.
Under
Linux
GCC
compiler
is
available
by
default
under
Linux
OS.
Just
check
that
the
compiler
is
installed
and
up
to
date
(via
Synaptic,
Yum
or
any
other
package
management
system).
Under
Mac
Download
Xcode
via
the
App
Store
(Mac
OS
≥
10.7)
or
via
the
CD
supplied
with
the
computer
(Mac
OS
10.5
and
10.6).
For
earlier
versions,
see
the
Apple
website.
Confirm
the
possibility
to
use
the
compiler
out
of
Xcode
environment.
To
do
so,
after
launching
Xcode,
go
to
"Settings",
then
"Downloads"
and
in
the
"Components"
tab,
select
the
"Check
for
and
install
updates
automatically"
box
and
install
the
"Command
Line
Tools"
extension.
Naturally,
if
you
already
have
an
installed
C
compiler
in
your
machine,
you
do
not
have
to
install
another
one.
To
check
that
Scilab
has
detected
a
compiler,
use
the
command
that
returns
%T
if
a
compiler
is
installed:
--> haveacompiler()