OpenLLM-France/wikimedia huggingface dataset:
# Dutch/Lesson 13A

| | Beginner level | Beginner level | Beginner level | Beginner level | Beginner level | Beginner level | Beginner level | Beginner level | Intermediate level | Intermediate level | Intermediate level | Intermediate level | Intermediate level | Intermediate level | Intermediate level | Intermediate level | Advanced level | Advanced level | Advanced level | Advanced level | Advanced level | Advanced level | Advanced level | Advanced level |
| | Cycle 1 | Cycle 1 | Cycle 1 | Cycle 1 | Cycle 2 | Cycle 2 | Cycle 2 | Cycle 2 | Cycle 3 | Cycle 3 | Cycle 3 | Cycle 3 | Cycle 4 | Cycle 4 | Cycle 4 | Cycle 4 | Cycle 5 | Cycle 5 | Cycle 5 | Cycle 5 | Cycle 6 | Cycle 6 | Cycle 6 |
| Main | Lesson 1 | Lesson 2 | Lesson 3 | Lesson 4 | Lesson 5 | Lesson 6 | Lesson 7 | Lesson 8 | Lesson 9 | Lesson 10 | Lesson 11 | Lesson 12 | Lesson 13 | Lesson 14 | Lesson 15 | Lesson 16 | Lesson 17 | Lesson 18 | Lesson 19 | Lesson 20 | Lesson 21 | Lesson 22 | Lesson 23 | Main |
| Practice | Lesson 1A | Lesson 2A | Lesson 3A | Lesson 4A | Lesson 5A | Lesson 6A | Lesson 7A | Lesson 8A | Lesson 9A | Lesson 10A | Lesson 11A | Lesson 12A | Lesson 13A | Lesson 14A | Lesson 15A | Lesson 16A | Lesson 17 | Lesson 18 | Lesson 19 | Lesson 20 | Lesson 21 | Lesson 22 | Lesson 23 | Practice |
| Examples | Vb. 1 | Vb. 2 | Vb. 3 | Vb. 4 | Vb. 5 | Vb. 6 | Vb. 7 | Vb. 8 | Vb. 9 | Vb. 10 | Vb. 11 | Vb. 12 | Vb. 13 | Vb. 14 | Vb. 15 | Vb. 16 | Lesson 17 | Lesson 18 | Lesson 19 | Lesson 20 | Lesson 21 | Lesson 22 | Lesson 23 | Examples |
| | Quiz | Quiz | Quiz | Quiz | Quiz | Quiz | Quiz | Quiz |
| | Main page | Main page | Main page | Main page | Introduction | Introduction | Introduction | Introduction | Pronunciation | Pronunciation | Pronunciation | Pronunciation | Vocabulary | Vocabulary | Vocabulary | Vocabulary | Index | Index | Index | Index | News | News | News | |
* Intermediate level: cycle 4
Lesson 13A ~ Lesson 13A
Bijvoeglijke naamwoorden ~ Adjectives

## Exercise 13A-1

    Add the [adjective] to the sentence in the comparative form
    [klein]: De grote boeken had hij hier gezet, de ..... stonden aan de andere kant
    [goed]: Hij heeft een ... auto gekocht
    [helder]: Het weer is nu wat ..... geworden.
    [groot]: Heb je niets .....?
    [klein]: Zij is in een ..... huis gaan wonen.
    [rood]: Hij werd alsmaar .... van de inspanning.
    [veel]: Hij heeft er nu .... dan voorheen
    [lekker]: Een .... kop soep had hij zich niet kunnen wensen
    [hoog]: Het water stond ..... dan normaal
    [mager]: Hij was ..... geworden
    De grote boeken had hij hier gezet, de kleinere stonden aan de andere kant
    Hij heeft een betere auto gekocht
    Het weer is nu wat helderder geworden.
    Heb je niets groters?
    Zij is in een kleiner huis gaan wonen.
    Hij werd alsmaar roder van de inspanning.
    Hij heeft er nu meer dan voorheen
    Een lekkerder kop soep had hij zich niet kunnen wensen
    Het water stond hoger dan normaal
    Hij was magerder geworden
* lekkerdere would get too long and the final -e is often clipped
# Dutch/Lesson 14A

| | Beginner level | Beginner level | Beginner level | Beginner level | Beginner level | Beginner level | Beginner level | Beginner level | Intermediate level | Intermediate level | Intermediate level | Intermediate level | Intermediate level | Intermediate level | Intermediate level | Intermediate level | Advanced level | Advanced level | Advanced level | Advanced level | Advanced level | Advanced level | Advanced level | Advanced level |
| | Cycle 1 | Cycle 1 | Cycle 1 | Cycle 1 | Cycle 2 | Cycle 2 | Cycle 2 | Cycle 2 | Cycle 3 | Cycle 3 | Cycle 3 | Cycle 3 | Cycle 4 | Cycle 4 | Cycle 4 | Cycle 4 | Cycle 5 | Cycle 5 | Cycle 5 | Cycle 5 | Cycle 6 | Cycle 6 | Cycle 6 |
| Main | Lesson 1 | Lesson 2 | Lesson 3 | Lesson 4 | Lesson 5 | Lesson 6 | Lesson 7 | Lesson 8 | Lesson 9 | Lesson 10 | Lesson 11 | Lesson 12 | Lesson 13 | Lesson 14 | Lesson 15 | Lesson 16 | Lesson 17 | Lesson 18 | Lesson 19 | Lesson 20 | Lesson 21 | Lesson 22 | Lesson 23 | Main |
| Practice | Lesson 1A | Lesson 2A | Lesson 3A | Lesson 4A | Lesson 5A | Lesson 6A | Lesson 7A | Lesson 8A | Lesson 9A | Lesson 10A | Lesson 11A | Lesson 12A | Lesson 13A | Lesson 14A | Lesson 15A | Lesson 16A | Lesson 17 | Lesson 18 | Lesson 19 | Lesson 20 | Lesson 21 | Lesson 22 | Lesson 23 | Practice |
| Examples | Vb. 1 | Vb. 2 | Vb. 3 | Vb. 4 | Vb. 5 | Vb. 6 | Vb. 7 | Vb. 8 | Vb. 9 | Vb. 10 | Vb. 11 | Vb. 12 | Vb. 13 | Vb. 14 | Vb. 15 | Vb. 16 | Lesson 17 | Lesson 18 | Lesson 19 | Lesson 20 | Lesson 21 | Lesson 22 | Lesson 23 | Examples |
| | Quiz | Quiz | Quiz | Quiz | Quiz | Quiz | Quiz | Quiz |
| | Main page | Main page | Main page | Main page | Introduction | Introduction | Introduction | Introduction | Pronunciation | Pronunciation | Pronunciation | Pronunciation | Vocabulary | Vocabulary | Vocabulary | Vocabulary | Index | Index | Index | Index | News | News | News | |
* Intermediate level: cycle 4
Lesson 14 ~ Lesson 14
Naamwoorden van handeling ~ Verbal nouns
# Control Systems/System Representations

| The Wikibook of: Control Systems and Control Engineering | Table of Contents | All Versions | PDF Version |
| The Wikibook of: Control Systems and Control Engineering | | | Glossary |

## System Representations

This is a table of times when it is appropriate to use each different type of system representation:
| Properties | State-Space Equations | Transfer Function | Transfer Matrix |
| Linear, Distributed | no | no | no |
| Linear, Lumped | yes | no | no |
| Linear, Time-Invariant, Distributed | no | yes | no |
| Linear, Time-Invariant, Lumped | yes | yes | yes |

## General Description

These are the general external system descriptions. y is the system output, h is the system response characteristic, and x is the system input. In the time-variant cases, the general description is also known as the convolution description.
General Description :
| Time-Invariant, Non-causal | $y(t)=\int _{-\infty }^{\infty }h(t-r)x(r)dr$ |
| Time-Invariant, Causal | $y(t)=\int _{0}^{t}h(t-r)x(r)dr$ |
| Time-Variant, Non-Causal | $y(t)=\int _{-\infty }^{\infty }h(t,r)x(r)dr$ |
| Time-Variant, Causal | $y(t)=\int _{0}^{t}h(t,r)x(r)dr$ |

## State-Space Equations

These are the state-space representations for a system. y is the system output, x is the internal system state, and u is the system input. The matrices A, B, C, and D are coefficient matrices.
[Analog State Equations]
State-Space Equations :
| Time-Invariant | $x'(t)=Ax(t)+Bu(t)$ $y(t)=Cx(t)+Du(t)$ |
| Time-Variant | $x'(t)=A(t)x(t)+B(t)u(t)$ $y(t)=C(t)x(t)+D(t)u(t)$ |
These are the digital versions of the equations listed above. All the variables have the same meanings, except that the systems are digital.
[Digital State Equations]
State-Space Equations :
| Time-Invariant | $x'[t]=Ax[t]+Bu[t]$ $y[t]=Cx[t]+Du[t]$ |
| Time-Variant | $x'[t]=A[t]x[t]+B[t]u[t]$ $y[t]=C[t]x[t]+D[t]u[t]$ |

## Transfer Functions

These are the transfer function descriptions, obtained by using the Laplace Transform or the Z-Transform on the general system descriptions listed above. Y is the system output, H is the system transfer function, and X is the system input.
[Analog Transfer Function]
Transfer Function :
* $Y(s)=H(s)X(s)$
[Digital Transfer Function]
Transfer Function :
* $Y(z)=H(z)X(z)$

## Transfer Matrix

This is the transfer matrix system description. This representation can be obtained by taking the Laplace or Z transforms of the state-space equations. In the SISO case, these equations reduce to the transfer function representations listed above. In the MIMO case, Y is the vector of system outputs, X is the vector of system inputs, and H is the transfer matrix that relates each input X to each output Y.
[Analog Transfer Matrix]
Transfer Matrix :
* $\mathbf {Y} (s)=\mathbf {H} (s)\mathbf {X} (s)$
[Digital Transfer Matrix]
Transfer Matrix :
* $\mathbf {Y} (z)=\mathbf {H} (z)\mathbf {X} (z)$
* Control Systems
# Control Systems/Systems Introduction

| The Wikibook of: Control Systems and Control Engineering | Table of Contents | All Versions | PDF Version |
| The Wikibook of: Control Systems and Control Engineering | | | Glossary |

## Preface

This is the Systems Introduction version of the Control Systems wikibook. This book will serve as a general introduction to the topic of systems engineering, by reusing existing text from the "master book", and leaving out some of the more advanced mathematical material.

### Warning

    This is a child version of the Control Systems parent book. The pages listed here constitute a suggested reading order of the material, and are not a stand-alone book. Do not edit these pages in a way that make them incompatable with the main narrative of the parent book. If you have questions, comments, or suggestions about this version, leave a message on the main book discussion page.
For a list of all child versions of this book, see All Versions.

## Table of Contents
### Systems Introduction

* Control Systems/Introduction
* Control Systems/System Identification
* Control Systems/Digital and Analog
* Control Systems/System Metrics
* Control Systems/System Modeling

### System Representation

* Control Systems/System Identification
* Control Systems/Digital and Analog
* Control Systems/System Metrics
* Control Systems/System Modeling

### Glossary and Licenses

* Control Systems/Glossary
* Control Systems/Resources
* Control Systems/Licensing
* Control Systems
# Control Systems/System Specifications

| The Wikibook of: Control Systems and Control Engineering | Table of Contents | All Versions | PDF Version |
| The Wikibook of: Control Systems and Control Engineering | ← Controllability and Observability | Controllers → | Glossary |

## System Specification

There are a number of different specifications that might need to be met by a new system design. In this chapter we will talk about some of the specifications that systems use, and some of the ways that engineers analyze and quantify technical systems.

## Sensitivity

The sensitivity of a system is a parameter that is specified in terms of a given output and a given input. The sensitivity measures how much change is caused in the output by small changes to the reference input. Sensitive systems have very large changes in output in response to small changes in the input. The sensitivity of system H to input X is denoted as:
    $S_{H}^{X}(s)$

## Disturbance Rejection

All physically-realized systems have to deal with a certain amount of noise and disturbance. The ability of a system to reject the noise is known as the disturbance rejection of the system.

## Control Effort

The control effort is the amount of energy or power necessary for the controller to perform its duty.
| ← Controllability and Observability | Control Systems | Controllers → |
# Expert Systems/Forward Chaining
## Forward Chaining

In a forward chaining system, the inference engine is presented with some starting information, and works forwards through its rule base to reach some conclusion. ...
# Expert Systems/Jess
## About Jess

Jess, a rule engine for the Java platform, is a superset of CLIPS, developed by Ernest Friedman-Hill of Sandia National Labs. It was first written in late 1995.
It provides rule-based programming suitable for automating an expert system, and is often referred to as an expert system shell. In recent years, intelligent agent systems have also developed, which depend on a similar capability.
Rather than a procedural paradigm, where a single program has a loop that is activated only one time, the declarative paradigm used by Jess continuously applies a collection of rules to a collection of facts by a process called pattern matching. Rules can modify the collection of facts, or they can execute any Java code.
Jess can be used to build Java servlets, EJBs, applets, and full applications that use knowledge in the form of declarative rules to draw conclusions and make inferences. Since many rules may match many inputs, there are few effective general purpose matching algorithms. The Jess rules engine uses the Rete algorithm.
While CLIPS is licensed as open source, Jess is not open source.

## Examples
### Example 1

Code examples:

### Example 2

Sample code:

## See also

* Official Website
# Expert Systems/MYCIN

Mycin was an early expert system developed over five or six years in the early 1970s at Stanford University; it was written in Lisp, by Edward Shortliffe under the direction of Bruce Buchanan and others; it derived from the earlier Dendra] expert system, but considerably modified and extended the basic Dendral software. This computer system was designed to diagnose infectious blood diseases and recommend antibiotics, with the dosage adjusted for patient's body weight — the name derived from the antibiotics themselves, as many antibiotics have the suffix "-mycin".

## Method

Mycin operated using a fairly simple inference engine,a knowledge base of ~500 rules. It would query the physician running the program via a long series of simple yes/no or textual questions. At the end, it provided a list of possible culprit bacteria ranked from high to low based on the probability of each diagnosis, its confidence in each diagnosis' probability, the reasoning behind each diagnosis (that is, Mycin would also list the questions and rules which led it to rank a diagnosis a particular way), and its recommended course of drug treatment.
Despite Mycin's success, it is sometimes now used as a cautionary tale in AI courses for people who generate their own ad hoc probability frameworks. Mycin had a limited depth to its reasoning hierarchy due to the noise introduced by its certainty factor system. This problem can be solved by using a rigorous probabilistic framework, such as Bayesian statistics.

## Results

Research conducted at the Stanford Medical School found Mycin to have a correct diagnosis rate of about 65%, which was better than most physicians who were not specialists in diagnosing bacterial infections, even though it was worse than those physicians who were themselves experts in the field who had average correct diagnosis rates of about 80%.

## Practical use

Mycin was never actually used in practice. This wasn't because of any weakness in its performance — in tests it outperformed members of the Stanford medical school. It was as much because of ethical and legal issues related to the use of computers in medicine — if it gives the wrong diagnosis, who can be held responsible? Issues with whether human experts would find it acceptable to use arose as well.
A difficulty that rose to prominence during the development of Mycin and subsequent complex expert systems has been the extraction of the necessary knowledge for the inference engine to use from the humans expert in the relevant fields into the rule base (the so-called knowledge engineering).

## References

* The AI Business: The commercial uses of artificial intelligence, ed. Patrick Winston and Karen A. Prendergast. ISBN 0-262-23117-4

## External links

* Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project -(edited by Bruce G. Buchanan and Edward H. Shortliffe; ebook version)
* TMYCIN, system based on MYCIN
* "Mycin Expert System: A Ruby Implementation"
* "MYCIN: A Quick Case Study"
* " SOME EXPERT SYSTEM NEED COMMON SENSE" -(by John McCarthy)
* "Expert Systems"
# Expert Systems/Productions

Productions are set of situation-action pairs. They are used in reaching conclusions and making decisions in problem solving situations. It is an algorithmic problem solving criteria, needed to make decision.
# Expert Systems/Introduction
## About This Book

This book is all about Expert Systems, an Artificial Intelligence (AI) programming technique.

## Target Audience

This book is designed for undergraduate and graduate students in computer science, computer engineering, or a related field. As this book is an introduction to the field of expert systems, and to artificial intelligence in general, students do not need to have a background in either of these areas.

## Prerequisites

Readers of this book are expected to be familiar with computer programming, and know at least one high level language. Students are also expected to have a background in logic, and probability. Some sections may require additional mathematics skills, such as calculus.
# Expert Systems/Knowledge Engineering
## Knowledge Engineering

Knowledge engineering is the task of extracting information from a human expert, typically through an interview, and translating that knowledge for use in an expert system.
# Expert Systems/Licensing
## License

The text of this book is released under the following license:
# Expert Systems/Introduction to Expert Systems
## Computer Intelligence

AI research has been one of the most frenzied areas of computer science since the inception of the discipline. However, despite the massive effort and money that has gone into research, computers are still unable to perform simple tasks that humans do on a regular basis. Many researchers believed that a comprehensive system of logic would enable computers to successfully complete high-level reasoning tasks that humans can perform. However, logical computer programs require knowledge on which to base decisions. Converting human knowledge into a form that is both meaningful and useful for a computer has proven to be a difficult task.

## Expert Systems

Expert systems are an area of AI research that attempts to codify the knowledge and reasoning processes of a human expert into a computer program.

### How Expert Systems Work

Expert systems interact with another entity, such as a human user or an application, to discover information about a problem, and evaluate possible solutions. The most simple form of an expert system is a question-and-answer system, where a human user is presented with questions. The user answers these questions, and those answers are used to further the reasoning process of the expert system.

### Uses of Expert Systems

Expert systems are used for problems where there is incomplete data about a subject, and insufficient theory available for the creation of an algorithmic solution. Some problems, such as medical diagnosis, are not easily solved with an algorithm, but instead require reasoning and induction.
Numerical algorithms are more efficient than expert systems, and are typically more exact. However, many problems are not suited to being easily modeled mathematically, and in these cases numerical algorithms are not possible. Other AI techniques, such as artificial neural networks are suited for problems where there is very little theory but a wealth of experimental data.
Expert systems tend to be slow, and often require extensive human interaction. However, well-designed expert systems can be very rigorous, and some expert systems have been shown to outperform the human experts that helped to develop them.

### Shortcomings of Expert Systems

Expert systems are based on human knowledge and reasoning patterns. This knowledge must be extracted from a human expert by a specialized knowledge engineer. Knowledge engineers ask the expert questions about his knowledge and his reasoning processes, and attempts to translate that into a computer-readable format known as a knowledge base. Expert systems generated in this way will be flawed if the information received from the expert is flawed, or if it is incorrectly translated by the knowledge engineer.
Expert systems, because they are focused on a single problem area, tend to fail catastrophically if presented with a problem or information that is outside their domain.
# Engineering Analysis/Matrix Forms

Matrices that follow certain predefined formats are useful in a number of computations. We will discuss some of the common matrix formats here. Later chapters will show how these formats are used in calculations and analysis.

## Diagonal Matrix

A diagonal matrix is a matrix such that:
    $a_{ij}=0,i\neq j$
In otherwords, all the elements off the main diagonal are zero, and the diagonal elements may be (but don't need to be) non-zero.

## Companion Form Matrix

If we have the following characteristic polynomial for a matrix:
    $|A-\lambda I|=\lambda ^{n}+a_{n-1}\lambda ^{n-1}+\cdots +a_{1}\lambda ^{1}+a_{0}$
We can create a companion form matrix in one of two ways:
    ${\begin{bmatrix}0&0&0&\cdots &0&-a_{0}\\1&0&0&\cdots &0&-a_{1}\\0&1&0&\cdots &0&-a_{2}\\0&0&1&\cdots &0&-a_{3}\\\vdots &\vdots &\vdots &\ddots &\vdots &\vdots \\0&0&0&\cdots &1&-a_{n-1}\end{bmatrix}}$
Or, we can also write it as:
    ${\begin{bmatrix}-a_{n-1}&-a_{n-2}&-a_{n-3}&\cdots &a_{1}&a_{0}\\0&0&0&\cdots &0&0\\1&0&0&\cdots &0&0\\0&1&0&\cdots &0&0\\0&0&1&\cdots &0&0\\\vdots &\vdots &\vdots &\ddots &\vdots &\vdots \\0&0&0&\cdots &1&0\end{bmatrix}}$

## Jordan Canonical Form

To discuss the Jordan canonical form, we first need to introduce the idea of the Jordan Block:

### Jordan Blocks

A jordan block is a square matrix such that all the diagonal elements are equal, and all the super-diagonal elements (the elements directly above the diagonal elements) are all 1. To illustrate this, here is an example of an n-dimensional jordan block:
    ${\begin{bmatrix}a&1&0&\cdots &0\\0&a&1&\cdots &0\\0&0&a&\cdots &0\\\vdots &\vdots &\vdots &\ddots &\vdots \\0&0&a&\cdots &1\\0&0&0&\cdots &a\end{bmatrix}}$

### Canonical Form

A square matrix is in Jordan Canonical form, if it is a diagonal matrix, or if it has one of the following two block-diagonal forms:
    ${\begin{bmatrix}D&0&\cdots &0\\0&J_{1}&\cdots &0\\\vdots &\vdots &\ddots &\vdots \\0&0&\cdots &J_{n}\end{bmatrix}}$
Or:
    ${\begin{bmatrix}J_{1}&0&\cdots &0\\0&J_{2}&\cdots &0\\\vdots &\vdots &\ddots &\vdots \\0&0&\cdots &J_{n}\end{bmatrix}}$
The where the D element is a diagonal block matrix, and the J blocks are in Jordan block form.
# Engineering Analysis/Minimization
## Khun-Tucker Theorem

The Khun-Tucker Theorem is a method for minimizing a function f(x) under the constraint g(x). We can define the theorem as follows:
    $L(x)=f(x)+\langle \Lambda ,g(x)\rangle$
Where Λ is the lagrangian vector, and < , > denotes the scalar product operation. We will discuss scalar products more later. If we differentiate this equation with respect to x first, and then with respect to Λ, we get the following two equations:
    ${\frac {\partial L(x)}{\partial x}}=x+A\Lambda$
    ${\frac {\partial L(x)}{\partial \Lambda }}=Ax-b$
We have the final result:
    $x=A^{T}[AA^{T}]^{-1}b$
# Engineering Analysis/Multi-Dimensional Fourier Series

The concept of the fourier series can be expanded to include 2-dimensional and n-dimensional function decomposition as well. Let's say that we have a function in terms of independent variables x and y. We can decompose that function as a double-summation as follows:
    $f(x,y)=\sum _{i=1}^{\infty }\sum _{j=1}^{\infty }a_{ij}\phi _{ij}(x,y)$
Where φ$_{ij}$ is a 2-dimensional set of orthogonal basis functions. We can define the coefficients as:
    $a_{ij}={\frac {\langle f(x,y),\phi _{ij}(x,y)\rangle }{\langle \phi _{ij}(x,y),\phi _{ij}(x,y)\rangle }}$
This same concept can be expanded to include series with n-dimensions.

## Further reading

* Basic Physics of Nuclear Medicine/Fourier Methods discusses using 2D and 3D Fourier reconstruction to get images of the interior of the human body.
* Kevin Cowtan's Book of Fourier: a book of pictorial 2-d Fourier Transforms.
The Feyman lectures Chapter 50 Harmonics
# Engineering Analysis/Quadratic Forms

If we have an n × 1 vector x, and an n × n symmetric matrix M, we can write:
    $x^{T}Mx=a$
Where a is a scalar value. Equations of this form are called quadratic forms.

## Matrix Definiteness

Based on the quadratic forms of a matrix, we can create a certain number of categories for special types of matrices:
* if $x^{T}Mx>0$ for all x, then the matrix is positive definite.
* if $x^{T}Mx\geq 0$ for all x, then the matrix is positive semi-definite.
* if $x^{T}Mx<0$ for all x, then the matrix is negative definite.
* if $x^{T}Mx\leq 0$ for all x, then the matrix is negative semi-definite.
These classifications are used commonly in control engineering.