System Modeling - Science topic

This Model has three critical elements -individual talent, domain knowledge (cultural and technical knowledge) and field influence (brand identity and market relevance) all three are directly related to aspects of fashion design and its publics support and acceptance.

The effectiveness of a school is directly linked to the implementation of specific guidelines for learning instructions and other activities accepted by educational leadership to improve students' learning outcomes. In 1996, Hallinger and Heck researched school effectiveness and proposed a model that, when consistently followed, enhances learning and promotes effective schools. Their model emphasizes following proper and specific guidelines on learning instructions and other activities accepted by educational leadership to improve student's learning outcomes. Hallinger, Philip, and Heck R.H.(1996) worked on school effectiveness research as Haliger and Merpy proposed a model that when religiously followed will enhance learning and promote effective schools.

A processor in a computer running Windows has two different modes: user mode and kernel mode. The processor switches between the two modes depending on what type of code is running on the processor. Applications run in user mode, and core operating system components run in kernel mode. A simple example to demonstrate the difference between user mode and kernel mode is that of a car driver and a mechanic. The driver is like an application running in user mode, while the mechanic is like an operating system component running in kernel mode .

You can take a look at this article for more information

Hello Fatima Aziz ,

You're welcome! Most of the simulators mentioned can indeed work with offloading algorithms or custom algorithms you write. To integrate your algorithm into the simulator, you may need to create a custom module or modify existing modules to fit your needs.

For instance, in ns-3, you can create custom application modules to implement your offloading algorithm, while in OMNeT++, you can extend existing modules or create new ones to incorporate your algorithm. In iFogSim and EdgeCloudSim, you can implement your offloading algorithm as part of the resource management and scheduling policies.

Regarding Mininet, while it is primarily focused on SDN, you can integrate it with other tools to create an environment that supports mobile edge computing. You might need to explore additional resources, such as ONOS (Open Network Operating System) or OpenDaylight, to create an SDN-based mobile edge computing environment in Mininet.

As you mentioned, exploring the documentation and example scenarios for each simulator will be helpful. Many simulators have active communities and mailing lists where you can ask for help or find resources related to your specific use case.

Good luck with your project.

Best regards!

Thanks Clement! I guess you are referring Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations? It looks great!

https://data.marine.copernicus.eu/product/OCEANCOLOUR_GLO_BGC_L4_MY_009_104/description

Many of the STS articles which are out there do not follow a prescribed methodology, but rather follow methodological guidance related to whichever field the STS under study resides within. See articles by Frank Geels (2002, 2005, 2007, 2014) for examples. SSM articles, conversely DO follow the prescribed SSM methodology of Checkland (1981).

You could provide an interesting contribution by integrating the two lit streams into one coherent analysis structure.

We used MESSAGE on national scale of Syria. Article: Formulating an optimal long-term energy supply.strategy for Syria using MESSAGE model,here in my profle

Here are the links of my two Modelings about engines :

Injection: https://www.researchgate.net/publication/339450942_Modeling_of_the_Automatic_Mechanical_Injection_System

ICE: https://www.researchgate.net/publication/352221791_The_Modeling_of_the_Alternative_Mechanical_Engines_the_mathematical_brake_work_and_the_mechanical_definition_of_the_availability

If you want to solve nonlinear differential equations (coupled or uncoupled) you can use 'odeint' function in python language. It uses the RK4 method of numerical integration. To use this function you have to define the nonlinear functions f(x,y), g(x,y) etc from the equations dx/dt=f(x,y) and dy/dt=g(x,y) and then supply these to odeint function as arguments. Along with this, you need to define a time range of integration and initial conditions for x and y.

You can start by solving the differential equation of the simple harmonic oscillator by odeint function in python. The system is defined by two coupled 1st order differential equations: dx/dt=y and dy/dt=-w*w*x. Where w is the frequency of the oscillator. Though it is not nonlinear but you can use this method for nonlinear differential equations also.

Dear Mehran!

Here is the link where you can apply:

Regards

Yahya

Weather is a good illustration of this difference. We forecast the weather by running a detailed computer model based on physics and meteorology starting at the present and extending out into the future with some assumptions about the boundary conditions. We predict the price of gas (or Twitter stock) next week based on the previous six weeks and a few assumptions (shape of the curve). I have done a lot of work with hydrological parameters, working as an applied mathematician with geologists and hydrologists. In that case I had to infer these parameters (which we can't directly measure) given data that we were able to measure. That was an exercise in nonlinear constrained optimization.

s. J Kim A robot control system, in short, manages commands, directs or regulates the movement and functions of various parts of the robot, and executes a specific set of forces and motions.

Dear Sandford:

You may benefit from these articles about your topic:

1- "System Modeling and Reliability Assessment of Microgrids: A Review"

@https://www.mdpi.com/2071-1050/14/1/126/htm.(section 5).

2- "Control Strategy of Microgrid Inverter Based on H∞ State Feedback Repeated Deadbeat Control"

@https://www.hindawi.com/journals/mpe/2021/8324926/.

3- "State Space Modelling and Control of the Modular Multilevel Converter".

.. The pdf file.

I hope it will be helpful...

Best wishes...

google it please

i think incremental model

Dear Sarah Kassir,

I suggest you to see links and attached files on topic.

Best regards

Try 'AQUASIM'. A trial pack of the same is available with official website.

Because you would need to determine the origin of the items that are taken for recycling at the end of their lives, blockchain would be tremendously helpful to track that information. Take a look at the following white papers and articles:

It is depend on the size of input vector, type and size of your data. if the size of data is small, you need features extraction. Remember you need meaningful features!

Hello Ani Ikechukwu

You could look for articles with the same topics that you're working on in any database. Then, you could identify the journals where those articles were published, and compile a list of related journals. As Princewill Ikpeka recommended, you could use the SCOPUS database, or even the Google Scholar site (some tips here: https://www.youtube.com/watch?v=dc-vKk205c8). However, you always should make a further review of the identified journals, to validate their scientific quality and credentials.

Best regards!

Dear Zarzisur Rahman Rony, it should be enough to add a "memory" block (you can find it in Simulink's Discrete toolbox) where algebraic loop occurs (e.g. after "system_approach_first/PV Array/Diode Rsh/Product5") then try with/without setting "inherit sample time" option. This allows breaking algebraic loop and it should work.

You may want to check the flags used when compiling the code. When the compiler optimize the code there is the possibility it changes instructions, which can lead to NaN computations. Also change the inputs to the problem to see if the NaNs disappear.

The multi-layer perceptron, the radial basis function network and the functional-link network

For more information:

S. CHEN &S. A. BILLINGS

1- Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

DOI: 10.1109/MCS.2016.2602087

2- Stochastic model predictive control — how does it work?

By: panelTor Aksel N.HeirungJoel A.Paulson

3- Stochastic Nonlinear Model Predictive Control with Probabilistic Constraints Ali Mesbah1 , Stefan Streif , Rolf Findeisen , and Richard D. Braatz

I agree with Mohamed-Mourad Lafifi

LEDS-EEP City Energy Planning Toolkit By:

Judit Bálint, Eduardas Kazakevicius, Thor Morante Brigneti

Mathematical Modeling of Kidney Transport

By: Anita T. Layton

By: Aurélie Edwards

Relationship between state-space and input-output models via observer Markov parameters

By: M.Q. Phan, R. W. Longman

Intelligent Autonomous Systems, Proceedings of the 15th International Conference IAS-15

Identifying the Most Important Independent Variables in Regression Models

By Jim Frost

With Matlab, we have:

With Matlab, we have:

https://www.mathworks.com/help/control/ug/continuous-discrete-conversion-methods.html#:~:text=The%20Zero%2DOrder%20Hold%20(ZOH,linear%20model%20H(s).

Also you can see this link :

In this article, you will find a new in the learning algorithms for the T-S fuzzy models:

Matlab (with Simulink) and or SciLab would be useful

(continued)

In case of more terms (all with different means m_j>0, j=1,2,...,n) the formulas are as follows (ti replaced by -s)

ch.f.(X_1+X_2+...+X_n)(t) = 1/ [ (1+m_1 s) (1 + m_2 s) ... (1 + m_n s)]

= \sum_{j=1}^n A_j / (1+m_j s),

where A_j = \prod_{k\ne j} [ 1 - m_k / m_j]^{-1}

Therefore, in such cases the density of the sum is equal to

\sum_{j=1}^n A_j / m_j \exp( - x/m_j ), for x>0.

If X_j in the sum is preceded by sign -, then the first two formulas remain valid after replacing m_j by - m_j. The last requires replacing the exponential density for positive variable by the opposite one.

I interested

Hi Bhaskar,

You should check the SoftRobotic Toolkit, I believe it is a very good starting point:

Good luck!

This signal can be cleaned from the high frequency noise superimposed on it by an average filtering. You need to sample this signal with the proper sampling frequency and then you can apply the sample on a sliding average filter. Such filter tool kit is associated with the mat lab.

You can use also analog low pass filter to filter out the high frequency ripples. You can use Butter worth filter. You can vary the cut off frequency and the order of the filter till you clean this signal.

With the signal processing tool kit in the mat lab there is no problem with with such type of noise.

I would like also that you see the attached paper in the link:

Best wishes

Hi I am Professor Soheil Sayed Hosseini See my post on satellite control article nmce is availebel

Dear Muhammad,

The more promising and externally challenging future for robotics in nuclear power plant (NPP). The key rationale of robotics application has always been to avoid human exposure to hazardous environments and tasks ranging from scrutiny and general maintenance to decontamination and post accidental activities. To execute these activities, robots need to incorporate artificial intelligence, improved sensors capability, enhanced data fusion and compliant human like leg and hand structures for efficient motions. Next generation robotic systems in NPPs are expected to work in full autonomous mode in contrast to the current semi-autonomous scenarios. Far future systems could deploy humanoid robots as well. This paper presents state-of-the-art of robotics developed for NPPs, associated challenges and finally comments on future directions.

The primary challenge of robots in NPPs is to ensure safety with precision, reliability and repeatability for any kind of operation in any state at any time under any circumstances. This challenge is due to technological limitations in terms of on-board processing speed, power requirements and unavailability of integrated sensors and actuators, initial robotic systems were highly task specific with little intelligence. The robots were almost unable to cope with any other scenario for which they were not designed. The robotic units were designed with stationary bases and with specific and limited work space. To make use of nuclear power for the benefit of human being in a risk-free and undisruptive manner is another challenge. To achieve this status, in addition to development of superior state-of-art technology, we need to devise better plans and systems to cater for cataclysmic circumstances. Reactor with cooling towers is the most sensitive part of a NPP. Radiation danger in these parts imposes special attentions for all kind of operations in this region and thus this is probably the most desired area of deployment and use of intelligent robotic systems. The challenges in this particular region are coped using various existing branches of robotic units such as mobile robots, stationary industrial arm manipulators or hybrid robots.

Simulators and computer modeling techniques are strongly recommended by the International Atomic Energy Agency (IAEA).Here the scopes are dynamic model study of sensitive region reactor with cooling tower. Re-designed robot model efficiency studies with an addition of an extendable arm studies using appropriate payload capacity, environmental hardening, sealed mechanisms and wide range of tools.

Hope it help you out. You would like my explanation.

Ashish

This paper

Dear Arun,

System modelling and simulation are based on mathematical and logical principles and concepts. It starts with a physical model of the system and then mathematically describing this system by a system of equations normally differential and algebraic equations. To determine the system performance parameters one needs to solve these equations. So, may be one of the best simulators is to use mathematical solving tool which may be from my point of view is the matlab/simulink. This environment in addition contains many specialized model libraries such as Filter design, digital digital signal processing

tool box, logic design tool box, communication tool box ....etc.

This wide utilization is because of easy use script language and the existing aid by the library of models of components, subsystems and subsystem.

There are also associated with it powerful compilers to covert the matlab code to a C-code or a VHDL code.The C-code can be implemented directly on the digital signal processors. The VHDL can be implemented on FPGAs or ASICs chips.

There are tool suits for FPGA design and for ASIC design that acceptsthe VHDL issued by the matlab as an input.

In addition to the matlab there is the ADS that can be a complement for the matlab for the analog system simulation.

Best wishes

Dear Mohammad, I developed many multi-carrier energy system models in MATLAB environment, if you are good in programming you may follow this way. Here you can find an example of my work on the topic (it is the only one published to date):

Dear S.Toquica, in our project we are not using kinematics models for robot control. I do think it would be very interesting and useful for real-world applications.

a few years back, we did some work on biomass using energy systems model. Project details can be found under https://www.psi.ch/en/eem/projects/chp-plant-swarm

get the mechanism and bulid the model by ode， then use data to get the accurate parameters

Dear Natesan Raajan ,

You can go through this article it might be helpful:

The Systems Model (or User Model) needs a Metamodel to ensure consistency between 9 SysML diagrams.

This is terrible a very bad modeling expereince for software intensive systems.

I would suggest you try the Structure-Behavior Coalescence (SBC) Process Algebra (PA) for the software modeling.

The SBC-PA is a metamodel as well as a systems model for software intensive systems.

-----------------------------------------------

Introduction of SBC-PA can be found in the paper

I used the quick start in Sys. Identification, it gave me a discrete time state space model that fits 100%, but in simulation i get very wrong values of the ouput..i dont understand why..please some help would be very appreciated

I believe that good starting point is

@ARTICLE{Sar:85,

author = {G.N. Saridis},

title = {An integrated theory of

intelligent machines by expressing the control performance as entropy},

journal = {Control Th.\ \& Advanced Technology},

year = {1985},

pages = {125-138},

number = {2},

}

and I hope that this paper is cited by its successors ... MK

Hi Nicolas,

There is a book that in my view backs up the spiral dynamics model, but maybe not in a strictly empirical way (if that's what you are looking for) by 'proving' that the model is historically accurate and that evolution 'in fact' happened this way. (The assumption of an 'objective truth' that could be discovered by empirical evidence is itself problematic.) The book is :

Chapman, Kelly (2015). Complexity and Creative Capacity. Rethinking knowledge transfer, adaptive management and wicked environmental problems. New York – London: Routledge.

It shows in general terms that the current global system evolves towards a higher (emergent) level of complexity and that we reach a point in which all previous (more stable, linear) systems are converging into a planetary interconnected complex (non-linear) flow of subsystems, leading to a new, specifically global dynamics.

In terms of system dynamics this may be the same as stating that the first tier systems converge and we enter a phase of 'second tier' dynamics. Chapman describes the process ontology and cocreative epistemology that will allow us to deal with complex challenges (incorporating e.g. 'indigenous knowledge'). The book does not literally refer to system dynamics, but as a reader I found it useful to link both approaches and explore similarities.

@ Kelly Chapman

Dear Fateme Bakhshande

You have an output regulation problem and you need to use Internal Model Principle (IMP). Assuming that your output is the first state and the reference to be track is a sinus, you have two options:

1- Use a dynamic controller: the dynamics of your controller should contain a 1-copy of the internal model of the reference signal. If you have p outputs in general, you need p-copy of the internal model. Then, the controller signal is a feedback from its internal state (the controller is dynamics so it has an internal state) and the state (or the output) of your system. This approach is robust.

2- Use a feedforward approach: Design a stabilizing controller for your system assuming that the reference is zero. Then, solve the output regulation equation to obtain the feedforward part. The controller is a sum of these two by some small modifications. Note that this method is not robust if you have uncertainty in the system.

A handy book on this topic is `` Nonlinear Output Regulation: Theory and Applications", by Jie Huang.

Maybe you could have a look at Sigma DDs:

I believe should say more about your problem. What is SAP2000, etc.

Dear Adel

Did you fixed your problem?

I have an idea

The procedure of the evaluation of and acceptance of SCI or SCIE is essentially the same. However, it exists a simple difference concerning since tCD/DVD format is used by SCI and online format is used by SCIE. In the following, you see some examples :

I hope at least with my stereo picture of the "Standard Model" that it is possible to show that what can be done by a very long Lagrange-Formula can also be constructed especially with the new media to get an instant overview direct for the eyes, what's sure better for everyman. The secret-mongering of the Philosopher gets only lost.

MP2 is great, but MP2 gradients are expensive and I am not sure about how large your systems are.

If you can afford to use MP2, then you can probably afford to use better DFT functionals than B3LYP - I would look into a good benchmark study, for example;

And pick one of the recommended functionals from there. They even have a breakdown of performance in terms of different chemical properties.

I would probably go with something like PW6B95-D3 and then perform singlepoints with a double-hybrid functional to get the MP2 correlation if nescessary.

The necessary power balance condition also requires instantaneous matching between the generation and the load for proper and reliable operating.

Please follow this

Dear Behzad Minaei,

I think you should take a look at the language Modelica for describing dynamical systems. There are also good open source implementations like JModelica, see http://www.jmodelica.org

You find good teaching material here https://www.xogeny.com/about/

and Mark Tiller has also written a couple of books and one recent book is an e-book for free. http://book.xogeny.com

I have myself used this software to develop applications in biotechnology during the last 5-6 years. Can take some questions if you need.

Best wishes

Jan Peter Axelsson

Assuming you have actual rock samples to work with, I agree with Jorge Costa-de-Moura. Benthic foraminifera will give you the best estimates for paleo water depth in shallow water environments, but nanno fossils are much better indicators for deepwater environments. Keep in mind that using micro-fossils is not an absolute answer, but rather a very good indicator. Storms, ocean bottom currents and debris flows can misplace micro-fossils from their original location of deposition. You may have to widen your research area in order to get a more accurate picture.

Dear Diego García-Gusano,

here are some literature with methods and tools for Smart City Energy Planning. Some of these may be interesting for you.

Best regards

Anatol Badach

Smart City Energy Planning: Integrating Data and Tools

A new way for Smart Energy Planning in cities: the INSMART solution

Study on City Energy Plan and Control

Extensible Energy Planning Framework for Preemptive Tasks

Methods and tools for community energy planning: A review

GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas

A review of urban energy system models: Approaches, challenges and opportunities

An Integrated Planning Framework for the Development of Sustainable and Resilient Cities – The Case of the InSMART Project

Research in city energy planning and it's development strategy

Developing a city energy modelling tool and approach

Gracias Néstor por tu pronta respuesta, ocurre que no logro entender la diferencia desde el punto de vista gerencial , es decir para toma de decisiones, en construir un SDEA o un DEA , si al final solo tendré un ranking de eficiencia técnica .

En un modelo probabilístico como SDEA debería poder , luego de llegar a un resultado con sus medias y varianzas en un entorno con valores , errores e ineficiencias,contestar algunas preguntas de sensibilidades , como " que. probabilidades tengo de lograr tal eficiencia", si la función de producción para esa firma toma tal valor o si tal recurso o tal producto toman tal valor.

He trabajado con MATLAB y lo he construido según los trabajos de Aigner y Lovell (hay un artículo muy bueno de estimación de eficiencias).

Antes me había zambullido en Tim Coelli y DEAP pero es muy rígido y no me permite modelar sobre otra función que no sea Cobb Douglas porque Translog no me anduvo.

Si estás en este tema quisiera

You can see the paper "State and parameter estimation: a nonlinear Luenberger approach"

in which an observer is used to estimate both state and parameters of a linear model.

If your goal is only to find the matrices A,B,C,D you can use least-square minimisation technique based

on your data 'y' and 'u'.

For this, you can fix the order of your model and use one of the controlable or observable canonical forms to represent it.

After that, solve the optimization problem (by using, e.g., fmincon function of Matlab) to find the entries of the matrices

Ac,Bc,Cc,Dc corresponding to one of the canonical forms.