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Design Optimization - Science topic

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What are your suggestions for innovative application and development in the field of Reliability-Based Design Optimization?
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Solution development based on proven technologies.
What are these proof-values: Reliability, ROI, TCO, ease of use.
How to check what's best: Monitoring, Predictive Measurement and Analytics, etc.
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I want to optimize the synthesis of nanoparticles using DOE.
My system is: lipid A and lipid B (total mass of two lipids stays the same), surfactant and water.
Which DOE should I choose?
  • Mixture - to make two lipids as a Mixture A and surfactant and water as a Mixture B?
  • Combined - to make two lipids as a Mixture A and surfactant as a numeric factor (not mentioning water here)
Just to be clear. Total mass of lipids does not change, only the ratio. Then I want to check different masses of surfactant (w/w surfactant/total lipids mass). Water is added, so total mass of sample is constant.
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This was my first idea, however I was concerned that such approach will interfere with calculations.
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This is for multivariate optimization in dispersive liquid liquid microextraction
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It depends on the selection of the star points.
My new paper (it is free) can show it as a practical example (Acid Red 18 vs Acid Brown 14 and Acid Orange 7):
Techno-economical aspects of electrocoagulation optimization in three acid azo dyes’ removal comparison
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For example :
structures & aerodynamics disciplines.Objective functions: weight, wing displacement and aerodynamic coefficient.
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I suggest you take a look on: https://www.mdpi.com/2226-4310/9/3/153
where aero-structural optimization is carried out using Particle Swarm Optimization.
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I'm working on some optimal strategies for an environmental surveillance network. My solution is almost based on the meta-heuristic solution. I have to know what the advantages or disadvantages are of heuristic and meta-heuristic optimizations.
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I am using Optimizations techniques for my research work on demand response. To deal with uncertain parameters and variables stochastic and robust optimization are used. I wanted to learn these techniques and subsequently implement in my research work. What are the good books and optimization softwares to start with?
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See Chapter 12 in:
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I am interested in  the optimal weight design of a gear train problem using optimization methods. There are a variety of studies on this topic.  One of them is "A Solution Method for Optimal Weight Design Problem of the Gear Using Genetic Algorithms" by Takao YOKOTA et al. The objective funticon and constraints are described as same formulations in not only the study of Yokota et al. but also the later studies. Nevertheless, I cannot find any detail about two terms. The former one is "b5" mentioned in the constraint "g5".  g5 is defined as the distance between the axes. The latter one is "l" mentioned in the objective function. "l" is defined as length of boss.
Could you inform me about these terms. Do the terms stand for a formulation or a constant value. 
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Dear Murat Dörterler, Greetings!
This systematic approach gives the minimum volume of gear.
But you have to read in Russian.
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Dear researchers,
I am coupling Design Optimization with ANSYS Fluent in Workbench 2020 R2. I can do the analysis. I am patching the initial Temperature values of the zones. However, the patch only works in the first project run. In the next runs, the patch is not run as it should, so the initial temperatures are not correctly assigned to my models' zones. How can I permanently initialize the flow field or automate the initialization process to run in the beginning of each run?
I thank you in advance,
Best regards,
Hugo Silva
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You could define variables in the project and use them for all the simulations.
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I have simulated a linear motor structure in the Maxwell software environment (version 16). The results are correct in the non-excitation state, but the results are not correct in the current excitation state. Can anyone tell me where the problem is? The simulation files are attached below.
reference paper:" Design Optimization and Performance Comparison of Two Linear Motor Topologies With PM-Less Tracks "
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Thank you Mr. Rebbah but I think the problem is something else .
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If one has do to some really complex shape optimization for the external aerodynamic problems . Is the adjoint solver has the enough capabilities to do the job without having command on MRF add on in Ansys Fluent .
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This is perfectly possible! At the moment we are developing it to optimse blade shapes of propellers.
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(Proposal) Oil Refinery Production: What is the company's goal?
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[Purpose: get Engineers & Scientists thinking outside their box ... think -large- problems. What's possible today vs. needs for tomorrow?]
Question: Are you interested in increasing your sales income by several orders of magnitude? Are you willing to think outside the box? If so, please read on. This is a large proposal, the size of NASA's Apollo Space program back in the early 1960s.
A new level of Computers and Software will be required for this Oil Production proposal. Today's Computers are Algebraic, i.e. bare bones, conceived designs that run similar to a 'model T' car. They 'run' along at a '30 mph' clip. We need fast super Computers like the Wright Brothers 'Airplane' that can run at a '3,000 mph' clip. These super Computers need 'Automatic Differentiation' based technologies; i.e. smart thinking abilities. NASA realized this when starting the Apollo space program; spent tons to get it and put us on the moon.
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📷
Oil production depends on many factors; e.g. Supply, Demand, present inventory, etc. An oil company may have many refineries with many distillation units. How can a company simulate extracting products 'a', 'b', and 'c' from its crude oil? Assume the company wants product 'a' on the west coast, 'b' in the middle of US, and 'c' on the east coast. Assume the company has refineries 'x' on west coast, 'y' in middle US, and 'z' on east coast. How does one model such a company's oil production so as to produce/refine the 'right' amounts of each product at each refinery site in order to meet the company's goal of maximizing profits?
Partial Differential Equations (PDEs) will be used to model the crude oil distillation for each distillation unit at each site; i.e. many PDEs must be solved at once! Are there computers large enough to handle such problems today? Are there plans for some super computer that will be able to handle many (1,000s) PDEs at once?
With maintenance of distillation units being continual, e.g. fix one, stop another, this will be a constant problem when trying to simulate the next day's crude oil work load. For example, assume a company has 600 distillation units overall. That means a computer program would be required to solve 600 PDEs ASAP; i.e. 10 hours of PDEs. My past experience with modeling in FortranCalculus™ language/compiler, I was taught that a modeling requiring 'Tmod' time to execute the model, would require around 2'Tmod' time for the optimal solution. That would then get us into the 20 hr. time range for 600 PDEs. Too long! Need faster computers and solvers to get into reasonable solution times. Ideas how this could be done today? For more, visit http://fortrancalculus.info/apps/fc-compiler.html ... Solves Algebraic Equations through Ordinary Differential Equations.
Many people thought that the Wright Brother's idea of an 'airplane' would never fly. But, what if it did? What if Oil sales income doubled or more? Would crude oil prices increase? (Everyone is going to want more for their piece of the pie, right?) How would this effect your company?
John D Rockefeller was quoted saying, "If you want to succeed you should strike out on new paths, rather than travel the worn paths of accepted success."
Any future John D Rockefeller's reading this proposal? Are you interested in increasing your company profits by several orders of magnitude? Does your company have a company goal or objective that all employees know about and follow? If so, continue reading on this proposal by reading my article "Company Goal: Increase Productivity?" (a dozen pages). Go to web page eBook on Engineering Design Optimization using Calculus level Methods, A Casebook Approach and click on the 'download' link, its free!
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Dear Phil,
The complexity of the refining business grows every day with new markets, new feedstocks and new regulations. Additionally, refineries are facing declining profit margins. I think, to sustain their profitability, refineries must leverage process simulation technology and capabilities to achieve best-in-class operational excellence. I think advance process simulation could be more helpful towards the inclining profit margin. Couple of areas where it can use effectively to reduce the market burden.
1. Heat exchanger maintenance and monitoring-thorough simulation of heat exchanger operations within the broader process simulation model. The heat exchanger design tool must also simulate all major heat exchanger types used in the refining industry. Furthermore, the solution should allow process engineers to easily develop and integrate their heat exchangers’ simulation as part of the refinery flowsheet without leaving their familiar process simulation environment.
2. Column operations troubleshooting-an integrated process simulator that accurately simulates the thermal and hydraulic behavior of the column unit to provide enough information to support column operations. With the correct process simulation software, users can accurately simulate thermo-hydraulic functioning of columns based on their construction and operating conditions. As a result, they can better understand the columns’ behavior and avoid operational mishaps. Simulating the operation of the column in the broader setting of the overall process enables users to identify root causes of the problems and determine the optimal point of operation for the overall process unit.
3. Integrated refining and gas plant analysis-Refineries need a solution that meticulously simulates the entire gas plant including acid gas treatment units, sulfur recovery, tail gas units and flare systems together with the mainstream refining process units, such as distillation units and reactor units. Advanced simulation technology would provide the refiners enough confidence to push the levels of sour crudes closer to the limit the refinery can process while meeting regulations. Feed flexibility, capacity creep and operating expenditure optimization, enabled using integrated refining and gas plant process simulation, can save refiners millions each year in operating margins while ensuring maximum reliability and plant uptime. In addition, the rigorous simulation of the gas plant operation offers refineries visibility and the ability to better document their emission levels. This capability is valuable for boosting their profit margins.
4. Planning model update for refineries-The ideal option is to give refinery process engineers the ability to maintain the planning models with the help of advanced process simulation software that can offer a streamlined workflow to update the planning models, enabling frequent updates when the models become out of sync with the operating range of the refinery.
5. Refinery-wide process analysis-With an advanced integrated solution for process simulation and refinery planning, refineries can develop a refinery-wide process model out of their refinery-wide planning model in a relatively short period of time. The accuracy of the simulation model can be enhanced by selectively incorporating rigorous models of reactor units to the refinery-wide flowsheet.
Ashish
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Between full factorial desgin and central composite design, which one gives optimum result when dealing with design of experiment?
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Optimum result from factorial design gives the good results, but takes more computation time.
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Which book is the best for designing a single phase induction motor for an industrial application or research paper which shows best efficiency optimization techniques.
Please share the link.
Thanks
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I recall the book
Wendland, H.: Scattered Data Approximation. Cambridge Monographs on Applied and Computational Mathematics, Cambridge, UK (2005).
A PhD student of mine used it thoroughly in her PhD project on simulation-based optimization in industry.
Check it out!
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Did any one work on reliability based optimal design of strcutural problems such as concrete structures and steel structures ?
I need a co-author who has experiences in the reliability based optimal design so that we could work on an optimization subject combined with finite element analysis in a paper.
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Hi Nazim Nariman
I am studying about the above topic!
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Hi, I am using four input variables for my process to maximise the yield(output). I want to get optimum point of variables through the integration of ANN and GA. Experiments are designed by central composite design. How can i integrate both of these?
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I prepared a complete tutorial about optimizing artificial neural networks using genetic algorithm with Python implementation. It is titled "Artificial Neural Networks Optimization using Genetic Algorithm with Python" It is available here:
You can read more and more in my 2018 book that covers GA in one of its chapters. The book is cited as “Ahmed Fawzy Gad ‘Practical Computer Vision Applications Using Deep Learning with CNNs’. Dec. 2018, Apress, 978–1–4842–4167–7” which is available here at Springer https://www.springer.com/us/book/9781484241660
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I have a structural component in Abaqus and I would like to optimize the shape. Please, how can I implement shape optimization in Abaqus?
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Samuel Ayinde @ Shape optimization with abaqus is very comman by using tosca. Tosca helps to improve existing design/shape for more reliable and durable components based on stress reduction through shape optimization. It also used for topology optimization for additive manufactured product. Here i am attaching abaqus/tosca manual: http://meet.cadcam-group.eu/pdf/SIMULIAseminar_April20/Georgi_Chakmakov_Tosca_Optimization_techniques.pdfThis attachment shows structural component design, shape and thickness optimization, which finally tells that there is weight reduction. This weight reduction means less material requirement, then the overall cost also decreases.Please also find the attachment tutorial for the same: https://www.4realsim.com/tosca/The relevant paper for optimization in the abaqus environment using TOSCA: http://www.simulia.com/download/pdf2009/Furbatto_SCC2009.pdf Tutorial for design optimization: https://www.youtube.com/watch?v=R5BFd5c4pfE
Tutorial for topology optimization: https://www.youtube.com/watch?v=vKmFfHPkWtw
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i have designed PCF-SPR sensor with two dual coating of plasmonic material( Ag/ITO) and numerically investigate the performance in term of confinement loss and wavelength sensitivity.
is it possible to optimize design (dimension,shape optimization)in COMSOL multiphysics software?.
kindly share the link related to optimisation of PCF-SPR sensor.
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Comsol is flexible in customizing the geometrical parameters.
Please look at few reference literature realted to your question using COMSOL software.
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suggest optimization method used for property optimization of liquid bio fuel mixture for designing optimized liquid bio fuel for diesel fired boiler
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Good work if results true obteind
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I need to set up structural shape optimization of an engineering component in MATLAB. I need:
1. A matlab code for structural shape optimization
2. An access to the procedure for writing the code in MATLAB.
Thank you.
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Hi Samuel
Are you familiar with these publications:
Topology optimization with a level set approach in Matlab [1]
Topology optimization with a density-based approach in Matlab [2] 
I think it provides, what you are looking for.
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Hi,
I am writing an assignment within one of my courses within my MSc. The course is named "Supply Chain Design and Optimisation", and the assignment requires us to find a good example of an organization using a locational strategy, in relation to where they should locate their manufacturing or warehousing hubs.
It seems like many of the papers that are publishing these types of research, are anonymizing the names of the companies.
I would really much appreciate if someone have any tips.
Thank you,
Martine Foerde
MSc Student, Logistics and Supply Chain Management
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Few more articles
'Greater Chinese'global production networks in the Middle East: the rise of the Jordanian garment industry
S Azmeh, K Nadvi - Development and Change, 2013 - Wiley Online Library
The expansion of ‘Greater Chinese’ capital from mainland China, Hong Kong and Taiwan into other parts of the developing world is increasingly noted. It is especially prominent in sub-Saharan Africa where Greater Chinese investments, firms and workers are found across a wide range of activities, from the extractive commodity sectors, to infrastructure projects, agriculture and manufacturing. One region where Greater Chinese investment is less well studied is the Middle East. This article focuses on the case of Jordan. Jordan has rapidly emerged as an important supplier of apparel to the United States, a consequence of a distinct preferential trade agreement. The article charts the ways in which this preferential trade agreement has stimulated the shifts of Greater Chinese garment manufacturers to Jordan. Using a global production networks (GPN) framework, and drawing on primary and secondary evidence, it assesses the dynamics behind Greater Chinese investments into Jordan; it also explores the ways in which Greater Chinese garment producers operating in Jordan organize their supply chains and are linked into the global garments GPNs. Finally, it considers the relationship between such capital flows and the influx of Asian migrant workers into the Jordanian export garment sector.
The Cases: Korean Vehicle Manufacturers in Europe (The 1990s)
JH Hyun - Korean Automotive Foreign Direct Investment in …, 2003 - Springer
This chapter attempts to trace the activities of Korean VMs in Europe. It is suggested that the internationalisation process of a company should be understood at the corporate level; the company’s specific history and changes in the competitive and regulatory environment (Nilsson et al. 1996). This study, therefore, selected two Korean companies in Europe as particular cases. These companies are HMC and DMC. In 1997, both companies comprised over 70 per cent of the country’s total in terms of production, sales and exports (Table 6.1). KMC is considered as a part of HMC based on the fact that Hyundai merged with KMC in October 1998.
A manufacturing network for generating added value from a geographical distance for the next generation
K Yukawa, T Kawakami - Journal of Machine Engineering, 2011 - yadda.icm.edu.pl
this study, we suggested a manufacturing network for generating added value from a geographical distance in terms of value creation from various management resources. Until today, most researchers focused on the Real Concentration of Production Base, which provides certain manufacturing benefits in these domains. However, "the Virtual Concentration of Production Bases" is realized by overcoming large physical distances and time differences between production bases, and creates greater added value for products. We attempted the simulation of the manufacturing network of creating the added value after having shown the new framework of network analysis in the manufacturing system.
Geography of multinational corporations and functional specialization in Chinese cities
C He, X Xiao - Symphonya, 2011 - search.proquest.com
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Almost all the optimization algorithms considers Function Evaluations to compare performance among various algorithms.
Do Function Evaluations number is the most important criteria? If yes/no why?
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Function evaluations are one of the most important criteria for comparison along with statistical analysis such as standard deviation, Friedmann's Test, Wilcoxon(due to the stochastic nature of metaheuristic), minimum fitness as well as average fitness. Function evaluation is important since several metaheurisitics have 2 or more stages in their optimization process (for example TLBO has teacher and student phase, GWO has only phase (searching and hunting), ABC also has two loops, Jaya has one , BFO has 5 etc). For all of them number of iterations may be same but that does not reflect the true computational requirement of the algorithm in terms of how much effort was required by the algorithm. Therefore, by using functional evaluations, a more fruitful comparison can be drawn since we are aiming for a common ground, basically the number of times the fitness function was called.
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I have a signal, and I want to add a plateau (Flattening) in my signal where ever I find local maxima.
What I am Doing:
I have 2 signals. 1st Reference and 2nd Measured Signal from Sensor. (I have taken 6 different measurement signals from 1 sensor).
The measured signals have delay, offset, flattening in it.
I want to make my reference signal to look alike my measured signal by putting delay, offset and flattening in signal to fulfill my needs.
I have done with delay and offset. The only problem I am facing is in Flattening.
To do flattening in my original signal I have observed that at every local maxima I have got flattening in signal and in every measurement flattening it is different.
So I want to make my signal flat at every local maxima.
Y axis: Height of flattening (I want to control the height of flattening at y axis)
Means I want to make a loop which contains varying values of flattening at y axis e.g In first attempt I want to give flattening of 0.1 then in 2nd attempt want to give flattening to al local maximas of 0.2 and observe their effect.
In short when I have local maxima so I want to give a flattening of (locs – 0.1) in the maxima on y axis. Means if I am getting peak at 5 so I want to give flattening at (5-0.1) at 4.9.
Then in 2nd loop want to check the effect of (locs-0.2) in the local maxima on y-axis.
Want to apply loop for values from 0.1 to 0.9.
X-axis: Width of flattening (I want to change the width of flattening)
Means I want to observe the effect of change in width of flattening by giving a range of values. This can be done by the help of loop.
In short when I have local maxima so I want to give a flattening width of 0.1 in the maxima on x axis.
Then in 2nd loop want to check the effect of 0.2 width change in the local maxima on x-axis.
Want to apply loop for values on x axis also for certain range.
Original Data Details :
In my original data x axis contains values in decimals so don’t want to interpolate data.
In my original data y axis also contains values in decimals.
size of my real data is 1x1666520.
Window effect:
The window is just to observe the behavior of change occurred at place of maxima means like if width of flattening is 0.1 so window is about 0.2.
I am sorry I remained unable to provide a good example data that explains exactly my situation in MATLAB so I have attached a handmade sketch.
I hope that I remained capable enough to make my question clear.
Below are the attached figures for explanation of flattening and How my Original signal looks like.
I am using MATLAB platform .
I am also attaching a rough example code on random data just to explain the concept  though it is not mature enough and not correct but it will provide an idea what i have done uptill now to achieve the results.
                t = 1:0.1:25 ;
                A = [1 0 1 2 3 5 0 1 0 0 0 2 3 6 7 0 0 8 0 1 1 2 3 4 2];
                [pks,locs] = findpeaks(A)
               % A(A>locs)=locs
               win1 = hamming(numel(A))';
               xw1 = win1.*A;
              figure
              plot(t,A,'b')
              A(locs+1) = A(locs);
              A(locs-1) = A(locs);
              hold all;plot(t,A,'r');
This is just my approach towards solving the problem which is just ok . If someone has a different approach so please share.
Thanks a lot in advance for your time and valuable and mature comments.
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Thanks a lot Prof Mohamed-Mourad Lafifi for your time and suggestions . This is a great addition to my knowledge bank.
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I am doing low-velocity impact analysis in order to obtain optimized shape for the vehicle's bumper beam, the bumper's element is Shell and also has a Non-Linear material. Please find the attached file.
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Dear Vahid, the direction of the displacement is correct and it follows my velocity direction, I got the problem which is referring to the wrong connection of my rigid region coupling and related DOFs.
Thanks for your attention.
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I want to measure the straightness of a line (Geometric Dimensioning & Tolerancing), which is represented by a pointset in 2D.
I build the convex hull of the pointset and calculate the "minimum width" of the convex hull, which is my estimation of the straightness of the line.
From measurement error, I have white noise on my pointset. By using the convex hull algorithm, I have a bias on my straightness-estimation - I systematically overestimate the straightness of the line.
Is there a method to minimize this bias for a given pointset?
Thanks in advance.
Philipp
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Thanks for the quick answer!
Maybe I should add some additional explanation of my application, as the question was a bit inaccurate.
I have a pointset with 2000-2050 points, they are ordered in x direction from right to left over a distance of 100 mm with an average distance of 0.05 mm between two neighbouring points (the points are ordered like pearls on a string).
They come from a laser pofile scanner (works with triangulation), which measures a straight line of a mechanical part. With the help of the pointset I want to identify the manufacturing precision of the part. For this purpose I want to measure the straightness of the line (standardized term in tolerancing), which could be defined as the "height of the line profile's bounding box with minimum height". Therefore I calculate the convex hull and measure it's 'minimal widht'.
A typical value of my straightness is 0.2 mm, so the bounding box has height 0.2 and length 100.
My problem now is, that the laser profile scanner has some uncertainty, which is some kind of random deviation of the point's position. This deviation mainly affects my straightness value, if points are deviating "away form the pointset", as the number of convex-hull-points is very small (only 10-20) compared to the number of all points and the requirement for a point to have an impact on the straightness is that it has to be part of the convex hull.
The problem with statistical estimation is, that i don't know how to estimate a value from a series of measurement, where all measurements are overestimations.
The idea with 6 standard deviations is very intersting. I think i first have to have a look at the range of my measurement error and subract it two times from my straightness value.
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I wish to simulate a simple cuboid of biological tissue, under compressive loading, obtain the stress/strain or force displacement curve, then recreate the same compressive loading model, however using artificial polymer material properties. I then need to optimise the topology of the 'polymer cuboid' so it reacts in the same way as the hyperelastic biological tissue sample. Is this possible, using the optimisation design response and objective functions in abaqus? Any help is much appreciated. 
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Hi Michael,
That picture is quite interesting - thank you for uploading it.
OK my original advice about switching to 2-D analysis is inappropriate here because the pores in your sample are irregularly shaped, i.e. there is no symmetry at all, let alone the particularly powerful type of symmetry that allows you to convert your 3-D simulations to 2-D simulations (axisymmetry). So ignore that if you haven't already.
With respect to modifying the interior topology, I have a further question. Are there any particular constraints? For instance, do you need the same volume fraction of pores in the polymer sample as in the original biological sample? Or the same surface area to volume ratio, number of vertical strands, strand diameter or similar?
My thought was that these constraints can be manipulated into mathematical expressions in the input file (Python) that ABAQUS can pick up, generate the geometry, mesh and input files automatically for and then optimise a particular result (your design variable), e.g. the reaction force at the top or bottom surface. Or did you have a different idea in mind?
Apologies - I am playing catch up here.
Milan.
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does Matlab R2014a support nonlinear constraint in multi objective optimization using optimization toolbox?
Thank you
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Yes. In fact there are some tutorials on YouTube. If R2014a features optimization toolbox, you can easily optimize your functions with its help.
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Dear Sir,
I am student of B.tech electrical engineering of Nirma University in final semester. I am doing my major project in which I am developing the code for BLDC motor design using particle swarm optimization.
I am making single objective multi-variable code for motor design, in this i am facing issue in calling multi-variable function. Can you show me the syntax of calling multi-variable function in PSO initialization?
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Hi, what do you mean by "issues in calling multi-variable function" ? I suppose you have coded this function in MATLAB or in another language. Is the problem related to the link between your function and the PSO code ?
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Dear Sir,
I am student of B.tech electrical engineering of Nirma University in final semester. I am doing my major project in which I am developing the code for BLDC motor design using particle swarm optimization.
I am making single objective multi-variable code for motor design, in this i am facing issue in calling multi-variable function. Can you show me the syntax of calling multi-variable function in PSO initialization?
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You may follow Mahamad Nabab Alam and read his paper and algorithm as in
Codes in MATLAB for Particle Swarm Optimization
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I applied two controllers with different gains to 2-DOF model for a period (t). Each controller is applied for sample period t1=0.5*t. I want to switch between these two controllers in order to minimize certain fitness function (sum of square errors).
Example: I want to give GA initial conditions to run these controllers on the model like [1,0]. Where [1] refer to controller no.1 and [0] refer to controller no.2. I want from GA to minimize the fitness function and then it automatically change the sequence of this controller and so on.
How can I switch between controllers using GA?
Hint: I use MATLAB in programming.
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Dear Prof. Ibraheem,
                                     If you are using a "Natural" Genetic Algorithm then you can by using some kind of randomising metadata like coin tossing program the system to choose the intiating controls. If however you want to use them sequentially,"Artificially" even then you will need an initiating step. Please see for example our proof of the "Public Health Field" and the Haag's theorem paper by one of us on www.researchgate.net/Soumitra K. Mallick.
Soumitra K. Mallick
for Soumitra K. Mallick, Nick Hamburger, Sandipan Mallick
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The idea is to allow construction contractors to propose alternative pavement designs that optimize individual means and methods to enhance highway project constructability.
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Thank you
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I would like to optimize the inspection and maintenance process in buildings, taking into account the future effects of climate change in their degradation. I mean, I would like to decrease the costs of them procedures, apply the suitable techniques in the accurate time, and increase the lifespan of the elements of the building rehabilitated.
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Dear Katharine,
I am very grateful for your collaboration. Thank you very much!.
Kind regards,
Pablo.
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I am working on parameter optimization of machine tools. I wish to use the response optimizer function in Minitab. I have used Taguchi L27 methodology to design the experiments and performed ANOA analysis. I want to learn the mathematics used for the response optimizer function in Minitab16
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Taguchi L27? Does that mean you are trying to optimise a response with respect to 13 independent factors from 27 observations?
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1. Recent or existing optimisation techniques which is applied to optimisation of thermal systems.
2. Recent optimisation tools used in thermal engineering applications.
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One major different between high thrust and low thrust trajectories are the thrust profiles for each of their propulsion systems.
For example in an orbit transfer using a solid rocket, the thrust profile is almost fixed and known with a predefined variation (neutral, progressive or regressive) and it is impossible to change the magnitude of the thrust during the burn time.
However, according to the literatures, it is possible to have variable thrust magnitude during the transfer in low thrust propulsion system.
I have read so many papers about low thrust trajectory design and optimization in different journals. Most of them contain profiles of thrust vector and its magnitude. The authors claim that they have found an “optimal” thrust variation that makes a trajectory which transfers the spacecraft from one orbit to another. We can find so many papers in which the transfer trajectory is modeled using Fourier series, Chebyshev polynomials or different kind of mathematical functions.
My question is:
“Are these thrust variations truly achievable in a real space mission?”
“Are low thrust propulsion systems nowadays capable of providing such thrust profiles?”
If yes then:
“How much thrust variation (Newton per second) is allowed in a low thrust trajectory?”
I already know that the limit of thrust magnitude can be up to 1 Newton typically in low thrust transfers. Please note that my question is about “the variation (the rate of increment or reduction)”, not the limit of the thrust profile.
I would appreciate if you answer with references.
Thanks in advance
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I can confirm that for a minimum-time transfer the thrust magnitude is constant and maximal. For a minimum-fuel transfer, the thrust magnitude alternates from maximal-thrust and zero-thrust.
Back to your question, I can only speak for electric propulsion, which is usually referred as low-thrust propulsion. In general, the thrust of an electric propulsion thruster can be throttled. Depending on the thruster type (arcjets, RITs, HET etc.), the throttle characteristic is very specific. The operation region varies from type to type. The power and/or the specific impulse shift from the initial operation point to one direction or the other. Every thruster type has its very own specific throttle characteristic. The challenge is modeling this characteristic in your optimization problem to find your optimal steering law.
Another aspect is the benefit-cost ratio. A thrust variation might save a fraction of fuel mass, but the downside is a more complex control of the propulsion system and a much more complex qualification process of such a thrust profile. Sometimes different operation points are given by the suppliers, see link.
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I would like to write a multi objective function for optimizing several variables. However, I would like only certain objective functions effect certain optimization variables. For example, for a multi objective function of F = [f1 f2 f3] and optimization variables x = [x1 x2 x3], I would like x1 to be effected by f1 but not f2 or f3 etc. Optimization of all the variables should be performed together and I cannot assume that x2 and x3 for example are constant while I optimize for x1 . Is there an optimization algorithm that can handle this problem?
Thanks,
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I am answering this question with respect to linear optimization.
Multi objective functions will take more than one objective functions. Let us say we want to maximize contribution (to profit) and maximize volume (in no of units) for a multiple product company. So it is necessary to use something like giving goal so that I can know how much weights we want to give each goal. So in a multi- objective framework it is necessary to assign right weights to the goals.
The slope of some of the variables, I understand that it is the coefficients will be different.
Let us say objective function is = w1(f1(X) + w2(f2(X)  
= w1(C11X1+ C12 X2+...+C1nXn) + w2 (C21X1+....+C2nXn) 
Given this , only requirement is how to assign the w1 and w2 and not C vector for 
Here we must understand that 
1) Should we solve for one goal or more than one goal . Optimal Solution for profit maximization will not be same for solution for utilization  maximization  
2) Optimal Solution for profit maximization will be different from that of cost minimization.
In my opinion finding the weights (W1 and W2) are more important that finding the
3) The coefficients are generally provided are  parameters. 
I hope it helps 
Goutam Dutta
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Hi,
My research is about revenue management on parking reservation system, especially in advance reservation system. In online reservation system, the parking management must make a decision immediately when a request is coming. so, I want to design an optimal policy for accepting or rejecting a request to a parking space base on user choice. By this decision, the system will assign the booking to available space based on customer choice. I have learned the basic policy such as First come first serve (FCFS) but it is not optimal policy to make an optimal utilization. the another method is using the opportunity cost matrix of losing more valuable booking request in the future. This method has some improvement of the FCFS method but it is not significant.
Do you have any idea?
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Thank you very much for sharing,
Good luck
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For my research work, i need to solve tri-objective (three objectives) design optimization of machine elements like gear, spring, bearing etc using NSGA-II.
suggest me any case study already solved by some other methods or idea for formulating a new case study?
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Dear Prof., we have worked on triple objective engineering design problems in the past. I am including some of our works, hopefully this helps you:
Ganesan, T., Vasant, P. and Elamvazuthi, I., 2013. Hybrid neuro-swarm optimization approach for design of distributed generation power systems. Neural Computing and Applications, 23(1), pp.105-117.
Ganesan, T., Vasant, P. and Elamvazuthi, I., 2013. Normal-boundary intersection based parametric multi-objective optimization of green sand mould system. Journal of Manufacturing Systems, 32(1), pp.197-205.
Ganesan, T., Elamvazuthi, I. and Vasant, P., 2015. Multiobjective design optimization of a nano-CMOS voltage-controlled oscillator using game theoretic-differential evolution. Applied Soft Computing, 32, pp.293-299.
T Ganesan, I Elamvazuthi, 2016,A Multiobjective Approach for Resilience-based Plant Design Optimization, Quality Engineering.
Thank you.
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Though we know that metallurgical, structural and mechanical changes occur due to machining that can alter the properties of the product, we still rely on those who assume that a high removal rate at low tool wear is the key to success and design optimization. What about the thermomechanical changes that might cause slow or fast degradation ? Must productivity take a back seat in favour of  understanding structural and thermomechanical changes that  degrade a machined product, though machining parameter optimization was carried out with out any regard for these? 
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In the aerospace industry, where safety is paramount, both material properties and high metal removal rates are important; one does not choose one over the other.
In the 1980s Rolls-Royce had very low removal rates on compressor and turbine disks. The view was to extend tool life and hence lower tool cost. However, they were very uncompetitive compared to GE and P&W who used much faster cutting techniques and throw away tools; the tool cost was insignificant compared with the product cost. The view at that time was that fast cutting would decimate material properties. However, when they did the experiments and had both properties and material removal rates as constraints, they could meet both requirements.
The research led to new and better cutting materials which put them in the lead (for a while anyway). This along with other innovations allowed them to go from a poor third to first or second (depending on how you measure it) gas turbine manufacturer in the world.
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I want to define a load in Ansys in specific location on an area of solid body. For this I need to have a node on that location. Using meshing tool, the mesh locate the nodes in arbitrary place on an area or volume which disable me to exert force in specific location.  
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You can define a Hard Point in area
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I was asked to optimize plant genomic DNA extraction protocol for Falcataria moluccana or locally known as batai. I have no idea how to start optimizing the extraction protocol. What i have in mind at the moment is to use CTAB method by Doyle & Doyle first, using it as a control to see how is the quality of the DNA will be. From there, I am not quite sure on which reagents i should optimize, like what is the optimum concentration i should use and the period of incubation for these reagents with the sample i used.
Are there any suggestions on what i should do next? Or any ideas on how to design an optimization extraction protocol?
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The critical part in CTAB is the NaCL concentration and amount of polysaccharides in the plant material. As long as you can balance it well, you can get good amounts. I would be tempted to use Liq Nitrogen grinding to break open the plant cells more effectively.
So in short, make sure the NaCl concentrations are right, perform two or more extractions in CTAB depending on ur polysaccharide content.
Though isoprop is better for precipitation, I would suggest a couple of rounds of ethanl washes to remove impurities.
It all depends on ur downstream application.
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I'm used to work with @Risk and Crystal Ball. As I don't have those tools available I would like to apply the BERT add on to EXCEL in order to do Monter Carlo analysis. These analysis include design optimization and Gantt Charts.
There is a lot of packages and I would like to know the experience of thes in conjunction with the BERT Excel add on.
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Checked the document. Could not find any reference to BERT. I appreciate your efffort oif helping me.
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Generally, there is special GA used for HFSS design optimization. my question is:
How we can use our prevate algorithme in the optimization using HFSS.
Thanks in advance
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The Optimization by the AG according to unknown to the objective function to be optimized
best regards
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I am beginner in the field of optimisation. I want to apply an optimisation algorithm for maxiimising the efficiency of brushless motor. But I dont have license module for coupling between MATLAB and FEA software. In what way can I  implement optimisation.
Please help me by giving your answers
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In general I agree with the answer of Claes Richard. This is the right procedure.
What you always need is a communication channel between the analysis solver (FEA) and the optimizer (Matlab). Because the output of FEA (stresses, displacements, etc) is the input for the optimizer and the output of the optimizer (new design variables) is the input for FEA and this goes on in cycles until convergence. This communication can be done either through memory (when you write code for both FEA and optimization), DLL communication or (the most simple solution) text files.
Given that Matlab can read/write to text files without any problem, you need a FEA analysis tool that can be called as exe from Matlab with the right arguments (analysis options, location of input file, etc), can read its input text file (prepared from matlab optimizer), analyze the model and produce an output text file (to be read by matlab optimizer for optimization constraint checking).
Any built-in matlab optimizer can be used (GA, SQP, etc) with any such analysis tool. It is rather easy to do the coupling yourself and this coupling can provide very impressive results!
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I now do the work of shape optimization by isogeometric analysi , but I need the area or volume constraints, how can I get these? Is the area surrounded by the control points?But I think this method is not very accurate.Beg your answer!Thank you very much!
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The area or the volume calculated using isogeometric analysis (NURBS-based discretization) is accurate. The calculation follows the standard numerical integration scheme. You firstly discretize the domain or region using finite element method or IGA, Then you integrate the area or volume of  each element. After that , you summarize them and you can get the volume or area. The integration scheme follows a similar way that you calculate  the local or global stiffness matrix. You may find the discrete shape gradient of the volume/aera with respect to the design control points in one of my papers about the isogeoemtric shape optimization. good luck.
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I find many strange problems in using ultra-low noise operational amplifier. It's very difficult to keep it in stable condition. The power supply is carefully designed and optimized. And everything is realized according to suggestions given in datasheet. I can't get noise performance given by ADA4898's datasheet. 
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I agree with Paul. and add to him, There is a device noise and circuit noise. In order to verify the performance parameters of a device you have to stick strictly to the given instructions in the manufacturer data sheet for measuring such parameters specially the noise performance.
The noise given in the data sheet is the root of the spectral noise power density in nV/ square root of Hz.  So if you measure the total noise you have to integrate the spectral noise density over the amplifier bandwidth.
You have to take into consideration the noise generated by the external input resistances or input sources. As Paul; said you have to choose metal film resistors which is less noise that the carbon film resistor. Also, the external resistance at the amplifier input must be as small as possible. Also, it may be very helpful to restrict the bandwidth of the amplifier to just the required frequency range.
Concerning the ripples you must suppress then by good regulation and filtering.
One reaming  note, what do you mean by the stability? is it the frequency stability?
or what? or noise? 
wish you success
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i am working on textile dyes' degradation for which i have to optimize different process parameters (atleast 8 factors). I have reviewed the literature and found both of these designs in different papers but mostly central composite design is used. 
Can anyone plz suggest me which one of these is the best and do i also need to carry out the single factor experiments as its reported that more than 4-5 factors cannot be studied using these designs?
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Placket-Burman is not suitable for optimization. The purpose of using Placket-Burman is to determine the most significant factors (Screen design). In general, Placket-Burman is not recommended because interactions are partially confounded or "aliased" with main effects.  Composite design is mainly used for optimization purposes with no confounding. Since you have 8 factors, CCD requires around 282 runs (Full design) which is very expensive and time consuming. Therefore, I recommend you to use first Placket-Burman to find few critical factors for the output responses, and then use CCD to optimize these critical factors. Please do NOT use face centered cubic design which is one of response surface designs because this method has multicollinearity if the problem has more than 5 factors. 
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After forming, the yield strength will change due to strain hardeneing. So how can I calculate the change in yield strength of the component after it has been formed. I have done forming simulation in lsdyna. This question is based on the forming simulation, the component is not manually tested. Will the ultimate strength  also change? If so how can I calculate it? I need to give the yield strength and uts as input for fatigue analysis. Kindly help me with this.
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You can calculate it using the Hollomon equations
Y=K*eps^n
in which K and n depends on the material and eps is the deformation you have.
Franco
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In physical design optimization of  data path having multiple cycle clock are avoided...why it so? Is multiple cycle path do not create timing issues while physical timing analysis?
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Yes they are not usually that critical. 
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Multiplication by a constant (C.X) is a fundamental operation in the linear time invariant (LTI) Systems. The design optimization of C.X leads to the design optimization of the whole system (from a circuit point of view speed/power/area). We proved in a previous work that for an N-bit constant, the upper-bound is equal to (N+1)/r+2^(r-2)-2, where r=2.W[((N+1).log(2))^0.5]/log(2) and W is the Lambert function. I am looking in the literature for a lower bound. This could be available in pure or applied mathematics journals rather than in circuit journals.  
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The work is described in my two recent papers. You can download them from my researchgate page.
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Is Cplex free or not, and how simple it is to use?
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InsyAllah I will explain in detail soon.
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In source localization applications, the geometry of the sensor array greatly affect the time delay estimates which are used to localize a source. I could not find comprehensive literature on this topic. Can anybody provide references for the optimization of array geometry to minimize the error in time delay estimation? Also references about methods to adaptively control the array geometry to optimize it for adaptive applications are needed. 
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Thank you all for your input. One of the reference that I found help is:
Nunes, L. O.; Martins, W. A.; Lima, M. V. S.; Biscainho, L. W. P.; Lee, B.; Said, A.; Schafer, R. W., "Discriminability Measure for Microphone Array Source Localization," Acoustic Signal Enhancement; Proceedings of IWAENC 2012; International Workshop on , vol., no., pp.1,4, 4-6 Sept. 2012
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I am working on automated assembly feasibility testing for which I need to obtain part bounding box coordinates from CATIA V5 for a 3D CAD model. Assuming a minimum axis aligned bounding box (Smallest cuboid in which a part is placed); The lowest and highest x, y, z coordinates are need to be obtained. 
For example
To obtain "arbitrarily oriented minimum bounding box co ordinate values" - measure inertia option can be directly used. similarly any direct or indirect way to obtain "axis aligned bounding box co ordinate values"?
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Dear sir,
Thanks for your answer
My requirement is slightly different from what I mentioned earlier. I am looking for a method/algorithm/option to obtain "axis aligned bounding box co ordinate values "
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I am studying the association of certain SNP with a specific disease, and due to several reasons I will use PCR-RFLP method for SNP genotyping, now I have two options:
a- To design and optimize my own PCR-RFLP method .
b- Or use previously optimized method
Which option is good for my research originality?
Thank a lot
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Either, but check wich options of endonucleases will cut/not cut your SNPs. You might find several options including isoschizomers that are cheaper or work better. Also worthed is to try the new enzymes that do the job in 15minutes.
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It should either be on the design and optimization pyrolysis machine or a pyrolysis system.
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The answer on the question is depended on kind of material for pyrolysis. Proposing it is usually waste tire, the answer is next depended on the size of that – is it shredded for chips of around 50mm size, or pieces of 200-250mm size, or even in kind of whole tires? To be short, the first approach is complicated with a tire shredding but it is most sure for continue processing with all of safety, health and environment standards to be observed, being carried out usually in a rotary kiln or auger pyrolysis reactor presented in one of the first indicative US patents such as 563580 (1997). Since 2007 the reactor auger design has been developed for double or even triple-set unit as you can see in my papers where use of steam that is co-generating and superheating with pyrolysis reactor heating, it is another question for consideration.
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I have doubts, because the experiments designs have treatment combinations that are at the midpoints of the edges of the experimental space and require at least three factors. When I try to optimize them by using RSM (Box Behnken) the Y1 is not significant due to the lack of fit is significant, Prob>F is lower than 0.05 (it should be linear as when conducting linear model the model is significant and the lack of fit is significant too) however the Y2 developed in RSM is not significant due to the lack of fit is significant (Prob>F is higher 0.05) but the interaction is significant to 10 %.
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In the case of Y1, if Prob>F is lower than 0.05, the lack-of-fit is not significant. Contrary to what you say about Y2 where Prob>F is higher than 0.05. Anyway, if variables Y1 and Y2 are not significant but the interaction is significant, staticians say that the final model must have both the pure variables and the interaction in the empirical equation to be real. Let your software propose the otimization design points. I hope this comment will be helpful.
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Hello! Sorry for my english.
I'm trying to simulate on Matlab using "m code" a time optimization problem for a non-linear system. I want to lead my system from my initial conditions to my final conditions(BPV) on the minimum time. Do you have any idea how to do that? or any example, or something?
My first problem it's because I already know initial conditions and final conditions of my system, but my initial conditions for my co-state of Hamilton equations are were unaware and final time too. I attached my problem.
Thank you so much in advance.
Arturo Gil
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You can consult the book "Applied mathematical Solving Problems with MATLAB" of the authors: Dingyü Xue and YangQuan Chen, where there is a example problam that answers your question.
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What investigation has been made in strength design optimization of a RC section subjected to combined compression and biaxial bending? 
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Please have a look at the following articles: You could search for the authors (Barros and Leps) and you will find more articles. 
de Medeiros, G. F., & Kripka, M. (2014). Optimization of reinforced concrete columns according to different environmental impact assessment parameters. Engineering Structures, 59, 185-194. doi: 10.1016/j.engstruct.2013.10.045
Barros, M. H. F. M., Martins, R. A. F., & Barros, A. F. M. (2005). Cost optimization of singly and doubly reinforced concrete beams with EC2-2001. Structural and Multidisciplinary Optimization, 30(3), 236-242. doi: 10.1007/s00158-005-0516-2
Lepš, M., & Šejnoha, M. (2003). New approach to optimization of reinforced concrete beams. Computers & Structures, 81(18-19), 1957-1966. doi: 10.1016/s0045-7949(03)00215-3
EL DEBS, A. L. H., NETO, A. C., CHAVES, I. A., SQUARCIO, R. M. F., & LIRA, S. A. Optimization of cross section of reinforced concrete beam using experimental design.
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I want to optimize four carbon and nitrogen sources along with pH, Temp, incubation time and size of inoculum. I am using design expert version 9. Is there any option through which all of these can be optimized at one time. Kindly help me with this.
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physiological parameters are also imp. as the nutrients of production medium. Temp., rpm, pH etc are those which can be optimized first as one factor at a time den u use PBD for other sources like c source and nitro sources and also mineral salts.
yes u can use each sources separately but it is recommended to use above 4 sources for each section as PBD rule says so.
All d best...
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Step by step procedure to apply GA using SciLab for design optimization.
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Thanks lot mem
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Currently I am working on a summer project for weight reduction of an component using LS-OPT tool.i need to understand the basic step by step process,how it creates response surface?on what basis? how it works on background?how to find out metamodel is accurate?pls help me out, thank you
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You can try Unscrabmler software. The CAMO company offers you 30 days free trial of software. You may find the required details in the help column.
with best wishes
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Can anyone help me clear my doubts by giving an example?
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Optimization is a very specialized field and can also be a complex area. As I was teach from the beginning, one main point is that the choice of the adequate algorithm depends on the formulation, as well as how to formulate it depend on the algorithm to use.
With this, I will try to start answer this question with my perspective and hope others, more specialized than me, may complement this answer or give others.
In deterministic optimization the input data for the given problem is known accurately. It is considered e.g. in many structural optimization problems.
There are a plenty of problems where the input data for the given problem is NOT known accurately. Then, the optimization under uncertainty, or stochastic optimization, is chosen in a way that the uncertainty be considered under assumption of a given (assumed known) probability distributions. E.g. it is the case where the human behavior has influence in the performance being optimized like for e.g. in economic applications.
Exists an intermediate special type of problem where parameters are known but only within certain bounds. For theses cases Robust optimization techniques are usually more adequate. E.g. cases where a structure is sometimes loaded differently in different scenarios, which do not occur simultaneously. In robust optimization, is like one is looking for a compromise between different optimums found in each scenario without calculating necessarily these different optimums.
It is not unusual that one gets a kind of problems that are computationally very very hard. Then the time spent in computations (e.g. line search techniques) is not adequate and there is some evidence that for these problems, heuristic search techniques (e.g. randomness is used to find better solutions) are better methods. Examples of heuristic optimization methods are simulated annealing, genetic algorithm and evolutionary algorithms.
If the examples mentioned are too generic, particular applications of specific interest can be easily found (with some spent of time) in the research literature.
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As per current ICH guidelines, Q8R2 is the current demand of the pharmaceutical formulation development to implement Quality By Design in the pharma industry. Therefore, the chemists and scientists should have the knowledge of QbD. However, academics show the least amount of interest.
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actually the concept of the study should be bit more practical and not just solving problems rather than sitting and solving complicated numerical .... lot of software are available now a days .... only thing required is the proper interface of the students with the concept of the topic of designs and QbD.
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There are many ways in which optimization has been done and decrease the number of transistors in the design. Some such trends follow a certain pattern or follow a law. Please suggest me an article that describes this topic.
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Karnaugh Maps (K-Maps) and Quine-Mc Clusky algorithms remain the best ways to reduce the digital designs in their minimum forms ( Sum of Product ) or (Product of Sum). However, I don't like the use of don't care terms as being arbitrarily logic 1 or logic 0. Don't care terms create spurious outputs if they are clubbed with 1s in KMAP minimization. Different computer programs have been developed to do the logic reduction for KMAP or Quine Mc Clusky.