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We traditionally think of bones as rigid structures that support the body. However, recent discussions suggest they may also play a role in sensory feedback, proprioception, and force regulation.
💡 Key observation: 🔹 Bone-conducted vibration is up to 30 times faster than neural proprioception. 🔹 If this is true in the jaw, could it also apply to other body parts?
📌 Potential Applications:1. Sensory Compensation in Neuropathy:
  • Could bone vibrations provide an alternative feedback mechanism for patients with diabetic neuropathy or peripheral nerve damage?
2. Balance & Posture in the Visually Impaired:
  • Blind individuals rely heavily on foot pressure and body posture.
  • Could bone-conducted feedback assist with maintaining balance when visual input is absent?
3. Prosthetic Limb Control:
  • Amputees often struggle with lack of sensory feedback from prosthetics.
  • Could incorporating bone-conducted vibrations into prosthetic design enhance sensory perception?
4. Athletic Performance & Force Optimization:
  • Elite athletes depend on precise proprioception for movement efficiency.
  • Could targeted bone vibration stimulation improve their force control?
🚀 Are we underestimating the sensory role of bones? 👉 Could bone-conducted vibration be a hidden feedback system we’ve overlooked?
Would love to hear insights from neuroscientists, physiologists, and orthopedic specialists! 🔬
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Given the pizeoelectric properties of bone, it would make sense that indeed there is some nerve stimulation from Bone-Conducted Vibration for preopriocetion. Depending on the biomechanics stresses that occur on the bone with forces applied depending on posture, electrical potential would be different both in frequency and location thereby stimulating different nerves accordingly .
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I am doing my first research project and would like to create a dynamical system simulating the human respiratory system to identify weaknesses.
After doing tons of reading, I have identified the major components being lung volume, airway pressure, airflow radius, diaphragm movement, lung compliance, airway resistance, neural control signals. However to create a model I need to have specific equations to represent the relationship between these different components.
Additionally, I would also like a dataset to test the validity of the model when complete.
I have been reading a lot of journals on human respiration but can't find any specific information to help me figure out the relationship in interactions between the different components.
Most of the reading is long and ended up being a waste of time where I got little to no understanding and didn't contribute to my project.
Could a experienced researcher give me some advice and guidance?
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Hi Theodore,
mathematical modelling of the respiratory systems is very obstacle because of the dynamic character of the respiratory dynamics during Inspiration vs. expiration. These dynamic characteristics differ significantly between physiological versus pathological circumstances and change constantly depending on the extent of the pathological processes (e.g. the extent of lung stiffness - elastance -, or airway obstruction - resistance -, which in turn is influenced by various factors (such as bronchoconstriction, airwas collaps, mucosal oedema, secretion retention). These latter factors also vary and combine). All these components influence muscle strength in addition to neuromuscular function. On the other hand, this is additionally modified by the resistance of the abdominal organs (obesity, ascites).
Beyond the above mentioned parameters you should also take the hysteresis effect of the airways during expiration in to account. This is an important factor that fundamentally distinguishes the lung from a balloon. Therefore, linear models do not accurately describe lung function, whether in a physiological or pathological state.
Furthermore, a distinction should be made between spontaneous breathing and breathing under mechanical ventilation (controlled vs supported ventilation).
The corresponding equations for the respiratory functions in normal or diseased lungs can be found in the various respiratory physiology books (among others):
1. John B.West, Andrew M. Lucs: West's Respiratory Physiology: The Essentials (Lippincott Connect)
2. John B.West, Andrew M. Lucs: West's Pulmonary Pathophysiology: The Essentials (Lippincott Connect)
3. Fishman’s Pulmonary Diseases and Disorders. PART 2: Scientific Basis of Lung Function in Health and Disease
4. Tammeling G.J., Quanjer Ph.H.: Contours of breathing 1-2. 1985 Boehringer Ingelheim
I admit that I have no experience with mathematical models and don't currently know of any.
In the later 1990s, we completed respiratory mechanics measurements under different forms of non-invasive ventilation using the so-called ‘Michigan lung’ model. With minimal modifications (to model the hysteresis effect), this mechanical lung model proved to be very helpful and usable (https://www.michiganinstruments.com/lung-simulators/).
Ref. (unfortunately, for internal reasons, this work was only published in the form of a poster):
1. Juhász J, Gröschel A, Hormann W, Sybrecht G.W. The impact of diverse mask pressure stability on the lung mechanics during CPAP in an experimental Model. Am J Respir Crit Care Med 1999;159(Suppl. 3):A427
2. Gröschel A, Juhász J, Hormann W, Sybrecht G.W. A comparison of the work of breathing in different CPAP-machines. Am J Respir Crit Care Med 1999;159(Suppl. 3):A428
I hope, this is some help for you. Best regards, János
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I am currently working on my undergraduate thesis which involves the "Non-Linear Neural Control of a Quadratic Tank Process with Delays using Internal Model Control". I have obtained the mathematical models for the plant under minimum phase and non-minimum phase operating conditions. The transfer function of the plant is a 2x2 transfer function matrix.
However, as a beginner, I am quite confused about the next step to take towards representing the built models in MATLAB & SIMULINK, obtaining data from the built models in MATLAB&SIMULINK, the algorithms to use to train the neural networks for modeling the plant, and the controller in the IMC structure, and how to validate the performance of the neural network algorithms generally.
I will appreciate any help or pointers to help me resolve my dilemma, Thank you!
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Similoluwa Okunowo Decoupling is a control approach for breaking down the dynamics of a multi-input, multi-output (MIMO) system into smaller single-input, single-output (SISO) subsystems. This can make it easier to develop a controller for the system and increase overall system performance.
Decoupling can be achieved by the use of a technology known as "decoupling control." This entails utilizing a controller to cancel out the connection between the system's various inputs and outputs.
Internal Model Control (IMC) is a standard way for constructing a decoupling controller. The IMC technique creates a controller by using a mathematical model of the plant to cancel out the connection between distinct inputs and outputs.
To begin incorporating IMC in your system, you must first identify a mathematical model of the plant. System identification techniques, such as frequency response or system identification software, can be used to accomplish this. Once you've created a plant model, you can use it to create a controller that can cancel out the coupling between multiple inputs and outputs.
The particular procedures for designing the IMC controller may vary depending on the dynamics of the plant and the required controller performance, but in general, they will include:
1. Discover the plant transfer function.
2. Find the best transfer function.
3. Discover the controller transfer function.
4. To produce the closed-loop transfer function, combine the plant and controller transfer functions.
5. To get the required performance, fine-tune the controller settings.
It is crucial to note that decoupling control is not always the optimal answer for all systems, and the trade-offs between decoupling and other control techniques such as robustness, noise tolerance, and control performance must be considered.
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If we fabricated a bench scale chemical process, will it give the same characteristics as of the production scale one?
We are plannig to make a graduation project that is based on the application of advanced control (fuzzy,neural) in the process engineering (specially, Ethanol fermentation from molasses). We thought that fabricating a bench scale process will give similar data, charactristics and measurement as the production scale, correct us please?
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Many thanks for the useful question and replies. I am thinking from a different angle. Advanced and new control techniques are tested via simulation (e.g., Aspen Dynamics) and are reported in academic journals. One can then use industrial scale flow rates and capacities. So, what are the pros and cons of simulation versus bench scale experimentation?
I belive bench scale experimentation will require considerable effort and budget but it will have include some practical aspects (e.g., measurement errors and noise). By the way, most of our control studies are via simulation for one reason or other. However, I welcome discussion on simulation versus bench scale experimenation.
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I would enjoy think tanking with you if I have peaked your interest.
Dennis (Dr Sha) (Dennis Shavelson DPM)
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Do you have a thought on how we can accomplish a 15 minute think tank?
Good fortune to you and your valuable work, no matter what.
Dennis (Dr Sha)
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Each of us has special abilities and talents. someone understands Mathematics well and another person is a good painter. But what is the reason that makes these differences? Do we have Different Neurons from others in our brain? Or we have developed or improved parts in it?
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Dear Aliasghar Khayati , natural talents are abilities that are part of who you are – they may be artistic talents, intelligence, physical strength, organizing ability and so on.
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What kind of projects are out there in neural system analysis?
What are the take aways?
I know it can be applied to many of the industries, commercials, and at home.
But I am bit vague or cannot grip on what the term stands for and what can be done within.
I honestly thought neural systems are somewhat related to bioengineerings.
Would you kindly give some explanations??
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Having hands on to data makes it easier to understand, Pick a language(matlab or python) and select an algorithm to see how it works on the data, look for youtube videos with simple examples..
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 When are multi-layer cellular neural networks the same?  
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Please re-frame your question.
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Why is distributed control necessary in automation of energy distribution systems? How would you distribute the automation logic across many controllers?
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I don't think it ist necessary to use a distributed control system - however in my opinion it is beneficial.
Distributed energy systems will have a lot of controllable devices in future. To realize a central control system a lot of effort is necessary for communication and data handling. In the end of the day, central control algorithms have to handle an increasing number of devices. So you have to ensure, that the whole system will not collapse by adding one additional device.
A distributed system can make use of cascading control systems: the base element may be "aggregators" which are able to act as one virtual single device in the distributed system - but effective it controls itself several devices. This approach of distributed control makes the system very flexible and stable.
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How to prove the stability of the whole system?
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Hi,
if you have interest in how this algorithm works, please check this youtube video
Regards
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My original research which is focused on assessing the performance of building from the perception of elderly and the disabled using neural computation is getting some challenges and becoming more interesting with an increased knowledge of artificial intelligence, data mining and machine learning. I am getting stronger in conviction to adopt and a new title/methodology - I need suggestions. For now I have proposed Building Performance - Machine Learning Based & Elderly / the disabled's Approach.
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I think you're looking for psychophysics. However it's a bit unclear from your language.
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It has zero delayed inputs and zero delayed outputs. Plus My network model has 4 inputs and 3 outputs, but while simulating/ plant identification in MATLAB's any of 3 controllers, it gives error, that it should have only 1 inport and 1 outport. Moreover 0 delayed input is not considered valid.
Moreover, how can I create some other NN Controller except from available controllers in MATLAB?
P.S. Kindly forgive if it is a basic and stupid question, but I am at a beginner's level, so cannot find the solution.
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N.Guersi
If you can get Matlab version 5.2, you can open the nnet toolbox, then nndemos, you can follow step by step how it performs identification (mfiles appcs1, appcs2), then the control of an inverted pendulum by RN (mfiles appcs2 , appcs2b). It is very educational and if you understand this procedure, you can design your own controllers.
Best regards