Knowledge Modeling - Science topic

In the author's interpretation we consider concepts and methods of science, such as science, knowledge, model, gnosticism and agnosticism, the principle of Ashby, facts, empirical regularity, empirical law, scientific law, and others. We have formulated the main problem of the science, concluding that cognitive abilities of a human are limited and do not provide effective knowledge in a very large volume of data. The solution to this problem is to look at ways of automation of scientific research. Traditionally, we use information-measuring systems and automated systems research (ASNI) for this. However, the mathematical methods used in these systems, impose strict impracticable requirements to the source data, which dramatically reduces the effectiveness and applicability of these systems in practice. Instead of having to submit to the source data impracticable requirements (like the normality of the distribution, absolute accuracy and complete replications of all combinations of values of factors and their full independence and additivity) automated system-cognitive analysis (ASC-analysis) offers (without any pre-processing) to understand the data and thereby convert them into information and then convert this information to knowledge by its application to achieve targets (i.e. for controlling) and for solution for problems of classification, decision support and meaningful empirical research of the modeled subject area. ASC-analysis is a systematic analysis, considered as a method of scientific cognition. This is a highly automated method of scientific knowledge that has its own developed and constantly improving software tool – an intellectual system called "Eidos". The system of "Eidos" has been developed in a generic setting, independent of any domain and can be applied in all subject areas, in which people apply their natural intelligence. The "Eidos" system is a tool of cognition, which greatly increases the possibility of natural intelligence, just like microscopes and telescopes multiply the possibilities of vision (but in this case only if you have this possibility). The study proposes a new view of the models: phenomenological meaningful model, which is currently represented only by systemic cognitive models, and which is currently in the middle between empirical and theoretical knowledge. The system called "Eidos" is considered as a tool of automation of the learning process, providing meaningful synthesis of phenomenological models directly on the basis of empirical data

Dear Sir/Mam,

I'm Mahesh from India & wanted to know more about Batteries & BMS used in Electric Vehicles.

Also, wanted to learn about the design & Development consideration for the BMS(Battery Management System) used in EVs/HEV.

Also, How I get more knowledge on Modeling & Simulation of EVs/HEV's & Electrical parameters used in same(Mostly in modelling & Simulation of batteries & BMS).

Please let me know the details with respect to technical perspective.

Kindly waiting for your positive response.

Thanks,

Mahesh Varpe

I have lots of variables and want to select several of them which will be able to explain a great part of variance. I assume PCA is satisfying. Can you recomend me other technique (or any usefull link to improve my knowledge in model selection techniques) to solve this problem?

I identified the parameters of a SISO linear parameter varying simulink model by NLSE tool-box of MATLAB R2013a. One of the model parameter is a function of the previous model output, which has been represented by using a unit delay/memory block in the simulink model.

Now I'm trying to get the output from the state space model of the same system by using the model parameter identified in earlier step. I order to get output, I used 'c2d' command at Ts=2s for discretizing the continuous state space model, however, the output is not matching with the output of the simulink model. According to my best knowledge, as the model parameters are same the output should be same for the simulink and state space model but the state space model output is underdamped in comparison of the simulink model output. Then I changed Ts=1.8s (by trial & error) then it is giving quite accurate result however the sample time in both the simulation and experimental data, based on which parameter estimation has been done, were 2 sec., that's why I set Ts=2s.

My question is, why for Ts=2s the discrete state space model is not giving accurate result?

Currently, I try to visualize knowledge about sustainable behaviour. I.e., I'd like to construct diagrams showing (primary causal) argumentations about why certain behaviours are (un-)sustainable. It would be an aim, to visualize the consequences of our actions and their environmental and societal impacts and to encourage discussions about the underlying argumentations, based on these visual representations. Ideally, such diagrams could be developed cooperatively.

Does anyone know about related work?

Does anyone even know about the existence of such "sustainability knowledge modelling languages"?

(Or do you think, the idea to visualize such sustainability-knowledge would be unproductive?)

When a teacher selects and uses examples shows an important part of their knowledge of mathematics (Subject matter Knowledge) and the mathematics teaching and learning. Following the most used teacher knowledge models, this knowledge can be placed in his or her Subject Matter Knowledge or Pedagogical Content Knowledge . In my opinion, the exemplification bring into play both areas of knowledge. The examples show the degree of knowledge of the discipline, both in its substantive and syntactic aspects, but also knowledge about mathematics teaching and learning characteristics. What do you think about?

we are working on knowledge repositories, there are many and many ways of modelling techniques, ontology is comprehensive and more detailed and tree based modelling techniques, whereas Meta modelling takes software engineers towards some mathematical and formal based solutions, so which should be preferred ?

I've been looking for a mouse model for AD research, an inducible model would be most suitable for the original experiment design. I found a few Jackson tetO transgenic models, but there seems to be few researches with these models since their early descriptions in 2005 and only tet-off strains were generated despite the fact that both tet-off models by tetO x tTA and tet-on models by tetO x rtTA were said to be feasible on the Jax mice description page. 

I was wondering why  researches with inducible AD mouse modes are done and if there are further advancements in the models themselves after the descriptions a decade ago.

Are there any limits in generation of tet-on AD models? 

little information available on the models makes me hesitate, since there are too many uncertainties. I'd really appreciate it if you can share your experience or knowledge with such models. Thanks.