Drifting of sand particles bouncing on a vibrating membrane of a Chladni experiment is characterized statistically. Records of trajectories reveal that bounces are circularly distributed and random. ...The mean length of their horizontal displacement is approximately proportional to the vibration amplitude above the critical level and amounts about one fourth of the corresponding bounce height. For the description of horizontal drifting of particles a model of vibration driven random walk is proposed that yields a good agreement between experimental and numerically simulated data.
•Bouncing of particles in the Chladni experiment is characterized statistically.•Statistical characteristics enable new modeling of Chladni pattern formation.•A new model of vibration driven random walk is introduced.•Good agreement between experimental and simulated data is demonstrated.
This article deals with experimental description of physical laws by probability density function of measured data. The Gaussian mixture model specified by representative data and related ...probabilities is utilized for this purpose. The information cost function of the model is described in terms of information entropy by the sum of the estimation error and redundancy. A new method is proposed for searching the minimum of the cost function. The number of the resulting prototype data depends on the accuracy of measurement. Their adaptation resembles a self-organized, highly non-linear cooperation between neurons in an artificial NN. A prototype datum corresponds to the memorized content, while the related probability corresponds to the excitability of the neuron. The method does not include any free parameters except objectively determined accuracy of the measurement system and is therefore convenient for autonomous execution. Since representative data are generally less numerous than the measured ones, the method is applicable for a rather general and objective compression of overwhelming experimental data in automatic data-acquisition systems. Such compression is demonstrated on analytically determined random noise and measured traffic flow data. The flow over a day is described by a vector of 24 components. The set of 365 vectors measured over one year is compressed by autonomous learning to just 4 representative vectors and related probabilities. These vectors represent the flow in normal working days and weekends or holidays, while the related probabilities correspond to relative frequencies of these days. This example reveals that autonomous learning yields a new basis for interpretation of representative data and the optimal model structure.
Nestabilnost prometnega toka na avtocestah vodi do zgostitev in prometnih zastojev z neprijetnimi posledicami. Zato, da bi se jim izognili, opredelimo optimalni kontrolni zakon za stabiliziranje ...prometnega toka. Za ta namen opišemo vpliv omejitve hitrosti na stabilni režim prometnega toka z novim osnovnim zakonom prometa, ki je izpeljan na podlagi eksperimentalnih podatkov in znanih lastnosti vožnje na avtocestah. Ustrezna povezava nam pove, kako je treba prilagoditi omejitev hitrosti na avtocesti gostoti prometa, če želimo doseči razmere za stabilen prometni tok.
Neugodne vozne razmere na avtocestah pogosto povzročijo nastajanje prometnih zastojev. Najpogosteje so posledica prometnih nesreč, neugodnega vremena in raznih del, okarakteriziramo pa jih lahko z ...zmanjšanjem zmogljivosti ceste. Na podlagi ocene zmanjšane zmogljivosti na kritičnem odseku ceste lahko prometnoobveščevalna služba napove razvoj zastoja in o tem vnaprej obvesti javnost. V članku je opisana nova matematična metoda, formulirana za ta namen. Z njo razvita inteligentna enota na podlagi posnetkov prometnega toka v preteklosti najprej napove prometni tok v kritični odsek ceste in ga nato skupaj z oceno zmanjšane zmogljivosti ceste uporabi za napoved lastnosti zastoja. Delovanje metode je prikazano na primeru napovedi razvoja zastoja na točki maksimalne prometne aktivnosti na avtocesti v Sloveniji.
In modelling laser-induced plasma plume formation, the proper description of laser absorption in the plasma plays an important role. In the present model, absorption is described by means of three ...different mechanisms: inverse bremsstrahlung (IB), photoionization (PI) and absorption by small condensed clusters. Numerical solutions of the model are given for KrF laser beam irradiation (wavelength
λ
=
248
nm) impinging on a nickel target at various fluences. The influence of particular absorption mechanisms on the absorbed laser beam energy in the plasma plume during the pulse is shown for different fluences. Using all three absorption mechanisms, the calculated plasma properties show good agreement with the experimental results of other authors.
Empirical modeling of the industrial antibiotic fed-batch fermentation process is discussed in this paper. Several methods including neural networks, genetic algorithms and feature selection are ...combined with prior knowledge in the research methodology. A linear model, a radial basis function neural network and a hybrid linear–neural network model are applied for the model formation. Two approaches to modeling of antibiotic fermentation process are presented: a dynamic modeling of the process and a modeling in the fermentation sample space. The first approach is focused on the current state of the fermentation process and forecasts the future product concentration. The second approach treats the fermentation batch as one sample which is characterized by the set of extracted features. Based on these features, the fermentation efficiency is predicted. Modeling in the fermentation sample space integrates the prior knowledge of experts with empirical information and can represent a basis for the control of the fermentation process.
A Method for Automatic Medical Diagnosis Grabec, Igor; Švegl, Eva; Sok, Mihael
Statistics, optimization & information computing,
2019, Letnik:
7, Številka:
1
Journal Article
Odprti dostop
This research paper presents a new method for the automatic diagnosis of diseases using a personal computer. Forming a basis for the characterization of diseases, a wide set of symptoms is ...introduced, and a particular disease is characterized by a set of statistical weights assigned to those symptoms. Information about the patient’s state is provided by a graphic interface in which the user confirms symptom indicators. Agreement between these symptoms and classified symptoms of a particular disease is then estimated by the sum of corresponding weights, where the disease corresponding to the maximal agreement is proposed as the result of the diagnosis. A disease likelihood estimator is calculated and presented to assess the reliability of the diagnosis. With regard to the automatic assessment of the diagnosis the corresponding algorithm and the properties of the computer program are included. Finally, the effectiveness of this method of medical diagnosis is demonstrated through four typical examples involving differently expressed symptoms. The diagnostic system resembles semantically driven sensory-neural network.
A general experimental description of chaotic phenomena is considered. A chaotic phenomenon is represented by an auto-regressive field whose evolution law is modeled by a non-linear mapping relation. ...This relation is formulated statistically by a conditional average estimator which is approximately calculated by nearest neighbor average. The structure of the model is expressed non-parametrically in terms of local state vectors which are reconstructed from data of recorded field. A novel reconstruction is presented using strongly and weakly correlated field values. The conditional average estimator is applicable for the prediction of field distribution outside some given initial domain on short and long scales. The method is demonstrated by predicting chaotic Kuramoto-Sivashinsky field and a turbulent vector field of ionization waves in plasma. The performance of statsitical modeling is estimated by comparing correlation functions of experimentally recorded and predicted fields.
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