Jerzy Jelonkiewicz

Genetic Algorithm for Observer Parameters Tuning in Sensorless Induction Motor Drive

The paper presents an example of genetic algorithm applied to speed adaptive flux observer parameters tuning in sensorless induction motor drive. The algorithm concerns an optimal gain matrix and PI controller gain coefficients tuning. The gain matrix is implemented in a neural network. Two methods of weights obtaining in the network is proposed. Presented approach selects an optimal operating point of the observer in the presence of measurement noise and inaccurate knowledge of mathematical model parameters.

Spin-glass Implementation of a Hopfield Neural Structure

Paper presents the hardware implementation of the Hopfield continuous neural network. We propose a molecular realization of a spin glass model. In particular, we consider a spin glass like structure that allows interconnection strengths change and neuron state test. Proposed device is based on SBA-15 mesoporous silica thin film, activated by Mn 12 molecular magnets. Our idea seems to be feasible from the technological point of view.

Molecular Approach to Hopfield Neural Network

The present article puts forward a completely new technology development , a spin glass-like molecular implementation of the Hopfield neural structure. This novel approach uses magnetic molecules homogenously distributed in mesoporous silica matrix, which forms a base for a converting unit, an equivalent of a neuron in the Hopfield network. Converting units interact with each other via a fully controlled magnetic fields, which corresponds to weighted interconnections in the Hopfield network. This novel technology enables building fast, high-density content addressable associative memories. In particular, it is envisaged that in the future this approach can be scaled up to mimic memory with human-like characteristics. This would be a breakthrough in artificial brain implementations and usher in a new type of highly intelligent beings. Another application relates to systems designed for multi-objective optimization (multiple criteria decision making).

Self-Correcting Neural Network for Stereo-matching Problem Solving

In the present paper we describe innovative architecture of artificial neural network based on Hopfield structure Self Correcting Neural Network (SCNN). It is implementation similar to dual mode Hopfield-like network for solving stereo matching problem. Considered network consists of basic layer of neurons implemented as analogue Hopfield-like network and supervising layer. Thanks to the supervising layer, there is a possibility of modification of the connection weights between the neurons in the basic layer. This enables the improvement of the network performance (accuracy). Authors propose a depth map use for image segmentation and objects auto-selection. High enough accuracy of these processes can be achieved when proposed network (SCNN) is applied. Similar idea can be applied also for images noise removal. In the present article we also describe in detail neurons dynamics in the basic and supervising layers of the SCNN. The network considered here was a subject of experimental tests using real stereo pictures as well as simulated stereo images. This enabled calculation of error and direct comparison with classic analogue Hopfield neural network.

Accuracy Improvement of Neural Network State Variable Estimator in Induction Motor Drive

Some accuracy improvement of the neural network (NN) estimator is proposed in the paper. The estimator approximates stator current components in the rotor flux reference frame. Two approaches are considered: data mining with GMDH algorithm and gradual training of the NN in the desired frequency range. In both cases the accuracy of the estimator is significantly improved. Provided tests confirmed this feature and encourage to implement such an estimator it in a sensorless vector controlled induction motor drive.

Iron Doped SBA-15 Mesoporous Silica Studied by Mössbauer Spectroscopy

Mesoporous silica SBA-15 containing propyl-iron-phosphonate groups were considered to confirm their molecular structure. To detect the iron-containing group configuration the Mossbauer spectroscopy was used. Both mesoporous silica SBA-15 containing propyl-iron-phosphonate groups and pure doping agent iron acetylacetate were investigated using Mossbauer spectroscopy. The parameters such as isomer shift, quadrupole splitting, and asymmetry in 57Fe Mossbauer spectra were analyzed. The differences in Mossbauer spectra were explained assuming different local surroundings of Fe nuclei. On this base we were able to conclude about activation of phosphonate units by iron ions and determinate the oxidation state of the metal ion. To examine bonding between iron atoms and phosphonic units the resonance Raman spectroscopy was applied. The density functional theory DFT approach was used to make adequate calculations. The distribution of active units inside silica matrix was estimated by comparison of calculated vibrational spectra with the experimental ones. Analysis of both Mossbauer and resonance Raman spectra seems to confirm the correctness of the synthesis procedure. Also EDX elemental analysis confirms our conclusions.

Influence of the Copper-Containing SBA-15 Silica Fillers on the Mechanical Properties of High Density Polyethylene

The paper concerns the mechanical properties of the high density polyethylene (HDPE) with the copper-containing SBA-15 silica filler. The considered filler is the SBA-15 mesoporous silica containing copper ions bounded inside channels via propyl-phosphonate anchoring groups. With its help, we can impart the biocidal properties to this plastic. Research covered mechanical properties, thermal analysis, colour, shine, and nanomolecular structure. Dynamical properties of the samples like modulus changes and mechanical core loss angle tangent versus temperature and vibration frequency were tested using DMTA method. Level of crystallinity was tested using DSC method while their structure was observed with going through light by optical microscope. Hardness and toughness of obtained samples were also defined. Colour and shine changes of the samples were observed for PE-HD with filler contents 0.5% and 1%. Modulus value changes versus temperature and frequency were notified for the samples with modifier. There were no differences in modulus changes versus temperature for samples with and without filler and frequencies 1 and 10 Hz. It was detected that melting enthalpy of the samples with the modifier decreases. Moreover, some influence of the samples with filler on colour and shine was observed.

Extensions of Hopfield Neural Networks for Solving of Stereo-Matching Problem

Paper considers three Hopfield based architectures in the stereo matching problem solving. Together with classical analogue Hopfield structure two novel architectures are examined: Hybrid-Maximum Neural Network and Self Correcting Neural Network.Energy functions that are crucial for the network performance and working algorithm are also presented.All considered structures are tested to compare their performance features. Two of them are particularly important: accuracy and computational time. For the experiment real and simulated stereo images are used. Obtained results lead to the conclusion about feasibility of considered architectures in the stereo matching problem solving for real time applications.

System for independent living --- new opportunity for visually impaired

The authors present the solution that supports the visually impaired persons. Proposed solution can help them to be more independent in everyday life. Described system will be able to assist the blind in unknown environment (stereo-vision based depth analysis module), to recognise familiar persons and read texts and labels. All information gathered by stereo-vision module and processed by the system (about obstacles in surrounding, result of face recognition, texts in visual field) will be available for the user thanks to tactile interface and the sound system. Considered here device can also help in moving around (system can be connected to a small vehicle).

New Class of Antimicrobial Agents

The paper is about a new class of antimicrobial functional nanomaterials. Proposed compounds are based on SBA-15 porous silica matrices and contain anchored copper ions. Thanks to the immobilization of functional groups the compounds are safer for environment than commonly used disinfectant agents. We prepared and examined silica based materials containing two concentrations of copper-containing groups: 10 and 5%. For the reference we prepared samples containing free-standing CuO molecules in the structure and checked their antimicrobial properties. Antibacterial effect of considered SBA-15-Cu material was tested on Escherichia coli bacteria. Antimicrobial tests were applied for the pure form of the material and as modifying agents for plastics. The obtained results showed that the sample with lower concentration of active copper-containing groups has stronger antimicrobial properties than the one with higher concentration of copper. Interestingly, silica containing free-standing CuO molecules has no antimicrobial properties. Considering the obtained results, we can conclude that the most probable antimicrobial mechanism in this case is an oxidation stress. When a plastic modifier is applied the material is enriched with bacterial inhibitory properties. It seems that SBA-15 silica containing low concentration of anchored copper ions is promising in terms of its antibacterial property and biomaterial potential for commercial use.