The role of weight normalization in competitive learning

The effect of different kinds of weight normalization on the outcome of a simple competitive learning rule is analyzed. It is shown that there are important differences in the representation formed depending on whether the constraint is enforced by dividing each weight by the same amount (''divisive enforcement'') or subtracting a fixed amount from each weight (''subtractive enforcement''). For the divisive cases weight vectors spread out over the space so as to evenly represent ''typical'' inputs, whereas for the subtractive cases the weight vectors tend to the axes of the space, so as to represent ''extreme'' inputs. The consequences of these differences are examined

Use of deep neural networks for segmentation of parenchymatous organs of the abdominal cavity of domestic pigs

Tato bakalářská práce se zabývá možností aplikace hlubokých neuronových sítí v medicinských úlohách, konkrétně úlohou segmentace parenchymatózních orgánů břišní dutiny prasete domácího. Pro realizaci jsme využili framework detectron2 založenou na architektuře Mask R-CNN. V práci jsou popsány technologie používané pro strojové zpracování obrazu. Důležitou částí práce je popis konvolučních neuronových sítí určených pro zpracování obrazu. Hlavní část práce se věnuje popisu Mask R-CNN a detectron2, a to prostřednictvím analýzy výsledku trénování této sítě na medicínských datech. Na závěr je uvedeno rozhodnutí, je-li síť vhodná pro využití v praxi.ObhájenoThis work deals with the possibility of applying deep neural networks in medical tasks, especially in the segmentation of parenchymatous organs of the abdominal cavity of the domestic pig. For this, we used "detectron2", which is based on "Mask R-CNN". In this work we described technology, which is used for digital image processing. Important part of this work is description of Convolutional Neural Networks and their usage for working with digital images. Main part describes "Mask R-CNN", "detectron2" and analyzes results of our training on medical dataset. In conclusion we gave a decision about the network (whether it is useful for practice)

ELECTRICITY FORECASTING FOR SMALL SCALE POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK

Short-term load forecast is an essential part of electric power system planning and operation. For this project, the main focus will be on the Gas District Cooling Plant (GDC) which acts as the primary source of energy for Universiti Teknologi PETRONAS (UTP). This project is looking into weekly forecast of the electricity production for the GDC plant using Artificial Neural Network Approach. This forecasting method will be very useful to support plant operation as the trending of load demand for an educational centre such as UTP is very much dependent on the university activities itself. The project involve MATLAB program for the STLF with Artificial Neural Network prediction model. The obtained results showed that introducing Multilayer Perceptron (MLP) Neural Network architecture improve the prediction significantly by obtaining a very small value of Mean Absolute Percent Error (MAPE). Besides that, by getting the smaller value of MAPE, it represents higher forecast accuracy of the model itself. The report consists of an introduction, problem statement, objectives, literature review and methodology used to solve the problem. It further looks into the obtained results with consistent discussion

ELECTRICITY FORECASTING FOR SMALL SCALE POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK

Short-term load forecast is an essential part of electric power system planning and operation. For this project, the main focus will be on the Gas District Cooling Plant (GDC) which acts as the primary source of energy for Universiti Teknologi PETRONAS (UTP). This project is looking into weekly forecast of the electricity production for the GDC plant using Artificial Neural Network Approach. This forecasting method will be very useful to support plant operation as the trending of load demand for an educational centre such as UTP is very much dependent on the university activities itself. The project involve MATLAB program for the STLF with Artificial Neural Network prediction model. The obtained results showed that introducing Multilayer Perceptron (MLP) Neural Network architecture improve the prediction significantly by obtaining a very small value of Mean Absolute Percent Error (MAPE). Besides that, by getting the smaller value of MAPE, it represents higher forecast accuracy of the model itself. The report consists of an introduction, problem statement, objectives, literature review and methodology used to solve the problem. It further looks into the obtained results with consistent discussion

Induction motor bearing fault detection using a sensorless approach

Continuous condition assessment of induction motors is very important due to its potential to reduce down-time and manpower needed in industry. Rolling element bearing faults result in more than 40% of all induction motor failures. Vibration analysis has been utilized to detect bearing faults for years. However, vibration sensors and expert vibration interpretation are expensive. This limitation prevents widespread monitoring of continuous bearing conditions in induction motors, which provides better performance compared to periodic monitoring, a typical practice for motor bearing maintenance in industry. A strong motivation exists for finding a costeffective approach for the detection of bearing faults. Motor terminal signals have attracted much attention. However, not many papers in the literature address this issue as it relates to bearing faults, because of the difficulties in effective detection. In this research, an incipient bearing fault detection method for induction motors is proposed based on the analysis of motor terminal voltages and currents. The basic idea of this method is to detect changes in amplitude modulation between the spatial harmonics caused by bearing faults and the supply fundamental frequency. This amplitude modulation relationship can be isolated using the phase coupling property. An Amplitude Modulation Detector (AMD), developed from higher order spectrum estimation, correctly captures the phase coupling and isolates these modulation relationships. In this research, in-situ bearing damage experiments are conducted so that the accelerated life span of the bearing can be recorded and investigated. Experimental results shown in this dissertation are based on different power supplies, load levels, VSI control schemes, and motor operating conditions. Taking the mechanical vibration indicator as a reference for fault detection, the proposed method is demonstrated to be effective in detecting incipient bearing faults in induction motors. If motors are operating at near steady state conditions, then experimental results show that the bearing fault detection rate of the proposed approach is 100%, while no false alarms are recorded

Caractérisation expérimentale et numérique du comportement des membranes nanocomposites en soufflage libre

Le développement des matériaux biocomposites, à base de fibres végétales, de renforts biocompatibles et de thermoplastiques, est en plein essor compte tenu de leurs propriétés mécaniques intéressantes ainsi que leur légèreté. Ces matériaux constituent une bonne alternative aux composites conventionnels. Dans le cadre de ce travail on s’intéresse au comportement en grandes déformations d’une membrane nanocomposite à base de PEHD (Polyéthylène à haute densité) comme matrice et de PMSQ (polyméthylsilsesquioxane) en renfort. Le but est de déterminer les constantes matérielles relatives au comportement hyperélastique de ces membranes en soufflage libre et au-dessus de leur température de transition vitreuse. Pour cela, nous considérons les modèles de comportement hyperélastiques de Mooney-Rivlin et d’Ogden. L’identification des paramètres mécaniques est effectuée par une approche hybride qui combine des données expérimentales, des données numériques (issues de la modélisation par éléments finis) et l’utilisation d’un algorithme d’optimisation de Levenberg-Marquardt au sens des moindres carrées

NEURAL NETWORK APPLICATION TO SHORT TERM LOAD FORECAST

Power system planning and operation is an important part for power systems industry. By having a good planning and operation, the quality of power supplied will be improved ensuring both consumer and power provider getting their share equally. In this case, the most challenging part is the prediction of how much the load that will be used by the consumer for a short period of time. This prediction is called load forecasting. This will be very useful to every power system company as the trending for the load demand is different for each geographical location. There are different methods to do the load forecasting. One of the project involved MATLAB program for the short term load forecasting (STLF) using Artificial Neural Network (ANN) model. We are using Multilayer Perceptron (MLP) Neural Network architecture, it will improve the forecast value significantly by obtain a very small mean absolute percentage error (MAPE). By getting a smaller MAPE, it represents higher forecast accuracy of the model itself. The elements in this report contain of an introduction, problem statement, objectives, literature review and methodology which was used to solve the forecasting problems. The discussion of the obtained results will be looked further in this project

Caractérisation des membranes thermoplastiques par une approche neurale

Dans ce travail, nous nous intéressons au problème de l’identification du comportement structural d’une membrane thermoplastique en Acrylonitrile Butadiène Styrène (ABS), en soufflage libre. Le modèle de comportement viscoélastique de Lodge est considéré. Pour l’identification des paramètres associés au modèle de comportement, on a utilisé une approche hybride qui combine, d’une part, des données expérimentales et les résultats de la simulation, et d’autre part, un algorithme des réseaux de neurones (algorithme de rétro propagation de l’erreur). Les résultats obtenus montrent que cette approche est bien adaptée au modèle de Lodge

Caractérisation hyperélastique des membranes thermoplastiques par une approche utilisant les réseaux de neurones

Dans ce travail, nous nous intéressons à la caractérisation du comportement des membranes thermoplastiques en soufflage libre. Les matériaux utilisés sont l'ABS (Acrylonitrile Butadiène Styrène) et l'HIPS (polystyrène à haut impact). Le modèle de comportement utilisé est de type hyperélastique de type Mooney-Rivlin. Pour l'identification des paramètres mécaniques, associés au modèle de comportement de ses membranes, on a utilisé une approche hybride qui combine, d'une part, des données expérimentales et les résultats de la simulation, et d'autre part, la technique des réseaux de neurones par application de l'algorithme de la rétropropagation de l'erreur. Les résultats obtenus montrent que cette approche est bien adaptée au modèle de comportement utilisé