Применение методов кластеризации для диагностики болезни Альцгеймера на основе ПЕТ-изображений

Робота присвячена використанню методів кластеризації в системах нечіткого виводу для класифікації ПЕТ-зображень з метою діагностики хвороби Альцгеймера. Оцінені характеристики кожного з трьох представлених кластеризаційних методів: Subtractive Clustering, C-means та Fuzzy Grid Partition. На-дані рекомендації щодо використання методу Subtractive Clustering у системах нечіткого виводу для автоматичної діагностики хвороби Альцгеймера, як методу, що показав найкращі результати з AUC=0,8791.This work was dedicated to clustering methods application in fuzzy inference system for Alzheimer’s disease diagnosis using PET-images. Three methods (Subtractive Clustering, C-means and Fuzzy Grid Partition) of clustering were discussed and their performance in Alzheimer’s disease diagnosis were measured. Recommendation of the future use of Subtractive Clustering algorithm in the computer-aided diagnosis system for Alzheimer’s disease are given. The performance of this algorithm is AUC=0,8791.Данная работа посвящена применению методов кластеризации в системах нечеткого вывода для классификации ПЭТ-изображений с целью диагностики болезни Альцгеймера. Оценены характеристики каждого из трех представленных методов кластеризации: Subtractive Clustering, C-means и Fuzzy Grid Partition. Представлены рекомендации касательно использования метода Subtractive Clustering в системах нечеткого вывода для автоматизированной диагностики болезни Альцгеймера, как метода, который показал наилучшие результаты с AUC=0,8791

Intelligent Based Terrain Preview Controller for a 3-axle Vehicle

Presented at 13th International Symposium on Advanced Vehicle Control, AVEC'16; Munich 13-16/09/2016The paper presents a six-wheel half longitudinal model and the design of a dual level control architecture. The first (top) level is designed using a Sugeno fuzzy inference feedforward architecture with and without preview. The second level of controllers are locally managing each wheel for each axle. As the vehicle is moving forward the front wheels and suspension units will have less time to respond when compared to the middle and rear units, hence a preview sensor is used to compensate. The paper shows that the local active suspensions together with the Sugeno Fuzzy, (locally optimised using subtractive clustering), Feedforward control strategy is more effective and this architecture has resulted in reducing the sprung mass vertical acceleration and pitch accelerations

Аналіз використання методу субтрактивної кластерізації при створені нечітких регуляторів електрогідравлічних слідкуючих приводів автомобілів

У статті розглянута задача створення нечіткого регулятора для електрогідравлічних слідкуючих приводів автомобілів з використанням методу субтрактивної кластерізації. Проведено дослідження перехідних процесів замкненої системи електрогідравлічного слідкуючого привода з нечітким регулятором, а також дослідження впливу методу субтрактивної кластерізації на якість таких нечітких регуляторівThe problem of creating a fuzzy controller for electrohydraulic servo drive vehicles using the subtractive clustering is considered in the article. The study of transient processes of closed systeme lectrohydraulic servo drive with fuzzy controller,and a study of the influence of the subtractive clustering method on the quality of the fuzzy controller are realiz

Fuzzy Subtractive Clustering Technique Applied to Demand Response in a Smart Grid Scope

AbstractThis paper focuses on demand response in a smart grid scope using a fuzzy subtractive clustering technique for modeling demand response. Domestic consumption is classified into profiles in order to favorable cover the adequate modeling. The fuzzy subtractive clustering technique is applied to a case study of domestic consumption demand response with three scenarios and a comparison of the results is presented. The demand response developed model intends to support consumer's decisions given a compromise between the consumption imperative needs and possible economical benefits due to reshape and reschedule

Content adaptive wavelet based method for joint denoising of depth and luminance images

In this paper we present a new method for joint denoising of depth and luminance images produced by time-of-flight camera. Here we assume that the sequence does not contain outlier points which can be present in the depth images. Our method first performs estimation of noise and signal covariance matrices and then performs vector denoising. Luminance image is segmented into similar contexts usina k-means algorithm, which are used for calculation of covariance matrices. Denoising results are compared with the ground truth images obtained by averaging of the multiple frames of the still scene

Automatic generation of fuzzy classification rules using granulation-based adaptive clustering

A central problem of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible extent. In this study, we present a method for automatic generation of fuzzy rules from data. The main advantage of the proposed method is its ability to perform data clustering without the requirement of predefining any parameters including number of clusters. The proposed method creates data clusters at different levels of granulation and selects the best clustering results based on some measures. The proposed method involves merging clusters into new clusters that have a coarser granulation. To evaluate performance of the proposed method, three different datasets are used to compare performance of the proposed method to other classifiers: SVM classifier, FCM fuzzy classifier, subtractive clustering fuzzy classifier. Results show that the proposed method has better classification results than other classifiers for all the datasets used

An adaptive neuro-fuzzy propagation model for LoRaWAN

This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios