Hierarchical fuzzy logic based approach for object tracking

In this paper a novel tracking approach based on fuzzy concepts is introduced. A methodology for both single and multiple object tracking is presented. The aim of this methodology is to use these concepts as a tool to, while maintaining the needed accuracy, reduce the complexity usually involved in object tracking problems. Several dynamic fuzzy sets are constructed according to both kinematic and non-kinematic properties that distinguish the object to be tracked. Meanwhile kinematic related fuzzy sets model the object's motion pattern, the non-kinematic fuzzy sets model the object's appearance. The tracking task is performed through the fusion of these fuzzy models by means of an inference engine. This way, object detection and matching steps are performed exclusively using inference rules on fuzzy sets. In the multiple object methodology, each object is associated with a confidence degree and a hierarchical implementation is performed based on that confidence degree.info:eu-repo/semantics/publishedVersio

Clustering-based analysis of semantic concept models for video shots

In this paper we present a clustering-based method for representing semantic concepts on multimodal low-level feature spaces and study the evaluation of the goodness of such models with entropy-based methods. As different semantic concepts in video are most accurately represented with different features and modalities, we utilize the relative model-wise confidence values of the feature extraction techniques in weighting them automatically. The method also provides a natural way of measuring the similarity of different concepts in a multimedia lexicon. The experiments of the paper are conducted using the development set of the TRECVID 2005 corpus together with a common annotation for 39 semantic concept

Ground test of large flexible structures

Many future mission models require large space (LSS) which have accurate surfaces and/or the capability of being accurately aligned. If ground test approaches which will provide adequate confidence of the structrual performance to the program managers are not developed, many viable structural concepts may never be utilized. The size and flexibility of many of the structural concepts will preclude the use of the current ground test methods because of the adverse effects of the terrestrial environment. The challenge is to develop new test approaches which will provide confidence in the capability of LSS to meet performance requirements prior to flight. The activities on ground testing of LSS are described. Since some of the proposed structural systems cannot be tested in entirety, a coordinated ground test analytical model program is required to predict structural performance in space. Several concepts of ground testing under development are addressed

Efektivitas Model POGIL untuk Meningkatkan Self Confidence dan Penguasaan Konsep Larutan Penyangga Peserta Didik

This study aims to describe the effectiveness of the POGIL model to increase students' self-confidence and mastery of the buffer solution concept. The research sample was taken using cluster random sampling technique, obtained class XI IPA 3 as the experimental class and XI IPA 4 as the control class. This study used a quasi-experimental method with pretest-posttest control group design. The POGIL model is said to be effective if it shows a significant difference in the value of n-Gain self confidence and students' mastery of concepts between the experimental class and the control class. The results showed that the POGIL model was effective in increasing students' self-confidence and mastery of the buffer solution concept.Keywords: POGIL models, self confidence, mastery of concepts, buffer solution Penelitian ini bertujuan untuk mendeskripsikan efektivitas model POGIL untuk meningkatkan self confidence dan penguasaan konsep larutan penyangga peserta didik. Sampel penelitian diambil menggunakan teknik cluster random sampling, diperoleh kelas XI IPA 3 sebagai kelas eksperimen dan XI IPA 4 sebagai kelas kontrol. Penelitian ini menggunakan metode kuasi eksperimen dengan pretest-postest control grup design. Model POGIL dikatakan efektif apabila menunjukkan perbedaan nilai n-Gain self confidence dan penguasaan konsep peserta didik yang signifikan antara kelas eksperimen dan kelas kontrol. Hasil penelitian menunjukkan bahwa model POGIL efektif dalam meningkatkan self confidence dan penguasaan konsep larutan penyangga peserta didik.Kata kunci: model POGIL, self confidence, penguasaan konsep, larutan penyangg

Application of physical parameter identification to finite element models

A time domain technique for matching response predictions of a structural dynamic model to test measurements is developed. Significance is attached to prior estimates of physical model parameters and to experimental data. The Bayesian estimation procedure allows confidence levels in predicted physical and modal parameters to be obtained. Structural optimization procedures are employed to minimize an error functional with physical model parameters describing the finite element model as design variables. The number of complete FEM analyses are reduced using approximation concepts, including the recently developed convoluted Taylor series approach. The error function is represented in closed form by converting free decay test data to a time series model using Prony' method. The technique is demonstrated on simulated response of a simple truss structure

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

Connections between the Sznajd Model with General Confidence Rules and graph theory

The Sznajd model is a sociophysics model, that is used to model opinion propagation and consensus formation in societies. Its main feature is that its rules favour bigger groups of agreeing people. In a previous work, we generalized the bounded confidence rule in order to model biases and prejudices in discrete opinion models. In that work, we applied this modification to the Sznajd model and presented some preliminary results. The present work extends what we did in that paper. We present results linking many of the properties of the mean-field fixed points, with only a few qualitative aspects of the confidence rule (the biases and prejudices modelled), finding an interesting connection with graph theory problems. More precisely, we link the existence of fixed points with the notion of strongly connected graphs and the stability of fixed points with the problem of finding the maximal independent sets of a graph. We present some graph theory concepts, together with examples, and comparisons between the mean-field and simulations in Barab\'asi-Albert networks, followed by the main mathematical ideas and appendices with the rigorous proofs of our claims. We also show that there is no qualitative difference in the mean-field results if we require that a group of size q>2, instead of a pair, of agreeing agents be formed before they attempt to convince other sites (for the mean-field, this would coincide with the q-voter model).Comment: 15 pages, 18 figures. To be submitted to Physical Revie