Joint Constraint Modelling Using Evolved Topology Generalized Multi-Layer Perceptron(GMLP)

The accurate simulation of anatomical joint models is important for both medical diagnosis and realistic animation applications.  Quaternion algebra has been increasingly applied to model rotations providing a compact representation while avoiding singularities.  This paper describes the application of artificial neural networks topologically evolved using genetic algorithms to model joint constraints directly in quaternion space.  These networks are trained (using resilient back propagation) to model discontinuous vector fields that act as corrective functions ensuring invalid joint configurations are accurately corrected.  The results show that complex quaternion-based joint constraints can be learned without resorting to reduced coordinate models or iterative techniques used in other quaternion based joint constraint approaches

DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS

Determination of pressure drop in pipeline system is difficult. Conventional methods (empirical correlations and mechanistic methods) were not successful in providing accurate estimate. Artificial Neural Networks and polynomial Group Method of Data Handling techniques had received wide recognition in terms of discovering hidden and highly nonlinear relationships between input and output patterns. The potential of both Artificial Neural Networks (ANN) and Abductory Induction Mechanism (AIM) techniques has been revealed in this study by generating generic models for pressure drop estimation in pipeline systems that carry multiphase fluids (oil, gas, and water) and with wide range of angles of inclination. No past study was found that utilizes both techniques in an attempt to solve this problem. A total number of 335 data sets collected from different Middle Eastern fields have been used in developing the models. The data covered a wide range of variables at different values such as oil rate (2200 to 25000 bbl/d), water rate (up to 8424 bbl/d), angles of inclination (-52 to 208 degrees), length of the pipe (500 to 26700 ft) and gas rate (1078 to 19658 MSCFD). For the ANN model, a ratio of 2: 1: 1 between training, validation, and testing sets yielded the best training/testing performance. The ANN model has been developed using resilient back-propagation learning algorithm. The purpose for generating another model using the polynomial Group Method of Data Handling technique was to reduce the problem of dimensionality that affects the accuracy of ANN modeling. It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. The best available empirical correlations and mechanistic models adopted by the industry had been tested against the data and the developed models. Graphical and statistical tools had been utilized for comparing the performance of the new models and other empirical correlations and mechanistic models. Thorough verifications have indicated that the developed Artificial Neural Networks model outperforms all tested empirical correlations and mechanistic models as well as the polynomial Group Method of Data Handling model in terms of highest correlation coefficient, lowest average absolute percent error, lowest standard deviation, lowest maximum error, and lowest root mean square error. The study offers reliable and quick means for pressure drop estimation in pipelines carrying multiphase fluids with wide range of angles of inclination using Artificial Neural Networks and Group Method of Data Handling techniques. Graphical User Interface (GUI) has been generated to help apply the ANN model results while an applicable equation can be used for Group Method of Data Handling model. While the conventional methods were not successful in providing accurate estimate of this property, the second approach (Group Method of Data Handling technique) was able to provide a reliable estimate with only three-input parameters involved. The modeling accuracy was not greatly harmed using this technique

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Design Of Computer Vision Systems For Optimizing The Threat Detection Accuracy

This dissertation considers computer vision (CV) systems in which a central monitoring station receives and analyzes the video streams captured and delivered wirelessly by multiple cameras. It addresses how the bandwidth can be allocated to various cameras by presenting a cross-layer solution that optimizes the overall detection or recognition accuracy. The dissertation presents and develops a real CV system and subsequently provides a detailed experimental analysis of cross-layer optimization. Other unique features of the developed solution include employing the popular HTTP streaming approach, utilizing homogeneous cameras as well as heterogeneous ones with varying capabilities and limitations, and including a new algorithm for estimating the effective medium airtime. The results show that the proposed solution significantly improves the CV accuracy. Additionally, the dissertation features an improved neural network system for object detection. The proposed system considers inherent video characteristics and employs different motion detection and clustering algorithms to focus on the areas of importance in consecutive frames, allowing the system to dynamically and efficiently distribute the detection task among multiple deployments of object detection neural networks. Our experimental results indicate that our proposed method can enhance the mAP (mean average precision), execution time, and required data transmissions to object detection networks. Finally, as recognizing an activity provides significant automation prospects in CV systems, the dissertation presents an efficient activity-detection recurrent neural network that utilizes fast pose/limbs estimation approaches. By combining object detection with pose estimation, the domain of activity detection is shifted from a volume of RGB (Red, Green, and Blue) pixel values to a time-series of relatively small one-dimensional arrays, thereby allowing the activity detection system to take advantage of highly capable neural networks that have been trained on large GPU clusters for thousands of hours. Consequently, capable activity detection systems with considerably fewer training sets and processing hours can be built