Research report on Bengla OCR training and testing methods

Includes bibliographical references (page 6-7).In this paper we present the training and recognition mechanism of a Hidden Markov Model (HMM) based multi-font Optical Character Recognition (OCR) system for Bengali character. In our approach, the central idea is to separate the HMM model for each segmented character or word. The system uses HTK toolkit for data preparation, model training and recognition. The Features of each trained character are calculated by applying the Discrete Cosine Transform (DCT) to each pixel value of the character image where the image is divided into several frames according to its size. The extracted features of each frame are used as discrete probability distributions which will be given as input parameters to each HMM model. In the case of recognition, a model for each separated character or word is built up using the same approach. This model is given to the HTK toolkit to perform the recognition using the Viterbi Decoding method. The experimental results show significant performance over models using neural network based training and recognition systems.Md. Abul Hasna

Classification non supervisée d’images 3D et extension à la segmentation exploitant les informations de couleur et de profondeur

Access to the 3D images at a reasonable frame rate is widespread now, thanks to the recent advances in low cost depth sensors as well as the efficient methods to compute 3D from 2D images. As a consequence, it is highly demanding to enhance the capability of existing computer vision applications by incorporating 3D information. Indeed, it has been demonstrated in numerous researches that the accuracy of different tasks increases by including 3D information as an additional feature. However, for the task of indoor scene analysis and segmentation, it remains several important issues, such as: (a) how the 3D information itself can be exploited? and (b) what is the best way to fuse color and 3D in an unsupervised manner? In this thesis, we address these issues and propose novel unsupervised methods for 3D image clustering and joint color and depth image segmentation. To this aim, we consider image normals as the prominent feature from 3D image and cluster them with methods based on finite statistical mixture models. We consider Bregman Soft Clustering method to ensure computationally efficient clustering. Moreover, we exploit several probability distributions from directional statistics, such as the von Mises-Fisher distribution and the Watson distribution. By combining these, we propose novel Model Based Clustering methods. We empirically validate these methods using synthetic data and then demonstrate their application for 3D/depth image analysis. Afterward, we extend these methods to segment synchronized 3D and color image, also called RGB-D image. To this aim, first we propose a statistical image generation model for RGB-D image. Then, we propose novel RGB-D segmentation method using a joint color-spatial-axial clustering and a statistical planar region merging method. Results show that, the proposed method is comparable with the state of the art methods and requires less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner. We believe that the methods proposed in this thesis are equally applicable and extendable for clustering different types of data, such as speech, gene expressions, etc. Moreover, they can be used for complex tasks, such as joint image-speech data analysisL'accès aux séquences d'images 3D s'est aujourd'hui démocratisé, grâce aux récentes avancées dans le développement des capteurs de profondeur ainsi que des méthodes permettant de manipuler des informations 3D à partir d'images 2D. De ce fait, il y a une attente importante de la part de la communauté scientifique de la vision par ordinateur dans l'intégration de l'information 3D. En effet, des travaux de recherche ont montré que les performances de certaines applications pouvaient être améliorées en intégrant l'information 3D. Cependant, il reste des problèmes à résoudre pour l'analyse et la segmentation de scènes intérieures comme (a) comment l'information 3D peut-elle être exploitée au mieux ? et (b) quelle est la meilleure manière de prendre en compte de manière conjointe les informations couleur et 3D ? Nous abordons ces deux questions dans cette thèse et nous proposons de nouvelles méthodes non supervisées pour la classification d'images 3D et la segmentation prenant en compte de manière conjointe les informations de couleur et de profondeur. A cet effet, nous formulons l'hypothèse que les normales aux surfaces dans les images 3D sont des éléments à prendre en compte pour leur analyse, et leurs distributions sont modélisables à l'aide de lois de mélange. Nous utilisons la méthode dite « Bregman Soft Clustering » afin d'être efficace d'un point de vue calculatoire. De plus, nous étudions plusieurs lois de probabilités permettant de modéliser les distributions de directions : la loi de von Mises-Fisher et la loi de Watson. Les méthodes de classification « basées modèles » proposées sont ensuite validées en utilisant des données de synthèse puis nous montrons leur intérêt pour l'analyse des images 3D (ou de profondeur). Une nouvelle méthode de segmentation d'images couleur et profondeur, appelées aussi images RGB-D, exploitant conjointement la couleur, la position 3D, et la normale locale est alors développée par extension des précédentes méthodes et en introduisant une méthode statistique de fusion de régions « planes » à l'aide d'un graphe. Les résultats montrent que la méthode proposée donne des résultats au moins comparables aux méthodes de l'état de l'art tout en demandant moins de temps de calcul. De plus, elle ouvre des perspectives nouvelles pour la fusion non supervisée des informations de couleur et de géométrie. Nous sommes convaincus que les méthodes proposées dans cette thèse pourront être utilisées pour la classification d'autres types de données comme la parole, les données d'expression en génétique, etc. Elles devraient aussi permettre la réalisation de tâches complexes comme l'analyse conjointe de données contenant des images et de la parol

Electrical switching in a diakoptics based tram traction simulation tool and its implementation in a SCADA environment

Safe electrical switching is a pre-requisite for secure and reliable operation and maintenance in any electrical utility and traction network. Electrical system safety regulatory bodies and corporate electrical regulations provide protocols including ‘no inadvertent system switching’ and are very strict regarding system safety policies and practices. An electrical High Voltage system ensures coded, legal and safe operational practices to achieve the required system safety, meeting, for instance, ‘on-time-every-time’ operational requirements. Every electrical entity needs to report their safe work practices in proper system safety documentation and effective coded demonstrations, and ensure safety through training-refresher programs to be accredited by technical commission and regulators. Electrical industries usually track real-time system parameters by remote monitoring, higher-level visual foot patrols, local-drone-online camera monitoring and preventative maintenance plans over the lifetime of the network system-switchgear maintenance regime. They undertake required maintenance and corrective progressive work with a systematically safe approach and in a documented manner. Safe electrical system isolation-restoration programs and effective workgroup safety is guaranteed by job specific risk assessment and job safety procedures. This thesis proposes an automated isolation-restoration switching method to be applied in the traction industry with special emphasis on system safety switching practices. It elaborates on how diakoptics, a mathematical method of tearing, stands out as one of the best methods to simulate and analyze a large-scale tram traction network. Examples based on traction systems in Adelaide, South Australia are used in this thesis as case studies on safe and effective isolation-restoration switching practices. The diakoptics algorithm splits a complex traction network into smaller pieces which are solved separately, and gets the optimized simulation of the whole electrical network in real time. Solutions of electrical subsections are combined to produce the correct representation of the entire network’s de-energized or energized switchgear state at a given time. The diakoptics - based ‘model tram traction simulator’ has been developed to cope with the system safety network switchgear orientation and system operational switching requirements. The model focuses on achieving electrical section-wise bottom to up topological power isolation, operational power restoration and entire network instantaneous electrical isolation-restoration in planned, unplanned and absolute emergency situations. A competent electrical operator, by working with the mimic of the traction simulator overhead and substation switchgear, can make an informed decision to progress. The on-duty electrical control officer updates the simulator to a system operational status. As the simulator switchgear connection-orientation mimics the real-time system switchgear operational state, the crew virtually makes a real-time patrol of the work location and the isolation limits, being able to plan safe maintenance work or prepare for a system upgrade. The system switching demonstrations, formally approved switching templates, related catenary system and detailed substation switchgear mimics which the maintainer requires are also included in the simulation tool. An automated isolation-restoration switching program to undertake any planned, unplanned and emergency maintenance work has been extensively tested and verified. The simulator has been upgraded to accommodate any future extensions and bypasses of the network. ‘One click’ immediate remote de-energization of the entire traction system has been included in the tool. Asset management, system safety management options, and system remote switching have been addressed. The tool is also capable of accommodating for future legislative changes to remote locking & tagging requirements.Thesis (Ph.D.) -- University of Adelaide, Electrical and Electronic Engineering, 202

A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids

This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. Capturing the topological information of the system through the GNN framework along with the state measurements can improve the performance of the detection mechanism. The problem is formulated as a classification problem through a GNN with message passing mechanism to identify abnormal measurements. The residual block used in the aggregation process of message passing and the gated recurrent unit can lead to improved computational time and performance. The performance of the proposed model has been evaluated through extensive simulations of power system states and attack scenarios showing promising performance. The sensitivity of the model to intensity and location of the attacks and model's detection delay versus detection accuracy have also been evaluated.Comment: 5 pages, 6 figures, accepted at ISGT conference of 202

Product platforms: influencing factors and effects

The product platform strategy is well known for its several positive effects. However, these effects differ under different market situations. Several product platform projects related decisions can influence these effects. This research work will show how these effects are influenced by decisions under different market situations, findings will help academics in enriching product platform theories and, it will help managers to take proper decisions to enhance the possibility of product platform project success