Investigation of FACTS devices to improve power quality in distribution networks

Flexible AC transmission system (FACTS) technologies are power electronic solutions that improve power transmission through enhanced power transfer volume and stability, and resolve quality and reliability issues in distribution networks carrying sensitive equipment and non-linear loads. The use of FACTS in distribution systems is still in its infancy. Voltages and power ratings in distribution networks are at a level where realistic FACTS devices can be deployed. Efficient power converters and therefore loss minimisation are crucial prerequisites for deployment of FACTS devices. This thesis investigates high power semiconductor device losses in detail. Analytical closed form equations are developed for conduction loss in power devices as a function of device ratings and operating conditions. These formulae have been shown to predict losses very accurately, in line with manufacturer data. The developed formulae enable circuit designers to quickly estimate circuit losses and determine the sensitivity of those losses to device voltage and current ratings, and thus select the optimal semiconductor device for a specific application. It is shown that in the case of majority carrier devices (such as power MOSFETs), the conduction power loss (at rated current) increases linearly in relation to the varying rated current (at constant blocking voltage), but is a square root of the variable blocking voltage when rated current is fixed. For minority carrier devices (such as a pin diode or IGBT), a similar relationship is observed for varying current, however where the blocking voltage is altered, power losses are derived as a square root with an offset (from the origin). Finally, this thesis conducts a power loss-oriented evaluation of cascade type multilevel converters suited to reactive power compensation in 11kV and 33kV systems. The cascade cell converter is constructed from a series arrangement of cell modules. Two prospective structures of cascade type converters were compared as a case study: the traditional type which uses equal-sized cells in its chain, and a second with a ternary relationship between its dc-link voltages. Modelling (at 81 and 27 levels) was carried out under steady state conditions, with simplified models based on the switching function and using standard circuit simulators. A detailed survey of non punch through (NPT) and punch through (PT) IGBTs was completed for the purpose of designing the two cascaded converters. Results show that conduction losses are dominant in both types of converters in NPT and PT IGBTs for 11kV and 33kV systems. The equal-sized converter is only likely to be useful in one case (27-levels in the 33kV system). The ternary-sequence converter produces lower losses in all other cases, and this is especially noticeable for the 81-level converter operating in an 11kV network

Secure Management of Personal Health Records by Applying Attribute-Based Encryption

The confidentiality of personal health records is a major problem when patients use commercial Web-based systems to store their health data. Traditional access control mechanisms, such as Role-Based Access Control, have several limitations with respect to enforcing access control policies and ensuring data confidentiality. In particular, the data has to be stored on a central server locked by the access control mechanism, and the data owner loses control on the data from the moment when the data is sent to the requester. Therefore, these mechanisms do not fulfil the requirements of data outsourcing scenarios where the third party storing the data should not have access to the plain data, and it is not trusted to enforce access control policies. In this paper, we describe a new approach which enables secure storage and controlled sharing of patient’s health records in the aforementioned scenarios. A new variant of a ciphertext-policy attribute-based encryption scheme is proposed to enforce patient/organizational access control policies such that everyone can download the encrypted data but only authorized users from the social domain (e.g. family, friends, or fellow patients) or authorized users from the professional\ud domain (e.g. doctors or nurses) are allowed to decrypt it

Classification of Microscopic Malaria Parasitized Images Using Deep Learning Feature Fusion

An infectious disease that causes a chronic and potentially life-threatening infection caused by microorganisms of the Plasmodium class, is malaria, or malarial disease. It is critical to detect the presence of Malaria parasites as early as possible to ensure that antimalarial treatment is adequate to cure the particular type of Plasmodium. This is to reduce death rates and to focus on various infections in the event of an adverse outcome. The purpose of this study was to develop an artificial intelligence approach capable of separating parasitized erythrocytes from normal basophilic erythrocytes as well as platelets overlying the red blood cells to overcome the high cost of Ma-laria diagnostic equipment. The tone and texture characteristics of erythrocyte images were extracted using histo-gram thresholds and watershed methods, and then fused with Squeeze Net and ShuffleNet algorithms. The measures included planning, preparing, approving, and testing Deep Convolution Neural Network Segmentation without preparation using a graphic processor unit. A total of 96 percent accuracy and specificity was obtained for the position of malaria in red blood cells based on the results of all of the tests. It has been demonstrated that deep learning can be effective in the field of clinical pathology. This provides new directions for development as well as increasing awareness of researchers in this field

Measuring Public Opinion Regarding Peaceful Solution of Palestine Issue: An Experimental Study of University Students in Pakistan, Iran and United Arab Emirates

This study aimed to measure public opinion in the Pakistan Iran and United Arab Emirates regarding peaceful solution of Palestine issue Data N 276 was collected from two universities one postgraduate college and one degree college in Pakistan two universities in Iran and two universities in United Arab Emirates Although Pakistan and Iran have theocratic environment and we got anti-Israel replies but there were 77 Pakistani and 41 Emirati students who presented their rational views about peaceful solution of this conflict There is a brief debate on One-State Solution Two-States Solution Three-States Solution and the status of Jerusalem The plan of forming union among the territories of Israel and Palestine single currency and Rail-Road plan for secular transportation from one region to another is also discussed in this study During comparing such public opinion with other previous international proposals for resolving this issue recommendations from the author are presented in the las

Optical remote sensing of water quality parameters retrieval in the Barents Sea

This thesis addresses various aspects of monitoring water quality indicators (WQIs) using optical remote sensing technologies. The dynamic nature of aquatic systems necessitate frequent monitoring at high spatial resolution. Machine learning (ML)-based algorithms are becoming increasingly common for these applications. ML algorithms are required to be trained by a significant amount of training data, and their accuracy depends on the performance of the atmospheric correction (AC) algorithm being used for correcting atmospheric effects. AC over open oceanic waters generally performs reasonably well; however, limitations still exist over inland and coastal waters. AC becomes more challenging in the high north waters, such as the Barents Sea, due to the unique in-water optical properties at high latitudes, long ray pathways, as well as the scattering of light from neighboring sea ice into the sensors’ field of view adjacent to ice-infested waters. To address these challenges, we evaluated the performances of state-of-the-art AC algorithms applied to the high-resolution satellite sensors Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI), both for high-north (Paper II) and for global inland and coastal waters (Paper III). Using atmospherically corrected remote sensing reflectance (Rrs ) products, estimated after applying the top performing AC algorithm, we present a new bandpass adjustment (BA) method for spectral harmonization of Rrs products from OLI and MSI. This harmonization will enable an increased number of ocean color (OC) observations and, hence, a larger amount of training data. The BA model is based on neural networks (NNs), which perform a pixel-by-pixel transformation of MSI-derived Rrs to that of OLI equivalent for their common bands. In addition, to accurately retrieve concentrations of Chlorophyll-a (Chl-a) and Color Dissolved Organic Matter (CDOM) from remotely sensed data, we propose in the thesis (Paper 1) an NN-based WQI retrieval model dubbed Ocean Color Net (OCN). Our results indicate that Rrs retrieved via the Acolite Dark Spectrum Fitting (DSF) method is in best agreement with in-situ Rrs observations in the Barents Sea compared to the other methods. The median absolute percentage difference (MAPD) in the blue-green bands ranges from 9% to 25%. In the case of inland and coastal waters (globally), we found that OC-SMART is the top performer, with MAPD Rrs products for varying optical regimes than previously presented methods. Additionally, to improve the analysis of remote sensing spectral data, we introduce a new spatial window-based match-up data set creation method which increases the training data set and allows for better tuning of regression models. Based on comparisons with in-water measured Chl-a profiles in the Barents Sea, our analysis indicates that the MSI-derived Rrs products are more sensitive to the depth-integrated Chl-a contents than near-surface Chl-a values (Paper I). In the case of inland and coastal waters, our study shows that using combined OLI and BA MSI-derived Rrs match-ups results in considerable improvement in the retrieval of WQIs (Paper III). The obtained results for the datasets used in this thesis illustrates that the proposed OCN algorithm shows better performance in retrieving WQIs than other semi-empirical algorithms such as the band ratio-based algorithm, the ML-based Gaussian Process Regression (GPR), as well as the globally trained Case-2 Regional/Coast Colour (C2RCC) processing chain model C2RCC-networks, and OC-SMART. The work in this thesis contributes to ongoing research in developing new methods for merging data products from multiple OC missions for increased coverage and the number of optical observations. The developed algorithms are validated in various environmental and aquatic conditions and have the potential to contribute to accurate and consistent retrievals of in-water constituents from high-resolution satellite sensors

A Hybrid Chaotic Image Encryption Scheme Bas~d on S-Box and Ciphertext Feedback

The fascinating developments in digital image processing and network communications during the past decade have created a great demand for real-time secure image transmission over the internet and through wireless networks. Due to some intrinsic features of images, such as bulk data capacity and high correlation among pixels, traditional encryption algorithms such as IDEA, DES and AES are not suitable for practical image encryption, especially for real time applications. In order to meet these challenges, a number of schemes have been proposed for encryption of digital images, making use of chaotic dynamical systems. The objective of the work undertaken in this thesis is two-fold - firstly to evaluate the security of a few representative chaotic ciphers by performing the cryptanalysis on them and secondly, to design an appropriate cipher that would fulfill the needs for both security and speed. The cryptanalysis is performed on two recently proposed chaotic ciphers by Pareek eta!. in [Pareek et a!., 2005] and [Pareek et a!., 2006]. The first cipher is a generic chaotic block cipher. It is shown that the proposed cipher is insecure against differential and knownplaintext attacks. We also show that the key space size of the proposed cipher is less than what is claimed by the authors. The second cipher of Pareek et a!. is a complete image encryption scheme. This scheme is also shown insecure against the differential attack in the thesis. It is also shown suffering from a few security defects and, therefore, is not suitable for real time secure encryption of digital images. In this work, a complete image encryption scheme - Hybrid Chaotic Image Encryption Scheme (HyChiES) is designed. HyChiES is based on a cryptosystem consisting of multiple piecewise linear chaotic maps (m-PLCMs), a generalized logistic map, AES S-box and ciphertext feedback. The analysis of the HyChiES shows that it is extremely sensitive to changes in pixels and, therefore, has an avalanche effect - a highly desirable property for any cipher. As a result, HyChiES randomizes plain images very effectively In this thesis, an AES like 128-bit block cipher is also designed, named as Hybrid-Chaotic Encryption Scheme (H-CES). The heart ofHyChiES and H-CES is the same cryptosystem that consists of AES S-box, generalized logistic map and ciphertext feedback. In order to analyze the differential characteristic probability of this cryptosystem, we consider it as a hybrid S-box. Based on the maximum differential probability of this hybrid S-box, differential characteristic probability for two rounds of H-CES is calculated and it is shown that H-CES is secure against differential cryptanalysis