A novel power management and control design framework for resilient operation of microgrids

This thesis concerns the investigation of the integration of the microgrid, a form of future electric grids, with renewable energy sources, and electric vehicles. It presents an innovative modular tri-level hierarchical management and control design framework for the future grid as a radical departure from the ‘centralised’ paradigm in conventional systems, by capturing and exploiting the unique characteristics of a host of new actors in the energy arena - renewable energy sources, storage systems and electric vehicles. The formulation of the tri-level hierarchical management and control design framework involves a new perspective on the problem description of the power management of EVs within a microgrid, with the consideration of, among others, the bi-directional energy flow between storage and renewable sources. The chronological structure of the tri-level hierarchical management operation facilitates a modular power management and control framework from three levels: Microgrid Operator (MGO), Charging Station Operator (CSO), and Electric Vehicle Operator (EVO). At the top level is the MGO that handles long-term decisions of balancing the power flow between the Distributed Generators (DGs) and the electrical demand for a restructure realistic microgrid model. Optimal scheduling operation of the DGs and EVs is used within the MGO to minimise the total combined operating and emission costs of a hybrid microgrid including the unit commitment strategy. The results have convincingly revealed that discharging EVs could reduce the total cost of the microgrid operation. At the middle level is the CSO that manages medium-term decisions of centralising the operation of aggregated EVs connected to the bus-bar of the microgrid. An energy management concept of charging or discharging the power of EVs in different situations includes the impacts of frequency and voltage deviation on the system, which is developed upon the MGO model above. Comprehensive case studies show that the EVs can act as a regulator of the microgrid, and can control their participating role by discharging active or reactive power in mitigating frequency and/or voltage deviations. Finally, at the low level is the EVO that handles the short-term decisions of decentralising the functioning of an EV and essential power interfacing circuitry, as well as the generation of low-level switching functions. EVO level is a novel Power and Energy Management System (PEMS), which is further structured into three modular, hierarchical processes: Energy Management Shell (EMS), Power Management Shell (PMS), and Power Electronic Shell (PES). The shells operate chronologically with a different object and a different period term. Controlling the power electronics interfacing circuitry is an essential part of the integration of EVs into the microgrid within the EMS. A modified, multi-level, H-bridge cascade inverter without the use of a main (bulky) inductor is proposed to achieve good performance, high power density, and high efficiency. The proposed inverter can operate with multiple energy resources connected in series to create a synergized energy system. In addition, the integration of EVs into a simulated microgrid environment via a modified multi-level architecture with a novel method of Space Vector Modulation (SVM) by the PES is implemented and validated experimentally. The results from the SVM implementation demonstrate a viable alternative switching scheme for high-performance inverters in EV applications. The comprehensive simulation results from the MGO and CSO models, together with the experimental results at the EVO level, not only validate the distinctive functionality of each layer within a novel synergy to harness multiple energy resources, but also serve to provide compelling evidence for the potential of the proposed energy management and control framework in the design of future electric grids. The design framework provides an essential design to for grid modernisation

Translanguage

This work proposes a hypothesis that could stand as a basis for empirical investigation of translation process without losing sight of translation product. The proposed hypothesis can provide guidelines to investigate three possible concerns: First, the developmental nature of translators’ transitional constructions before settling on a “final” version. Second, the role of the non-native language in translating. Third, the type of language that is deployed in a translation.Cet article propose une hypothèse qui pourrait jeter les bases pour la recherche empirique du processus de traduction sans perdre de vue le produit de la traduction. L’hypothèse avancée fournit des principes pour trois enjeux possibles : d’abord, la nature développementale des constructions transitionnelles avant d’établir une version « finale », en deuxième lieu, le rôle de la langue étrangère dans la traduction, et enfin, le type de langue

Graph Spectral Domain Watermarking for Unstructured Data from Sensor Networks

The modern applications like social networks and sensors networks are increasingly used in the recent years. These applications can be represented as a weighted graph using irregular structure. Unfortunately, we cannot apply the techniques of the traditional signal processing on those graphs. In this paper, graph spread spectrum watermarking is proposed for networked sensor data authentication. Firstly, the graph spectrum is computed based on the eigenvector decomposition of the graph Laplacian. Then, graph Fourier coefficients are obtained by projecting the graph signals onto the basis functions which are the eigenvectors of the graph Laplacian. Finally, the watermark bits are embedded in the graph spectral coefficients using a watermark strength parameter varied according to the eigenvector number. We have considered two scenarios: blind and non-blind watermarking. The experimental results show that the proposed methods are robust, high capacity and result in low distortion in data. The proposed algorithms are robust to many types of attacks: noise, data modification, data deletion, rounding and down-sampling

Graph spectral domain blind watermarking

This paper proposes the first ever graph spectral domain blind watermarking algorithm. We explore the recently developed graph signal processing for spread-spectrum watermarking to authenticate the data recorded on non-Cartesian grids, such as sensor data, 3D point clouds, Lidar scans and mesh data. The choice of coefficients for embedding the watermark is driven by the model for minimisation embedding distortion and the robustness model. The distortion minimisation model is proposed to reduce the watermarking distortion by establishing the relationship between the error distortion using mean square error and the selected Graph Fourier coefficients to embed the watermark. The robustness model is proposed to improve the watermarking robustness against the attacks by establishing the relationship between the watermark extraction and the effect of the attacks, namely, additive noise and nodes data deletion. The proposed models were verified by the experimental results

First record of Octopus vulgaris (Cuvier, 1797) (Octopodidae) in the Iraqi coastal waters, NW Arabian Gulf

The present study identifies one species of the genus Octopus in the order: Octopoda (Cephalopoda: Mollusca), recorded forthe first time in the Iraqi coastal waters and Arabian-Persian Gulf. The study extended from January 2019 to December of thesame year. The Octopus specimens were seasonally obtained from the fishing trawlers operating in the Iraqi coastal waters inthe South of Al- Fao District, Basrah- Iraq, NW Arabian Gulf. The Octopus was identified as O. vulgaris in Iraqi coastal watersdepending on morphological features. The habitats of living specimens are briefly described. Some observations were reported on the occurrence of this species and the measurement of some environmental factors. The species was identified up to spe-cies level using standard literature. This species looks similar morphologically to the species which is already identified from the other areas around the world. The present study records significant expansion in the distribution range of this species

Adaptive Indexing of Documents Using Genetic Algorithms and Relevance Feedback

مقدمة: في هذه الورقة، تم البحث في مشكلة استرجاع الوثائق الصحيحة التي تحقق رغبات المستخدم. الهدف الرئيسي في أنظمة استرجاع المعلومات هو استرداد جميع الوثائق ذات الصلة فقط. طرق العمل: تم استخدام الخوارزمية الجينية لتحقيق هذا الهدف. أوصاف المستندات تم تكييفها وتغييرها باستخدام الخوارزمية الجينية، اعتمادًا على الأحكام التي اطلقها المستخدم (والتي تم جمعها والاحتفاظ بها) حول اهمية المستندات بالنسبة له. الخوارزمية الجينية هي أداة قوية تعتمد على مبادئ الداروينية وتقنيات التطور للبحث في فضاءات البحث المعقدة. يسهل استخدام الخوارزمية الجينية تكييف فهارس المستندات. تم تنفيذ ثلاثة طرق في الانتخاب: نمذجة عجلة الروليت ، ونمذجة عجلة الروليت مع النخبة والنمذجة الشاملة التصادفية. يتم حساب دالة الصلاحية باستخدام معامل Jaccard الذي يقيس التقارب بين الاستعلام وفهرس المستند. الاستنتاجات: توجد بين الكلمات المفتاحية المستخدمة لوصف محتوى الوثائق اعتماديات إحصائية. من الصعب استيعاب هذه الاعتماديات في نظام الاسترجاع. يمكن للخوارزمية الجينية أن تأخذ في الاعتبار هذه الاعتماديات أثناء عملها. وفقًا لنظرية المخطط وفرضية حجر البناء [10] ، يتم نشر المخططات الأكثر صلاحية من جيل إلى جيل ، حيث يتم أخذ عينات منها وإعادة تجميعها وتحويلها وإعادة تشكيلها لتشكيل سلاسل ذات صلاحية أعلى. هناك جانب آخر يمكن أن تقدمه الخوارزمية الجينية ، وهو الاعتماد على التغذية الراجعة المقدمة من مستخدمي نظام الاسترجاع لتكييف أوصاف المستندات، وإنتاج مجموعة جديدة من الأوصاف الأقرب إلى حاجات المستخدمين. تم استخدام ثلاثة انواع من الانتخاب المتناسب مع الصلاحية ، وهي نمذجة عجلة الروليت ، ونمذجة عجلة الروليت ذات النخبة ، والنمذجة الشاملة التصادفية. أظهرت النتائج تفوق النوع الثالث على الأول والثاني.Background: In this paper, the problem of retrieving the correct documents that satisfy the user's concerns is investigated. The main aim in information retrieval systems is to retrieve all and only relevant documents. Materials and Methods: The genetic algorithm is utilized to adapt and change the documents indexes, depending on relevance judgments collected from users. Genetic algorithm is a powerful tool that depends on the Darwinian principles and evolution techniques to search complex spaces. The use of genetic algorithm facilitates the adaptation of documents indexes. Sampling operation is performed using roulette wheel, roulette wheel with elitism and stochastic universal sampling. The fitness function is computed using Jaccard's coefficient that measure the closeness between query and document index. Results: The results show that the new descriptions are more efficient and closer to the population of users that use the information retrieval system. In addition, the stochastic universal sampling gave the best results. Conclusion: The keywords used to describe the content of documents have statistical dependencies among them. It is difficult to accommodate these dependencies in retrieval system. Genetic algorithm can consider these dependencies during its action. According to schema theorem and building block hypothesis [10], the fittest schemata are propagated from generation to generation, where they are sampled, recombined, mutated and resampled to form strings of potentially higher worth. Another aspect genetic algorithm can offer, is the reliance on the feedback provided by users of the retrieval system to adapt documents descriptions and selections variations were experimented with roulette sampling, with elitism, and with produce new set of descriptions closer to the population of users' needs. Three fitness proportionate selection variations are used, roulette wheel sampling, roulette wheel with elitism and  stochastic universal sampling. The results have indicated the superiority of the third over the first two.&nbsp

Ecological study of two gastropods species Melanoides turbuculata and Melanopsis preaemorsa from Euphrates river - Basrah, Iraq

Biological and taxonomic studies of snails in the waters of Iraq's desert have been enhanced through seasonal recording of them, and it is one of the means to follow their distribution and determine the density. Aiming to determine occurrence and density of two snails Melanoides tuberculata and  Melanopsi preaemorsa, in two stations at the Euphrates river-Basrah, Governorate, in Al-Madina and Al-Qurna. Three replicates within each station were chosen for sample collection of these two species using the quadrate (25 × 25) cm. Many environmental factors including temperature, salinity, pH, dissolved oxygen (DO), and organic material in sediment were measured, 5,330 samples were obtained from the stations. The highest annual density was recorded by snail M. turbuculata with 2,745 and 1,072 ind/m² in two stations respectively, while M. preaemorsa with 912 and 601 ind/m² in (St.1 and 2), respectively. The densities of snail M. turbuculata from 480 ind./m2, June 2019 at (St. 1) to 57 ind./m2 in December 2019. And in (St. 2) from 256 ind./m2 in May 2019 to 0 ind./m2 in August - December 2019. While snail species M. preaemorsa (Linnaeus, 1758) in (St. 1) from 128 ind./m2 in June and July to 0 ind./m2 in January-February, November-December 2019 and in (St. 2) from 144 ind./m2 in April to 0 ind./m2 in August-December 2019. The statistical analysis found significant differences in DO, temperatures, and snail density in two stations. This is the first study in the region on these two species in particular, and their abundance

Quadtree partitioning scheme of color image based

Image segmentation is an essential complementary process in digital image processing and computer vision, but mostly utilizes simple segmentation techniques, such as fixed partitioning scheme and global thresholding techniques due to their simplicity and popularity, in spite of their inefficiency. This paper introduces a new split-merge segmentation process for a quadtree scheme of colour images, based on exploiting the spatial and spectral information embedded within the bands and between bands, respectively. The results show that this technique is efficient in terms of quality of segmentation and time, which can be used in standard techniques as alternative to a fixed partitioning scheme

Making sense of neuromorphic event data for human action recognition

Neuromorphic vision sensors provide low power sensing and capture salient spatial-temporal events. The majority of the existing neuromorphic sensing work focus on object detection. However, since they only record the events, they provide an efficient signal domain for privacy aware surveillance tasks. This paper explores how the neuromorphic vision sensor data streams can be analysed for human action recognition, which is a challenging application. The proposed method is based on handcrafted features. It consists of a pre-processing step for removing the noisy events followed by the extraction of handcrafted local and global feature vectors corresponding to the underlying human action. The local features are extracted considering a set of high-order descriptive statistics from the spatio-temporal events in a time window slice, while the global features are extracted by considering the frequencies of occurrences of the temporal event sequences. Then, low complexity classifiers, such as, support vector machines (SVM) and K-Nearest Neighbours (KNNs), are trained using these feature vectors. The proposed method evaluation uses three groups of datasets: Emulator-based, re-recording-based and native NVS-based. The proposed method has outperformed the existing methods in terms of human action recognition accuracy rates by 0.54%, 19.3%, and 25.61% for E-KTH, E-UCF11 and E-HMDB51 datasets, respectively. This paper also reports results for three further datasets: E-UCF50, R-UCF50, and N-Actions, which are reported for the first time for human action recognition on neuromorphic vision sensor domain