Design, Development & Performance Evaluation of an Anechoic Chamber for

The design, erection and evalution procedures for it microwave anechuic chamber competed at Cochin University for antenna studies are presented the chamber has an average reflectivity level of - 32 dB on at X-band frequencies, and it i, comparable to international standards.

DESIGN AND DEVELOPMENT OF COMPACT ASYMMETRIC COPLANAR STRIP FED ANTENNAS

Antennas are indispensable component of any wireless communication device. An antenna is a transducer between the transmitter and the free space waves and vice versa. They efficiently transfer electromagnetic energy from a transmission line into free space. But the present day communication applications require compact and ultra wide band designs which cannot be catered by simple microstrip based designs. PIFAs have solved the problem to some extend, but the field of antennas needs more innovative designs In this thesis the design and development of compact planner antenna are presented. Emphasis is given to the design of the feed as well as the radiator resulting in simple compact uniplanar geometries. The Asymmetric coplanar feed used to excite the antennas is found to be a suitable choice for feeding compact antennas.The main objectives of the study are the design of compact single, dual and multi band antennas with uniplanar structure and extension of the design for practical GSM/WLAN applications and Ultra compact antennas using the above techniques and extension of the design to antennas for practical applications like RFID/DVB-H. All the above objectives are thoroughly studied. Antennas with ultra compact dimensions are obtained as a result of the study. Simple equations are provided to design antennas with the required characteristics. The design equations are verified by designing different antennas for different applications.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Design And Development Of A Named Entity Based Question Answering System For Malayalam Language

This is a Named Entity Based Question Answering System for Malayalam Language. Although a vast amount of information is available today in digital form, no effective information access mechanism exists to provide humans with convenient information access. Information Retrieval and Question Answering systems are the two mechanisms available now for information access. Information systems typically return a long list of documents in response to a user’s query which are to be skimmed by the user to determine whether they contain an answer. But a Question Answering System allows the user to state his/her information need as a natural language question and receives most appropriate answer in a word or a sentence or a paragraph. This system is based on Named Entity Tagging and Question Classification. Document tagging extracts useful information from the documents which will be used in finding the answer to the question. Question Classification extracts useful information from the question to determine the type of the question and the way in which the question is to be answered. Various Machine Learning methods are used to tag the documents. Rule-Based Approach is used for Question Classification. Malayalam belongs to the Dravidian family of languages and is one of the four major languages of this family. It is one of the 22 Scheduled Languages of India with official language status in the state of Kerala. It is spoken by 40 million people. Malayalam is a morphologically rich agglutinative language and relatively of free word order. Also Malayalam has a productive morphology that allows the creation of complex words which are often highly ambiguous. Document tagging tools such as Parts-of-Speech Tagger, Phrase Chunker, Named Entity Tagger, and Compound Word Splitter are developed as a part of this research work. No such tools were available for Malayalam language. Finite State Transducer, High Order Conditional Random Field, Artificial Immunity System Principles, and Support Vector Machines are the techniques used for the design of these document preprocessing tools. This research work describes how the Named Entity is used to represent the documents. Single sentence questions are used to test the system. Overall Precision and Recall obtained are 88.5% and 85.9% respectively. This work can be extended in several directions. The coverage of non-factoid questions can be increased and also it can be extended to include open domain applications. Reference Resolution and Word Sense Disambiguation techniques are suggested as the future enhancements

Learning disability prediction tool using ANN and ANFIS

Learning Disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively

Design and Analysis of Zero Cogging Brushless DC Motor for Spacecraft Applications

This paper presents the optimal design of a sur- face mounted permanent magnet Brushless DC mo- tor (PMBLDC) meant for spacecraft applications. The spacecraft applications requires the choice of a torques motor with high torque density, minimum cogging torque, better positional stability and high torque to inertia ratio. Performance of two types of machine con¯gurations viz Slotted PMBLDC and Slotless PMBLDC with halbach array are compared with the help of analytical and FE methods. It is found that unlike a Slotted PMBLDC motor, the Slotless type with halbach array develops zero cogging torque without reduction in the developed torque. Moreover, the machine being coreless provides high torque to inertia ratio and zero magnetic stiction

Design and Finite Element Analysis of Hybrid Stepper Motor for Spacecraft Applications

This paper presents the design and analysis of a 400-step hybrid stepper motor for spacecraft applications. The design of the hybrid stepper motor for achieving a specific performance requires the choice of appropriate tooth geometry. In this paper, a detailed account of the results of two-dimensional finite-element (FE) analysis conducted with different tooth shapes such as square and trapezoidal, is presented. The use of % more corresponding increase in detent torque and distorted static torque profile. For the requirements of maximum torque density, less-detent torque, and better positional accuracy and smooth static torque profile, different pitch slotting with equal tooth width has to be provided. From the various FE models subjected to analysis trapezoidal teeth configuration with unequal tooth pitch on the stator and rotor is found to be the best configuration and is selected for fabrication. The designed motor is fabricated and the experimental results is compared with the FE results

Optimal Design of a Surface Mounted Permanent-Magnet BLDC Motor for Spacecraft Applications

This paper presents the optimal design of a surface mounted permanent-magnet (PM) Brushless direct-current (BLDC) motor meant for spacecraft applications. The spacecraft applications requires the choice of a motor with high torque density, minimum cogging torque, better positional stability and high torque to inertia ratio. Performance of two types of machine configurations viz Slotted PMBLDC and Slotless PMBLDC with Halbach array are compared with the help of analytical and finite element (FE) methods. It is found that unlike a Slotted PMBLDC motor, the Slotless type with Halbach array develops zero cogging torque without reduction in the developed torque. Moreover, the machine being coreless provides high torque to inertia ratio and zero magnetic stiction

FEM based Virtual Prototyping and Design of Third Harmonic Excitation System for Low Voltage Brushless Alternators

Salient pole brushless alternators coupled to IC engines are extensively used as stand-by power supply units for meeting in- dustrial power demands. Design of such generators demands high power to weight ratio, high e ciency and low cost per KVA out- put. Moreover, the performance characteristics of such machines like voltage regulation and short circuit ratio (SCR) are critical when these machines are put into parallel operation and alterna- tors for critical applications like defence and aerospace demand very low harmonic content in the output voltage. While designing such alternators, accurate prediction of machine characteristics, including total harmonic distortion (THD) is essential to mini- mize development cost and time. Total harmonic distortion in the output voltage of alternators should be as low as possible especially when powering very sophis- ticated and critical applications. The output voltage waveform of a practical AC generator is replica of the space distribution of the ux density in the air gap and several factors such as shape of the rotor pole face, core saturation, slotting and style of coil disposition make the realization of a sinusoidal air gap ux wave impossible. These ux harmonics introduce undesirable e ects on the alternator performance like high neutral current due to triplen harmonics, voltage distortion, noise, vibration, excessive heating and also extra losses resulting in poor e ciency, which in turn necessitate de-rating of the machine especially when connected to non-linear loads. As an important control unit of brushless alternator, the excitation system and its dynamic performance has a direct impact on alternator's stability and reliability. The thesis explores design and implementation of an excitation i system utilizing third harmonic ux in the air gap of brushless al- ternators, using an additional auxiliary winding, wound for 1=3rd pole pitch, embedded into the stator slots and electrically iso- lated from the main winding. In the third harmonic excitation system, the combined e ect of two auxiliary windings, one with 2=3rd pitch and another third harmonic winding with 1=3rd pitch, are used to ensure good voltage regulation without an electronic automatic voltage regulator (AVR) and also reduces the total harmonic content in the output voltage, cost e ectively. The design of the third harmonic winding by analytic methods demands accurate calculation of third harmonic ux density in the air gap of the machine. However, precise estimation of the amplitude of third harmonic ux in the air gap of a machine by conventional design procedures is di cult due to complex geome- try of the machine and non-linear characteristics of the magnetic materials. As such, prediction of the eld parameters by conven- tional design methods is unreliable and hence virtual prototyping of the machine is done to enable accurate design of the third har- monic excitation system. In the design and development cycle of electrical machines, it is recognized that the use of analytical and experimental methods followed by expensive and in exible prototyping is time consum- ing and no longer cost e ective. Due to advancements in com- putational capabilities over recent years, nite element method (FEM) based virtual prototyping has become an attractive al- ternative to well established semi-analytical and empirical design methods as well as to the still popular trial and error approach followed by the costly and time consuming prototyping. Hence, by virtually prototyping the alternator using FEM, the important performance characteristics of the machine are predicted. Design of third harmonic excitation system is done with the help of results obtained from virtual prototype of the machine. Third harmonic excitation (THE) system is implemented in a 45 KVA ii experimental machine and experiments are conducted to validate the simulation results. Simulation and experimental results show that by utilizing third harmonic ux in the air gap of the ma- chine for excitation purposes during loaded conditions, triplen harmonic content in the output phase voltage is signi cantly re- duced. The prototype machine with third harmonic excitation system designed and developed based on FEM analysis proved to be economical due to its simplicity and has the added advan- tage of reduced harmonics in the output phase voltage.

Research through DESIGN through research - a problem statement and a conceptual sketch

This paper re-addresses the issue of a lacking genuine design research paradigm. It tries to sketch an operational model of such a paradigm, based upon a generic design process model, which is derived from basic notions of evolution and learning in different domains of knowing (and turns out to be not very different from existing ones). It does not abandon the scientific paradigm but concludes that the latter has to be embedded into / subordinated under a design paradigm.

Gender and Space in Tehran

Abstract: This dissertation generally concentrates on the relationships between “gender” and “space” in the present time of urban life in capital city of Tehran. “Gender” as a changing social construct, differentiated within societies and through time, studied this time by investigation on “gender attitude” or “gender identity” means attitudes towards “gender” issues regarding Tehran residences. “Space” as a concept integrated from physical and social constituents investigated through focus on “spatial attitude” means attitudes towards using “living spaces” including private space of “house”, semi private semi public space of neighborhood and finally public spaces of the city. “Activities and practices” in space concentrated instead of “physical” space; this perspective to “space” discussed as the most justified implication of “space” in this debate regarding current situations in city of Tehran. Under a systematic approach, the interactions and interconnections between “gender” and “space” as two constituent variables of social organization investigated by focus on the different associations presented between different “gender identities” and their different “spatial identities”; in fact, “spatial identity” manifests “gender identity” and in opposite direction, “spatial identity” influences to construction of “gender identity”. The hypotheses of case study in Tehran defined as followed: • “Gender identity” is reflected on “spatial identity”. Various “gender identities” in Tehran present different perspectives of “space” or they identify “space” by different values. • As “gender identity” internalizes patriarchal oppression, it internalizes associated “spatial” oppression too. • Within the same social class, different “gender identities” related to men and women, present interconnected qualities, compared with “gender identities” related to men or women of different social classes. This situation could be found in the “spatial” perspectives of different groups of men and women too. • Following the upper hypotheses, “spatial” oppression differs among social classes of Tehran living in different parts of this city. This research undertook a qualitative study in Tehran by interviewing with different parents of both young daughter and son regarding their attitudes towards gender issues from one side and activities and behaviors of their children in different spaces from the other side. Results of case study indicated the parallel changes of parents’ attitudes towards “gender” and “spatial” issues; it means strong connection between “gender” and “space”. It revealed association of “equal” spatial attitudes with “open, neutral” gender attitudes, and also the association of “biased, unequal” spatial identities with “conservative patriarchal” gender identities. It was cleared too that this variable concept – gender space - changes by “sex”; mothers comparing fathers presented more equitable notions towards “gender spatial” issues. It changes too by “social class” and “educational level”, that means “gender spatial” identity getting more open equitable among more educated people of middle and upper classes. “Breadwinning status in the family” also presents its effect on the changes of “gender spatial” identity so participant breadwinners in the family expressed relatively more equitable notions comparing householders and housekeepers. And finally, “gender spatial” identity changes through “place” in the city and regarding South – North line of the city. The illustration of changes of “gender spatial” identity from “open” to “conservative” among society indicated not only vertical variation across social classes, furthermore the horizontal changing among each social class. These results also confirmed hypotheses while made precision on the third one regarding variable of sex. More investigations pointed to some inclusive spatial attitudes throughout society penetrated to different groups of “gender identities”, to “opens” as to “conservatives”, also to groups between them, by two opposite features; first kind, conservative biased spatial practices in favor of patriarchal gender relations and the second, progressive neutral actions in favor of equal gender relations. While the major reason for the inclusive conservative practices was referred to the social insecurity for women, the second neutral ones associated to more formal & safer spaces of the city. In conclusion, while both trends are associated deeply with the important issues of “sex” & “body” in patriarchal thoughts, still strong, they are the consequences of the transitional period of social change in macro level, and the challenges involved regarding interactions between social orders, between old system of patriarchy, the traditional biased “gender spatial” relations and the new one of equal relations. The case study drew an inhomogeneous illustration regarding gender spatial aspects of life in Tehran, the opposite groups of “open” and “conservative”, and the large group of “semi open semi conservative” between them. In macro perspective it presents contradicted social groups according their general life styles; they are the manifestations of challenging trends towards tradition and modernity in Iranian society. This illustration while presents unstable social situations, necessitates probing solutions for social integration; exploring the directions could make heterogeneous social groups close in the way they think and the form they live in spaces. Democratic approaches like participatory development planning might be helpful for the city in its way to more solidarity and sustainability regarding its social spatial – gender as well – development, in macro levels of social spatial planning and in micro levels of physical planning, in private space of house and in public spaces of the city.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.

The Informational Complexity of Learning from Examples

This thesis attempts to quantify the amount of information needed to learn certain tasks. The tasks chosen vary from learning functions in a Sobolev space using radial basis function networks to learning grammars in the principles and parameters framework of modern linguistic theory. These problems are analyzed from the perspective of computational learning theory and certain unifying perspectives emerge.

The Architect's Collaborator: Toward Intelligent Tools for Conceptual Design

In early stages of architectural design, as in other design domains, the language used is often very abstract. In architectural design, for example, architects and their clients use experiential terms such as "private" or "open" to describe spaces. If we are to build programs that can help designers during this early-stage design, we must give those programs the capability to deal with concepts on the level of such abstractions. The work reported in this thesis sought to do that, focusing on two key questions: How are abstract terms such as "private" and "open" translated into physical form? How might one build a tool to assist designers with this process? The Architect's Collaborator (TAC) was built to explore these issues. It is a design assistant that supports iterative design refinement, and that represents and reasons about how experiential qualities are manifested in physical form. Given a starting design and a set of design goals, TAC explores the space of possible designs in search of solutions that satisfy the goals. It employs a strategy we've called dependency-directed redesign: it evaluates a design with respect to a set of goals, then uses an explanation of the evaluation to guide proposal and refinement of repair suggestions; it then carries out the repair suggestions to create new designs. A series of experiments was run to study TAC's behavior. Issues of control structure, goal set size, goal order, and modification operator capabilities were explored. In addition, TAC's use as a design assistant was studied in an experiment using a house in the process of being redesigned. TAC's use as an analysis tool was studied in an experiment using Frank Lloyd Wright's Prairie houses.