5975 research outputs found
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GWO Based Optimal Reactive Power Coordination of DFIG, ULTC and Capacitors
No full textWind is available with free of cost anywhere in the world, this wind can be used for power generation due to many advantages. This attracts the researchers to work on wind power plants. The presence of wind power plants on distribution system causes major influence on voltage controlled devices (VCDs) in terms of life of the devices. Therefore, this paper proposes grey wolf optimization method (GWO) together with forecasted load one day in advance. VCDs are on load tap changer (ULTC) and capacitors (CS), there are two main objectives first one is curtail of distribution network (DN) loss and second one is curtailing of ULTC and CS switching’s. Objectives are achieved by controlling the reactive power of DFIG in coordination with VCDs. The proposed method is planned and applied in Matlab/Simulink on 10KV practical system with DFIG located at different locations. To validate the efficacy of GWO, results are compared with conventional and dynamic programming methods without profane grid circumstances
A Power Efficient Self Biased OTA Design Based on g_m/I_D Methodology with Considering Load Variation, Temperature Variation and Power Supply Variation
No full textThe present work addresses the design of power efficient fully self biased OTA using a design methodology based on the transistor characteristics. This analog module was analyzed, designed and prototyped in TSMS 0.35μm CMOS technology. Simulation results are presented, in order to validate the methodology. The OTA has Gain of 41.35 dB and 3db bandwidth of 138.73 kHz and the UGB of 12.40MHz with the current consumption of 65.50 μA. The circuit does not have need of any DC external biasing circuit, only need to apply VDD (3.3 V). Here self biasing has been introduced with power consumption of 216.15μW. The results have been taken with load variations, temperature variations, and power supply variations. This circuit used in real time high frequency applications as in RF communication
Dual Axes Solar Tracker
No full textPhotovoltaic (PV) is one of the most important sources of renewable energy in the world. Its current efficiency could be increased up to 60% by using dual axes solar tracker, which maximise PV exposure to sun. The most important component in dual axes solar tracker is sensing location of the sun. Four light dependent resistors (LDR) are used as the sensors, connected to potentiometers to increase their accuracy. Arduino UNO is used as the controller to control two stepper motors. Two experiments have been carried out, where the tolerance of the LDR has been found to be 0.05V and the calibration of the four LDRs to have the error of 0.03V. Both experiments proved the capability of LDR for dual axes solar tracker and potentiometer to increase their accuracy
Gamification for Elementary Mathematics Learning in Indonesia
No full textThe purpose of this research is to combine multimedia elements and mathematics learning material to a mathematic learning interactive application. Research and design methodology that used is Game Development Life Cycle (GDLC) which consist of initiation, pre-production, production, testing and release. Content inside the game is made using gamification and expert system concept. The result of this research is an interactive learning game to support student to understand mathematic materials. The purpose of this application is to help student to learn mathematic in an interactive and interesting way, to deliver mathematic material easily
An Enhancement Role and Attribute Based Access Control Mechanism in Big Data
No full textTo be able to leverage big data to achieve enhanced strategic insight and make informed decision, an efficient access control mechanism is needed for ensuring end to end security of such information asset. Attribute Based Access Control (ABAC), Role Based Access Control (RBAC) and Event Based Access Control (EBAC) are widely used access control mechanisms. The ABAC system is much more complex in terms of policy reviews, hence analyzing the policy and reviewing or changing user permission are quite complex task. RBAC system is labor intensive and time consuming to build a model instance and it lacks flexibility to efficiently adapt to changing user’s, objects and security policies. EBAC model considered only the events to allocate access controls. Yet these mechanisms have limitations and offer feature complimentary to each other. So in this paper, Event-Role-Attribute based fine grained Access Control mechanism is proposed, it provide a flexible boundary which effectively adapt to changing user’s, objects and security policies based on the event. The flexible boundary is achieved by using temporal and environment state of an event. It improves the big data security and overcomes the disadvantages of the ABAC and RBAC mechanisms. The experiments are conducted to prove the effectiveness of the proposed Event-Role-Attribute based Access Control mechanism over ABAC and RBAC in terms of computational overhead
Conceptual Sentiment Analysis Model
No full textBag-of-words approach is popularly used for Sentiment analysis. It maps the terms in the reviews to term-document vectors and thus disrupts the syntactic structure of sentences in the reviews. Association among the terms or the semantic structure of sentences is also not preserved. This research work focuses on classifying the sentiments by considering the syntactic and semantic structure of the sentences in the review. To improve accuracy, sentiment classifiers based on relative frequency, average frequency and term frequency inverse document frequency were proposed. To handle terms with apostrophe, preprocessing techniques were extended. To focus on opinionated contents, subjectivity extraction was performed at phrase level. Experiments were performed on Pang & Lees, Kaggle’s and UCI’s dataset. Classifiers were also evaluated on the UCI’s Product and Restaurant dataset. Sentiment Classification accuracy improved from 67.9% for a comparable term weighing technique, DeltaTFIDF, up to 77.2% for proposed classifiers. Inception of the proposed concept based approach, subjectivity extraction and extensions to preprocessing techniques, improved the accuracy to 93.9%
Symbolic-Connectionist Representational Model for Optimizing Decision Making Behavior in Intelligent Systems
No full textModeling higher order cognitive processes like human decision making come in three representational approaches namely symbolic, connectionist and symbolic-connectionist. Many connectionist neural network models are evolved over the decades for optimizing decision making behaviors and their agents are also in place. There had been attempts to implement symbolic structures within connectionist architectures with distributed representations. Our work was aimed at proposing an enhanced connectionist approach of optimizing the decisions within the framework of a symbolic cognitive model. The action selection module of this framework is forefront in evolving intelligent agents through a variety of soft computing models. As a continous effort, a Connectionist Cognitive Model (CCN) had been evolved by bringing a traditional symbolic cognitive process model proposed by LIDA as an inspiration to a feed forward neural network model for optimizing decion making behaviours in intelligent agents. Significanct progress was observed while comparing its performance with other varients
Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network
No full textClustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency
Removal of Fixed-valued Impulse Noise based on Probability of Existence of the Image Pixel
No full textThis paper proposes a new approach for restoring images distorted by fixed-valued impulse noise. The detection process is based on finding the probability of existence of the image pixel. Extensive investigations indicate that the probability of existence of a pixel in an original image is bounded and has a maximum limit. The tested pixel is judged as original if it has probability of existence less than the threshold boundary. In many tested images, the proposed method indicates that the noisy pixels are detected efficiently. Moreover, this method is very fast, easy to implement and has an outstanding performance when compared with other well-known methods. Therefore, if the proposed filter is added as a preliminary stage to many filters, the final results will be improve
Opinion Mining of Light Rail Transit Development in Indonesia
No full textLight rail transit (LRT), or fast tram is urban public transport using rolling stock similar to a tramway, but operating at a higher capacity, and often on an exclusive right-of-way. Indonesia as one of developing countries has been developed the LRT in two cities of Indonesia, Palembang and Jakarta. There are opinions toward the development of LRT, negative and positive opinions. To reveal the level of LRT development acceptance, this research uses machine learning approach to analyze the data which is gathered through social media. By conducting this paper, the data is modeled and classified in order to analyze the social sentiment towards the LRT development