663 research outputs found
Forecasting load demand using artificial neural network (ANN) / Alias Khamis
No full textRecently, Artificial Neural Network is widely used in intelligent computational technology for data compression, pattern recognition and machine learning. This paper presents the development of artificial neural network (ANN) using Back-propagation to forecast the load demand at Maintenance Building at UiTM Shah Alam campus. The data is taken from the Maintenance Department that has installed remote metering system. The programme was developed using Borland C++ version 5.02
Pushing Alias Resolution to the Limit
Get PDFIn this paper, we show that utilizing multiple protocols offers a unique opportunity to improve IP alias resolution and dual-stack inference substantially. Our key observation is that prevalent protocols, e.g., SSH and BGP, reply to unsolicited requests with a set of values that can be combined to form a unique device identifier. More importantly, this is possible by just completing the TCP hand-shake. Our empirical study shows that utilizing readily available scans and our active measurements can double the discovered IPv4 alias sets and more than 30× the dual-stack sets compared to the state-of-the-art techniques. We provide insights into our method's accuracy and performance compared to popular techniques.Cyber Securit
An alias of I
No full textAn Alias of I is a manuscript of poems that emerge from familial myth into the sensoria and materiality of the Anthropocene, expanding from claustrophobic New Jersey suburbia to ancestral and rented homes in Malacca, Hong Kong, New York, California, and elsewhere. Blending temporality and locality, these movements through space incite explorations of the self, parsing consciousness, interiority, and perception. These poems reckon with inherited violence and the throughline of colonialism, immigration, and intergenerational trauma.
An Alias of I investigates identity and inheritance through the lens of queerness, neurodivergence, and Asian American diaspora. At once familiar and alien, these poems render modernity and selfhood into a landscape where the boundaries between bodies, environments, and stories—past and present—all blur. These meditations on trauma, loss, and mundanity reveal new possibilities that materialize through joy, intimacy, and found signs. An Alias of I embraces hybrid forms, blending prose, lyric, visual, and narrative poems, to explore emotional processing and rationalization through various angles and perspectives, attempting to capture the movement—focus points, detours, ruminations—of an evasive mind working toward an end.
As the title insinuates, the poems interrogate the mutability and instability of personhood. Through these poems, readers are asked to reckon with the gap between externality—who they want to be, how they want to be perceived, desires—and the internal, elusive self. From this tension emerges the difficulty and transcendence of intimacy and community. These poems guide readers through these spaces and worlds with humor, nonchalance and a sense of possibility, inviting readers to keep an open eye.M.F.A.Includes bibliographical reference
Design And Simulation Of A High Thrust Linear Oscillatory Actuator
Get PDFAn actuator is widely used in many applications either in automation, transportation, productions, robotics, logistics, etc. There are many types of actuator available in the market. An actuator is a device that converts energy into limited mechanical motion. The form of energy could be electric, hydraulic or pneumatic. Electric actuator is much superior compare to other energy form. It gives efficiency, controllability, cost and environmental safety.
This thesis is a study on designing a linear oscillatory actuator based on electromagnetic theory. The aim of this study is to develop a linear oscillatory actuator for mechanical cutter with high thrust. Linear oscillatory actuator (LOA) is a type of linear actuator whereby its motion is in single axis and moves continuously. In this research, the design starts from magnetic analysis using Finite Element Method (FEM). This software can simulate the flux density, flux flow, thrust, cogging force, normal force on the element and material in the motor including electromagnet element. The LOA was designed to have a view of its structure before simulate the design by Microcal Origin software. Then, simulation was done to obtain the best thrust, cogging force and normal force value. Few modifications on the structure are done during this simulation to identify the highest thrust, lowest cogging force and normal force.
Simulations of all designed modelling are compared. Future recommendation has been provided to help other researcher for further development of this LOA
Modeling and Simulation of Small Scale Microgrid System
No full textA microgrid systems is a new technology for improving reliability and providing alternative energy supplies to the grid system. Low voltage faults in the system are one of the critical issues that require distributed generating sources to disconnect from grid provide energy to the load. Therefore the techniques used in the microgrid system with microsoures can be important in reducing the problems in the grid system. In this paper two different microsources photovoltaic (PV) and wind turbine (WT) with battery storage for a small scale microgrid system are simulated. The aim is to observe the effect of microsources parameter on the outputs at the point of common coupling. Most of the results can be used for develop a small scale microgrid system for practical applications
Intelligent algorithm based modeling of renewable and green energy resources for microgrid optimization
Get PDFThe reduction of fossil fuels, rising oil prices and environmental awareness have attracted attention to the use of renewable energy (RE)-based distributed generation (DG) systems. Among the various types of renewable energy-based DG, photovoltaic (PV) and fuel cell (FC) technology have shown great potential in electricity generation due to rapid technological development, high efficiency, clean operation and slight influence by weather conditions. To ensure optimal DG output, the RE system must be coordinated using a voltage controller and optimisation techniques to determine the optimal DG output voltage and power value. To improve the AC bus arrangement, battery power is connected to a down/up converter to ensure continuous power flow between the Alternating Current (AC) bus and the battery. In order to control the voltage source inverter (VSI) of the PV/fuel cell/battery cell system, conventional methods of control voltage modes and currents with improved controllers of the artificial intelligence (AI) of both the internal current control loop and the output voltage were built. The proposed tuned Artificial Neural Network (ANN) controller has an advantage over the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller while maintaining the simplicity and robustness of the Proportional Integral (PI) controller. The inverter-based DG model is applied to the microgrid system to review its effectiveness as a complete model as well as to evaluate the performance of its use in large network systems. Since the VSI model is built on a P-Q control scheme that allows separate control of active and reactive power output, DG can operate based on active and reactive power reference on the inverter. A new smart technique has been developed to manage active and reactive power reference for DG by using ANN to ensure that the DG unit operates at optimal power values while reducing the amount of power loss as well as maintaining the voltage profile within acceptable limits. The results showed that the proposed tuned ANN technique could accurately predict the active and reactive power references of DG with minimal error. A comparison was made between the ANN DG controller and the ANFIS DG controller for the power management strategy in terms of the generation by standard forecasting metrics. The comparison between the proposed AI controller and the conventional PI controller has been conducted, and the results showed that the proposed tuned artificial NN technique could accurately predict the active and reactive power references of DG with minimal error. For active power of Battery, is 0.23%, Fuel Cell is 0.23%, reactive power of Battery is 0.0175%, Fuel Cell is 0.097%, Photovoltaic PV1, 0.078% and PV2 is 0.021%. At the end of the research, the AI controller was evaluated/validated for effectiveness by comparative means also conducted to assess the performance and forecast accuracy of the tuned AI that has been chosen by forecasting metrics, which show good estimation performance in only 1.6E-14% for the coefficient of determination (R²), 5.86E-05% for root mean square error (RMSE), 9.1E-06% mean absolute error (MAE) and 0.011% for mean absolute percentage error (MAPE)
Two Steps Forward, One Step Back: The Selling of Charlie\u27S Angels and Alias
No full textThe author analyzes various elements of the promotional campaigns for two recent female-driven action narratives, the film Charlie\u27s Angels and the television series Alias. The analysis suggests that these inconsistent and often regressive campaigns weaken the potentially progressive gender images offered by the film and series
Using N-gram Analysis for Forensic Author Identification and Text Relatedness
No full textAM Session
Using N-gram Analysis for Forensic Author Identification and Text Relatedness
Carole Chaski, ALIAS Technology LLC and Institute for Linguistic Evidence, Inc, US
Dynamic load balancing for petascale quantum Monte Carlo applications: The Alias method
Get PDFDiffusion Monte Carlo is a highly accurate Quantum Monte Carlo method for electronic structure calculations of materials, but it requires frequent load balancing or population redistribution steps to maintain efficiency on parallel machines. This step can be a significant factor affecting performance, and will become more important as the number of processing elements increases. We propose a new dynamic load balancing algorithm, the Alias Method, and evaluate it theoretically and empirically. An important feature of the new algorithm is that the load can be perfectly balanced with each process receiving at most one message. It is also optimal in the maximum size of messages received by any process. We also optimize its implementation to reduce network contention, a process facilitated by the low messaging requirement of the algorithm: a simple renumbering of the MPI ranks based on proximity and a space filling curve significantly improves the MPI Allgather performance. Empirical results on the petaflop Cray XT Jaguar supercomputer at ORNL show up to 30% improvement in performance on 120,000 cores. The load balancing algorithm may be straightforwardly implemented in existing codes. The algorithm may also be employed by any method with many near identical computational tasks that require load balancing.Journal ArticlePublishe
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