597 research outputs found
Switching Flow-Graph nonlinear modeling technique
A unified graphical modeling technique, “Switching Flow-Graph” is developed to study the nonlinear dynamic behavior of pulse-width-modulated (PWM) switching converters. Switching converters are variable structure systems with linear subsystems. Each subsystem can be represented by a flow-graph. The Switching Flow-Graph is obtained by combining the flowgraphs of the subsystems through the use of switching branches. The Switching Flow-Graph model is easy to derive, and it provides a visual representation of a switching converter system. Experiments demonstrate that the Switching Flow-Graph model has very good accuracy
Factors affecting the success of Information Systems projects
This thesis looks at how accurately project managers can pre-predict what problems may arise during an Information Systems project they are about to commence based on their experience of previous projects. The second part investigates what actions a project manager is likely to take to expedite a troubled project. Both of these questions are investigated and it is found that while project managers can confidently predict which problems could arise, and how to take actions to rectify such actions when they occur, these problems still arise
A New Approach using Deep Learning and Reinforcement Learning in HealthCare: Skin Cancer Classification
Nowadays, skin cancer is one of the most important problems faced by the world, due especially to the rapid development of skin cells and excessive exposure to UV rays. Therefore, early detection at an early stage employing advanced automated systems based on AI algorithms plays a major job in order to effectively identifying and detecting the disease, reducing patient health and financial burdens, and stopping its spread in the skin. In this context, several early skin cancer detection approaches and models have been presented throughout the last few decades to improve the rate of skin cancer detection using dermoscopic images. This work proposed a model that can help dermatologists to know and detect skin cancer in just a few seconds. This model combined the merits of two major artificial intelligence algorithms: Deep Learning and Reinforcement Learning following the great success we achieved in the classification and recognition of images and especially in the medical sector. This research included four main steps. Firstly, the pre-processing techniques were applied to improve the accuracy, quality, and consistency of a dataset. The input dermoscopic images were obtained from the HAM10000 database. Then, the watershed algorithm was used for the segmentation process performed to extract the affected area. After that, the deep convolutional neural network (CNN) was utilized to classify the skin cancer into seven types: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma melanocytic nevi, melanoma vascular skin lesions. Finally, in regards to the reinforcement learning part, the Deep Q_Learning algorithm was utilized to train and retrain our model until we found the best result. The accuracy metric was utilized to evaluate the efficacy and performance of the proposed method, which achieved a high accuracy of 80%. Furthermore, the experimental results demonstrate how reinforcement learning can be effectively combined with deep learning for skin cancer classification tasks
Machine Learning for Metasurfaces Design and Their Applications
Metasurfaces (MTSs) are increasingly emerging as enabling technologies to
meet the demands for multi-functional, small form-factor, efficient,
reconfigurable, tunable, and low-cost radio-frequency (RF) components because
of their ability to manipulate waves in a sub-wavelength thickness through
modified boundary conditions. They enable the design of reconfigurable
intelligent surfaces (RISs) for adaptable wireless channels and smart radio
environments, wherein the inherently stochastic nature of the wireless
environment is transformed into a programmable propagation channel. In
particular, space-limited RF applications, such as communications and radar,
that have strict radiation requirements are currently being investigated for
potential RIS deployment. The RIS comprises sub-wavelength units or meta-atoms,
which are independently controlled and whose geometry and material determine
the spectral response of the RIS. Conventionally, designing RIS to yield the
desired EM response requires trial and error by iteratively investigating a
large possibility of various geometries and materials through thousands of
full-wave EM simulations. In this context, machine/deep learning (ML/DL)
techniques are proving critical in reducing the computational cost and time of
RIS inverse design. Instead of explicitly solving Maxwell's equations, DL
models learn physics-based relationships through supervised training data. The
ML/DL techniques also aid in RIS deployment for numerous wireless applications,
which requires dealing with multiple channel links between the base station
(BS) and the users. As a result, the BS and RIS beamformers require a joint
design, wherein the RIS elements must be rapidly reconfigured. This chapter
provides a synopsis of DL techniques for both inverse RIS design and
RIS-assisted wireless systems.Comment: Book chapter, 70 pages, 12 figures, 2 tables. arXiv admin note:
substantial text overlap with arXiv:2101.09131, arXiv:2009.0254
Air Force Institute of Technology Research Report 1997
This report summarizes the research activities of the Air Force Institute of Technology\u27s Graduate School of Engineering and the Graduate School of Logistics and Acquisition Management. It describes research interests and faculty expertise; list student theses/dissertations; identifies research sponsors and contributions; and outlines the procedure for contacting either school
Seasat data utilization project
During the three months of orbital operations, the satellite returned data from the world's oceans. Dozens of tropical storms, hurricanes and typhoons were observed, and two planned major intensive surface truth experiments were conducted. The utility of the Seasat-A microwave sensors as oceanographic tools was determined. Sensor and geophysical evaluations are discussed, including surface observations, and evaluation summaries of an altimeter, a scatterometer, a scanning multichannel microwave radiometer, a synthetic aperture radar, and a visible and infrared radiometer
Online Gaming and the Social Construction of Virtual Victimization
Online computer gaming is becoming an increasingly popular leisure activity as well as a growing context for social networking and social interaction in general. Drawing from a cyber-ethnography conducted in one such online game, I analyze the process by which the notion of victimization is socially constructed within the online gaming community. I contextualize this analysis within the framework of social construction theories, specifically addressing how internal and external norms, beliefs and values influence the assessment of the severity of virtual harm and the subsequent validity of victim claims. The reported findings suggest a distinction between virtual violence and theft within the context of the game; the latter being assessed as more harmful to the cohesiveness of the online community as well as the individual victim. Reasons for this distinction as well as a broader analysis of the interaction between online and offline culture is discussed
Online Gaming and the Social Construction of Virtual Victimization
Online computer gaming is becoming an increasingly popular leisure activity as well as a growing context for social networking and social interaction in general. Drawing from a cyber-ethnography conducted in one such online game, I analyze the process by which the notion of victimization is socially constructed within the online gaming community. I contextualize this analysis within the framework of social construction theories, specifically addressing how internal and external norms, beliefs and values influence the assessment of the severity of virtual harm and the subsequent validity of victim claims. The reported findings suggest a distinction between virtual violence and theft within the context of the game; the latter being assessed as more harmful to the cohesiveness of the online community as well as the individual victim. Reasons for this distinction as well as a broader analysis of the interaction between online and offline culture is discussed
Critical path analysis type scheduling in a finite capacity environment
In order to cope with more realistic production scenarios, scheduling theory has
been increasingly considering assembly job shops. Such an effort has raised
synchronization of operations and components as a major scheduling issue. Most
effective priority rules designed for assembly shops have incorporated measures
to improve coordination when scheduling assembly structures. However, by
assuming a forward loading, the priority rules designed by these studies schedule
all operations as soon as possible, which often leads to an increase of the workin-
progress level.
This study is based on the assumption that synchronization may be improved by
sequencing rules that incorporate measures to cope with the complexity of
product structures. Moreover, this study favours the idea that, in order to
improve synchronization and, consequently, reduce waiting time, backward
loading should be considered as well. By recognizing that assembly shop
structures are intrinsically networks, this study investigates the feasibility of
adopting the Critical Path Method as a sequencing rule for assembly shop.
Furthermore, since a Critical Path type scheduling requires a precise
determination of production capacity, this study also includes Finite Capacity as
a requisite for developing feasible schedules.
In order to test the above assumptions, a proven and effective sequencing rule is
selected to act as a benchmark and a simulation model is developed. The
simulation results from several experiments showed significant reduction on the
waiting time performance measure due to the adoption of the proposed critical
path type priority rule.
Finally, a heuristic procedure is proposed as a guideline for designing scheduling
systems which incorporate Critical Path based rules and Finite Capacity
approach
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