597 research outputs found

    Switching Flow-Graph nonlinear modeling technique

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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

    Full text link
    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

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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
    • …
    corecore