For high transmission latency from users, cloud computing (CC) is not the best choice for processing latency sensitive applications that require real time response with minimal delay. To tolerate this issue, a new com...
For high transmission latency from users, cloud computing (CC) is not the best choice for processing latency sensitive applications that require real time response with minimal delay. To tolerate this issue, a new computing paradigm named fog computing (FC) was introduced to make cloud services closer to the user. It has been proposed to support real-time applications. Resource allocation and Task assignment are important issues for fog environment. Inefficient resource allocation causes poor performance for the system. Some tasks are constrained with a time limit called deadline. In case of real time applications, tasks need to be scheduled in the fog environment without violating its deadline. A new algorithm called Modified Deadline Aware Resource Allocation (MDARA) was proposed in this paper. Its aim is minimizing the completion time of applications, minimizing average response time, augment usage of resources under a constraint of deadline for enhancing the performance of fog environment. The proposed algorithm is compared with one of effective load balancing algorithms as Modified Round Robin algorithm (MRR) and compared with one of the recent algorithms like DRAM algorithm, which focuses on load balancing and resource utilization without taking into consideration the overall completion time and user-defined deadline of tasks. According to the results, the proposed algorithm outperformed both MRR and DRAM algorithms.
In the context of studying periodic processes, this paper investigates first under which conditions switching affine systems in the plane generate stable limit cycles. Based on these conditions, a design methodology i...
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This paper proposes a novel grid-connected inverter with LCL filters applied to IRM-ILQ (Inverse Reference Model - Inverse Linear Quadratic) control strategy. The IRM, which is feed-forward control, allows for realizi...
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ISBN:
(数字)9798350375589
ISBN:
(纸本)9798350375596
This paper proposes a novel grid-connected inverter with LCL filters applied to IRM-ILQ (Inverse Reference Model - Inverse Linear Quadratic) control strategy. The IRM, which is feed-forward control, allows for realizing the fast response to current control without delay. The ILQ control gives an analytical solution for optimal control, and response speed in the closed-loop system is controlled. The IRM-ILQ control method simultaneously provides target tracking and error convergence, basically the IRM-ILQ control is the two-degree of freedom control. Owing to it, it can suppress surge current less than 58.6% during momentary power failure and make power factor keep unity even when command current value changes. The proposed grid-connected inverter is confirmed current tracking and an impressive adaptability to varying amplitude and phase of current by numerical simulations.
Identifying the elec.romagnetic features of an unknown antenna and evaluating its elec.romagnetic vulnerability are crucial for enhancing the effectiveness of high-power elec.romagnetic attacks or defenses. Sparse mea...
Identifying the elec.romagnetic features of an unknown antenna and evaluating its elec.romagnetic vulnerability are crucial for enhancing the effectiveness of high-power elec.romagnetic attacks or defenses. Sparse measurement of the radiation pattern combined with Infinitesimal Dipole Modeling (IDM) can be an effective method for antenna modeling. IDM uses mathematical representations of infinitesimal current-carrying elements to model the behavior of the unknown antenna. The more measurement data used, the higher the correlation with the original pattern. However, given the practical limitations of the observation environment, a method of obtaining high correlation with minimal data is necessary. In this paper, a method is presented to probabilistically increase the recognition performance of antenna characteristics, even when IDM is applied with limited data. This is achieved by utilizing general elec.romagnetic properties of antennas and data augmentation. A simulation and measurement were conducted using a rigid horn antenna. The results showed that the same correlation performance can be achieved with IDM enhanced by data augmentation, compared to traditional IDM, even when using fewer measurement points.
An economic zone is bound to have continuous monitoring and control by an autonomous surveillance system for better production competency and security. Wireless Rechargeable Sensor Networks (WRSNs) have gained popular...
An economic zone is bound to have continuous monitoring and control by an autonomous surveillance system for better production competency and security. Wireless Rechargeable Sensor Networks (WRSNs) have gained popularity to provide reliable and sustainable energy supply for continual network operations. In economic zones, numerous sensor nodes are deployed for diverse activities that require continuous energy supply. However, the integration of WRSNs with UAVs has increased the efficiency of recharging the sensor nodes deployed in areas that are difficult to reach by mobile chargers. Nevertheless, UAVs’ limited battery life limits their performance and range which requires efficient deployment of charging stations. This paper proposes an integration of the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithms to address the charging scheduling problem through efficient deployment of charging stations and finding the optimal charging scheduling path for UAVs. Simulation results demonstrate that the proposed scheme produces a shorter planning path, leading to a longer network lifespan than alternative approaches.
The automated and reliable delineation of atherosclerotic carotid plaques in ultrasound (CUS) videos is of significant clinical relevance for management of the disease and the prediction of future stroke events. To fa...
The automated and reliable delineation of atherosclerotic carotid plaques in ultrasound (CUS) videos is of significant clinical relevance for management of the disease and the prediction of future stroke events. To facilitate stroke risk assessment, in this study, we propose an integrated software system for the automated segmentation and classification of atherosclerotic carotid plaques in longitudinal CUS videos, which was evaluated using 10 CUS videos, from 10 patients (5 Asymptomatic, AS, and 5 Symptomatic, SY). The proposed methodology involves the following steps: a) CUS video frame (VF) resolution and intensity normalization, b) speckle reduction filtering, c) Motion-mode state-based cardiac cycle (CC) identification, d) deep learning (DL)-based plaque segmentation, e) extraction and selec.ion of plaque region of interest (ROI)-specific textural features, and f) machine learning (ML)-based plaque classification. Initially, one CC (cardiac diastole-systole-diastole) was selec.ed per CUS video, and the CC’s consecutive VFs were identified and reduced in number to exclude redundant VFs. All standardized VFs per patient were extracted, cropped and resized to mainly accommodate the ROI and were fed into a priorly trained and evaluated 2-dimensional DL plaque segmentation model. For each VF, the DL-based segmented plaque ROI was projected onto its primary resolution-normalized VF counterpart, from which textural and amplitude modulation-frequency modulation (AM-FM) plaque ROI features were extracted. Statistical analysis on the total AS and SY VFs was used for feature selec.ion. We identified 2 plaque-originating AM-FM features, which exhibited statistically significant differences between the AS and SY standardized VFs (p<0.05), followed by 3 textural features (p<0.05). To finalize our system, in a future study, the strong AM-FM AS/SY descriptors, identified here, will be evaluated alone or in combinations with other plaque-descriptive features, in machine learnin
In the context of studying periodic processes, this paper investigates first under which conditions switching affine systems in the plane generate stable limit cycles. Based on these conditions, a design methodology i...
In the context of studying periodic processes, this paper investigates first under which conditions switching affine systems in the plane generate stable limit cycles. Based on these conditions, a design methodology is proposed by which the phase portraits of the switching systems are determined to obtain globally stable limit cycles from simple specifications, such as given amplitudes and frequencies of desired oscillations. As an application, the paper finally shows that an oscillator model can be derived with a small effort from data measured for an unknown oscillating system.
This paper investigates the problem of zero-day malicious software (Malware) detection through unsupervised deep learning. We built a sequence-to-sequence auto-encoder model for learning the behavior of normal softwar...
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As robots become more prevalent, the complexity of robot-robot, robot-human, and robot-environment interactions increases. In these interactions, a robot needs to consider not only the effects of its own actions, but ...
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The SCHC framework is a tool that enables IPv6 communication over Low-Power Wide-Area Networks (LPWANs). SCHC allows to carry large data packets, such as IPv6 datagrams, in LPWANs thanks to its compression and fragmen...
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