Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip(MPSoC)has become an integral chip design issue for multiprocessor *** performance measurement of computation...
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Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip(MPSoC)has become an integral chip design issue for multiprocessor *** performance measurement of computational systems is changing with the advancement in *** to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded *** operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor(CMOS)circuits because high on-chip temperature adversely affects the life span of the *** this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first(EA-EDF)scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip(SOC).Dynamic power management(DPM)enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task *** migration avoids peak temperature values in the multicore *** utilization factor(ui)on central processing unit(CPU)core consumes more energy and increases the temperature *** technique switches the core bymigrating such task to a core that has less temperature and is in a low power *** proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature *** effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and *** simulation results show the improvement in performance by optimizing 4.8%on u_(i) 9%,16%,23%and 25%at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can
This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shut...
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This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shutoff (PSPS) strategy is applied to actively cut some vulnerable lines which may easily cause wildfires, and reinforce some lines that are connected to critical loads. To mitigate load shedding caused by active line disconnection in the PSPS strategy, network reconfiguration is applied before the wildfire occurrence. During the restoration period, repair crews (RCs) repair faulted lines, and network reconfiguration is also taken into consideration in the recovery strategy to pick up critical loads. Since there exists possible errors in the wildfire prediction, several different scenarios of wildfire occurrence have been taken into consideration, leading to the proposition of a stochastic multi-period resilient model for the VSC-based AC-DC HDS. To accelerate the computational performance, a progressive hedging algorithm has been applied to solve the stochastic model which can be written as a mixed-integer linear program. The proposed model is verified on a 106-bus AC-DC HDS under wildfire conditions, and the result shows the proposed model not only can improve the system resilience but also accelerate computational speed.
In this paper, we propose a cloud-based virtual commissioning Web platform for IEC 61499 compliant distributed automation systems that support Create-Read-Update-Delete operations for SoftPLC containers from various v...
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This paper proposes an extension to IEC 61499 architecture to better support flexibility and reconfigurability of automation systems while reducing communication overhead. The extension concerns adapter connections be...
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This research presents a navigation robotic system designed for the concurrent tasks of line following and obstacle avoidance in partially-known environments with presence of obstacles. By applying a strategically pos...
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The roll-out of a flexible ramping product provides independent system operators(ISOs)with the ability to address the issues of ramping capacity *** procure flexible ramping capability by committing more generating un...
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The roll-out of a flexible ramping product provides independent system operators(ISOs)with the ability to address the issues of ramping capacity *** procure flexible ramping capability by committing more generating units or reserving a certain amount of headrooms of committed *** this paper,we raise the concern of the possibility that the procured flexible ramping capability cannot be deployed in realtime operations due to the unit shut-down in a look-ahead commitment(LAC)*** a solution to the issues of ramping capacity shortage,we provide a modified ramping product formulation designed to improve the reliability and reduce the expected operating *** trajectories of start-up and shutdown processes are also considered in determining the ramping capability.A new optimization problem is formulated using mixed integer linear programming(MILP)to be readily applied to the practical power system *** performance of this proposed method is verified through simulations using a small-scale system and IEEE 118-bus *** simulation results demonstrate that the proposed method can improve the generation scheduling by alleviating the ramping capacity shortages.
Temperature sensing on a flexible platform is essential to many healthcare, e-skin, and robotic applications. In this regard, a polymer-based temperature sensor is fabricated where a conductive polymer material i.e. P...
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Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, ...
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Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE
The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interes...
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The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.
Flapping wing aerial vehicles are nowadays in demand due to surveillance, civil needs, espionage and border missions.A lot of challenges exists in the development of autonomous flight missions for the flapping wing ae...
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