The growing interest in health and fitness has resulted in an increase in the number of people joining fitness clubs. However, maintaining member engagement remains a challenge for fitness club operators. The goal of ...
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Despite decades of intensive study on grouping for small to medium-sized data sets, research into huge data clustering is gaining more and more attention from both business and academia. Contrary to clustering algorit...
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In over modulated low voltage cascaded multi-level converters are subjected to power imbalance problems. The scheme of reactive power control is used to maintain the cascaded multilevel inverter under power imbalanced...
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The integration of IoT and fog computing technologies has the potential to revolutionize a number of sectors, including healthcare, agriculture, urbanization, and transportation. Real-time human health monitoring, sen...
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When modeling many natural problems, it is necessary to deal with the control of processes that are subject to extreme instantaneous influences of a pulsed nature, which leads to complex nonlinear behavior of the obje...
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With the advent of the Internet of vehicles (IoV), the explosive growth of data from vehicular sensors places a heavy computing burden on green-enabled intelligent transportation systems. In this study, a paradigm of ...
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ISBN:
(纸本)9781538674628
With the advent of the Internet of vehicles (IoV), the explosive growth of data from vehicular sensors places a heavy computing burden on green-enabled intelligent transportation systems. In this study, a paradigm of vehicular fog computing (VFC) is introduced, which is able to offload computation tasks to the nearby fog nodes. In addition, considering the advantage of non-orthogonal multiple access (NOMA), a NOMA-enabled VFC is proposed, in which a task vehicle, a main fog access point (F-AP), an idle vehicle, and other cooperative F-APs are involved to process the task. By jointly optimizing NOMA power allocation, task allocation ratio, bandwidth allocation and time slot allocation, the offloading data maximization problem is investigated. After simplifying the problem through mathematical analysis, the offloading data maximization problem is solved by the proposed interior-point method based on successive convex approximation (SCA). Simulation results show that the proposed offloading scheme performs better than other existing schemes in terms of offloading data.
This paper presents a data-driven methodology for monitoring and quantifying the degradation of plastic chain conveyors, a relevant asset in industrial automation. The proposed approach leverages system vibrations and...
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ISBN:
(纸本)9798331516246;9798331516239
This paper presents a data-driven methodology for monitoring and quantifying the degradation of plastic chain conveyors, a relevant asset in industrial automation. The proposed approach leverages system vibrations and employs the synchrosqueezing Short-Time Fourier Transform and convolutional autoencoders to generate a compact representation space for the data. This space enables the construction of control charts to monitor the extracted metrics. The method is intended to provide a comprehensive assessment of system degradation. Applied to a plastic conveyor chain on a dataset collected over a four-month period, the methodology seeks to identify and quantify the two most significant degradation mechanisms: the chain's elongation due to joint wear and the wear and tear of the slide rail. This research intends to address a significant gap in the literature, offering a practical and automatic solution for condition monitoring based on vibration for industrial equipment. Two aspects will be considered for the evaluation of the method: the capability to "reduce" the storage usage (dimensionality reduction) and the anomaly detection capabilities of the system.
Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estim...
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ISBN:
(纸本)9798350323658
Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estimation of the robot posture, in particular base height. In this paper, we propose a novel approach for combining visual-inertial odometry (VIO) with leg odometry in an extended Kalman filter (EKF) based state estimator. The VIO module uses a stereo camera and IMU to yield low-drift 3D position and yaw orientation and drift-free pitch and roll orientation of the robot base link in the inertial frame. However, these values have a considerable amount of latency due to image processing and optimization, while the rate of update is quite low which is not suitable for low-level control. To reduce the latency, we predict the VIO state estimate at the rate of the IMU measurements of the VIO sensor. The EKF module uses the base pose and linear velocity predicted by VIO, fuses them further with a second high-rate IMU and leg odometry measurements, and produces robot state estimates with a high frequency and small latency suitable for control. We integrate this lightweight estimation framework with a nonlinear model predictive controller and show successful implementation of a set of agile locomotion behaviors, including trotting and jumping at varying horizontal speeds, on a torque-controlled quadruped robot.
The use of online computing has grown quickly across a range of institutions and businesses. The storage of information in the clouds, which enables individuals and businesses to store their personal information large...
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In new computing architectures, data is outsourced and sensitive data becomes vulnerable in various areas. Owners must guarantee that their data are secure from breaches and unauthorized users and available anytime. V...
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