The Industrial Internet of Things (1IoT) gradually becomes a new paradigm for information exchange in the industrial production environment. To ensure the high reliability of IloT services, an efficient resource alloc...
The Industrial Internet of Things (1IoT) gradually becomes a new paradigm for information exchange in the industrial production environment. To ensure the high reliability of IloT services, an efficient resource allocation method with good robustness is urgently needed under complex industrial environments. This paper considers the distributed constraint- coupled resource allocation problem with noisy information exchange over an undirected network, where each agent holds a private cost function and obtains the solution via only local communications. Communication noise poses a challenge to gradient-tracking based algorithm as the impact of noise will accumulate and its variance tends to infinity when the noise is persistent. Adopting noise-tracing scheme, we propose an exact noise-robust distributed gradient-tracking algorithm to achieve cost-optimal distribution of resources, which can avoid noise-accumulation in the tracking step. Moreover, noise suppression parameters are introduced to further attenuate the impact of noise. With diminishing suppression parameters, it is theoretically proved that the proposed algorithm is able to achieve exact convergence to the optimal solution. Finally, a numerical example is provided for verification.
learner’s cognitive and metacognitive are key personal profile for individualized teaching. To evaluate learner’s comprehensive characteristics, existing learner model were reviewed. Two challenges of constructing a...
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learner’s cognitive and metacognitive are key personal profile for individualized teaching. To evaluate learner’s comprehensive characteristics, existing learner model were reviewed. Two challenges of constructing an accurate and comprehensive learner model integrating cognitive and metacognitive were summarized. A plan of constructing a comprehensive learner model was made based on analysis of existing massive online learning environment, sensor information technology and educational data-mining. As a case study, a method of how to map learning data onto learners’ cognitive and metacognitive was proposed based on an analysis of a number of pupils’ Scratch projects. Three mapping table were established. Pupil’s cognitive skill could be evaluated from technology shown from Scratch project, namely, data structure, algorithm, computational practices and overall evaluation. Content shown from Scratch project were used to infer pupil’s cognitive style. Meta-cognitive ability can be measured from computational practices and behavior in programming process.
With the popularization of smart meters, power companies can collect massive amounts of data from users for non-invasive load detection, electricity theft detection, etc. However, due to faults in smart meters and abn...
With the popularization of smart meters, power companies can collect massive amounts of data from users for non-invasive load detection, electricity theft detection, etc. However, due to faults in smart meters and abnormal communication, the dataset often has missing values, making it difficult for data-driven methods to be widely applied. In this paper, we propose an unsupervised learning method based on TCN-Attention to fill in customers' electricity consumption data. Firstly, we extract electricity consumption features through TCN. In order to improve the feature extraction capability, we introduce a channel attention mechanism to TCN. The attention mechanism makes TCN focus on the effective features in the data by assigning weights. Meanwhile, we use mask tokens to cover a certain proportion of data, forcing the proposed model to learn potential feature representations of the data during the training process. Finally, the features are restored to data through feedforward neural networks. An example is given to verify the effectiveness of the proposed method.
Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller ...
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The physical mechanism of gain motivation is the main theoretical bottleneck that restricts the signal-to-noise ratio(SNR)and results in a mono-merit implementation for the existing stimulated Brillouin scattering-bas...
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The physical mechanism of gain motivation is the main theoretical bottleneck that restricts the signal-to-noise ratio(SNR)and results in a mono-merit implementation for the existing stimulated Brillouin scattering-based fiber sensors.A phase-chaos laser(PCL)is proposed and introduced in the Brillouin optical correlation domain analysis(BOCDA)scheme to promote the SNR and achieve a high-accuracy *** PCL characteristics are presented,and a theoretical model of chaos gain accumulation and extraction is ***,the simulation results reveal that the SNR is improved by 5.56 dB,and the signal-to-background noise ratio(SBR)of the Brillouin gain spectrum(BGS)is promoted by 8.28 dB with a 100-km sensing ***,the PCL is experimentally *** the proof-of-concept experiment,the accuracy of the Brillouin frequency shift is upgraded to 0.64 MHz,and the SBR of BGS is improved by 10.77 *** PCL provides a new research direction for optical chaos,and the PCL-BOCDA showcases a promising future for optimal-merit-coupling sensing and its application.
We proposed a tightly-coupled Lidar-visual-inertial odometry and mapping method, which takes advantage of measurement of Lidar, visual and inertial sensors to achieve highly accurate, real-time 6DoF state estimation a...
We proposed a tightly-coupled Lidar-visual-inertial odometry and mapping method, which takes advantage of measurement of Lidar, visual and inertial sensors to achieve highly accurate, real-time 6DoF state estimation and map-building in GNSS-denied environments. The proposed odometry is a tightly-coupled optimization-based method, obtains robust and low drift odometry by fusing pre-integrated IMU measurements, visual features from the image, and geometric features from Lidar data. Further, we adapt an online method to mitigate degeneracy in optimization problems to improve robustness in environmentally degenerate cases. Simulation and real-world experiments show that the proposed method exhibits similar or better robustness and accuracy with the state-of-the-art SLAM methods.
This paper proposes a new power supply module combining the solar panels with an energy storage device, which can achieve the plug-and-play function. Photovoltaic (PV) cells supply power to the micro-grid via an inver...
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ISBN:
(纸本)9781509054190
This paper proposes a new power supply module combining the solar panels with an energy storage device, which can achieve the plug-and-play function. Photovoltaic (PV) cells supply power to the micro-grid via an inverter controlled by maximum power point tracking (MPPT) strategy to extract the maximum power from the PV arrays. The energy storage system is used to maintain the voltage and frequency via an inverter governed by droop-control to maintain the stable operation of the system even under disturbances. This power supply module will have a droop characteristic when connected to a variable load. The combined module is modelled and simulated with MATLAB/SIMULINK for different working conditions, such as connecting to different load and working in the daytime or at night. The simulation results show the rationality and feasibility of the proposed module.
An adaptive backstepping dynamic surface sliding mode controller based on nonlinear disturbance observer (NDOABSMC) is designed to track zigzag motion of underwater glider (UG). Firstly, a nonlinear disturbance observ...
An adaptive backstepping dynamic surface sliding mode controller based on nonlinear disturbance observer (NDOABSMC) is designed to track zigzag motion of underwater glider (UG). Firstly, a nonlinear disturbance observer is devised to observe ocean current. Then, the backstepping sliding mode control is used to devise motion attitude controller of UG to ensure that UG can track the target trajectory quickly. Additionally, the stability of UG's controlsystem is analyzed. In the end, the proposed control strategy is compared with other approaches.
Aiming at the problem that the current depth estimation of single image mostly uses the ground public data set, and there is less research on aerial images, this paper uses the collected visible and infrared aerial im...
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Aiming at the problem that the current depth estimation of single image mostly uses the ground public data set, and there is less research on aerial images, this paper uses the collected visible and infrared aerial image data sets to study the depth estimation. We used FCRN and LapDRN to extract the depth estimation results of visible aerial image and infrared aerial image under global and single object respectively, and compared the results extracted from the two types of networks by qualitative and quantitative methods. The results show that the depth estimation accuracy of visible aerial image is higher than that of infrared image, but the increase rate of infrared image accuracy index is higher than the increase rate of image depth range. The image depth estimation results extracted by LapDRN method are superior to FCRN in accuracy, structure consistency and edge clarity, which indicates that LapDRN is more effective in the estimation of aerial image depth information.
Wind energy has become one of the most promising new energy sources in the context of the global energy interconnection. However, the inherent uncertainty of wind power and the volatility of its output make it difficu...
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