Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field a...
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This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effec...
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This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effect of measurement outliers in data transmission,a self-adaptive saturation function is ***,to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization,a DETS is adopted to regulate the frequency of data *** the addressed MSNSSs,our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS;the local upper bound(UB)on the filtering error covariance(FEC)is derived by solving the difference equations and minimized by designing proper filter ***,according to the local filters and their UBs,a DFF algorithm is presented in terms of the inverse covariance intersection fusion *** such,the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers,thereby improving the estimation ***,the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is ***,the validity of the developed algorithm is checked using a simulation example.
During the calibrating of star sensor, the calibration accuracy is greatly affected by the mismatch between the color temperature of the light and the to-be-measured star, which further affects the attitude measuremen...
During the calibrating of star sensor, the calibration accuracy is greatly affected by the mismatch between the color temperature of the light and the to-be-measured star, which further affects the attitude measurement accuracy. This paper studied the near-infrared spectra of stars with different color temperatures, and analyzed the errors on star positioning and magnitude measurement of star sensor due to the color temperature mismatch. The results showed that in the central field of view, the spot centroid deviation caused by spectral mismatch is smaller than that in the edge field of *** the defocus of the imaging surface also affects the spot centroid deviation. Besides, when calibrating with 6000K color temperature light, the maximum measurement error can reach -1.9126 magnitude.
The integration of signals from physical, social and cyber spaces, known as Cyber-Physical-Social systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M)...
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The integration of signals from physical, social and cyber spaces, known as Cyber-Physical-Social systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M) is collaborative optimized among the three sub-systems. Though some technologies and optimization methods have been studied since its proposition, there is a lack of a systemic architecture as well as an overall implementation about how to efficiently exploit the social signals. For this reason, this paper proposes a general framework of CPSS for urban transportation and presents a feasible solution for traffic optimization based on knowledge automation. The specific implementation includes basic modeling of CPSS, knowledge evolution and reasoning, and collaborative optimization of C&P strategies. As a remarkable highlight, the influence of both individual activities and social learning is concerned during knowledge evolution and reasoning part. A case study from the application in the city of Dongguan is also given to validate our proposed framework and methods, showing that they can efficiently improve the average speed of the actual transportation.
Cyber-Physical-Social systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-System (HMTS), focusing on the fusion of ternary space: social network...
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Cyber-Physical-Social systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-System (HMTS), focusing on the fusion of ternary space: social network of human society, network of machines and the Internet of things. In this paper, we propose a specific implementation framework of CPSS for Smart City based on intelligent loops, including basic modeling and interactive fusion, state perception and cognition, and adaptive learning. On this basis, an overall architecture of the CPSS platform is designed, which is applied in the urban transportation management in Hangzhou. The application results demonstrate that the intelligent loop could optimize the control and management strategies for actual urban transportation.
Sample generation is an effective method to improve the performance of hyperspectral image classification by generating virtual samples for training sample expansion in the training process of classification. However,...
Sample generation is an effective method to improve the performance of hyperspectral image classification by generating virtual samples for training sample expansion in the training process of classification. However, there are some defects existing in the previous sample generation methods including the lack of spatial information, the redundant generation and the damage of the original spectral components. In this paper, we propose conditional band selection generative adversarial net, named CBS-GAN, to handle this problem. Firstly, the band selection net of CBS-GAN is utilized to avoid redundant bands and keep original spectral information, then the generation net of CBS-GAN generates the spatial-spectral data blocks by selected bands for sample generation. The experiments of classification are also used to demonstrate the availability of virtual samples generated by our method.
3D printing technology has rapidly advanced, yet the variability in deployment environments poses significant challenges for monitoring systems, because the dynamic deployment environments such as changes in lighting ...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
3D printing technology has rapidly advanced, yet the variability in deployment environments poses significant challenges for monitoring systems, because the dynamic deployment environments such as changes in lighting and monitoring camera status, can severely impact the effectiveness and robustness of monitoring systems. To tackle this problem in monitoring system, this paper introduces a novel approach for deep neural network (DNN) adaptation to the dynamic environments through self-supervised learning and applies it to during in-situ monitoring. Specifically, we introduce a self-supervised learning strategy that leverages the auxiliary reconstruction task during in-situ monitoring, subsequently applying self-supervised fine-tuning to classification tasks with a new imbalanced-aware classification loss. Our methodology was rigorously evaluated using a real-world dataset for 3D printing defect detection. The experimental outcomes affirm the robustness of our approach, showcasing a higher defect detection accuracy rate than baselines. This substantially mitigates the adverse effects associated with printing defects, thereby increasing the reliability and quality of 3D printing processes.
Driving risk entropy, based on entropy law, is an innovative concept proposed for intelligent driving systems. The concept deals with the driving risks caused by the human-vehicle-road system from the driving informat...
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Polysilicon is mainly obtained by chemical vapor deposition(CVD) reaction, in which hydrogen gas and trichlorosilane(TCS) are fed into the CVD reactor to produce polysilicon. However, the feeding parameters are mainly...
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Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recordin...
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