A lot of vision systems have been embedded in devices around us, like mobile phones, vehicles and UAVs. Many of them still need interactive operations of human users. However, specifying accurate object information co...
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The recently proposed dynamic constrained multi-objective evolutionary algorithm (DCMOEA) is effective to handle constrained optimization problems (COPs). However, one drawback of DCMOEA is it mainly searches the glob...
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In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of (industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate, ...
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In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of (industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate, fast, and robust power system state estimation (PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states (i.e., nodal voltage phasors). Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramer-Rao lower bound is developed. Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.
In this paper, the prediction-based distributed filtering problem is discussed for a class of time-varying stochastic systems with communication delay and different types of noises over sensor networks. The communicat...
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In this paper, the prediction-based distributed filtering problem is discussed for a class of time-varying stochastic systems with communication delay and different types of noises over sensor networks. The communication delay is characterized when the state estimations are transmitted between adjacent sensor nodes. In order to compensate the effects induced by the communication delay, the prediction-based idea is employed and then the active compensation estimation is provided when designing the time-varying distributed filter. In particular, both the prediction-based state estimation and its own innovation measurements are utilized in terms of the concerned sensor networks under given topological structure. Subsequently, a locally minimum upper bound of the filtering error covariance is given by determining the filter gain at each time step. Finally, the validity and advantages of the presented prediction-based distributed filtering method are illustrated by some simulations.
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.
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.
A new concept of a synchronous detector for Giant Magneto-Impedance (GMI) sensors is presented. This concept combines a lock-in amplifier, with outstanding capabilities, high speed and a feedback approach that ensures...
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This paper concers the H∞ control for singular systems with time-varying delay. Firstly, an augmented Lyapunov-Krasovskii functional (ALKF) is constructed by adding some integral terms which are dependent on the sing...
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This paper investigates the problem of finite-time H∞ state estimation for discrete-time stochastic switched genetic regulatory networks (GRNs) with time-varying delays and exogenous disturbances. A new discrete time...
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To improve the accuracy of Electroencephalogram (EEG) emotion recognition, a stacking emotion classification model is proposed, in which different classification models such as XGBoost, LightGBM and Random Forest are ...
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