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	1. Modelling and optimal control of a conveyor-serviced production station with dynamic pickup point
	Hao Tang, Panfei Wu, Li Quan, Qi Tan, Jing Sun
	Information Science and System Science 22 December 2017
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (12891K B)

	Abstract:In this paper, we propose an optimal control strategy for a production line composed of a conveyor-serviced production station. The station is equipped with an industrial robot used to unload parts from the conveyor belt considering dynamic pickup points. Compared to traditional stations with fixed pickup points, both the mathematical model and optimal control of the lookahead ranges are more complicated when considering dynamic pickup points. In fact, the randomness of part arrival and processing implies that the unloading and service operations are stochastic. Therefore, we established a semi-Markov decision process model for optimisation problem by defining the buffer vacancy and position of the robot gripper as the system states, and the lookahead range as the control variable. From this process, both numerical and learning-based optimisation methods can be applied to determine optimal or suboptimal lookahead policies depending on the availability of system parameters. We validated the effectiveness of the proposed control strategy from simulations. The results suggest that production considering a dynamic pickup point outperforms that considering a fixed pickup point in various aspects, including the expected long-run average cost, production rate, and cycle time.
	TO cite this article:Hao Tang, Panfei Wu, Li Quan, et al. Modelling and optimal control of a conveyor-serviced production station with dynamic pickup point[OL].[22 December 2017] http://en.paper.edu.cn/en_releasepaper/content/4742948


	2. The Depth Estimation Based on Acitve Vision
	Lin Anping,Sun Wei
	Information Science and System Science 05 May 2017
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (4K B)

	Abstract:In the eye-in-hand system, the depth is very important for visual positioning system. The image of the object moves as the camera moving, according to the image position change of the same object and the distance the camera made, the depth can be estimated. This paper deduced the relation between camera moving and the image of object moving, adopted centroid method and local voting method to detecting circle. Then used the table tennis to do the radial and the axial motion experiments and estimate the depth. The experiments proved that moving camera alongside either radial or axis direction and taking photos in different position, the depth can be calculated according to the distance the camera moved and the image changes of the same object.
	TO cite this article:Lin Anping,Sun Wei. The Depth Estimation Based on Acitve Vision[OL].[ 5 May 2017] http://en.paper.edu.cn/en_releasepaper/content/4730728


	3. Density Clustering Pruning Method Based on Reconstructed Support Vectors for Sparse LS-SVM
	Si Gangquan,Shi Jianquan,Guo Zhang
	Information Science and System Science 06 May 2015
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (4K B)

	Abstract:In least squares support vector machine (LSSVM), nonlinear function estimation is done by solving a linear set of equations instead of solving a quadratic programming problem, and a nonsparse solution is obtained. Several sparse algorithms have been developed to obtain reduced support vectors to improve the generalization performance of LSSVM. However, all of them iteratively look for support vectors in training datasets, which may are not the most superior choice for building the function model. In this paper, we propose a method of reconstructed support vectors based on the training datasets. The support vectors reconstructed are near the hyper plane of target function and uniformly distributed, which have more contribution to target function. In addition, the method we proposed converges at a faster rate than those iterative algorithms, because one-step selecting strategy is adapted without repeated training. To show the efficacy and feasibility of our proposed algorithm, some comparing experiments are conducted, which are all favorable for our viewpoints. That is, the method we proposed needs less number of support vectors to reach the almost same generalization performance, most important, which has the better robustness and accuracy prediction for the real operating mode.
	TO cite this article:Si Gangquan,Shi Jianquan,Guo Zhang. Density Clustering Pruning Method Based on Reconstructed Support Vectors for Sparse LS-SVM[OL].[ 6 May 2015] http://en.paper.edu.cn/en_releasepaper/content/4641959


	4. Real-Time Payoff-Maximization for Aggregator in Dynamic Aggregator-PHEVs System
	CHEN Jie,YANG Bo,GUO Zhuoyu,CHEN Cailian,GUAN Xinping
	Information Science and System Science 15 July 2014
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (4K B)

	Abstract:In recent years, more and more plug-in hybrid electric vehicles (PHEVs) have been put to use in smart grid. In this paper, we consider a dynamic aggregator-PHEVs system, where the aggregator convinces the PHEVs to use electricity rather than gas by setting an appropriate charging price dynamically. We propose a payoff-maximizing algorithm for the aggregator to decide not only the charging price but also the electricity amount purchased from real-time power market based on Lyapunov optimization. Furthermore, we transform the power purchase problem into the energy allocation problem among all the PHEVs. The proposed algorithm operates in real time and does not require any prior knowledge of the statistical information of the system. Theoretically, we demonstrate the proposed algorithm can guarantee network stability and achieve a result that is away from the optimum by O(1/V), where V is a control parameter. The effectiveness and robustness of the algorithm is validated through simulation results.
	TO cite this article:CHEN Jie,YANG Bo,GUO Zhuoyu, et al. Real-Time Payoff-Maximization for Aggregator in Dynamic Aggregator-PHEVs System[OL].[15 July 2014] http://en.paper.edu.cn/en_releasepaper/content/4601808

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