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Full-Text Articles in Science and Technology Studies
Stochastic Modeling And Optimization Of Multi-Plant Capacity Planning Problem, Anoop Verma, Nagesh Shukla, S.K Tyagi, Nishikant Mishra
Stochastic Modeling And Optimization Of Multi-Plant Capacity Planning Problem, Anoop Verma, Nagesh Shukla, S.K Tyagi, Nishikant Mishra
Nagesh Shukla
n this paper the problem of capacity planning under risk from demand and price/cost uncertainty of the finished products is addressed. The deterministic model is extended into a two-stage stochastic model with fixed recourse by means of various expected levels of demand as random. A recourse penalty is also included in the objective for both shortage and surplus in the finished products. The model is analyzed to quantify the risk using Markowitz mean-variance model.
Competitive Adsorption Of Metals On Cabbage Waste From Multi-Metal Solutions, M Hossain, H Ngo, W Guo, L Nghiem, F Hai, S Vigneswaran, Thanh Vinh Nguyen
Competitive Adsorption Of Metals On Cabbage Waste From Multi-Metal Solutions, M Hossain, H Ngo, W Guo, L Nghiem, F Hai, S Vigneswaran, Thanh Vinh Nguyen
Faisal I Hai
This study assessed the adsorption capacity of the agro-waste ‘cabbage’ as a biosorbent in single, binary, ternary and quaternary sorption systems with Cu(II), Pb(II), Zn(II) and Cd(II) ions. Dried and ground powder of cabbage waste (CW) was used for the sorption of metals ions. Carboxylic, hydroxyl, and amine groups in cabbage waste were found to be the key functional groups for metal sorption. The adsorption isotherms obtained could be well fitted to both the mono- and multi-metal models. In the competitive adsorption systems, cabbage waste adsorbed larger amount of Pb(II) than the other three metals. However, the presence of the …
Depth Image Super-Resolution Using Multi-Dictionary Sparse Representation, Haoheng Zheng, Abdesselam Bouzerdoum, Son Lam Phung
Depth Image Super-Resolution Using Multi-Dictionary Sparse Representation, Haoheng Zheng, Abdesselam Bouzerdoum, Son Lam Phung
Haoheng Zheng
In this paper, we propose a new depth super-resolution technique based on multiple dictionary learning. A novel dictionary selection method using basis pursuit is proposed to generate multiple dictionaries adaptively. A sparse representation of each low-resolution input patch is derived based on the learned dictionaries, and then used to reconstruct the corresponding high-resolution patch. Experimental results are presented which show that the proposed multi-dictionary scheme outperforms existing depth super-resolution methods.