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Full-Text Articles in Engineering
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 …
Optimal Sensor Distribution For Multi-Station Assembly Process Using Chaos-Embedded Fast-Simulated Annealing, N Shukla, M Tiwari, R Shankar
Optimal Sensor Distribution For Multi-Station Assembly Process Using Chaos-Embedded Fast-Simulated Annealing, N Shukla, M Tiwari, R Shankar
Nagesh Shukla
This paper presents a novel methodology for the allocation of sensors in multi-station assembly processes. It resolves two core issues pertaining to the determination of an optimal number of sensors to be employed and their best locations. To make the traditional approach more effective, the effect of noise on sensor placement is minimized by maximizing the determinant of the Fischer information matrix. A state-space approach is adopted to model the variation propagation pertaining to the transfer of parts in a given multi-station assembly process. Further, the objective function conceived is significant over other contributions with respect to adding the effect …
Multi Station Assembly Process And Determining The Optimal Sensor Placement Using Chaos Embedded Fast Simulated Annealing, Nagesh Shukla, Manoj Tiwari, Ravi Shankar
Multi Station Assembly Process And Determining The Optimal Sensor Placement Using Chaos Embedded Fast Simulated Annealing, Nagesh Shukla, Manoj Tiwari, Ravi Shankar
Nagesh Shukla
This paper presents a new methodology for allocation of sensors in Multi Station Assembly processes. It resolves two core issues i.e. determining the optimal number of sensors to be used and the best locations for each of sensors. The effect of noise on the sensor placement has been minimized by maximizing the determinant of Fisher information matrix. The paper conceives objective function that is significant over other contributions in respect of adding the effect of noise coupled with the sensor data. To optimize the proposed objective function, a new algorithm is developed that combines Chaotic sequences with traditional Evolutionary Fast …
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.