Research a Novel Optimization Mechanism of Parameters Based on Hybrid NN and GA

2009 ◽  
pp. 451-460
Author(s):  
Yansong Liu ◽  
Rulong Wang ◽  
Gang Yi
Keyword(s):  
Optimization Mechanism

scholarly journals Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

2020 ◽  
Vol 10 (4) ◽  
pp. 1257 ◽  
Author(s):  
Liang Zhang ◽  
Quanshen Wei ◽  
Lei Zhang ◽  
Baojiao Wang ◽  
Wen-Hsien Ho
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relationships ◽  
Two Stage ◽  
Proposed Model ◽  
The Social ◽  
Item List ◽  
Ranking Strategy ◽  
High Prediction ◽  
Optimization Mechanism

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

scholarly journals Effect of Gas-Liquid Contact Intensification on Heat and Mass Transfer in Deflector and Rod Bank Desulfurization Spray Tower

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1269
Author(s):  
Yuzhen Jin ◽  
Weida Zhao ◽  
Zeqing Li
Keyword(s):  
Mass Transfer ◽  
Pressure Drop ◽  
Heat And Mass Transfer ◽  
Temperature Difference ◽  
Flue Gas ◽  
Flue Gas Desulfurization ◽  
Spray Tower ◽  
Porous Media Model ◽  
Desulfurization Efficiency ◽  
Optimization Mechanism

The deflector and the rod bank are commonly used to optimize flue gas distribution in the original spray tower (OST) of a wet flue gas desulfurization system (WFGD). In this paper, the internal optimization mechanism of the deflector desulfurization spray tower (DST) and the rod bank desulfurization spray tower (RBST) are studied. Based on the Euler–Lagrange method, the standard k-ε turbulence model, an SO2 absorption model and a porous media model, the numerical simulation of the desulfurization spray tower is carried out with the verification of the model rationality. The results show that there are gas-liquid contact intensification effects in DST and RBST. Compared with OST, gas-liquid contact intensification enhances the heat and mass transfer effects of DST and RBST. The temperature difference between inlet and outlet of flue gas increased by 3.3 K and the desulfurization efficiency of DST increased by 1.8%; the pressure drop decreased by 37 Pa. In RBST, the temperature difference between the flue gas inlet and outlet increased by 5.3 K and the desulfurization efficiency increased by 3.6%; the pressure drop increased by 33 Pa.

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