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97 results on '"Cui, Yonghao"'

11. ONECG: Online Negotiation Environment for Coalitional Games

Author

Chen, Siqi, Cui, Yonghao, Shang, Cong, Hao, J., Weiss, Gerhard, RS: FSE DACS, Dept. of Advanced Computing Sciences, and RS: FSE DACS RAI

Subjects
Cooperative game theory, Human-agent interaction, TheoryofComputation_GENERAL, Coalitional negotiation games, ComputingMethodologies_ARTIFICIALINTELLIGENCE, Negotiation
Abstract

Coalitional games can be used to model a variety of problems in the real world. In coalitional game theory, how players form coalitions and divide payoffs is one fundamental issue to be answered. This demo presents an online negotiation environment for coalitional games (ONECG), in which coalitional negotiation can be conduced in a distributed way between people, agents, or in mixed settings via offer exchange and natural language communication. ONECG also allows configuration of specifications of coalitional games, and supports the rapid development of new negotiating agents through a set of well-defined APIs. This new environment is helpful to facilitate research on training human negotiation skills in coalitional games as well as the design of negotiation agents.

Published
2019

12. Towards Fire Prediction Accuracy Enhancements by Leveraging an Improved Naïve Bayes Algorithm

Author

Liang Shu, Wei Chen, Yingmin You, Cui Yonghao, and Zhang Haigen

Subjects
fire prediction, Physics and Astronomy (miscellaneous), Computer science, General Mathematics, five level orthogonal design, 020101 civil engineering, 02 engineering and technology, Interference (wave propagation), 0201 civil engineering, Compensation (engineering), Naive Bayes classifier, Prior probability, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Orthogonal array testing, double weighted naive Bayes, business.industry, lcsh:Mathematics, characteristic attributes, Pattern recognition, lcsh:QA1-939, Chemistry (miscellaneous), NIST, 020201 artificial intelligence & image processing, Artificial intelligence, experimental combustion material, business
Abstract

To improve fire prediction accuracy over existing methods, a double weighted naive Bayes with compensation coefficient (DWCNB) method is proposed for fire prediction purposes. The fire characteristic attributes and attribute values are all weighted to weaken the assumption that the naive Bayes attributes are independent and equally important. A compensation coefficient was used to compensate for the prior probability, and a five-level orthogonal testing method was employed to properly design the coefficient. The proposed model was trained with data collected from the National Institute of Standards and Technology (NIST) fire database. Simulation comparisons show that the average prediction accuracy of the proposed method is 98.13%, which is 5.08% and 2.52% higher than the methods of naive Bayes (NB) and double weighted naive Bayes (DWNB), respectively. The experimental results show that the average accuracies of the DWCNB method for test fire and interference sources were 97.76% and 98.24%. Prediction accuracies were 5.06% and 3.74% higher than those of the NB and DWNB methods.

Published
2021

35. Polymer-Clad Silicon on Insulator Slot Modulator

Author

Chen, Xi, primary, Espinoza, David S., additional, Dudley, Eric F., additional, Li, Zheng, additional, Mohamed, Moustafa, additional, Cui, Yonghao, additional, Park, Wounjhang, additional, Shang, Li, additional, and Mickelson, Alan R., additional

Published
2011
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47. Aerobic acetone-butanol-isopropanol (ABI) fermentation through a co-culture of Clostridium beijerinckiiG117 and recombinant Bacillus subtilis1A1

Author

Cui, Yonghao, He, Jianzhong, Yang, Kun-Lin, and Zhou, Kang

Abstract

An engineered B. subtilis1A1 strain (BsADH2) expressing a secondary alcohol dehydrogenase (CpSADH) was co-cultured with C. beijerinckiiG117 under an aerobic condition. During the fermentation on glucose, B. subtilisBsADH2 depleted oxygen in culture media completely and created an anaerobic environment for C. beijerinckiiG117, an obligate anaerobe, to grow. Meanwhile, lactate produced by B. subtilisBsADH2 was re-assimilated by C. beijerinckiiG117. In return, acetone produced by C. beijerinckiiG117 was reduced into isopropanol by B. subtilisBsADH2 via expressing the CpSADH, which helped maintain the redox balance of the engineered B. subtilis. In the symbiotic system consisting of two strains, 1.7 ​g/L of acetone, 4.8 ​g/L of butanol, and 0.9 ​g/L of isopropanol (with an isopropanol/acetone ratio of 0.53) was produced from 60 ​g/L of glucose. This symbiotic system also worked when oxygen was supplied to the culture, although less isopropanol was produced (0.9 ​g/L of acetone, 4.9 ​g/L of butanol, and 0.2 ​g/L of isopropanol). The isopropanol titer was increased substantially to 2.5 ​g/L when we increased the inoculum size of B. subtilisBsADH2 and optimized other process parameters. With the Bacillus-Clostridiumco-culture, switching from the original acetone-butanol (AB) fermentation to an aerobic acetone-butanol-isopropanol (ABI) fermentation can be easily achieved without genetic engineering of Clostridium. This strategy of employing a recombinant Bacillusto co-culture with Clostridiumshould be potentially useful to modify traditional acetone-butanol-ethanol fermentation for the production of other value-added chemicals.

Published
2020
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