The Experts below are selected from a list of 600612 Experts worldwide ranked by ideXlab platform
Abduljalil A. Al-abidi - One of the best experts on this subject based on the ideXlab platform.
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Artificial neural network analysis of liquid desiccant regenerator performance in a solar hybrid air-conditioning system
Sustainable Energy Technologies and Assessments, 2013Co-Authors: Abdulrahman Th. Mohammad, Sohif Mat, M. Y. Sulaiman, Kamaruzzaman Sopian, Abduljalil A. Al-abidiAbstract:Abstract In this paper, experimental tests are carried out to investigate the performance of a counter flow regenerator using lithium chloride (LiCl) solution as the desiccant. A single and multilayer artificial neural network (ANN) is used to predict the performance of the regenerator. Five parameters are used as inputs to the ANN, namely: air and desiccant flow rates, air inlet humidity ratio, and air and desiccant inlet temperatures. The outputs of the ANN are the temperature, humidity ratio, moisture removal rate (MRR), and the effectiveness. ANN predictions for these parameters are compared with the experimental values. The results show that the optimum Testing Model for MRR in the regenerator was the 5-5-5-1 structure with R2 = 0.93, whereas the optimum Testing Model for effectiveness was the 5-11-1 structure with R2 = 0.95. The maximum temperature and humidity ratio difference between the ANN Model and experimental are 1.4 °C and 2.1 g/kg, respectively. The MRR and effectiveness of regenerator increase slowly as function of air inlet temperature. It was found that the MRR and effectiveness increased about 0.79% and 1.1%, respectively. The moisture removal rate decreased with increasing air inlet humidity ratio and increased with desiccant inlet temperature.
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Artificial neural network analysis of liquid desiccant dehumidifier performance in a solar hybrid air-conditioning system
Applied Thermal Engineering, 2013Co-Authors: Abdulrahman Th. Mohammad, Sohif Mat, M. Y. Sulaiman, Kamaruzzaman Sopian, Abduljalil A. Al-abidiAbstract:Abstract A new solar hybrid liquid desiccant air conditioning system has been tested and simulated to investigate the technical feasibility of cooling systems for greenhouse applications using weather data for Malaysia. In this paper, experimental tests are carried out to investigate the performance of a counter flow dehumidifier using lithium chloride (LiCl) solution as the desiccant. A single and multilayer artificial neural network is used to predict the performance of the dehumidifier. Five parameters are used as inputs to the ANN, namely: air and desiccant flow rates, air inlet humidity ratio, and air and desiccant inlet temperatures. The outputs of the ANN are the temperature, humidity ratio, moisture removal rate, and the effectiveness. ANN predictions for these parameters are compared with the experimental values. The results show that the optimum Testing Model for moisture removal rate in the dehumidifier was the 5-5-5-1 structure with R 2 = 0.91, whereas the optimum Testing Model for effectiveness was the 5-11-11-1 structure with R 2 = 0.79. The maximum temperature and humidity ratio difference between the ANN Model and experimental are 1.2 °C and 1.9 g/kg, respectively.
Zhao Hong-bin - One of the best experts on this subject based on the ideXlab platform.
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Object-oriented Web Application Testing Model
Computer Science, 2010Co-Authors: Zhao Hong-binAbstract:In order to guarantee Web quality and reliability,people attach more and more importance to Web application Testing. Based on good analysis and understanding to Web application,good Testing Models and methods can be put forward so as to test Web application effectively.An object-oriented Web application Testing Model and Testing methods were offered so as to support navigation Testing and state Testing.
Gu Xi-qian - One of the best experts on this subject based on the ideXlab platform.
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Behavior-based software Testing process Model and its application
Journal of Computer Applications, 2007Co-Authors: Gu Xi-qianAbstract:On the basis of a thorough analysis of the software integration Testing technique,compared with the common software Testing Model,a new behavior-based software Testing process Model was proposed.It covered the whole Testing process including test design,test plan,test case generation,test execution,test results analysis and reTesting.These activities were merged into the lifecycle process of software development.The behavior-based Testing Model was applied to a software Testing project of large application software called American On-Line Media Player(AMP) project and the result that the behavior-based software Testing Model has some advantages in digging the early bugs and efficiency of regression Testing was obtained.
Abdulrahman Th. Mohammad - One of the best experts on this subject based on the ideXlab platform.
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Artificial neural network analysis of liquid desiccant regenerator performance in a solar hybrid air-conditioning system
Sustainable Energy Technologies and Assessments, 2013Co-Authors: Abdulrahman Th. Mohammad, Sohif Mat, M. Y. Sulaiman, Kamaruzzaman Sopian, Abduljalil A. Al-abidiAbstract:Abstract In this paper, experimental tests are carried out to investigate the performance of a counter flow regenerator using lithium chloride (LiCl) solution as the desiccant. A single and multilayer artificial neural network (ANN) is used to predict the performance of the regenerator. Five parameters are used as inputs to the ANN, namely: air and desiccant flow rates, air inlet humidity ratio, and air and desiccant inlet temperatures. The outputs of the ANN are the temperature, humidity ratio, moisture removal rate (MRR), and the effectiveness. ANN predictions for these parameters are compared with the experimental values. The results show that the optimum Testing Model for MRR in the regenerator was the 5-5-5-1 structure with R2 = 0.93, whereas the optimum Testing Model for effectiveness was the 5-11-1 structure with R2 = 0.95. The maximum temperature and humidity ratio difference between the ANN Model and experimental are 1.4 °C and 2.1 g/kg, respectively. The MRR and effectiveness of regenerator increase slowly as function of air inlet temperature. It was found that the MRR and effectiveness increased about 0.79% and 1.1%, respectively. The moisture removal rate decreased with increasing air inlet humidity ratio and increased with desiccant inlet temperature.
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Artificial neural network analysis of liquid desiccant dehumidifier performance in a solar hybrid air-conditioning system
Applied Thermal Engineering, 2013Co-Authors: Abdulrahman Th. Mohammad, Sohif Mat, M. Y. Sulaiman, Kamaruzzaman Sopian, Abduljalil A. Al-abidiAbstract:Abstract A new solar hybrid liquid desiccant air conditioning system has been tested and simulated to investigate the technical feasibility of cooling systems for greenhouse applications using weather data for Malaysia. In this paper, experimental tests are carried out to investigate the performance of a counter flow dehumidifier using lithium chloride (LiCl) solution as the desiccant. A single and multilayer artificial neural network is used to predict the performance of the dehumidifier. Five parameters are used as inputs to the ANN, namely: air and desiccant flow rates, air inlet humidity ratio, and air and desiccant inlet temperatures. The outputs of the ANN are the temperature, humidity ratio, moisture removal rate, and the effectiveness. ANN predictions for these parameters are compared with the experimental values. The results show that the optimum Testing Model for moisture removal rate in the dehumidifier was the 5-5-5-1 structure with R 2 = 0.91, whereas the optimum Testing Model for effectiveness was the 5-11-11-1 structure with R 2 = 0.79. The maximum temperature and humidity ratio difference between the ANN Model and experimental are 1.2 °C and 1.9 g/kg, respectively.
K S Chen - One of the best experts on this subject based on the ideXlab platform.
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developing one sided specification six sigma fuzzy quality index and Testing Model to measure the process performance of fuzzy information
International Journal of Production Economics, 2019Co-Authors: K S Chen, Ching Hsin Wang, Shun Fung ChiuAbstract:Abstract Depending on the quality characteristic, a process capability index (PCI) can be used for one-sided specifications or for bilateral specifications. A number of researchers have investigated the statistical properties of one-sided specification indices and proposed methods for applications. The later introduction of the Six Sigma approach also assisted many firms in effectively enhancing their production capacities, reducing waste, and increasing effectiveness. Chen et al. (2017a) modified the PCI for one-sided specifications and proposed the Six Sigma Quality Index (SSQI), which coincidently equals the quality level and has a one-to-one relationship with yield. However, uncertainty in quality characteristic measurements is common in practice, which can lead to judgment errors in conventional process capability assessment methods. This study therefore developed an SSQI for one-sided specifications based on the fuzzy Testing method created by Buckley (2005) and developed a Six Sigma fuzzy evaluation index and Testing Model. In addition to having a simpler calculation procedure, the Model takes the process capability and Six Sigma quality level into consideration and can process the uncertainties in the data to make it more convenient for the industry to solve engineering issues. Finally, we presented a practical example to demonstrate the applications. The Model proposed in this study can provide the industry with a practical approach to assess process quality in a fuzzy environment.
