Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Norhaliza Abdul Wahab"'
Autor:
Erlianasha Samsuria, Mohd Saiful Azimi Mahmud, Norhaliza Abdul Wahab, Muhammad Zakiyullah Romdlony, Mohamad Shukri Zainal Abidin, Salinda Buyamin
Publikováno v:
Applications of Modelling and Simulation, Vol 8, Pp 225-238 (2024)
In a highly automated Flexible Manufacturing System (FMS), optimal utilization and scheduling of resources and equipment are paramount. This necessity underpins the full utilization of automation capabilities, leading to increased productivity and mi
Externí odkaz:
https://doaj.org/article/53076dac13af44acaa17ff5c32cade3c
Autor:
Syahira Ibrahim, Norhaliza Abdul Wahab
Publikováno v:
Water Science and Technology, Vol 89, Iss 7, Pp 1701-1724 (2024)
Hyperparameter tuning is an important process to maximize the performance of any neural network model. This present study proposed the factorial design of experiment for screening and response surface methodology to optimize the hyperparameter of two
Externí odkaz:
https://doaj.org/article/c8bb7ab8ab2345c4b2debbad56d53d08
Publikováno v:
International Islamic University Malaysia Engineering Journal, Vol 24, Iss 1 (2023)
The pH neutralization process is a highly non-linear process and time delay system that is difficult to control and to accurately model mathematically. Therefore, the empirical method, which needs reliable experimental data to represent the process d
Externí odkaz:
https://doaj.org/article/1e34b6a62f5e49cfaf4ef759dbe60cef
Publikováno v:
Membranes, Vol 13, Iss 3, p 285 (2023)
Water is one of the main sources of life’s survival. It is mandatory to have good-quality water, especially for drinking. Many types of available filtration treatment can produce high-quality drinking water. As a result, it is intriguing to determi
Externí odkaz:
https://doaj.org/article/97fbcdd039ff4adfbcd202ee41d087e9
Autor:
Syahira Ibrahim, Norhaliza Abdul Wahab
Publikováno v:
Membranes, Vol 12, Iss 8, p 726 (2022)
This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural ne
Externí odkaz:
https://doaj.org/article/0603cffb851549838ad7c57a3d46f1fe
Autor:
Nur Sakinah Ahmad Yasmin, Norhaliza Abdul Wahab, Fatimah Sham Ismail, Mu’azu Jibrin Musa, Mohd Hakim Ab Halim, Aznah Nor Anuar
Publikováno v:
Membranes, Vol 11, Iss 8, p 554 (2021)
Support vector regression (SVR) models have been designed to predict the concentration of chemical oxygen demand in sequential batch reactors under high temperatures. The complex internal interaction between the sludge characteristics and their influ
Externí odkaz:
https://doaj.org/article/de8bcf7e52e4478a98e95d3a333c11b9
Publikováno v:
Sensors & Transducers, Vol 153, Iss 6, Pp 200-208 (2013)
The signal conditioning circuit of the electrostatic sensor is a small signal amplifier that collects and amplifies the detected electrostatic noise. Due to its high amplification properties, the output signal is highly susceptible to be defaced by i
Externí odkaz:
https://doaj.org/article/7533e0ed742d491a88ce01d24c48d689
Publikováno v:
Bulletin of Electrical Engineering and Informatics. 9:2278-2285
Two main challenges in activated sludge wastewater treatment plant (WWTP) are cost and effluent quality, which has forced the wastewater treatment operator to find an alternative to improve the existing control strategy. The Benchmark Simulation Mode
Publikováno v:
Bulletin of Electrical Engineering and Informatics. 9:1835-1843
Aerobic granular sludge (AGS) is one of the treatment methods often used in wastewater systems. The dynamic behavior of AGS is complex and hard to predict especially when it comes to a limited data set. Theoretically, support vector machine (SVM) is
Publikováno v:
Lecture Notes in Electrical Engineering ISBN: 9789811939228
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec61aed6a228bbb54c6dedb6e869db16
https://doi.org/10.1007/978-981-19-3923-5_46
https://doi.org/10.1007/978-981-19-3923-5_46