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International Conference on Computer and Automation Engineering, ICCAE

作者： Muhammad Naeim Mohd Aris Shalini Nagaratnam Nurul Nnadiah Zakaria Muhammad Fadhirul Anuar Mohd Azami Muhammad Afiq Ikram Samsudin Ernee Sazlinayati Othman Department of American Degree Transfer Program School of American Education Sunway University Bandar Sunway Malaysia Department of Econometrics and Business Statistics School of Business Monash University Malaysia Bandar Sunway Malaysia Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Seri Iskandar Malaysia Department of American Degree Transfer Programme School of American Education Sunway University Bandar Sunway Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Seremban 3 Malaysia Centre of Foundation Studies Universiti Teknologi MARA Cawangan Selangor Kampus Dengkil Dengkil Malaysia

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

This study explores and compares the capability of Gaussian process machine learning (GPML) with time series analysis techniques, which are autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) and cubic spline interpolation, in modeling unemployment rate in Malaysia over the period of 1991 to 2022. Six predictive models are developed based on the observations. The predictive performance of each model is quantified using mean absolute error (MAE) and mean squared error (MSE). GPML demonstrated the best predictive model, exhibiting minimal MAE and MSE compared to the time series analysis techniques. The statistical analysis also concluded no significant mean difference between the GPML model and the actual observations, implying a robust predictive model in predicting the unemployment rate in Malaysia.

关键词： Analytical models Interpolation Statistical analysis Time series analysis Gaussian processes Machine learning Predictive models

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Improving the Energy Consumption of the Binary Azeotropic Separation of Dichloromethane/Methanol: Process Design, Control, and Intensification

SSRN

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SSRN 2022年

作者： Kong, Zong Yang Yang, Ao Segovia-Hernández, Juan Gabriel Putranto, Aditya Sunarso, Jaka Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia College of Safety Engineering Chongqing University of Science & Technology Chongqing401331 China Universidad de Guanajuato División de Ciencias Naturales y Exactas Departamento de Ingeniería Química Campus Guanajuato Noria Alta s/n Gto Guanajuato36050 Mexico Discipline of Chemical Engineering School of Engineering Monash University Malaysia Bandar Sunway Selangor 47500 Malaysia

Here we explored the advantages and drawbacks of the different energy-intensified extractive distillation-based processes, i.e. side-stream extractive distillation (SSED), thermally coupled extractive distillation (TCED), and extractive dividing wall column (EDWC), for the binary azeotropic separation of dichloromethane and methanol. The EDWC was found to give the best improvement in terms of energy consumption, total annual cost (TAC), and CO 2 emissions by about 21%, 18%, and 21%, relative to conventional extractive distillation (CED). These however were traded-off by the increase in conditional number (CN) by 186 times, signifying a complex dynamic characteristic for the EDWC. Thus, the SSED was suggested as an alternative option as it provides identical improvement in energy consumption, TAC, and CO 2 emissions as those of EDWC, albeit to a lower extent by about 18%, 12%, and 18%, relative to the CED, whilst providing the least drawback on the operational controllability, as evidenced by the marginal increase in the CN by about 1.5 times. We also investigated the dynamic performance of the SSED and found that the SSED provides identical dynamic performance in handling both ± 10% feed flowrate and ± 5% feed composition disturbances as those of CED. © 2022, The Authors. All rights reserved.

关键词： Dichloromethane

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Employing derivatives for creating texture masks for digital images

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AIP Conference Proceedings 2024年第1期2898卷

作者： Taha Basheer Taha Ruzelita Ngadiran Dujan B. Taha IT Department Faculty of Applied Science Tishk International University Erbil Kurdistan Region Iraq Faculty of Electronic Engineering Technology Universiti Malaysia Perlis 02600 Arau Perlis Malaysia Centre of Excellence Advanced Computing (AdvComp) Universiti Malaysia Perlis 02600 Arau Perlis Malaysia Software Engineering Department College of Computer Science and Mathematics Mosul University Mosul Iraq

Texture masking has been used as a significant factor in creating perceptual maps that simulates human vision system in observing noise in digital images. In this paper, the possibility of creating texture masks using first and second derivatives is investigated and tested. Results shows that after applying the derivatives, areas of higher texture were highlighted and recognized, which will lead to simple implementation of texture masks using numerical analysis.

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Adsorption kinetics of amoxicillin, ampicillin, and doripenem on organobentonite

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AIP Conference Proceedings 2024年第1期3073卷

作者： Jason Yi Juang Yeo Aqsha Aqsha Suryadi Ismadji Jaka Sunarso Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Kuching 93350 Sarawak Malaysia Department of Bioenergy and Chemurgy Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Jawa Barat Indonesia Department of Chemical Engineering Widya Mandala Surabaya Catholic University Kalijudan 37 Surabaya 60114 Indonesia

The frequent discharge of antibiotic compounds to the environment has accelerated the emergence and spread of superbugs, causing a global public health crisis. Clay material such as bentonite represents a promising solution for removing antibiotics from wastewater given its high porosity, abundance, and environmental compatibility. Here, the adsorption kinetics of amoxicillin, ampicillin, and doripenem on an organobentonite modified with chitosan are studied across temperature range of 30-50 oC. The kinetic data were correlated by various kinetic models such as pseudo-first-order (PFO), pseudo-second-order (PSO), Elovich, and intra-particle diffusion. PSO was shown to provide the most accurate representation for the adsorption kinetics of all three antibiotics at 30-50 oC, whereas the fitted parameters such as mass ratio of adsorbed adsorbate to adsorbent (mg g-1) and adsorption rate (g mg-1 min-1) were given as 35.538-53.569 mg g-1 and 5.263×10-3-6.056×10-3 g mg-1 min-1 for amoxicillin, respectively; 46.688-55.869 mg g-1 and 5.098×10-3-6.886×10-3 g mg-1 min-1 for ampicillin, respectively; and 54.032-59.606 mg g-1 and 6.262×10-3-6.709×10-3 g mg-1 min-1 for doripenem, respectively. In addition, multiple rate-controlling steps were also indicated in the correlation using intra-particle diffusion model.

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Kurdish end-to-end speech synthesis using deep neural networks

Natural Language Processing Journal

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Natural Language Processing Journal 2024年 8卷

作者： Sabat Salih Muhamad Hadi Veisi Aso Mahmudi Abdulhady Abas Abdullah Farhad Rahimi Computer Science Department Faculty of Science Soran University Soran Kurdistan Iraq College of Interdisciplinary Science and Technologies University of Tehran Tehran Iran School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Melbourne Australia Artificial Intelligence and Innovation Centre University of Kurdistan Hewlêr Erbil Kurdistan Region Iraq Department of Computer Engineering Faculty of Engineering and agriculture Islamic Azad University Arak Branch Arak Iran

This article introduces an end-to-end text-to-speech (TTS) system for the low-resourced language of Central Kurdish (CK, also known as Sorani) and tackles the challenges associated with limited data availability. We have compiled a dataset suitable for end-to-end text-to-speech that includes 21 h of CK female voice paired with corresponding texts. To identify the optimal performing system, we employed Tacotron2, an end-to-end deep neural network for speech synthesis, in three training experiments. The process involves training Tacotron2 using a pre-trained English system, followed by training two models from scratch with full and intonationally balanced datasets. We evaluated the effectiveness of these models using Mean Opinion Score (MOS), a subjective evaluation metric. Our findings demonstrate that the model trained from scratch on the full CK dataset surpasses both the model trained with the intonationally balanced dataset and the model trained using a pre-trained English model in terms of naturalness and intelligibility by achieving a MOS of 4.78 out of 5.

关键词： Central Kurdish language Deep learning Speech synthesis Tacotron2 End-to-end WaveGlow vocoder

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Green hydrogen production from oil palm wastes: A techno-economic and environmental perspective

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Energy Conversion and Management 2025年 341卷

作者： Zong Yang Kong Tiffany Jia Ning Ang Hwai Chyuan Ong Tao Shi Chien Ing Yeo Ao Yang School of Engineering Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway 47500 Selangor Darul Ehsan Malaysia Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga 93350 Kuching Sarawak Malaysia School of Civil and Environmental Engineering Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia School of Chemistry and Chemical Engineering Chongqing University Chongqing 400044 PR China Sunway Biofunctional Molecules Discovery Centre School of Medical and Life Sciences Sunway University No. 5 Jalan Universiti Bandar Sunway 47500 Selangor Darul Ehsan Malaysia College of Safety Science and Engineering Chongqing University of Science and Technology Chongqing 401331 PR China

As the second-largest producer of palm oil globally, the palm oil industry in Malaysia generates substantial amount of oil palm wastes. These wastes can emit harmful gases and lead to environmental issues. To repurpose the abundantly available palm oil wastes, this study aims to conduct a preliminary techno-economic and environmental analysis via both experimental and simulation study to compare hydrogen production. Four different types of oil palm wastes using biomass gasification are investigated, i.e., oil palm fronds, empty fruit bunches, oil palm trunks, and palm kernel shells. In-house experimental work was conducted to determine the proximate and ultimate composition of these feedstocks, and these data are subsequently input into Aspen Plus for simulation, techno-economic, and environmental analysis. Overall, all four cases can recover the total capital cost within 19 years, with oil palm trunk feedstock being the most superior. It was found that oil palm trunks resulted in the highest hydrogen yield of 118.65 kg h −1 and possessed superior economic performance (i.e., lowest payback period of 10.78 years and highest net present value of 8.31 million USD). In terms of the environmental impact, the use of oil palm trunks contributed to the lowest global warming potential of 1.95 kg CO 2 -eq kg −1 H 2 . This demonstrated the environmental benefit of using the waste gas from the biomass gasification process to generate electricity for the upstream demand. On the contrary, the empty fruit bunches case resulted in the least satisfactory economic and environmental performance. Overall, this work provides insights into the economic feasibility and environmental impacts of using various oil palm wastes for hydrogen production through gasification to address the environmental issues caused by the underutilized oil palm wastes.

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Complementary role of large language models in educating undergraduate design of distillation column: Methodology development

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Digital Chemical Engineering 2023年 9卷

作者： Kong, Zong Yang Adi, Vincentius Surya Kurnia Segovia-Hernández, Juan Gabriel Sunarso, Jaka Department of Engineering School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Department of Chemical Engineering National Chung Hsing University Taichung 40227 Taiwan Universidad de Guanajuato Campus Guanajuato División de Ciencias Naturales y Exactas Departamento de Ingeniería Química Noria Alta s/n Gto Guanajuato 36050 Mexico Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching 93350 Malaysia

This paper explores the integration of large language models (LLMs), such as ChatGPT, in chemical engineering education, departing from conventional practices that may not be universally accepted. While there is ongoing debate surrounding the acceptance of LLMs, driven by concerns over computational instability and potential inconsistencies, their inevitability in shaping our communication and interaction with technology cannot be ignored. As educators, we are positioned to play a vital role in guiding students toward the responsible, effective, and synergetic use of LLMs. Focusing specifically on distillation column design in undergraduate mass-transfer courses, this study demonstrates how ChatGPT can be utilized as an auxiliary tool to create interactive learning environments and simulate real-world engineering thinking processes. It emphasizes the need for students to develop critical thinking skills and a thorough understanding of LLM principles, taking responsibility for their use and creations. While ChatGPT should not be solely relied upon, its integration with fundamental principles of chemical engineering is crucial. The effectiveness and limitations of ChatGPT are exemplified through two case studies, showcasing the importance of manual calculations and established simulation software as primary tools for guiding and validating engineering results and analyses. This paper also addresses the pedagogical implications of integrating LLMs into mass transfer courses, encompassing curriculum integration, facilitation, guidance, and ethical considerations. Recommendations are provided for incorporating LLMs effectively into the curriculum. Overall, this study contributes to the advancement of chemical engineering education by examining the benefits and limitations of LLMs as educational aids in the design process. © 2023 The Author(s)

关键词： ChatGPT Chemical engineering education Distillation Industry 4.0 Large language models Mass transfer

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A Deep Learning Network for the Classification of Intracardiac Electrograms in Atrial Tachycardia

arXiv

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arXiv 2022年

作者： Chen, Zerui Teo, Sonia Xhyn Ochtman, Andrie Saw, Shier Nee Cheng, Nicholas Lim, Eric Tien Siang Lyu, Murphy Lee, Hwee Kuan Singapore Singapore Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Cardiology National Heart Centre Singapore Singapore Singapore Abbott Medical Singapore Singapore School of Computing National University of Singapore Singapore Singapore Singapore Singapore Singapore Singapore Rehabilitation Research Institute of Singapore Singapore Singapore Singapore Institute for Clinical Sciences Singapore Singapore

A key technology enabling the success of catheter ablation treatment for atrial tachycardia is activation mapping, which relies on manual local activation time (LAT) annotation of all acquired intracardiac electrogram (EGM) signals. This is a time-consuming and error-prone procedure, due to the difficulty in identifying the signals’ activation peaks for fractionated signals. This work presents a Deep Learning approach for the automated classification of EGM signals into three different types - "normal", "abnormal", and "unclassified", which forms part of the LAT annotation pipeline, and contributes towards bypassing the need for manual annotations of the LAT. The Deep Learning network, the CNN-LSTM model, is a hybrid network architecture which combines convolutional neural network (CNN) layers with long short-term memory (LSTM) layers. 1452 EGM signals from a total of 9 patients undergoing clinically-indicated 3D cardiac mapping were used for the training, validation and testing of our models. From our findings, the CNN-LSTM model achieved an accuracy of 81% for the balanced dataset. For comparison, we separately developed a rule-based Decision Trees model which attained an accuracy of 67% for the same balanced dataset. Our work elucidates that analysing the EGM signals using a set of explicitly specified rules as proposed by the Decision Trees model is not suitable as EGM signals are complex. The CNN-LSTM model, on the other hand, has the ability to learn the complex, intrinsic features within the signals and identify useful features to differentiate the EGM signals. © 2022, CC BY.

关键词： Decision trees

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Nonequilibrium reaction rate theory: Formulation and implementation within the hierarchical equations of motion approach

arXiv

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arXiv 2022年

作者： Ke, Yaling Kaspar, Christoph Erpenbeck, André Peskin, Uri Thoss, Michael Institute of Physics Albert-Ludwig University Freiburg Hermann-Herder-Strasse 3 Freiburg79104 Germany Department of Physics University of Michigan Ann ArborMI48109 United States Schulich Faculty of Chemistry Technion-Israel Institute of Technology Haifa32000 Israel EUCOR Centre for Quantum Science and Quantum Computing Albert-Ludwig University Freiburg Hermann-Herder-Strasse 3 Freiburg79104 Germany

The study of chemical reactions in environments under nonequilibrium conditions has been of interest recently in a variety of contexts, including current-induced reactions in molecular junctions and scanning tunneling microscopy experiments. In this work, we outline a fully quantum mechanical, numerically exact approach to describe chemical reaction rates in such nonequilibrium situations. The approach is based on an extension of the flux correlation function formalism to nonequilibrium conditions and uses a mixed real and imaginary time hierarchical equations of motion approach for the calculation of rate constants. As a specific example, we investigate current-induced intramolecular proton transfer reactions in a molecular junction for different applied bias voltages and molecule-lead coupling strengths. © 2022, CC BY.

关键词： Equations of motion

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Towards a Deadline-Based Simulation Experimentation Framework Using Micro-Services Auto-Scaling Approach

Towards a Deadline-Based Simulation Experimentation Framewor...

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2019 Winter Simulation Conference, WSC 2019

作者： Anagnostou, Anastasia Taylor, Simon J. E. Tijjani Abubakar, Nura Kiss, Tamas Deslauriers, James Gesmier, Gregoire Terstyanszky, Gabor Kacsuk, Peter Kovacs, Jozsef Brunel University London Modelling Simulation Group UxbridgeUB8 3PH United Kingdom University of Westminster Centre for Parallel Computing Faculty of Science and Technology LondonW1W 6UW United Kingdom Hungarian Academy of Science Budapest1111 Hungary

ISBN: (纸本)9781728132839

There is growing number of research efforts in developing auto-scaling algorithms and tools for cloud resources. Traditional performance metrics such as CPU, memory and bandwidth usage for scaling up or down resources are not sufficient for all applications. For example, modeling and simulation experimentation is usually expected to yield results within a specific timeframe. In order to achieve this, often the quality of experiments is compromised either by restricting the parameter space to be explored or by limiting the number of replications required to give statistical confidence. In this paper, we present early stages of a deadline-based simulation experimentation framework using a micro-services auto-scaling approach. A case study of an agent-based simulation of a population physical activity behavior is used to demonstrate our framework. © 2019 IEEE.

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