| 1. |
- Masrur Hossain, M., et al.
(författare)
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Analysis and optimization of a modified Kalina cycle system for low-grade heat utilization
- 2021
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Ingår i: Energy Conversion and Management. - : Elsevier Ltd. - 2590-1745. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Kalina cycle system (KCS) offers an attractive prospect to produce power by utilizing low-grade heat sources where traditional power cycles cannot be implemented. Intending to explore the potential of exploiting low-grade heat sources for conversion to electrical energy, this study proposes two modified power generation cycles based on KCS-34. A multi-phase expander is positioned between the Kalina separator and the second heat regenerator in the proposed X-modification. In contrast, it is located between the mixer and second regenerator for Y-modification. To explore the potential benefits and limitations of the proposed modifications contrasted with the KCS-34, thermodynamic modeling and optimization have been conducted. The influence of critical decision parameters on overall cycle performance is analyzed. The result elucidates that by implementing an additional multi-phase expander, a significant amount of energy can be extracted from a lean ammonia water loop and X-modification can deliver superior thermodynamic performance compared with the Y-modification and the original KCS-34. With a reduced turbine inlet pressure of 58 bar and an ammonia concentration of 80%, the X-modified cycle's efficiency reaches a peak value of 17% and a net power yield of 1015 kW. An increase of 6.35% can be achieved compared with the conventional KCS-34 operating at the same conditions. Maximum exergy destruction of the working substance was observed in the condenser.
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| 2. |
- Ahmed, B. S., et al.
(författare)
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Optimum design of PIλDμ controller for an automatic voltage regulator system using combinatorial test design
- 2016
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 11:11
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Tidskriftsartikel (refereegranskat)abstract
- Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly. © 2016 Ahmed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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| 3. |
- Najib, Muhammad Sharfi, et al.
(författare)
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Agarwood classification: A Case-Based Reasoning approach based on E-nose
- 2012
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Ingår i: Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, CSPA 2012. - 9781467309615 ; , s. 120-126
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Konferensbidrag (refereegranskat)abstract
- Using an array of sensors (E-nose) to classify Agarwood has proven to be successful and produced performance close to an expert level (90% of expert level performance) but it has proven difficult to eliminate misclassifications without over-fitting. In our effort to improve our result we explored a self-improving Case-Based Reasoning approach and reached 100% correct classification. Case-Based Reasoning is an approach that will learn from every new classified case and hence the risk for misclassification is reduced. Also when new cases have to be classified that have never occurred before the system will avoid misclassification (similarity measurement is low). The approach also enables indeterminism; in reality a sample may be both close to a good case and a bad case and need further exploration by experts. The approach also handles natural variants in the wood samples well; both low-quality and high-quality samples may spread considerably in the context of E-nose readings and there is no model available of low or high quality.
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| 4. |
- Saif-ul-Allah, M. W., et al.
(författare)
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Gated Recurrent Unit Coupled with Projection to Model Plane Imputation for the PM2.5 Prediction for Guangzhou City, China
- 2022
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Ingår i: Frontiers in Environmental Science. - : Frontiers Media S.A.. - 2296-665X. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Air pollution is generating serious health issues as well as threats to our natural ecosystem. Accurate prediction of PM2.5 can help taking preventive measures for reducing air pollution. The periodic pattern of PM2.5 can be modeled with recurrent neural networks to predict air quality. To the best of the author’s knowledge, very limited work has been conducted on the coupling of missing value imputation methods with gated recurrent unit (GRU) for the prediction of PM2.5 concentration of Guangzhou City, China. This paper proposes the combination of project to model plane (PMP) with GRU for the superior prediction performance of PM2.5 concentration of Guangzhou City, China. Initially, outperforming the missing value imputation method PMP is proposed for air quality data under consideration by making a comparison study on various methods such as KDR, TSR, IA, NIPALS, DA, and PMP. Secondly, it presents GRU in combination with PMP to show its superiority on other machine learning techniques such as LSSVM and two other RNN variants, LSTM and Bi-LSTM. For this study, data for Guangzhou City were collected from China’s governmental air quality website. Data contained daily values of PM2.5, PM10, O3, SOx, NOx, and CO. This study has employed RMSE, MAPE, and MEDAE as model prediction performance criteria. Comparison of prediction performance criteria on the test data showed GRU in combination with PMP has outperformed the LSSVM and other RNN variants LSTM and Bi-LSTM for Guangzhou City, China. In comparison with prediction performance of LSSVM, GRU improved the prediction performance on test data by 40.9% RMSE, 48.5% MAPE, and 50.4% MEDAE.
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| 5. |
- Ahmed, Bestoun S., 1982-, et al.
(författare)
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Handling constraints in combinatorial interaction testing in the presence of multi objective particle swarm and multithreading
- 2017
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Ingår i: Information and Software Technology. - : Elsevier. - 0950-5849 .- 1873-6025. ; 86, s. 20-36
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Tidskriftsartikel (refereegranskat)abstract
- Context: Combinatorial testing strategies have lately received a lot of attention as a result of their diverse applications. In its simple form, a combinatorial strategy can reduce several input parameters (configurations) of a system into a small set based on their interaction (or combination). In practice, the input configurations of software systems are subjected to constraints, especially in case of highly configurable systems. To implement this feature within a strategy, many difficulties arise for construction. While there are many combinatorial interaction testing strategies nowadays, few of them support constraints. Objective: This paper presents a new strategy, to construct combinatorial interaction test suites in the presence of constraints. Method: The design and algorithms are provided in detail. To overcome the multi-judgement criteria for an optimal solution, the multi-objective particle swarm optimisation and multithreading are used. The strategy and its associated algorithms are evaluated extensively using different benchmarks and comparisons. Results: Our results are promising as the evaluation results showed the efficiency and performance of each algorithm in the strategy. The benchmarking results also showed that the strategy can generate constrained test suites efficiently as compared to state-of-the-art strategies. Conclusion: The proposed strategy can form a new way for constructing of constrained combinatorial interaction test suites. The strategy can form a new and effective base for future implementations. (C) 2017 Elsevier B.V. All rights reserved.
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| 6. |
- Degas, A., et al.
(författare)
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A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management : Current Trends and Development with Future Research Trajectory
- 2022
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:3
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Forskningsöversikt (refereegranskat)abstract
- Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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| 7. |
- Hao, D., et al.
(författare)
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Solar energy harvesting technologies for PV self-powered applications : A comprehensive review
- 2022
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Ingår i: Renewable energy. - : Elsevier Ltd. - 0960-1481 .- 1879-0682. ; 188, s. 678-697
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Tidskriftsartikel (refereegranskat)abstract
- Many key aspects of society, such as transport, housing and health care, have been significantly improved by the advent of a range of electricity applications, and the power generation for electricity applications has been a major field of research. Photovoltaic (PV) self-powered technologies are promising technologies for addressing applications' power supply challenges and alleviating conventional electricity load and environmental pollution. This study reviews solar energy harvesting (SEH) technologies for PV self-powered applications. First, the PV power generation and scenarios of PV self-powered applications are analyzed. Second, analysis of system design for PV self-powered applications is presented. Third, key components for PV self-powered applications, including maximum power point tracking (MPPT) techniques and power management (PM) systems are discussed in detail. Furthermore, numerous PV self-powered applications and utilizations of energy harvesting are summarized. Finally, some recommendations are proposed for further research.
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| 8. |
- Sakao, T., et al.
(författare)
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AI-LCE : Adaptive and Intelligent Life Cycle Engineering by applying digitalization and AI methods-An emerging paradigm shift in Life Cycle Engineering
- 2021
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Ingår i: Procedia CIRP. - : Elsevier B.V.. - 2212-8271. ; , s. 571-576
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Konferensbidrag (refereegranskat)abstract
- This paper presents a vision for a much-needed paradigm shift in Life Cycle Engineering (LCE), which is termed Adaptive and Intelligent Life Cycle Engineering (AI-LCE). To do so, interdisciplinary analysis of literature in domains of AI and LCE is performed. Needed concepts and methods are described: key enabling technologies are Artificial Intelligence (AI), the Internet of Things, and data lakes, which are becoming cost-effective and increasingly implemented in industry. Both artificial and human intelligence are used in combination. Its advantages compared with the conventional LCE include shorter time for changing activities in the lifecycle and the accuracy of changes made.
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