Today solar or wind energy resources are coupled with the AC grid supply in the household using inverters and battery banks. However, the maximum power point tracking-based battery chargers and inverters have signific...
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ISBN:
(数字)9798350375589
ISBN:
(纸本)9798350375596
Today solar or wind energy resources are coupled with the AC grid supply in the household using inverters and battery banks. However, the maximum power point tracking-based battery chargers and inverters have significant losses in the renewable-to-AC grid link. By using supercapacitor modules with extra low-frequency power converters, we can increase the end-to-end efficiency of whiteware such as refrigerators, washing machines, dryers, and heat pumps. Given that the modern inverter-driven whiteware carries brush-less DC motors driven by an inverter supplied by an internal DC bus derived from the line frequency AC supply, these systems can be easily powered by a direct DC source for the reduction of losses associated with the renewable energy link in a household. In addition, this could also reduce the converter losses within the white-good. The paper presents the theoretical and experimental details on how commercial whiteware can be easily modified to operate from a DC rail with a supercapacitor module used for the dual purpose of power conversion and energy buffering based on an example of a refrigerator.
The growing use of IoT devices in smart agriculture makes the industry vulnerable to several cyber risks. This study introduces an innovative hybrid Intrusion Detection System (IDS) utilising Gated Recurrent Units (GR...
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ISBN:
(数字)9798331521691
ISBN:
(纸本)9798331521707
The growing use of IoT devices in smart agriculture makes the industry vulnerable to several cyber risks. This study introduces an innovative hybrid Intrusion Detection System (IDS) utilising Gated Recurrent Units (GRUs) to improve the security of IoT-enabled smart agriculture. compared to current LSTM and CNN-based intrusion detection systems, the proposed model effectively incorporates sequential data dependencies, hence minimising false positives and enhancing detection accuracy. Experimental findings indicate that the suggested model surpasses conventional techniques, with an accuracy of 97.5%, an F1 score of 0.96, and a recall of 0.97. These developments create a strong, efficient, and scalable solution for safeguarding IoT-based agricultural systems from cyber-attacks.
Fatigue assessment of bolted ring-flanges in offshore wind turbine structures is a critical step in the overall structural design process. Analytical and numerical methods are employed to predict bolt stress, offering...
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Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from...
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Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by enhancing exact solvers. The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers.
Estimation of univariate regression function by a neural network with one hidden layer is considered, where the weight vector is determined by applying gradient descent to a regularized empirical L2 risk. Here the num...
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Fuzzy Cognitive Maps (FCMs) are graph-based simulation models commonly used to model complex systems. They are often built by participants and aggregated to compare the viewpoints of homogenous groups (e.g., anglers a...
ISBN:
(纸本)9798350369663
Fuzzy Cognitive Maps (FCMs) are graph-based simulation models commonly used to model complex systems. They are often built by participants and aggregated to compare the viewpoints of homogenous groups (e.g., anglers and ecologists) and increase the reliability of the FCM. However, the default approach for aggregation may propagate the errors of an individual participant, producing an aggregate FCM whose structure and simulation outcomes do not align with the system of interest. Alternative aggregation methods exist; however, there are no criteria to assess the quality of aggregation methods. We define nine desirable criteria for FCM aggregation algorithms and demonstrate how three existing aggregation procedures from social choice theory can aggregate FCMs and fulfill desirable criteria, enabling the assessment and comparison of FCM aggregation procedures to support modelers in selecting an aggregation algorithm. Moreover, we classify existing aggregation algorithms to provide structure to the growing body of aggregation approaches.
This study predicts software reusability at the class level using machine learning and a dataset of 65 Java applications. The reuse rates were calculated using cohesion, coupling, complexity, inheritance, documentatio...
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ISBN:
(数字)9798331540760
ISBN:
(纸本)9798331540777
This study predicts software reusability at the class level using machine learning and a dataset of 65 Java applications. The reuse rates were calculated using cohesion, coupling, complexity, inheritance, documentation, and size metrics. The data was segmented by project size (all, small, medium, and big). Several classifiers were tested once the labels were changed to multi-class labels (low: 0, moderate: 1, high: 2). On large projects, the voting model was 93% accurate. The results demonstrate how well machine learning predicts software reusability and provide recommendations for increasing software quality measures.
software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Beca...
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software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software ***,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and *** the requirements are not clear to the development team,it has a significant effect on the quality of the software *** study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)***,solutions to overcome these challenges are also *** data analysis is performed on the interview data collected from software industry ***,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven *** study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.
Spatio-temporal video grounding (or STVG) task aims at locating a spatio-temporal tube for a specific instance given a text query. Despite advancements, current methods easily suffer the distractors or heavy object ap...
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ISBN:
(数字)9798350353006
ISBN:
(纸本)9798350353013
Spatio-temporal video grounding (or STVG) task aims at locating a spatio-temporal tube for a specific instance given a text query. Despite advancements, current methods easily suffer the distractors or heavy object appearance variations in videos due to insufficient object information from the text, leading to degradation. Addressing this, we propose a novel framework, context-guided STVG (CG-STVG), which mines discriminative instance context for object in videos and applies it as a supplementary guidance for target localization. The key of CG-STVG lies in two specially designed modules, including instance context generation (ICG), which focuses on discovering visual context information (in both appearance and motion) of the instance, and instance context refinement (ICR), which aims to improve the instance context from ICG by eliminating irrelevant or even harmful information from the context. During grounding, ICG, together with ICR, are deployed at each decoding stage of a transformer architecture for instance context learning. Particularly, instance context learned from one decoding stage is fed to the next stage, and leveraged as a guidance containing rich and discriminative object feature to enhance the target-awareness in decoding feature, which conversely benefits generating better new instance context to improve localization finally. Compared to existing methods, CG-STVG enjoys object information in text query and guidance from mined instance visual context for more accurate target localization. In experiments on HCSTVG-v1/-v2 and VidSTG, CG-STVG sets new state-of-the-arts in m_tIoU and m_vIoU on all of them, showing efficacy. Code is released at https://***/HengLan/CGSTVG.
Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward func...
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