Inner speech recognition is a modern advancement in Brain computer interfaces (BCI) that facilitates a communication between the computer and the brain in a direct way. It is particularly beneficial for individuals wh...
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Autonomous driving demands sophisticated control systems that optimize safety, performance, passenger comfort, and fuel efficiency. This study proposes a steering control system that integrates the Deep Deterministic ...
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Complex engineering projects (CEP) delivered by alliance is a form of system of systems with intricate interactions and are inevitably plagued with diverse risks. This research adopts the system modeling approach to d...
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ISBN:
(纸本)9798350358810;9798350358803
Complex engineering projects (CEP) delivered by alliance is a form of system of systems with intricate interactions and are inevitably plagued with diverse risks. This research adopts the system modeling approach to develop an alliance risk ontology framework, a systematic Literature Review (SLR) method for alliance risk extraction, and NVivo qualitative analysis for data analytics and risk visualization. The research developed the system of systems (SOS) alliance risk ontology framework and found two hundred and seven alliance risks from interactions of systems. Research provides the fundamental framework for risk categorization, analytical depth, and data treatment method, which can be significant to pre-analyze and mitigate critical risks in engineering projects involving alliances.
Industrial companies developing complex systems have face challenges with undesired unforeseen system behavior emerging in late development stages or during the system's operational use. This paper proposes a syst...
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ISBN:
(纸本)9798350365924;9798350365917
Industrial companies developing complex systems have face challenges with undesired unforeseen system behavior emerging in late development stages or during the system's operational use. This paper proposes a systematic approach from a systemsengineering perspective to overcome these challenges. Combining Design of Experiments with regression analysis while conveying a beneficial human vs machine task balance enables us to shift the focus from individual requirements to the overall system design for system testing without overwhelming efforts. We aim to keep the specific performance stated through requirements while ensuring a minimum performance throughout the parameter space. Actively using measurements during the development enables monitoring of the system performance throughout the parameter space facilitating detection and subsequent elimination or reduction of inherent detrimental emergent behavior. The proposed procedure also gives a solid rationale for the case company in what to test and not.
This paper tackles the problem of event-triggered unified performance state estimation in neural networks with time-varying delays. A novel event-triggered methodology is introduced, aiming to balance the performance ...
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This paper proposes a classification method for five types of images represented by the word 'kawaii.' 'kawaii images' do not have fixed concept or object, which makes it to classify them simply using ...
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Intuitive robotic tele-operation is a necessary but often difficult task for users because the structure of a robot is different from that of a human body. Our previously developed novel interface allows a user to ope...
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This research work investigates the effect of gain parameter tuning of the Sliding Mode Controller (smc) using Genetic Algorithm (GA) on the speed control of the DC motor. The smc tuned by GA shows a superior result c...
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This paper discusses the shift from robustness paradigm towards resilience within systems of systems (SoS), recognizing the existence of failures in such complex systems. In response, we introduce a new framework call...
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ISBN:
(纸本)9798350365924;9798350365917
This paper discusses the shift from robustness paradigm towards resilience within systems of systems (SoS), recognizing the existence of failures in such complex systems. In response, we introduce a new framework called the multi-level stochastic hypergraph (MLSHG) model to enhance the resilience of SoS, specifically acknowledging the stochastic processes inherent in some component systems (CSs). Our framework validates the inclusion of redundancy, including stand-in and stand-by mechanisms, as well as adaptability, making it easier to adapt the system and recover from failure. In addition, we propose an algorithm based on functional decomposition and performance analysis for efficient recovery strategies. To validate our approach, we conduct a case study on a mushroom farm, demonstrating the potential of the proposed framework for improving resilience in complex SoS.
Various types of machine learning methods are being used to predict and estimate power stability in smart grids. These include both supervised and unsupervised machine learning methods. This paper proposes the use of ...
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ISBN:
(纸本)9798350365924;9798350365917
Various types of machine learning methods are being used to predict and estimate power stability in smart grids. These include both supervised and unsupervised machine learning methods. This paper proposes the use of a semi-supervised learning method using a graph model for proactive learning in smart grids. The proposed method starts by labeling the unlabeled bus voltage data using information from the partially labeled bus voltage data. Then, it evaluates the score of the labeling and predicts the system stability by using correlation matrices. The method is applied for five power system test cases including the ieee 14 bus, 30 bus, 39 bus, 57 bus, and 118 bus systems. The results confirm a high degree of correlation between the predicted results and the actual values of the terminal voltages of all the nodes in the experimental systems. The method can be used to predict the effects of disturbances in smart grids and related systems of systems.
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