The Metaverse is transforming digital engagement across sectors like education, healthcare, and technology. This survey examines how edge computing, combined with 5G, IoT, and AI, supports real-time, immersive experie...
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This study uses machine learning models as potent analytical tools to look into the issue of inventory cost and profit prediction in the automobile industry. Throughout a ten-year dataset from 2012 to 2023, the study ...
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This paper proposes a fuzzy optimal adaptive prescribed performance control strategy for nonlinear multi-agent systems. Firstly, to solve the prescribed performance problem, an error transformation function is used to...
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Empowering Healthcare systems with Machine Learning: Mechanisms, Classifications, and Applications' examines how machine learning (ML) and healthcare are dynamically combining. It explains how ML algorithms manage...
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The learning environment has experienced an evolution in this era of advanced instructional technologies. Smart classrooms are currently equipped with interactive devices and resources to enhance the effectiveness of ...
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In recent years, the emergence of the Internet and E-commerce has steered significant growth in digital transactions. Businesses today need mobile wallets, credit and debit cards, and e-cash to digitize payments. Digi...
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This paper analyzes the effect of control parameters of feed-forward and inner loop velocity controller in an admittance control scheme on the performance and passivity. The interaction force, inertia, and damping com...
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ISBN:
(数字)9781665479271
ISBN:
(纸本)9781665479271
This paper analyzes the effect of control parameters of feed-forward and inner loop velocity controller in an admittance control scheme on the performance and passivity. The interaction force, inertia, and damping compensation were considered as the feed-forward input. Sufficient conditions and guidelines for each parameter were provided to enable the implementation of a wide range of desired admittance satisfying passivity. The proposed guidelines were verified through experiments.
The proceedings contain 72 papers. The topics discussed include: a collaborative method for multi AGVs scheduling and path planning considering dynamic arrival of task;a projection-based exploration method for multi-a...
ISBN:
(纸本)9798400710025
The proceedings contain 72 papers. The topics discussed include: a collaborative method for multi AGVs scheduling and path planning considering dynamic arrival of task;a projection-based exploration method for multi-agent coordination;disturbance-observer-based adaptive neural command filtered control of nonlinear strict-feedback systems with input delay;performance degradation study for a proton exchange membrane fuel cell in underwater vehicle applications;finite-time cooperative guidance laws of impact time and angle with input saturation constraint;variable structure sliding mode control method for hypersonic vehicles;attitude stabilization control algorithm based on dynamic sliding mode under angular velocity and control torque saturation;and time coordination entry guidance with combined lateral and longitudinal adjustments.
Machine learning is commonly used to detect anomalies in industrial controlsystems (ICS). In general, building an anomaly detection model requires massive training data and computational resources. Therefore, an idea...
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ISBN:
(纸本)9781665475785
Machine learning is commonly used to detect anomalies in industrial controlsystems (ICS). In general, building an anomaly detection model requires massive training data and computational resources. Therefore, an ideal solution is to use a pre-trained model instead of building each model completely from scratch. However, we cannot directly use a pre-trained model because each ICS dataset has its own unique features and characteristics. This paper proposes a practical transfer learning technique dubbed MENDEL (tiMe sEries aNomaly Detection using transfEr Learning) to efficiently build anomaly detection models, respectively, for different ICS domains. MENDEL first applies principal components analysis (PCA) to each model to obtain a fixed number of reduced features compatible with other models and then finds a reasonable mapping between different models' reduced features systemically for effective transfer learning. We evaluate the performance of MENDEL on two datasets (SWaT and WADI) with two models (InterFusion and USAD). Our evaluation results show that MENDEL can overall achieve high F1 scores even when a model is retrained with only a small proportion of the training dataset. For example, when we first train InterFusion with the SWaT train dataset and then retrain the trained model with only 10% of the entire WADI train dataset, the retrained InterFusion achieves an F1 score of 72%, which is better than an F1 score of 44% achieved by InterFusion with the entire SWAT training dataset.
The temperature sensors DS18B20 and Raspberry Pi based platform are used to collect the data for the compressor machine elements. Sample data is being analyzed using Machine Learning for predicting health of the compr...
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