We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution of this paper is the introduction of ...
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In modern power systems, efficient energy scheduling is essential for ensuring system stability and optimizing resource allocation. At present, Deep Reinforcement learning (DRL) algorithms perform well in continuous a...
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The onboard high-speed detection is a critical development direction for the operation and maintenance of railway networks. Rail corrugation is the most common track irregularity in metro systems, causing a series of ...
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In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risky. However, designing dense reward si...
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Artificial intelligence systems are becoming increasingly common and used in the educational industry. The paper examines the various possibilities of using artificial intelligence (AI) in education. Artificial intell...
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Federated learning (FL), as a privacy-preserving machinelearning paradigm, trains a global model across devices without exposing local data. However, resource heterogeneity and inevitable stragglers in wireless netwo...
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The cracks of bridges are not always completely open during vibrations but undergo an open and closed cycle mode under moving vehicles. Therefore, this study proposes a long short-term memory (LSTM)-based detection mo...
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The cracks of bridges are not always completely open during vibrations but undergo an open and closed cycle mode under moving vehicles. Therefore, this study proposes a long short-term memory (LSTM)-based detection model for breathing cracks with multiple damage positions and degrees of beam-like bridges for the first time. The contact point displacement variation (CPDV), serving as an evaluation indicator of the bridge damage, is calculated by analyzing the acceleration history data of the vehicle that has passed over a randomly damaged bridge. At the same time, CPDV is also used as the input variable of an LSTM neural network to monitor the damage location and degree of the bridge. By employing the finite element simulation of the bridge half-vehicle model, a dataset was created for the training and prediction of LSTM. The numerical results show that the proposed method can accurately identify complex breathing crack information at different vehicle speeds, and it is also robust to road roughness. In addition, a laboratory experiment was conducted to verify the proposed method. The experimental results show that CPDV is sensitive to bridge cracks and LSTM can realize higher damage prediction.
For accurate pH measurement and voltage prediction, solution properties need to be converted into measurable electrical signals. Reliable and precise data from pH sensors are achieved by calibrating the sensors with k...
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ISBN:
(纸本)9783031837821;9783031837838
For accurate pH measurement and voltage prediction, solution properties need to be converted into measurable electrical signals. Reliable and precise data from pH sensors are achieved by calibrating the sensors with known solutions, supporting effective monitoring and maintenance of water quality to safeguard organism health in aquaculture applications. In this study, fabricated pH strips are used for data measurement, and a dataset spanning various pH levels has been developed. Using machinelearning algorithms, voltage is predicted across these pH values, with random forest regression yielding the best prediction accuracy for different pH ranges. This approach enhances sensor precision and reliability, minimizing the need for manual calibration. Additionally, it enables real-time pH monitoring and effectively captures complex parameter relationships, offering a more efficient and sustainable solution for optimal water quality management in aquaculture. The random forest model worked remarkably well, with a low RMSE of 0.01V and a R-2 value of 0.99, indicating great accuracy and minimum error in voltage predictions. The high R-2 score indicates that the model explains nearly all of the variance in the data.
The proceedings contain 12 papers. The special focus in this conference is on Advanced Analytics and learning on Temporal data. The topics include: EDGAR: Embedded Detection of Gunshots by AI in Real-ti...
ISBN:
(纸本)9783031243776
The proceedings contain 12 papers. The special focus in this conference is on Advanced Analytics and learning on Temporal data. The topics include: EDGAR: Embedded Detection of Gunshots by AI in Real-time;identification of the Best Accelerometer Features and Time-Scale to Detect Disturbances in Calves;ODIN AD: A Framework Supporting the Life-Cycle of Time Series Anomaly Detection Applications;clustering of Time Series Based on Forecasting Performance of Global Models;experimental Study of Time Series Forecasting Methods for Groundwater Level Prediction;fast Time Series Classification with Random Symbolic Subsequences;RESIST: Robust Transformer for Unsupervised Time Series Anomaly Detection;window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark;application of Attention Mechanism Combined with Long Short-Term Memory for Forecasting Dissolved Oxygen in Ganga River;data Augmentation for Time Series Classification with Deep learning Models;Dimension Selection Strategies for Multivariate Time Series Classification with HIVE-COTEv2.0.
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