Predictive maintenance techniques are designed to determine the state of equipment in action to help us know when we can intervene to perform maintenance on it. Predictive maintenance design applies artificial intelli...
Predictive maintenance techniques are designed to determine the state of equipment in action to help us know when we can intervene to perform maintenance on it. Predictive maintenance design applies artificial intelligence techniques such as machine learning to analyze data and monitor efficiency. In this paper, we predict the maintenance of any equipment before it stops to reduce unplanned equipment maintenance by means of machine learning algorithms. We adopted in our work the following supervised machine learning algorithms: Random Forest, Support Vector Machine, KNN, Decision Tree, Logistic Regression, Naïve Bayes, and XGBOOST. Simulations and results show that the Random Forest and XGBOOST have almost the same performance. The XGBOOST machine learning algorithm is preferred for bigger datasets compared to Random Forest and it works more effectively in the case of small datasets. Finally, the proposed predictive maintenance system is successful in identifying possible failure indicators and reducing some production stoppages as shown by our simulation results.
We demonstrate a broadband low-loss nonvolatile 2×2 silicon directional coupler switch based on phase-change material Sb2Se3. The switch has a low insertion loss of ~0.5 dB, a compact footprint of ~41 µm, an...
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Bus-clamping Pulse Width Modulation (PWM) is an effective method to reduce the switching loss in a three-phase voltage source inverter (VSI). In bus-clamping PWM scheme, the phase legs are switched using high frequenc...
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Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing activities at sea have limitations. A...
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Intention prediction (IP) is a challenging task for intelligent vehicle’s perception systems. IP provides the likelihood, or probability, of a target vehicle to perform a maneuver subjected to a finite set of possibi...
Intention prediction (IP) is a challenging task for intelligent vehicle’s perception systems. IP provides the likelihood, or probability, of a target vehicle to perform a maneuver subjected to a finite set of possibilities. There are many factors that influence the decision-making process of a driver, which should be considered in a prediction framework. In addition, the lack of labeled large-scale dataset with maneuver annotation imposes another challenge to the task. This paper proposes an Interaction-aware Maneuver Prediction framework, called IAMP, using interaction graphs to extract complex interaction features from traffic scenes. In addition, we explored a semi-supervised approach called Noisy Student to take advantage of unlabeled data in the training step. Experimental results show relevant improvement when using unlabeled data, increasing the average performance of a classifier by 7.17% of accuracy. Moreover, this approach also made it possible to obtain an intention predictor with similar results to a classifier., even when using a shorter observation horizon.
With the development of 5G/6G communication technology, precise channel analysis considering the spatial characteristics of waves has become important. Traditional methods for analyzing random environments often rely ...
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This paper considers electric automated buses traveling in inter-urban roads and following a given route including stops, that must be reached according to a given timetable. Some of these stops are provided with a ch...
This paper considers electric automated buses traveling in inter-urban roads and following a given route including stops, that must be reached according to a given timetable. Some of these stops are provided with a charging infrastructure allowing to charge the bus batteries. The paper proposes a decentralized control scheme for determining the optimal speed profiles, the dwell and charging times of the buses, by taking into account the traffic conditions along the road through a suitable traffic flow prediction model. Two objectives are considered contemporarily: the minimization of the deviations from the timetable and the minimization of the energy lack at the end of the bus route. To attain both these conflicting objectives, a lexicographic approach is adopted to design the controller which considers that, depending on the system state, the priority of the two objectives can change. Accordingly, the proposed control scheme changes the objective prioritization in real time and switches between two different lexicographic-based optimal control solutions. Some tests are discussed in the paper to show the effectiveness of the proposed control scheme.
Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation of specific knowledge memories and th...
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This paper presents a transformer-based improved multi-path noise-canceling (IMNC) low noise amplifier (LNA) for K-band satellite communications. The design enhances the dual-path noise cancellation and significantly ...
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ISBN:
(数字)9798331509606
ISBN:
(纸本)9798331509613
This paper presents a transformer-based improved multi-path noise-canceling (IMNC) low noise amplifier (LNA) for K-band satellite communications. The design enhances the dual-path noise cancellation and significantly reduces the noise figure. The proposed LNA employs a three-coil transformer with dual-eight-shaped inductor to boost gain, and introduces a noise cancellation path for the common-gate transistor to optimizes noise performance. During the circuit design phase, a multicomponent integrated modeling (MIM) technique is applied, which accurately characterizes the EM field of the LNA and ensures high consistency between simulation and measurement results. The LNA, fabricated in a 40 nm CMOS process, consumes 28.8 mW of power and achieves a peak gain of 12.6 dB. Its 3-dB bandwidth ranges from 19.9 to 25 GHz with a minimum noise figure of 2 dB. The core area of the LNA is $0.16 ~\text{mm}^{2}$ .
This paper proposes an energy efficient resource allocation design algorithm for an intelligent reflecting surface (IRS)-assisted downlink ultra-reliable low-latency communication (URLLC) network. This setup features ...
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