This study presents a machine learning-based approach to forecast Allocative Localization Error (ALE) in Wireless Sensor Networks (WSNs), addressing challenges such as dynamic network topologies and resource constrain...
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Phishing attacks are a major cybersecurity threat that resulted in over 1.2 million incidents in the first half of 2020. These attacks caused substantial financial losses and posed risks to individuals and organizatio...
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This study presents a three-dimensional localization of a ball drop in a multi-surface environment using cost-effective data acquisition devices, and proposes two learning-based methods to improve the baseline classic...
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Simulation is commonly adopted in developing building automated fault detection and diagnosis (AFDD) strategies. However, simulations often fall short in accurately representing real-world scenarios, which hinders the...
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ISBN:
(纸本)9798350358513;9798350358520
Simulation is commonly adopted in developing building automated fault detection and diagnosis (AFDD) strategies. However, simulations often fall short in accurately representing real-world scenarios, which hinders the efficacy of models trained on such data for identifying faults in actual buildings. To tackle this challenge, we present a new approach for feature extraction that leverages entropy obtained from graph structures. These structures are constructed based on features that can distinguish between normal and faulty conditions. This method includes acquiring graph structures from simulated data, extracting their entropies as features to train AFDD models. Then, the process of obtaining entropies from graphs is replicated for real building data, and the trained AFDD model is applied to conduct tests on them. Empirical findings illustrate that our suggested approach enables fault detection in real-world scenarios, even when the model is trained with simulated data. The features extracted by our proposed approach surpass the baseline, which consists of GNN embedded features, in terms of fault detection performance. Therefore, we infer that our method has the potential to take advantage of simulation for real building fault detection.
In the last years, various kinds of Petri Nets were conceived for solving all types of software problems, each Petri Net kind having its own features and limitations. Some of the most outstanding types are: Petri Nets...
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ISBN:
(纸本)9781665479332
In the last years, various kinds of Petri Nets were conceived for solving all types of software problems, each Petri Net kind having its own features and limitations. Some of the most outstanding types are: Petri Nets (PN), Enhanced Time Petri Nets (ETPN), Fuzzy Logic Enhanced Time Petri Nets (FLETPN), Unified Enhanced Time Petri Nets (UETPN), Object Enhanced Time Petri Nets (OETPN), Stochastic OETPN, Quantum Petri Nets (QPN). The current research goals are to analyze and compare their power for solving practical application problems. As example, we approach the problem of a moving robot in a warehouse (moving entity problem). A feature analysis of various PN model types is performed based on this example.
In this paper we present a novel strategy for reactive collision-free feasible motion planning for robotic manipulators operating inside an environment populated by moving obstacles. The proposed strategy embeds the D...
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ISBN:
(纸本)9781728196817
In this paper we present a novel strategy for reactive collision-free feasible motion planning for robotic manipulators operating inside an environment populated by moving obstacles. The proposed strategy embeds the Dynamical System (DS) based obstacle avoidance algorithm into a constrained non-linear optimization problem following the Model Predictive Control (MPC) approach. The solution of the problem allows the robot to avoid undesired collision with moving obstacles ensuring at the same time that its motion is feasible and does not overcome the designed constraints on velocity and acceleration. Simulations demonstrate that the introduction of the MPC prediction horizon helps the optimization solver in finding the solution leading to obstacle avoidance in situations where a non predictive implementation of the DS-based method would fail. Finally, the proposed strategy has been validated in an experimental work-cell using a Franka-Emika Panda robot.
Aiming at the issue that vehicle detection accuracy is easily influenced by abnormal weather conditions such as rain, snow, and frog, et al. This paper studies the methods of expressway vehicle detection based on deep...
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ISBN:
(纸本)9798350345780
Aiming at the issue that vehicle detection accuracy is easily influenced by abnormal weather conditions such as rain, snow, and frog, et al. This paper studies the methods of expressway vehicle detection based on deep learning. First, methods for detecting expressway vehicles based on FasterRCNN, YOLOV3, and SSD are compared and analyzed. Then, based on SEU expressway vehicle detection dataset under abnormal weather conditions, the training of the vehicle detection model and the research of compare experiments on Faster-RCNN, YOLOv3, and SSD are carried out by manually labeling and collecting specific regions of vehicles. Theoretical analysis and experimental results show that the YOLOv3-based detection model of expressway vehicle detection under abnormal weather conditions outperforms the other two methods, with an average accuracy of 99.2%.
Driving automation is gradually replacing human driving maneuvers in different applications such as adaptive cruise control and lane keeping. However, contemporary driving automation applications based on expert syste...
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ISBN:
(纸本)9781728196817
Driving automation is gradually replacing human driving maneuvers in different applications such as adaptive cruise control and lane keeping. However, contemporary driving automation applications based on expert systems or predefined control strategies are not in line with individual human driver's preference. To overcome this problem, we propose a Personalized Adaptive Cruise Control (P-ACC) system that can learn the driver's car-following preferences from historical data using model-based maximum entropy Inverse Reinforcement Learning (IRL). Once activated in real-time, the P-ACC system first classifies the driver type and the weather type (at that moment). The vehicle is then controlled using the pre-trained IRL model on the cloud of the associated class. The personalized IRL model on the cloud will be updated as more human driving data is collected from various scenarios. Numerical simulation with real-world naturalistic driving data shows that, the accuracy of reproducing the real-world driving profile improves up to 30.1% in terms of speed and 36.5% in terms of distance gap, when P-ACC is compared with the Intelligent Driver Model (IDM). Game engine-based human-in-the-loop simulation demonstrates that, the takeover frequency of the driver during the usage of P-ACC decreases up to 93.4%, compared with that during the usage of IDM-based ACC.
Learning from demonstration (LfD) provides a fast, intuitive and efficient framework to program robot skills, which has gained growing interest both in research and industrial applications. Most complex manipulation t...
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ISBN:
(纸本)9781728196817
Learning from demonstration (LfD) provides a fast, intuitive and efficient framework to program robot skills, which has gained growing interest both in research and industrial applications. Most complex manipulation tasks are longterm and involve a set of skill primitives. Thus it is crucial to have a reliable coordination scheme that selects the correct sequence of skill primitive and the correct parameters for each skill, under various scenarios. Instead of relying on a precise simulator, this work proposes a human-in-the-loop coordination framework for LfD skills that: builds parameterized skill models from kinesthetic demonstrations;constructs a geometric task network (GTN) on-the-fly from human instructions;learns a hierarchical control policy incrementally during execution. This framework can reduce significantly the manual design efforts, while improving the adaptability to new scenes. We show on a 7-DoF robotic manipulator that the proposed approach can teach complex industrial tasks such as bin sorting and assembly in less than 30 minutes.
Accurate gait phase detection is a fundamental technology for wearable rehabilitation robots, enabling them to deliver effective, safe, and personalized assistance that closely aligns with the therapeutic needs and sa...
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ISBN:
(纸本)9789819607853;9789819607860
Accurate gait phase detection is a fundamental technology for wearable rehabilitation robots, enabling them to deliver effective, safe, and personalized assistance that closely aligns with the therapeutic needs and safety requirements of users. Unlike force sensors or inertial measurement units, surface electromyography (sEMG) signals establish a direct link to the wearer's motion intentions, making them ideally suited for real-time applications. This study investigates the utilization of sEMG signals to identify gait phases using three types of machine learning models: k-nearest neighbors (KNN), support vector machines (SVM), and artificial neural networks (ANN). The models are trained on a publicly available dataset (i.e., SIAT-LLMD), which consists of data from 40 healthy individuals and utilizes three training methods: holdout, 10-fold cross-validation, and leave-one-subject-out cross-validation. ANN achieved the best results, demonstrating an overall accuracy of 92.6%+/- 2.6% for detecting stance and swing phases, and 94.4%+/- 4.2% for detecting five gait sub-phases (i.e., stance flexion phase, stance extension phase, preswing phase, swing flexion phase, and swing extension phase).
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