To enhance the performance of user personalized recommendation algorithms, this study offers a user personalized recommendation calculation based on deep learning network. Efficient and accurate personalized recommend...
To enhance the performance of user personalized recommendation algorithms, this study offers a user personalized recommendation calculation based on deep learning network. Efficient and accurate personalized recommendation algorithms can effectively increase user experience fulfillment. Considering user behavior, this research suggests a useful advice. We offer a preference network that might gather user preferences based on item qualities since customers intuitively convey their considerations based on some specific attributes of items. Moreover, weighted affiliation rules are used to identify these patterns to enhance the nature of recommendations because there are some sequential patterns in item purchases. We offer a preference network that might gather user preferences based on item qualities since customers intuitively convey their contemplations based on some specific attributes of items. Moreover, weighted affiliation rules are used to identify these patterns to enhance the nature of recommendations because there are some sequential patterns in item purchases. The methodology solves the sparsity problem and outperforms existing algorithms. Implementing a user behavior-based recommendation method, which gauges users' interests based on certain evaluations of item qualities, is the primary commitment. Furthermore, this strategy makes use of a sequential purchase pattern to raise the caliber of recommendations.
Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust n...
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Current techniques of gait assessment rely on wearable sensors, pressure-sensitive walkway systems, and optical motion capture systems. A less invasive and more portable solution could be represented by a mobile robot...
Current techniques of gait assessment rely on wearable sensors, pressure-sensitive walkway systems, and optical motion capture systems. A less invasive and more portable solution could be represented by a mobile robot that follows the user during the gait activity. The main idea behind this work relies on finding the best robot configuration for less invasive gait analysis with high acceptability from the end users. To this aim, two follow-me configurations have been designed: human-leader (i.e. the robot follows the person from behind), and robot-leader (i.e. robot follows the person from the front). We asked 27 young participants to test both modalities and to evaluate their perception of the robot in 5 domains: comfort, expected conformity, safety, trust, and unobtrusiveness. Additionally, we extracted quantitative parameters related to the walking experience from the data recorded by the platform and we analyzed them in tandem with the qualitative results. The results reported that robot-leader configuration tended to be more appreciated in terms of comfort, trust, and safety. On the contrary, the human-leader configuration is perceived as less obtrusive, less invasive, and in line with users' expectations. Considering the gait assessment application, we expect the human-leader configuration to return more promising and accurate results.
Simultaneous Localization and Mapping (SLAM) is one of the fundamental problems in autonomousrobotics. Over the years, many approaches to solve this problem for 6D poses and 3D maps based on LiDAR sensors or depth ca...
Simultaneous Localization and Mapping (SLAM) is one of the fundamental problems in autonomousrobotics. Over the years, many approaches to solve this problem for 6D poses and 3D maps based on LiDAR sensors or depth cameras have been proposed. One of the main drawbacks of the solutions found in the literature is the required computational power and corresponding energy consumption. In this paper, we present an approach for LiDAR-based SLAM that maintains a global truncated signed distance function (TSDF) to represent the map. It is implemented on a System On Chip (SoC) with an integrated FPGA accelerator. The proposed system is able to track the position of a Velodyne VLP-16 LiDAR in real time, while maintaining a global TSDF map that can be used to create a polygonal map of the environment. We show that our implementation delivers competitive results compared to state-of-the-art algorithms while drastically reducing the power consumption compared to classical CPU or GPU-based methods.
Social assistive robots usually encompass a great compromise between the advanced perception models that one can use and their computing capabilities. The ideal approaches are always oriented towards low power consump...
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ISBN:
(数字)9798350314403
ISBN:
(纸本)9798350314410
Social assistive robots usually encompass a great compromise between the advanced perception models that one can use and their computing capabilities. The ideal approaches are always oriented towards low power consumption while maintaining a higher order of responsiveness to the surroundings. Therefore, we present in this paper, an improvement of the follow-me system on ASTRO. In detail, we propose the use of Mediapipe SDK for human detection and tracking, when ASTRO is meant to accompany someone that is walking. A study on the new velocity and distance profiles that the robot keeps from the individuals is presented and we have also evaluated how it affects their perception of being safe. The presented results show that our new approach allows the system to achieve real-time performance by becoming
$\approx 9.1\times \mathbf{faster}$
, smoothing, and keeping a more natural distance from the user.
The development of connected autonomous vehicles (CAVs) facilitates the enhancement of traffic efficiency in complicated scenarios. Difficulties remain unsolved in developing an effective and efficient coordination st...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
The development of connected autonomous vehicles (CAVs) facilitates the enhancement of traffic efficiency in complicated scenarios. Difficulties remain unsolved in developing an effective and efficient coordination strategy for CAVs. In this paper, we formulate the cooperative autonomous driving task of CAVs as an optimal control problem with safety conditions enforced as hard constraints, and propose a computationally-efficient parallel optimization framework to generate strategies for CAVs with the travel efficiency improved and the hard safety constraints satisfied. Specifically, all constraints involved are addressed appropriately with convex approximation, such that the convexity property of the reformulated optimization problem is exhibited. Then, a parallel optimization algorithm is presented to solve the reformulated optimization problem, with an embodied iterative nearest neighbor search strategy to determine the optimal passing sequence. It is noteworthy that the travel efficiency is enhanced and the computation burden is considerably alleviated with the proposed innovation development. We also examine the proposed method in CARLA simulator and perform thorough comparisons to demonstrate the effectiveness and efficiency of the proposed approach.
State of health estimation of battery is crucial to ensure the safety and durability of electric vehicles. This paper presents six methods to extract the battery health indicator from electric vehicle field testing da...
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ISBN:
(数字)9781665463188
ISBN:
(纸本)9781665463195
State of health estimation of battery is crucial to ensure the safety and durability of electric vehicles. This paper presents six methods to extract the battery health indicator from electric vehicle field testing data. The methods for extracting health indicators from the discharge cycle show the ability to cope with the variable driving condition. In total, 157 health indicators are extracted from the collected data. Pearson correlation coefficient and Spearman's rank correlation coefficient are used to measure the correlation between the health indicators and the state of health. The results suggest that health indicators extracted by the presented methods have high correlations to the battery state of health.
To avoid the task failure caused by joint breakdown during the collaborative motion planning of dual-redundant robot manipulators, a neural dynamic fault-tolerant (NDFT) scheme is proposed and applied. To do so, a joi...
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To avoid the task failure caused by joint breakdown during the collaborative motion planning of dual-redundant robot manipulators, a neural dynamic fault-tolerant (NDFT) scheme is proposed and applied. To do so, a joint fault-tolerant strategy is first designed, and it is formulated as a time-varying equality constraint. Second, combining the robot position and orientation control, joint limit constraint, joint fault-tolerant equality constraint, and considering the repetitive motion optimization criterion, a fault-tolerant framework for the dual-redundant robot manipulators based on quadratic programming (QP) is constructed. Then, a varying-parameter recurrent neural network (VP-RNN) is designed to solve the QP issue. The fault-tolerant framework and the VP-RNN constitute NDFT scheme. With the NDFT scheme, the impact of faulty joints on the whole system can be remedied by healthy joints, thereby the end-effectors of the robot can complete the given end-effector task. Finally, computer simulations and physical experiments are implemented to verify the availability, physical realizability, and accuracy of the proposed NDFT scheme in the collaborative execution of end-effector tasks. Comparative experimental results with conventional repetitive motion planning schemes based on neural networks show higher accuracy and smaller joint angle drift.
The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). ...
The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations.
To develop robots that can show cognitive functions, we must learn from the knowledge of human cognition. Existing biological and psychological evidence suggests that self-face perception and sensorimotor learning mec...
To develop robots that can show cognitive functions, we must learn from the knowledge of human cognition. Existing biological and psychological evidence suggests that self-face perception and sensorimotor learning mechanisms play a crucial role in self-recognition. However, one of the most important self-identity cues – facial information – has not been extensively studied in the robot self-recognition task. Current research on robot self-recognition primarily relies on the recognition of high-precision targets and tracking of manipulator motions, where the self-perception of facial information is not well studied. In this work, we propose a novel approach to achieve self-recognition via self-perception of facial expressions. Specifically, we developed a Conditional Generative Adversarial Network (CGAN) model using the knowledge on human cognitive and sensorimotor functions. It allows the robot to be aware of self-face (i.e., off-line model). Passing the observed visual variations in a mirror and comparing them to self-perceptive information, the robot can recognize the self through an online Bayesian learning regression. The results of our first experiment show that the robot can recognize itself in a mirror. The results from the second experiment show that our algorithm could be tricked by a similar robot with the same facial expressions, which is similar to the rubber hand illusion (RHI).
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