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This paper presents a new damage detection and localization technique based on the changes in vibration parameters using BAT and Particle Swarm Optimization algorithm. The finite element method is used to apply damage at specific element(s) of the considered beams. The damage is represented by a reduction in Young's modulus and the identification of damage is formulated as an optimization problem using objective function based on Modal Scale Factor and changes in natural frequencies. A procedure for detecting and locating damage of beam-like structures based on BAT algorithm is used. This approach presents a method that can be used to detect the single and multiple-damage positions and the rate of damage in structural elements with high accuracy after the first iteration. The results obtained using BAT algorithm are compared to those obtained using Particle Swarm Optimization Algorithm. By taking noise into account in the damage detection and localization problem, it is shown that our approach based on BAT algorithm can detect the damage locations with high accuracy. [ABSTRACT FROM AUTHOR]

Currently, with the power electronics evolution, a major research axis is oriented towards the diagnosis of converters supplying induction machines. Indeed, a converter such as the inverter is susceptible to have structural failures such as faulty leg and/or opencircuit IGBT faults. In this paper, the detection of the faulty leg and the localization of the open-circuit switch of an inverter are investigated. The fault detection technique used in this work is based essentially upon the monitoring of the root mean square (RMS) value and the calculation of the mean value of the three-phase currents. In the first part of the paper work, the faulty leg is detected by monitoring the RMS value of the three-phase currents and comparing them to the nominal value of the phase current. The second part, the open-circuit IGBT fault is localized simply by knowing the polarity of the calculated mean value current of the faulty phase. The work is first accomplished using simulation work and then the obtained simulation results are validated by experimental work conducted in our LDEE laboratory to illustrate the effectiveness, simplicity and rapidity of the proposed technique. [ABSTRACT FROM AUTHOR]

With the development of communication technology, the interference from passive intermodulation to the communication system has become increasingly severe. Locating the source of passive intermodulation and then repairing or replacing the device experiencing passive intermodulation is a reliable method. Therefore, this paper proposes a dual-frequency signal localization method based on the principle of phase ranging to accurately locate the passive intermodulation sources in RF cables. This method switches the test signal frequency to obtain the phase of passive intermodulation signals at different frequencies to localize the passive intermodulation source. This paper analyzes the principle of the passive intermodulation localization method based on dual-frequency signals, establishes a localization system for experiments, and finally, analyzes the experimental error to propose an optimization strategy. Through experimental verification, this method can stabilize the positioning error of a passive intermodulation source in a transmission line less than 0.06 m. Meanwhile, this method has high positioning accuracy and the advantages of slight computational complexity, fast positioning speed, and only a small number of requirements on hardware resources. It can work in communication scenarios like satellite and base stations, providing a new technical solution for localizing passive intermodulation interference sources in 5G and 6G communication systems. [ABSTRACT FROM AUTHOR]

Recent geometric approaches to visual‐inertial odometry have shown impressive accuracy with real‐time performance in autonomous tracking applications in several fields including virtual and augmented reality (VR & AR) as well as robotics. But these methods are still not robust to challenging conditions due to their dependence on hand‐engineered features, heuristics, sensor calibration and manual synchronization (when using visual and inertial sensors). In this paper, we review the recent advances in deep learning based approaches to odometry and identify some future research directions. [ABSTRACT FROM AUTHOR]

This paper presents a visual compass method utilizing global features, specifically spherical moments. One of the primary challenges faced by photometric methods employing global features is the variation in the image caused by the appearance and disappearance of regions within the camera's field of view as it moves. Additionally, modeling the impact of translational motion on the values of global features poses a significant challenge, as it is dependent on scene depths, particularly for non-planar scenes. To address these issues, this paper combines the utilization of image masks to mitigate abrupt changes in global feature values and the application of neural networks to tackle the modeling challenge posed by translational motion. By employing masks at various locations within the image, multiple estimations of rotation corresponding to the motion of each selected region can be obtained. Our contribution lies in offering a rapid method for implementing numerous masks on the image with real-time inference speed, rendering it suitable for embedded robot applications. Extensive experiments have been conducted on both real-world and synthetic datasets generated using Blender. The results obtained validate the accuracy, robustness, and real-time performance of the proposed method compared to a state-of-the-art method. [ABSTRACT FROM AUTHOR]

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Authenticity is commonly discussed by scholars specialized in game localization. Their discussions range from the challenge professional localizers face to the role of using authentic materials in the classroom to train new localization professionals. This paper discusses the issue of localization practices and game authenticity, addressing how game developers and localization professionals debate this issue. To do so, the paper defines localization and authenticity and presents a literature review on the theme. After that, we present the results from interviews and fieldwork at game conventions on three different themes: culture and authenticity in games, game changes during localization process, and authenticity definitions. We conclude that the discussion between authenticity and localization should be based on the localizer's language so the research about this topic can be further investigated. [ABSTRACT FROM AUTHOR]

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs' localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios. [ABSTRACT FROM AUTHOR]

In everyday life, 5G-based localization technology is commonly used, but non-line-of-sight (NLOS) environments can block the propagation of the localization signal, thus preventing localization. In order to solve this problem, this paper proposes a reconfigurable intelligent surface non-line-of-sight time difference of arrival (TDOA) localization (RNTL) algorithm. Firstly, a model of a reflective-surface-based intelligent localization (RBP) system is constructed, which utilizes multiple RISs deployed in the air to reflect signals. Secondly, in order to reduce the localization error, this paper establishes the optimization problem of minimizing the distance between each estimated coordinate and the actual coordinate and solves it via the piecewise linear chaotic map–gray wolf optimization algorithm (PWLCM-GWO). Finally, the simulation results show that the RNTL algorithm significantly outperforms the traditional gray wolf optimization and particle swarm optimization algorithms in different signal-to-noise ratios, and the localization errors are reduced by 46% and 53.5%, respectively. [ABSTRACT FROM AUTHOR]

We propose a novel point cloud alignment algorithm, namely PCR-DAT, for radar inertial ranging and localization. In environments with complex feature variations, the distribution trend of features is always changing, and the traditional alignment algorithms often fall into local optimums when dealing with regional point clouds with a combination of rich and sparse feature points, thus affecting the accuracy and stability of point cloud alignment. This paper addresses this issue by constructing a cost function composed of distance factors obtained from lidar measurements, normal distribution factors, and IMU pre-integration measurement factors. The core idea involves analyzing and classifying features in the target environment, defining different residual factors based on feature categories. Sparse features correspond to distance factors, while rich features correspond to distribution factors. Subsequently, a nonlinear optimization process is employed to estimate the robot's pose. We evaluate the accuracy and robustness of the algorithm in various scenarios, including experiments on the KITTI dataset and field data collected during UGV movement. The results demonstrate that the DAT point cloud registration algorithm effectively addresses the pose prediction problem in the presence of feature degradation. [ABSTRACT FROM AUTHOR]

To improve the speed and accuracy of the methods used for the recognition and positioning of strawberry plants, this paper is concerned with the detection of elevated-substrate strawberries and their picking points, using a strawberry picking robot, based on the You Only Look Once version 7 (YOLOv7) object detection algorithm and Red Green Blue-Depth (RGB-D) sensing. Modifications to the YOLOv7 model include the integration of more efficient modules, incorporation of attention mechanisms, elimination of superfluous feature layers, and the addition of layers dedicated to the detection of smaller targets. These modifications have culminated in a lightweight and improved YOLOv7 network model. The number of parameters is only 40.3% of that of the original model. The calculation amount is reduced by 41.8% and the model size by 59.2%. The recognition speed and accuracy are also both improved. The frame rate of model recognition is increased by 19.3%, the accuracy of model recognition reaches 98.8%, and mAP@0.95 reaches 96.8%. In addition, we have developed a method for locating strawberry picking points based on strawberry geometry. The test results demonstrated that the average positioning success rate and average positioning time were 90.8% and 76 ms, respectively. The picking robot in the laboratory utilized the recognition and positioning method proposed in this paper. The error of hand–eye calibration is less than 5.5 mm on the X-axis, less than 1.6 mm on the Y-axis, and less than 2.7 mm on the Z-axis, which meets the requirements of picking accuracy. The success rate of the picking experiment was about 90.8%, and the average execution time for picking each strawberry was 7.5 s. In summary, the recognition and positioning method proposed in this paper provides a more effective method for automatically picking elevated-substrate strawberries. [ABSTRACT FROM AUTHOR]