Localization Accuracy Research Articles

A hybrid intelligent diagnostic approach for spool jamming faults of hydraulic directional valves

Directional valves are critical functional components in hydraulic systems for diverting the flow of hydraulic oil and controlling oil pressure. Faults of the valves could lead to failure and even serious accidents in hydraulic systems. Fault diagnostic accuracy is directly influenced by the effectiveness of signal processing. Due to the harsh operating environment of hydraulic systems, the condition monitoring signal acquired from the systems contains a large amount of redundant information and environmental noise. This makes the training processes of fault diagnostic approaches time-consuming and inefficient. In this paper, a new hybrid intelligent approach to spool jamming fault diagnostics for hydraulic directional valves is presented. The approach is designed based on the combined strengths of wavelet packet decomposition (WPD), empirical mode decomposition (EMD), variable mode decomposition (VMD), and the multi-layer perceptron (MLP) to improve the efficiency and accuracy of the fault diagnostic process. Five evaluation indices, including processing time (PT), local average accuracy (LAA), stability of accuracy (SA), weighted average of the neuron number (WANN), and robustness of WANN (ROB), are applied to benchmark the approach using ablation experiments. The experimental results show that the hybrid approach has a strong capability to extract fault-related features from the signal and perform fault diagnostics with high accuracy, efficiency, and robustness.

YLS-SLAM: a real-time dynamic visual SLAM based on semantic segmentation

Purpose Traditional visual simultaneous localization and mapping (SLAM) systems are primarily based on the assumption that the environment is static, which makes them struggle with the interference caused by dynamic objects in complex industrial production environments. This paper aims to improve the stability of visual SLAM in complex dynamic environments through semantic segmentation and its optimization. Design/methodology/approach This paper proposes a real-time visual SLAM system for complex dynamic environments based on YOLOv5s semantic segmentation, named YLS-SLAM. The system combines semantic segmentation results and the boundary semantic enhancement algorithm. By recognizing and completing the semantic masks of dynamic objects from coarse to fine, it effectively eliminates the interference of dynamic feature points on the pose estimation and enhances the retention and extraction of prominent features in the background, thereby achieving stable operation of the system in complex dynamic environments. Findings Experiments on the Technische Universität München and Bonn data sets show that, under monocular and Red, Green, Blue - Depth modes, the localization accuracy of YLS-SLAM is significantly better than existing advanced dynamic SLAM methods, effectively improving the robustness of visual SLAM. Additionally, the authors also conducted tests using a monocular camera in a real industrial production environment, successfully validating its effectiveness and application potential in complex dynamic environment. Originality/value This paper combines semantic segmentation algorithms with boundary semantic enhancement algorithms to effectively achieve precise removal of dynamic objects and their edges, while ensuring the system's real-time performance, offering significant application value.

Asynchronous localization of dynamic node through underwater acoustic sensor networks

In most cases, the operation of underwater acoustic sensor networks (UASNs) relies on accurate sensor location information. However, UASNs present more challenges than terrestrial sensor networks, such as the propagation speed variation with depth (i.e., stratification effect), asynchronous clock, node mobility, etc. In this paper, an efficient method of asynchronous localization is proposed by taking into account the stratification effect and node mobility. Firstly, a network is constructed that consists of surface buoys, AUVs, and target sensors. Secondly, an iterative least squares (LS) method is developed to locate AUV by establishing the relationship between propagation delay and location estimation. Thirdly, the passive mobility velocity of AUV is used to develop and solve the mobility model of the target sensors. Then, with the support of AUV, an asynchronous localization method is developed to estimate the target position, which improves the localization accuracy through motion and stratification compensation strategies. Moreover, the proposed method is validated through Cramer Rao lower bound (CRLB) analysis. Finally, simulation is performed to show that the proposed method is more efficient in reducing the estimation errors.

Improved Binaural Hearing Effects and Sound Source Localization in Listeners with Simulated Unilateral Conductive Hearing Loss: Effect of a Bone Conduction Device.

Objectives: It has been proven that patients with unilateral conductive hearing loss (UCHL) may encounter typical problems associated with asymmetric hearing, especially in challenging listening environments. In this study, we aimed to determine how UCHL affects speech recognition under multisource competing environments and the ability of sound source localization, as well as whether assistance with a bone conduction device (BCD) can confer hearing benefits in such listening tasks. Design: Acquired UCHL was simulated using an earplug combined with an earmuff in 10 listeners (mean age: 29.9 ± 4.77 years) with bilateral normal hearing (NH), and a within-subject repeated-measures design was used to compare hearing results among three listening conditions: NH (both ears open, C1), unilateral plug (UP) (simulated UCHL, C2), and UP + BCD (simulated UCHL aided with a BCD, C3). The speech reception threshold (SRT) of summation, squelch, and head shadow (HS) effects and the mean absolute error of sound source localization were used as markers for binaural hearing abilities. Results: BCD assistance (C3) improved the summation and HS effects in all listeners with simulated UCHL, resulting in a lower (i.e., better) SRT than that observed in C2; however, no significant differences in squelch effects were observed between C2 and C3. Notably, most listeners exhibited more accurate sound source localization in C3 than in C2. Further, BCD assistance mainly improved localization accuracy when the noise stimuli were presented at low intensities and on the hearing-impaired (plugged) side, suggesting that the benefits of BCD for sound localization are not based on the reacquisition of binaural processing. Conclusions: The current results have clinical implications for the promotion of BCDs in patients with UCHL, especially those with acquired UCHL who are unable to undergo surgery.

Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items

ABSTRACT Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model – EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model’s efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications.

Off-Grid Underwater Acoustic Source Direction-of-Arrival Estimation Method Based on Iterative Empirical Mode Decomposition Interval Threshold.

To solve the problem that the hydrophone arrays are disturbed by ocean noise when collecting signals in shallow seas, resulting in reduced accuracy and resolution of target orientation estimation, a direction-of-arrival (DOA) estimation algorithm based on iterative EMD interval thresholding (EMD-IIT) and off-grid sparse Bayesian learning is proposed. Firstly, the noisy signal acquired by the hydrophone array is denoised by the EMD-IIT algorithm. Secondly, the singular value decomposition is performed on the denoised signal, and then an off-grid sparse reconstruction model is established. Finally, the maximum a posteriori probability of the target signal is obtained by the Bayesian learning algorithm, and the DOA estimate of the target is derived to achieve the orientation estimation of the target. Simulation analysis and sea trial data results show that the algorithm achieves a resolution probability of 100% at an azimuthal separation of 8° between adjacent signal sources. At a low signal-to-noise ratio of -9 dB, the resolution probability reaches 100%. Compared with the conventional MUSIC-like and OGSBI-SVD algorithms, this algorithm can effectively eliminate noise interference and provides better performance in terms of localization accuracy, algorithm runtime, and algorithm robustness.

Target localization and defect detection of distribution insulators based on ECA‐SqueezeNet and CVAE‐GAN

AbstractInsulators, as typical equipment for distribution networks, provide good electrical insulation between live conductors and earth. Timely and accurate detection is essential for insulator detection issues. However, as the complexity of neural networks increases, the detection efficiency is often lower. Therefore, this paper proposes a fast insulator positioning and defect detection method. Firstly, for insulator target localization, the SqueezeNet network is improved using ECA attention mechanism. In addition, to address the issue of low defect detection accuracy, a joint algorithm has been proposed. The integration of convolutional variational autoencoder (CVAE) and generative adversarial network (GAN) solve their own shortcomings due to different image focus angles. The target localization accuracy reaches 94.30%, and the defect detection accuracy reaches 89.60%. It solves the problems of difficulty in locating small targets in a large field of view and inaccurate detection due to a small number of abnormal samples. This method has been tried and tested in practical distribution network systems.

Experimental and Numerical Investigation of Acoustic Emission Source Localization Using an Enhanced Guided Wave Phased Array Method.

To detect damage in mechanical structures, acoustic emission (AE) inspection is considered as a powerful tool. Generally, the classical acoustic emission detection method uses a sparse sensor array to identify damage and its location. It often depends on a pre-defined wave velocity and it is difficult to yield a high localization accuracy for complicated structures using this method. In this paper, the passive guided wave phased array method, a dense sensor array method, is studied, aiming to obtain better AE localization accuracy in aluminum thin plates. Specifically, the proposed method uses a cross-shaped phased array enhanced with four additional far-end sensors for AE source localization. The proposed two-step method first calculates the real-time velocity and the polar angle of the AE source using the phased array algorithm, and then solves the location of the AE source with the additional far-end sensor. Both numerical and physical experiments on an aluminum flat panel are carried out to validate the proposed method. It is found that using the cross-shaped guided wave phased array method with enhanced far-end sensors can localize the coordinates of the AE source accurately without knowing the wave velocity in advance. The proposed method is also extended to a stiffened thin-walled structure with high localization accuracy, which validates its AE source localization ability for complicated structures. Finally, the influences of cross-shaped phased array element number and the time window length on the proposed method are discussed in detail.

Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study.

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

Optimize data association of point cloud to improve the quality of mapping and positioning

Purpose Accurate mapping is crucial for the positioning and navigation of mobile robots. Recent advancements in algorithms and the accuracy of LiDAR sensors have led to a gradual improvement in map quality. However, challenges such as lag in closing loops and vignetting at map boundaries persist due to the discrete and sparse nature of raster map data. The purpose of this study is to reduce the error of map construction and improve the timeliness of closed loop. Design/methodology/approach In this letter, the authors introduce a method for dynamically adjusting point cloud distance constraints to optimize data association (ODA-d), effectively addressing these issues. The authors propose a dynamic threshold optimization method for matching point clouds to submaps during scan matching. Findings Large deviations in LiDAR sensor point cloud data, when incorporated into the submap, can result in irreparable errors in correlation matching and loop closure optimization. By implementing a data association framework with double constraints and dynamically adjusting the matching threshold, the authors significantly enhance submap quality. In addition, the authors introduce a dynamic fusion method that accounts for both submap size and the distance between submaps during the mapping process. ODA-d reduces errors between submaps and facilitates timely loop closure optimization. Originality/value The authors validate the localization accuracy of ODA-d by examining translation and rotation errors across three open data sets. Moreover, the authors compare the quality of map construction in a real-world environment, demonstrating the effectiveness of ODA-d.

Ultrasonic guided wave damage localization method for composite fan blades based on damage-scattered wave difference

Abstract Ultrasonic guided wave (UGW) has a wide monitoring range and high accuracy, holding promise for monitoring damage in engine large-area composite fan blades. However, the multi-curvature characteristics of engine composite fan blades with their anisotropic material properties make damage localization impossible with conventional UGW monitoring methods. In order to realize the UGW damage monitoring of the blade, this paper proposes a damage localization method based on damage-scattered wave difference. The method utilizes irregular sensing arrays to achieve localization of damage in multi-curvature composite blades with a small amount of data. First, the difference between the mutual excitation in a pair of sensors and the damage-scattered waves captured at reception was analyzed. It is concluded that the closer the damage is to the received sensor, the greater the damage index (DI). Next, a DI ratio of the mutually excited and received signals is computed for each sensor pair. This ratio is utilized to make a vertical line on the propagation path, which is identified as the damage likelihood line (DLL). Finally, a DLL corresponding to the three largest DIs was selected, and their intersections were used for damage localization. A time-domain truncated signal processing method is proposed to enable DI to more accurately represent the effects of damage, and improve the localization accuracy of the method. An experiment on damage localization was carried out on a homemade composite fan blade, where the damage was tested at various locations and sizes. The results show that the damage localization on the blade is good and 3mm tiny damage localization is achieved.

An accurate smartphone-based indoor pedestrian localization system using ORB-SLAM camera and PDR inertial sensors fusion approach

Using one’s own smartphone for indoor navigation, especially for visually impaired individuals, can be a valuable and empowering tool to promote independence and safe mobility. Additionally, the integration of Pedestrian Dead Reckoning (PDR), which relies on smartphone sensors like accelerometers and gyroscopes, is a well-known and widely used approach in the field of indoor navigation. However, despite continuous improvements in sensor technology, addressing drift issues remains a principal problem in the effective implementation of PDR systems. On the other hand, although the use of Simultaneous Localization and Mapping (SLAM) technologies in smartphones for indoor navigation has also gained significant adoption in recent years, there are indeed challenges associated with SLAM, including low localization accuracy and potential tracking divergence. To overcome the above limitations, this paper presents a novel and promising solution for smartphone-based indoor localization, addressing the limitations of both PDR and SLAM through their synergistic integration. In fact, to correct the accumulated errors of the proposed localization system and thus improve the accuracy, our approach uses an ORB-SLAM camera and PDR inertial sensors via a Kalman filter to achieve back-end fusion. Experimental results show that our localization system significantly reduces the mean localization error by 62%, marking a considerable improvement over the standalone PDR and SLAM systems. Moreover, compared with state-of-the-art techniques, our system achieves high localization accuracy, with an improvement of 59%, making it highly suitable for today’s real-time applications, especially beneficial for visually impaired people.

Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization

Wi-Fi round-trip time (RTT) ranging has proven successful in indoor localization. Here, it is shown to be useful outdoors as well—and more accurate than smartphone code-based GNSS when used near buildings with Wi-Fi access points (APs). A Bayesian grid with observation and transition models is used to update a probability distribution of the position of the user equipment (UE). The expected value (or the mode) of this probability distribution provides an estimate of the UE location. Localization of the UE using RTT ranging depends on knowing the locations of the Wi-Fi APs. Determining these positions from floor plans can be time-consuming, particularly when the APs may not be accessible (as is often the case in order to prevent unauthorized access to the network). An alternative is to invert the Bayesian grid method for locating the UE—which uses distance measurements from the UE to several APs with known position. In the inverted method we instead locate the AP using distance measurements from several known positions of the UE. In localization using RTT, at any given time, a decision has to be made as to which APs to range to, given that there is a cost associated with each “range probe” and that some APs may not respond. This can be problematic when the APs are not uniformly distributed. Without a suitable ranging strategy, one can enter a dead-end state where there is no response from any of the APs currently being ranged to. This is a particular concern when there are local clusters of APs that may “capture” the attention of the RTT app. To avoid this, a strategy is developed here that takes into account distance, signal strength, time since last “seen”, and the distribution of the directions to APs from the UE—plus a random contribution. We demonstrate the method in a situation where there are no line-of-sight (LOS) connections and where the APs are inaccessible. The localization accuracy achieved exceeds that of the smartphone code-based GNSS.

Indoor fingerprint localization algorithm based on WKNN and LightGBM-GA

Abstract WiFi-based indoor fingerprint localization is widely used in indoor localization owing to its high accuracy and low deployment costs. Changes in the indoor signal environment directly affect localization accuracy. To improve localization accuracy and stability, this paper proposes a novel indoor fingerprint localization algorithm based on Weighted K-Nearest Neighbors (WKNN) and an enhanced Light Gradient Boosting Machine (LightGBM). First, in the offline phase, Gaussian filtering and K-Nearest Neighbors-Random Forest information completion algorithm with fusion of Euclidean and Manhattan distances are used to remove outliers from the fingerprint database dataset and fill in missing fingerprint information, ensuring the integrity of the fingerprint database. During the online phase, the fingerprint database is divided into training and testing sets. The LightGBM algorithm is used for modeling. Additionally, Genetic Algorithm (GA) is use d to optimize the parameters of LightGBM algorithm to find the best parameters by fitness evaluation. Then, the nearest neighbor set found by the WKNN algorithm is introduced into the LightGBM-GA model. Combining the predictions from the standalone LightGBM algorithm and performing weighted fusion yields the final predicted coordinates. The experiments are conducted in 8 m × 10 m laboratory containing 5 access points and 80 reference points to collect the Received Signal Strength Indication values of 5 WiFi hotspots. The experimental results show that the average localization error of the proposed algorithm is 1.11 m, which is reduced by 6.7%–38.3% compared to K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), LightGBM, KNN + XGBoost, WKNN + LightGBM, and WKNN + XGBoost-GA localization algorithms. The localization curve is smoother, and the cumulative distribution function converges faster. Moreover, the localization time is reduced by 13.3%–36.7%, effectively enhancing localization accuracy and decreasing localization time.

Three-dimensional grid-free sound source localization method based on deep learning

Sound source localization (SSL) technology is a popular method for identifying the locations of noise sources, which serves as a prerequisite for noise control. Deep learning, as a data-driven tool, shows broad perspectives in the field of SSL with its powerful nonlinear fitting ability. The existing deep learning-based SSL methods only provide a two-dimensional (2D) representation of the sound source location and cannot obtain the specific coordinates of the sound source in three-dimensional (3D) space. Although traditional beamforming methods can be directly generalized to 3D scenes in principle, they suffer from the limitations of insufficient vertical resolution and high computational cost. Therefore, a 3D grid-free SSL method (3DGF) informed by deep learning is suggested in this study to enhance the accuracy and computational efficiency of 3D localization. First, the number of data channels is compressed to respect limited memory resources during the training process. Subsequently, a dense convolutional neural network (DenseNet) model is utilized to obtain the 3D spatial coordinates of the sound source using the processed 3D beamforming map as input. Since the coordinates are continuous and are not constrained by the grid of the beamforming map, the grid-free strategy presents more accurate localization results. Then, the effects of the volume of training data and the compression ratio are analyzed, respectively, in simulation, and the localization performance with different signal-to-noise ratios (SNRs) is also tested. Finally, by comparing 3DGF with DAMAS, both simulation and experimental results demonstrate that 3DGF improves the accuracy and efficacy of 3D localization. Meanwhile, its satisfactory generalization ability and robustness against noise highlight its potential for practical applications.

Tiny-Object Detection Based on Optimized YOLO-CSQ for Accurate Drone Detection in Wildfire Scenarios

Wildfires, which are distinguished by their destructive nature and challenging suppression, present a significant threat to ecological environments and socioeconomic systems. In order to address this issue, the development of efficient and accurate fire detection technologies for early warning and timely response is essential. This paper addresses the complexity of forest and mountain fire detection by proposing YOLO-CSQ, a drone-based fire detection method built upon an improved YOLOv8 algorithm. Firstly, we introduce the CBAM attention mechanism, which enhances the model’s multi-scale fire feature extraction capabilities by adaptively adjusting weights in both the channel and spatial dimensions of feature maps, thereby improving detection accuracy. Secondly, we propose an improved ShuffleNetV2 backbone network structure, which significantly reduces the model’s parameter count and computational complexity while maintaining feature extraction capabilities. This results in a more lightweight and efficient model. Thirdly, to address the challenges of varying fire scales and numerous weak emission targets in mountain fires, we propose a Quadrupled-ASFF detection head for weighted feature fusion. This enhances the model’s robustness in detecting targets of different scales. Finally, we introduce the WIoU loss function to replace the traditional CIoU object detection loss function, thereby enhancing the model’s localization accuracy. The experimental results demonstrate that the improved model achieves an mAP@50 of 96.87%, which is superior to the original YOLOV8, YOLOV9, and YOLOV10 by 10.9, 11.66, and 13.33 percentage points, respectively. Moreover, it exhibits significant advantages over other classic algorithms in key evaluation metrics such as precision, recall, and F1 score. These findings validate the effectiveness of the improved model in mountain fire detection scenarios, offering a novel solution for early warning and intelligent monitoring of mountain wildfires.

Deep learning-based source term estimation of hydrogen leakages from a hydrogen fueled gas turbine

Hydrogen fueled gas turbine is an efficient and environmentally friendly energy conversion equipment. However, it is prone to gas leakages from corrosion-prone components and pipe connections. In order to address gas leakage problems, source term estimation (STE) can be applied to estimate the location and intensity of the leakage sources. Traditional STE methods are based on an optimization procedure using an atmospheric transport and dispersion model, which requires considerable computation efforts and cannot meet the need of real-time implementation. The complex flow field around the gas turbine, the possible existence of multiple leakage sources, and the high-dimensional time series data further increase the difficulty of the STE problem. To address these challenges, in the present study, a deep learning based STE approach is proposed. The basic idea is to use long short-term memory auto-encoder (LSTM-AE) network to extract the encoded features of multi-sensor data and then use a deep neural network (NN) to establish the relationship between the encoded features and the source term parameters of hydrogen leakages. The multi-sensor data are generated using computational fluid dynamics (CFD) analysis to simulate relevant hydrogen leakage scenarios of concern. The results show that compared with other machine learning algorithms, the proposed approach has an overall best performance in terms of the STE accuracy. Specifically, its hydrogen leakage source localization accuracy is 0.9798, and the leakage strength estimation R-squared (R2) can reach 0.9632. When the training data proportion is only 20–30%, the proposed approach can still perform well, indicating the ability of the model to effectively predict the location and strength of the leakage source even with relatively less training data.

Impact of Ventilator Settings on Pulmonary Nodule Localization Accuracy in a Hybrid Operating Room: A Single-Center Study.

Background: Pulmonary nodule localization in a hybrid operating room (OR) followed by thoracoscopic operation presents a viable alternative for early lung cancer treatment, potentially supplanting conventional two-stage preoperative computed tomography-guided localization. This hybrid OR technique enables lesion localization under positive ventilation, contrasting with the traditional method requiring concurrent respiratory motion. This study aimed to evaluate our experience with different ventilator settings and the accuracy of pulmonary nodule localization. Methods: We retrospectively analyzed 176 patients with multiple pulmonary nodules who had localization procedures in our hybrid operating room. Ninety-five patients were assigned to the traditional ventilator setting group (tidal volume 8-10 mL/kg) and 81 to the lung-protective strategy group (tidal volume < 8 mL/kg). Localization accuracy was assessed via hybrid computed tomography imaging, ensuring that the needle-to-lesion distance was ≤5 mm. Between-group differences were assessed using the chi-squared test, Fisher's exact test, and the Mann-Whitney U test, as appropriate. Results: Pathological findings revealed primary lung malignancy in 150 patients, inclusive of invasive adenocarcinoma, adenocarcinoma in situ, and minimally invasive adenocarcinoma. Multivariate regression analysis identified tidal volume, nodule count, and localization depth as significant predictors of localization accuracy. Conclusions: This study demonstrated that ventilator settings with a tidal volume of 8-10 mL/kg significantly enhanced localization accuracy and slightly improved patient oxygenation. However, additional randomized controlled trials are warranted to validate these findings and establish definitive guidelines for future interventions.

Counter-directional mixing of longitudinal guided waves to localize multiple micro-damages in a pipe

Abstract Nonlinear guided wave has been considered as an effective means to detect early micro-damage in structures. In particular, the guided wave mixing technique has received great attention due to its ability to effectively isolate nonlinearity of measurement system and to localize micro-damage. Previous research mostly focuses on guided wave mixing in plate-like structures, and the research on guided wave mixing in pipes is very limited. Recent studies have been conducted on the co-directional mixing of guided waves in pipes. However, this mixing method is difficult to accurately localize multiple micro-damages because of the large mixing zone. In order to overcome these shortcomings, this study was carried out to investigate the counter-directional mixing of longitudinal guided waves in a pipe. Finite element simulations were performed on the counter-directional mixing of two primary guided waves in the pipe, and the generation of harmonic at the sum frequency was observed. The combined harmonic was used for the characterization and localization of two micro-damages in the pipe. The results show that by controlling the mixing zone of the two primary guided waves, micro-damage areas with an axial separation of 30mm can be effectively localized, which significantly improves the resolution of multiple micro-damages compared with the co-directional mixing method. Meanwhile, the relationship between the size of the mixing zone and the localization accuracy is discussed. This study provides a promising means for multiple micro-damages detection in pipes.

Auditory Localization Performance in Cochlear Implant Recipients With Single-Sided Deafness: The Challenges and Limitations of Current Outcome Metrics.

Acoustic localization accuracy metrics currently employed in clinical literature both overestimate and underestimate performance benefit of cochlear implantation (CI) for single-sided deafness (SSD). Although localization in SSD with CI has been investigated, performance characterization has relied heavily on average error. Although attractively concise, this measure may misrepresent performance. Here, we characterize frequency-specific localization on a granular level in subjects with CI for SSD as a critical analysis of localization outcome metrics. Eight CI recipients with SSD were recruited. Stimuli of broadband (BBN) and narrowband noise (NBN) at low (500 Hz), mid (1000 Hz), and high (4000 Hz) frequencies were presented in a semianechoic chamber. Localization accuracy was quantified in mean angular error (MAE) and linear regression slope. Use of a CI for SSD subjects improved localization performance by slope for all stimuli ( p ≤ 0.0033) to a level that was equal to normal-hearing controls at 1 and 4 kHz ( p ≥ 0.2281). MAE was also significantly improved for SSD subjects using CI for BBN stimuli ( p ≪ 0.0001); however, no statistically significant improvement in MAE was seen for NBN ( p ≥ 0.5773) with CI use. Descriptive analysis of individual subject performance highlights the reasons for contradictory results. There is inherent challenge in characterizing localization benefit for individuals with CI for SSD. Our data demonstrate the limitations of utilization of average error as the sole metric for outcome benefit. We emphasize the importance of continued research investigating alternative outcome measures as we work toward a more refined understanding of the potential benefits and limitations of cochlear implantation for SSD.