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This study aims to identify certain phonetic and lexical determinants of major durational variations in spontaneous speech. Three duration samples (SHORT, STANDARD and LONG) were extracted from a corpus of French conversational speech. With high concentrations of liquids and glides in SHORT, vowels show strong temporal distortion towards lengthening. Conversely, fricatives and plosives represent stable temporal supports. Some function words tend to promote reduction while the lengthening of phonemes is due, in large part, to their presence in some frequent items in conversation (discourse markers, interjections). The duration of specific phonemes (/l/, /ʀ/, /w/, /m/) is strongly conditioned by their lexical or communicative context. Phonemic durations are therefore determined, among other things, by the nature of phonemes and by the properties of lexicon and its use in conversation.

Whispering-gallery mode microresonators have gained wide popularity as experimental platforms for different applications, ranging from biosensing to nonlinear optics. Typically, the resonant modes of dielectric microresonators are stimulated via evanescent wave coupling, facilitated using tapered optical fibers or coupling prisms. However, this method poses serious shortcomings due to fabrication and access-related limitations, which could be elegantly overcome by implementing a free-space coupling approach; although additional alignment procedures are needed in this case. To address this issue, we have developed a new algorithm to excite the microresonator automatically. Here, we show the working mechanism and the preliminary results of our experimental method applied to a home-made silica microsphere, using a visible laser beam with a spatial light modulator and a software control.

The field harvesting process of harvesting machinery is often affected by high workload and environmental factors that can impede/delay manual rowing, thereby leading to lower efficiency and quality in the residual film collector. To address this challenge, an automatic rowing control system using the 4mz-220d self-propelled residual film collector as the experimental carrier was proposed in this study. Cotton stalks in the ridges were chosen as the research object, and a comprehensive application of key technologies, machinery, and electronic control was used, thereby incorporating a pure tracking model as the path-tracking control method. To achieve the automatic rowing function during the field traveling process, the fuzzy control principle was implemented to adjust the forward distance within the pure tracking model dynamically, and the expected steering angle of the steering wheel was determined based on the kinematic model of the recovery machine. The MATLAB/Simulink software was utilized to simulate and analyze the proposed model, thus achieving significant improvements in the automation level of the residual film collector. The field harvesting tests showed that the average deviation of the manual rowing was 0.144 m, while the average deviation of the automatic rowing was 0.066 m. Moreover, the average lateral deviation of the automatic rowing was reduced by 0.078 m with a probability of deviation within 0.1 m of 95.71%. The research study demonstrated that the designed automatic rowing system exhibited high stability and robustness, thereby meeting the requirements of the autonomous rowing operations of residual film collectors. The results of this study can serve as a reference for future research on autonomous navigation technology in agriculture.

The efficiency of power transfer is a critical issue for wireless charging applications in electric vehicles. The misalignment between the transmitter coil and the receiver coil in wireless charging leads to a significant reduction in efficiency. This article investigates improving coil misalignment performance in wireless power transfer for electric vehicles using magnetic flux density analysis. The objective is to study the effect of the automatic alignment transmitter system’s movement on error distance. The automatic alignment transmitter system was integrated with a wireless power transfer system to realign the transmitter coil whenever lateral misalignment occurred between the transmitter and receiver coils. The experiment was performed with a horizontal misalignment of 0.35 m and was repeated three times. The gap between the coils was held constant at 0.15 m. The wireless charging system was designed according to the Society of Automotive Engineers (SAE) standard. The experimental results demonstrated that the movement error distance was 0.001 m, with an average error of 0.33%. These findings indicate that the automatic alignment transmitter system achieved an operational effectiveness of 99.67%. The maximum wireless charging efficiencies of 75.78% and 75.59% were recorded for the X-axis and Y-axis adjustments, respectively.

Aiming to meet the growing need for automated harvesting, an automatic alignment method based on Euclidean clustering and K-means clustering is proposed to address issues of driver fatigue and inaccurate driving in manually operated corn harvesters. Initially, the corn field environment is scanned using LiDAR to obtain point cloud data, which are then subjected to pass-through filtering and statistical filtering to remove noise and non-corn contour points. Subsequently, Euclidean clustering and K-means clustering methods are applied to the filtered point cloud data. To validate the impact of Euclidean clustering on subsequent clustering, two separate treatments of the obtained point cloud data were conducted during experimental validation: the first used the K-means clustering algorithm directly, while the second involved performing Euclidean clustering followed by K-means clustering. The results demonstrate that the combined method of Euclidean clustering and K-means clustering achieved a success rate of 81.5%, representing a 26.5% improvement over traditional K-means clustering. Additionally, the Rand index increased by 0.575, while accuracy improved by 57% and recall increased by 61%.

The current shafting alignment process primarily relies on manual adjustments, resulting in low efficiency and limited precision. This study designs an automatic alignment system for multi-support shafting to improve alignment efficiency and accuracy, advancing the development of intelligent systems. The overall design of the automatic alignment system is proposed, including the establishment of shafting and actuator models. The alignment performance of two control strategies under different shafting deviations is validated through simulations. The results indicate that both the full-state feedback control strategy and the model predictive control strategy can regulate bearing load errors to within 1%. Compared to manual alignment, both strategies demonstrate significantly higher efficiency. The proposed automatic alignment system is feasible and lays a foundation for the development of high-precision shafting alignment systems.

Purpose The purpose of this study is to provide a micro-nano chip automatic alignment system. Used for micron and nanometer channel alignment of microfluidic chip. Design/methodology/approach In this paper, combined with the reconstructed micro–nanoscale Hough transform theory, a “clamp–adsorb–rotate” chip alignment method is proposed. The designed alignment system includes a microscopic identification device, a clamping device and a suction device. After assembly, the straightness of the linear slide rail in the horizontal and vertical directions was tested, respectively. The results show that in the horizontal and vertical directions, the linearity error of the linear slide is +0.29 and 0.30 µm, respectively, which meets the requirement of chip alignment accuracy of 15 µm. In the direction of rotation, the angular error between the microchannel and the nanochannel is ±0.5°. In addition, an alignment flow experiment of the chip is designed. The results demonstrate that the closer the angle between the microchannel and the nanochannel is to 90°, the fluid fills the entire channel. Compared with the conventional method, the method and the assembly system realize fully automatic double-layer chip alignment. Findings A mechanical device designed by Hough transform theory can realize microfluidic chip alignment at nanometer and micron level. Originality/value The automatic alignment device adopts Hough transform principle and can be used for microfluidic chip alignment.

Human body motion analysis based on wearable inertial measurement units (IMUs) receives a lot of attention from both the research community and the and industrial community. This is due to the significant role in, for instance, mobile health systems, sports and human computer interaction. In sensor based activity recognition, one of the major issues for obtaining reliable results is the sensor placement/assignment on the body. For inertial motion capture (joint kinematics estimation) and analysis, the IMU-to-segment (I2S) assignment and alignment are central issues to obtain biomechanical joint angles. Existing approaches for I2S assignment usually rely on hand crafted features and shallow classification approaches (e.g., support vector machines), with no agreement regarding the most suitable features for the assignment task. Moreover, estimating the complete orientation alignment of an IMU relative to the segment it is attached to using a machine learning approach has not been shown in literature so far. This is likely due to the high amount of training data that have to be recorded to suitably represent possible IMU alignment variations. In this work, we propose online approaches for solving the assignment and alignment tasks for an arbitrary amount of IMUs with respect to a biomechanical lower body model using a deep learning architecture and windows of 128 gyroscope and accelerometer data samples. For this, we combine convolutional neural networks (CNNs) for local filter learning with long-short-term memory (LSTM) recurrent networks as well as generalized recurrent units (GRUs) for learning time dynamic features. The assignment task is casted as a classification problem, while the alignment task is casted as a regression problem. In this framework, we demonstrate the feasibility of augmenting a limited amount of real IMU training data with simulated alignment variations and IMU data for improving the recognition/estimation accuracies. With the proposed approaches and final models we achieved 98.57% average accuracy over all segments for the I2S assignment task (100% when excluding left/right switches) and an average median angle error over all segments and axes of 2.91 ° for the I2S alignment task.

Non-contact optical detection of ultrasound critically depends on the amount of light collected from the detection surface. Although it can be optimized in multiple ways for an ideal flat polished surface, industrial non-destructive testing and evaluation (NDT&E) usually requires optical detectors to be robust for unpolished material surfaces that are usually rough and curved. Confocal detectors provide the best light collection but must trade off sensitivity with depth of field. Specifically, detection efficiency increases with the numerical aperture (NA) of the detector, but the depth of field drops. Therefore, fast realignment of the detector focal point is critical for in-field applications. Here, we propose an optical distance and angle correction system (DACS) and demonstrate it in a kHz-rate laser-ultrasound inspection system. It incorporates a Sagnac interferometer on receive for the fast scanning of aircraft composites, which minimizes the required initial alignment. We show that DACS performs stably for different composite surfaces while providing ±2° angular and ±2 mm axial automatic correction with a maximum 100 ms realignment time.

Coastal vegetation is effective in dissipating incident wave energy during storm conditions, which offers valuable protection to coastal communities. Determining vegetation drag coefficient (CD) is of great importance to the quantification of vegetation-induced wave dissipation. Recently, a direct measuring approach has been developed to derive vegetation drag coefficient more accurately compared to the conventional calibration approach. However, as this approach requires perfectly in-phase force and velocity signals, there are two difficulties associated with it. The first difficulty is the availability of a suitable force sensor to compose synchronized force–velocity measuring systems. The second difficulty is related to realigning the obtained timeseries of force and velocity data. This technical note develops a new synchronized force–velocity measuring system by using standard force sensors and an acoustic doppler velocimeter (ADV). This system is applied together with an automatic realignment algorithm to ensure in-phase data for CD deviation. The algorithm reduces the phase shift between force–velocity signals from ca. 0.26 s to 0.003 s. Both time-varying and period-averaged CD can be obtained using this method. The derived CD can be used to accurately reproduce the measured maximum total acting force on vegetation (R2 = 0.759), which shows the reliability of the automatic alignment algorithm. The newly-developed synchronized force–velocity measuring system and alignment algorithm are expected to be useful in future experiments on vegetation–wave interactions with various hydrodynamic and vegetation settings.