Line-Features-Based Pose Estimation Method for the Disc Cutter Holder of Shield Machine.

To achieve automatic disc cutter replacement of shield machines, measuring the accurate pose of the disc cutter holder by machine vision is crucial. However, under polluted and restricted illumination conditions, achieving pose estimation by vision is a great challenge. This paper proposes a line-features-based pose estimation method for the disc cutter holder of the shield machine by using a monocular camera. For the blurring effect of rounded corners on the image edge, a rounded edge model is established to obtain edge points that better match the 3D model of the workpiece. To obtain the edge search box corresponding to each edge, a contour separation method based on an adaptive threshold region growing method is proposed. By preprocesses on the edge points of each edge, the efficiency and the accuracy of RANSAC linear fitting are improved. The experimental result shows that the proposed pose estimation method is highly reliable and can meet the measurement accuracy requirements in practical engineering applications.

A New Fusion Fault Diagnosis Method for Fiber Optic Gyroscopes.

The fiber optic gyroscope (FOG) is a high precision inertial navigation device, and it is necessary to ensure its reliability for effective use. However, the extracted fault features are easily distorted due to the interference of vibrations when the FOG is in operation. In order to minimize the influence of vibrations to the greatest extent, a fusion diagnosis method was proposed in this paper. It extracted features from fault data with Fast Fourier Transform (FFT) and wavelet packet decomposition (WPD), and built a strong diagnostic classifier with a sparse auto encoder (SAE) and a neural network (NN). Then, a fusion neural network model was established based on the diagnostic output probabilities of the two primary classifiers, which improved the diagnostic accuracy and the anti-vibration capability. Then, five fault types of the FOG under random vibration conditions were established. Fault data sets were collected and generated for experimental comparison with other methods. The results showed that the proposed fusion fault diagnosis method could perform effective and robust fault diagnosis for the FOG under vibration conditions with a high diagnostic accuracy.

Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision.

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine's cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.

An Integrated Compensation Method for the Force Disturbance of a Six-Axis Force Sensor in Complex Manufacturing Scenarios.

As one of the key components for active compliance control and human-robot collaboration, a six-axis force sensor is often used for a robot to obtain contact forces. However, a significant problem is the distortion between the contact forces and the data conveyed by the six-axis force sensor because of its zero drift, system error, and gravity of robot end-effector. To eliminate the above disturbances, an integrated compensation method is proposed, which uses a deep learning network and the least squares method to realize the zero-point prediction and tool load identification, respectively. After that, the proposed method can automatically complete compensation for the six-axis force sensor in complex manufacturing scenarios. Additionally, the experimental results demonstrate that the proposed method can provide effective and robust compensation for force disturbance and achieve high measurement accuracy.

A Camera Intrinsic Matrix-Free Calibration Method for Laser Triangulation Sensor.

Laser triangulation sensors (LTS) are widely used to acquire depth information in industrial applications. However, the parameters of the components, e.g., the camera, of the off-the-shelf LTS are typically unknown. This makes it difficult to recalibrate the degenerated LTS devices during regular maintenance operations. In this paper, a novel one-dimensional target-based camera intrinsic matrix-free LTS calibration method is proposed. In contrast to conventional methods that calibrate the LTS based on the precise camera intrinsic matrix, we formulate the LTS calibration as an optimization problem taking all parameters of the LTS into account, simultaneously. In this way, many pairs of the camera intrinsic matrix and the equation of the laser plane can be solved and different pairs of parameters are equivalent for displacement measurement. A closed-form solution of the position of the one-dimensional target is proposed to make the parameters of the LTS optimizable. The results of simulations and experiments show that the proposed method can calibrate the LTS without knowing the camera intrinsic matrix. In addition, the proposed approach significantly improves the displacement measurement precision of the LTS after calibration. In conclusion, the proposed method proved that the precise camera intrinsic matrix is not the necessary condition for LTS displacement measurement.

Development and Application of a Tandem Force Sensor.

In robot teaching for contact tasks, it is necessary to not only accurately perceive the traction force exerted by hands, but also to perceive the contact force at the robot end. This paper develops a tandem force sensor to detect traction and contact forces. As a component of the tandem force sensor, a cylindrical traction force sensor is developed to detect the traction force applied by hands. Its structure is designed to be suitable for humans to operate, and the mechanical model of its cylinder-shaped elastic structural body has been analyzed. After calibration, the cylindrical traction force sensor is proven to be able to detect forces/moments with small errors. Then, a tandem force sensor is developed based on the developed cylindrical traction force sensor and a wrist force sensor. The robot teaching experiment of drawer switches were made and the results confirm that the developed traction force sensor is simple to operate and the tandem force sensor can achieve the perception of the traction and contact forces.

Adaptive-Neuro-Fuzzy-Based Information Fusion for the Attitude Prediction of TBMs.

In a tunneling boring machine (TBM), to obtain the attitude in real time is very important for a driver. However, the current laser targeting system has a large delay before obtaining the attitude. So, an adaptive-neuro-fuzzy-based information fusion method is proposed to predict the attitude of a laser targeting system in real time. In the proposed method, a dual-rate information fusion is used to fuse the information of a laser targeting system and a two-axis inclinometer, and then obtain roll and pitch angles with a higher rate and provide a smoother attitude prediction. Considering that a measurement error exists, the adaptive neuro-fuzzy inference system (ANFIS) is proposed to model the measurement error, and then the ANFIS-based model is combined with the dual-rate information fusion to achieve high performance. Experimental results show the ANFIS-based information fusion can provide higher real-time performance and accuracy of the attitude prediction. Experimental results also verify that the ANFIS-based information fusion can solve the problem of the laser targeting system losing signals.

Depression and perceived stress among the medical residents under the "unified double-track" training systems. / "双轨合一"培训模式下住院医师的抑郁症状和知觉压力.

OBJECTIVES: It is an important reform for medical education in China to combine professional postgraduate training with standardized resident training. This study aims to evaluate the depression and perceived stress in postgraduate students of clinical medicine and residents from society and to determine the relation between depression and perceived stress in medical residents. METHODS: Chinese Perceived Stress Scale (CPSS) and Self-Rating Depression Scale (SDS) were applied to 330 residents (including 235 postgraduate students of clinical medicine and 95 residents from society) from a Class-A Grade-3 genernal hospital in Hunan Province to evaluate and compare the depression and perceived stress in postgraduate students of clinical medicine and residents from society. Pearson correlation analysis was performed to assess the association between depression and perceived stress. Stress resources between 2 groups of residents were observed and compared. RESULTS: Of the 235 postgraduate students of clinical medicine, 148 (63.0%) showed depression and 162 (68.9%) showed elevated perceived stress. Main stress resources were academic pressure, scientific research pressure, and employment pressure. Of the 95 residents from society, 52 (54.7%) showed depression and 58 (61.1%) showed elevated perceived stress. Main stress resources were economic stress, employment pressure, and academic pressure. The scores of CPSS and SDS were significantly higher in postgraduate students of clinical medicine than those in residents from society (t=2.110, P=0.036; t=2.810, P=0.005, respectively), while gender showed no difference in the scores of CPSS and SDS (t=-0.968, P=0.334; t=0.462, P=0.644, respectively). There was a significant positive correlation between depression and perceived stress (r=0.854, P<0.001). CONCLUSIONS: Residents (including postgraduate students of clinical medicine and residents from society) possess depression and elevated perceived stress with positive correlation. The postgraduate students of clinical medicine show higher level of depression and perceived stress than the residents from society under the "unified double-track" training system.

Research and engineering application of layout optimization for lockbolt structures in railway wagons considering multidimensional failure modes based on MSNSGA-III.

The lockbolt structure is essential in railway wagons, and a scientific lockbolt layout can ensure uniform load distribution, thereby preventing failure. However, current engineering lacks layout optimization methods that address multidimensional failure modes. This paper presents a new lockbolt structure layout optimization method based on submodel, parametric models, and a multi-strategy integrated NSGA-III (MSNSGA-III), adhering to the DVS EFB 3435-2 standard. This method simultaneously optimizes the number and spacing of lockbolts to prevent tensile, bearing, shear, and other static failure modes under specified load conditions. The proposed method was applied during the design phase of a container flatcar. Optimization results indicate that, compared to NSGA-III, this method achieves the best IGD and HV values across multiple complex test functions, demonstrating superior performance in solving complex Pareto front optimization problems. Additionally, the optimized lockbolt structure's safety margins increased by a maximum of 59.81%, passing the full vehicle strength test and significantly enhancing resistance to multidimensional failure modes. These results highlight the method's significant practical application value in addressing the optimization of railway wagon lockbolt structures under complex multidimensional failure modes.

Research on Load Reverse Engineering and Vibration Fatigue Analysis Technology of Rapid Box Wagon.

The overall stiffness and modal frequency of the car body of a rapid box car are reduced by the design of the full-side open movable side door structure. The vibration fatigue performance of the welded structure in this car body needs to be verified. The rigid-flexible coupling model of the rapid box wagon was established first, and the model was verified by modal test data. By the application of the virtual iteration method on this model, the displacement excitation loads of this vehicle were acquired. The effectiveness of the load reverse obtaining technology was verified through the comparison between calculated data and the experimental data. Based on the rigid-flexible coupling model and the load obtained by reverse engineering, the fatigue life of the welded structure in the car body was evaluated through the modal structural stress method. The calculated results show that the car body structure obtains obvious modal vibration, which leads to short fatigue life in several weld lines. According to the application requirements of this wagon, the local improvement scheme was proposed, and the effect of the improvement program was evaluated. In this paper, a new fatigue evaluation technology based on the load reverse method of test data was proposed, which provides a theoretical basis for the structural design and program improvement of railway vehicles.

A Study of Response Inhibition in Overweight/Obesity People Based on Event-Related Potential.

Objective: To investigate the characteristics of response inhibition of overweight/obese people, using behavior experiments combine with neural electrophysiological technology and discussing the difference in impulse level between obesity/overweight and normal-weight people through EEG data, questionnaire, and behavior experiment. Method: (1) All participants completed the Go/Nogo task; meanwhile, behavior data and 64 channel EEG data were recorded. (2) Participants completed the Stop-Signal task and behavior date was recorded. Results: (1) During Go/Nogo task, no significant differences were found in reaction time, omission errors of the Go task between the two groups, while commission errors of the Nogo task of the control group were significantly greater than the overweight/obesity group. (2) About SSRT during the Stop-Signal Task, the interaction of stimulus type (high-calorie food picture, low-calorie food picture) and group (control group, overweight/obesity group) was significant (p = 0.008). (3) No significant differences were found between the two groups in amplitude and latency of N2. About the amplitude of P3, the interaction of task type (Go task, Nogo task), electrode point (Cz, CPz, Pz), and groups were significant (p = 0.041), the control group P3 amplitude was significantly greater than overweight/obesity group during the Nogo task. Regarding about latency of P3, the interaction of group and electrode point were not significant (p = 0.582), but the main effect of task type was significant (p = 0.002). Conclusion: (1) In terms of behavioral outcomes, overweight-obese subjects had lower dominant response inhibition and response cessation compared to normal-weight subjects. (2) In terms of EEG results, overweight-obese subjects showed no difference in processing speed and level of conflict monitoring for early inhibitory processing compared to normal-weight subjects, but there was a deficit in behavioral control for late inhibitory processing.

Neural-Network-Based Iterative Learning Control for Multiple Tasks.

Iterative learning control (ILC) can synthesize the feedforward control signal for the trajectory tracking control of a repetitive task, even when the system has strong nonlinear dynamics. This makes ILC be one of the most popular methods for trajectory tracking control. Restriction on a repetitive task, however, limits its application to multiple trajectories. This article proposes a neural-network-based ILC (NN-ILC) to deal with nonrepetitive tasks very effectively. A position-based ILC is designed to compensate the tracking error, based on which the multiple outputs of the ILC (ILC outputs) for multiple tasks are expressed as a function of the reference position, velocity, and acceleration. The proposed NN-ILC divides the ILC outputs of multiple tasks into two parts: the linear and nonlinear portions. The first part is expressed by a linear function, which is the linear portion of the function of the ILC outputs. The second part is expressed by a nonlinear function, which is estimated by complementary neural networks including a general neural network and a switching neural network. Finally, the two parts are combined and the ILC outputs of multiple tasks are expressed as a neural-network-based function. Two advantages of the proposed NN-ILC are emphasized. First, the ILC outputs of multiple tasks are compressed into a function by the proposed method, and thus, the memories can be saved. Second, in terms of generalizability, the neural-network-based function of the ILC outputs can easily predict position compensation for multiple tasks without extra iterative learning processes. Experimental results on a robot arm show that the proposed NN-ILC method can easily realize the ILC of multiple tasks. It can save memory comparing with the method of storing the data of multiple tasks and can predict the ILC output of any task, which can accelerate the iterative learning process.

Insensitivity to Success and Failure: An Experimental Study of Performance-Based Feedback in Depression.

OBJECTIVE: This experimental study set out to examine the effects of performance feedback (success or failure) on depressed emotions and self-serving attribution bias in inpatients suffering from major depressive disorder (MDD). METHODS: The study was based on a 2 × 2 experimental design in which 71 MDD patients and 59 healthy controls participated. Both groups (MDD and controls) were randomly assigned to two conditions: success or failure in the performance feedback. A section of Raven's Standard Progressive Matrices (SPM) was used as a bogus test of the participants' reasoning abilities, and the Core Depressive Factor of the Zung Self-Rating Depression Scale was used to measure changes in depressed emotion in the subjects following the performance feedback. Participants then rated the accuracy of the SPM as a measure of their reasoning capacity. RESULTS: The levels of depressed emotions in patients with MDD did not differ significantly under the two feedback conditions. In contrast, depressed emotion levels increased significantly in healthy individuals in response to failure feedback but did not change in response to success feedback. With regard to the ratings of SPM accuracy, there was no significant difference across the two feedback conditions for depressed patients; however, the accuracy ratings were higher in the success condition than in the failure condition for the controls. CONCLUSION: Individuals with MDD exhibit blunted emotional reactivity when experiencing new positive or negative social stimuli, supporting the theory of Emotion Context Insensitivity. In addition, self-serving attribution bias does not occur in MDD, which is consistent with the theory of learned helplessness in depression.

CMOS-Compatible Fabrication for Photonic Crystal-Based Nanofluidic Structure.

Photonic crystal (PC)-based devices have been widely used since 1990s, while PC has just stepped into the research area of nanofluidic. In this paper, photonic crystal had been used as a complementary metal oxide semiconductors (CMOS) compatible part to create a nanofluidic structure. A nanofluidic structure prototype had been fabricated with CMOS-compatible techniques. The nanofluidic channels were sealed by direct bonding polydimethylsiloxane (PDMS) and the periodic gratings on photonic crystal structure. The PC was fabricated on a 4-in. Si wafer with Si3N4 as the guided mode layer and SiO2 film as substrate layer. The higher order mode resonance wavelength of PC-based nanofluidic structure had been selected, which can confine the enhanced electrical field located inside the nanochannel area. A design flow chart was used to guide the fabrication process. By optimizing the fabrication device parameters, the periodic grating of PC-based nanofluidic structure had a high-fidelity profile with fill factor at 0.5. The enhanced electric field was optimized and located within the channel area, and it can be used for PC-based nanofluidic applications with high performance.

A Nanofluidic Biosensor Based on Nanoreplica Molding Photonic Crystal.

A nanofluidic biosensor based on nanoreplica molding photonic crystal (PC) was proposed. UV epoxy PC was fabricated by nanoreplica molding on a master PC wafer. The nanochannels were sealed between the gratings on the PC surface and a taped layer. The resonance wavelength of PC-based nanofluidic biosensor was used for testing the sealing effect. According to the peak wavelength value of the sensor, an initial label-free experiment was realized with R6g as the analyte. When the PC-based biosensor was illuminated by a monochromatic light source with a specific angle, the resonance wavelength of the sensor will match with the light source and amplified the electromagnetic field. The amplified electromagnetic field was used to enhance the fluorescence excitation result. The enhancement effect was used for enhancing fluorescence excitation and emission when matched with the resonance condition. Alexa Fluor 635 was used as the target dye excited by 637-nm laser source on a configured photonic crystal enhanced fluorescence (PCEF) setup, and an initial PCEF enhancement factor was obtained.