Time-Frequency Multi-Domain 1D Convolutional Neural Network with Channel-Spatial Attention for Noise-Robust Bearing Fault Diagnosis.

This paper proposes a noise-robust and accurate bearing fault diagnosis model based on time-frequency multi-domain 1D convolutional neural networks (CNNs) with attention modules. The proposed model, referred to as the TF-MDA model, is designed for an accurate bearing fault classification model based on vibration sensor signals that can be implemented at industry sites under a high-noise environment. Previous 1D CNN-based bearing diagnosis models are mostly based on either time domain vibration signals or frequency domain spectral signals. In contrast, our model has parallel 1D CNN modules that simultaneously extract features from both the time and frequency domains. These multi-domain features are then fused to capture comprehensive information on bearing fault signals. Additionally, physics-informed preprocessings are incorporated into the frequency-spectral signals to further improve the classification accuracy. Furthermore, a channel and spatial attention module is added to effectively enhance the noise-robustness by focusing more on the fault characteristic features. Experiments were conducted using public bearing datasets, and the results indicated that the proposed model outperformed similar diagnosis models on a range of noise levels ranging from -6 to 6 dB signal-to-noise ratio (SNR).

Latent profile analysis of post-traumatic stress and post-traumatic growth among firefighters.

Background: Due to the job characteristics, firefighters are repeatedly exposed to trauma incidents. However, not all firefighters exhibit the same level of post-traumatic stress disorder (PTSD) or post-traumatic growth (PTG). Despite this, few studies have looked into firefighters' PTSD and PTG.Objective: This study identified subgroups of firefighters based on their PTSD and PTG levels, and investigated the influence of demographic factors and PTSD/PTG-related factors on latent class classification.Method: Latent profile analysis was used to examine the patterns of PTSD and PTG among 483 firefighters in South Korea. Using a cross-sectional design, demographic factors and job factors were examined as group covariates through a three-step approach. PTSD-related factors such as depression and suicide ideation, as well as PTG-related factors such as emotion-based response were analysed as differentiating factors.Results: Four classes were identified and named 'Low PTSD-low PTG (65.2%),' 'Mid PTSD-mid PTG (15.5%),' 'Low PTSD-high PTG (15.3%),' and 'High PTSD-mid PTG (3.9%).' The likelihood of belonging to the group with high trauma-related risks increased with more rotating shift work and years of service. The differentiating factors revealed differences based on the levels of PTSD and PTG in each group.Conclusions: 34.8% of firefighters experienced changes due to traumatic events while on the job, and some required serious attention. Modifiable job characteristics, such as the shift pattern, indirectly affected PTSD and PTG levels. Individual and job factors should be considered together when developing trauma interventions for firefighters.

Firefighters were classified into four groups based on their levels of PTSD and PTG. 65.2% of the participants belonged to the 'Low PTSD-low PTG' group.The shift pattern and years of service predicted the likelihood of group classification.This implies that, despite being frequently exposed to threatening events, not all firefighters experience trauma, and that job characteristics influence trauma-related vulnerabilities.

Effects of Carbon Nanotube and Graphene Oxide Incorporation on the Improvements of Magneto-Induced Electrical Sensitivity of Magneto-Rheological Gel.

Magneto-rheological gel (MRG) has been the subject of recent research due to its versatile applications. Especially, the magneto-induced electrical properties of MRGs under different levels of magnetic field enables them to be used as magneto-sensors. However, conventional MRG shows a low level of electrical conductivity, complicating its use in sensor applications. In this regard, in the present study, the carbon nanotube (CNT) and graphene oxide (GO) are added to fabricate new types of MRG. Herein, four different MRG samples were fabricated with reference to an amount of CNT and GO. The microstructural images of carbonyl iron powder (CIP)-based chain structures with CNT and GO were observed using SEM images. Then, their magneto-induced electrical impedances were investigated under four levels of magnetic field (i.e., 0, 50, 100, and 150 mT) and input frequencies (1, 2, 5, and 10 Hz). Based on the experimental results, three electrical models, including first-order series and parallel, and first- and half-order complex models, were proposed, and their accuracy was examined, showing the highest accuracy when first- and half-order complex models were used. The simulated results indicated that the incorporation of both CNT and GO can improve the magneto-induced electrical sensitivity; thus, it can be concluded that MRG with CNT and GO can be a possible method to be used in magneto-sensor applications.

Maize harvester gearbox design modification for improved fatigue life.

The gearbox has the advantage of being able to change the torque and rotational speed according to the gear ratio and has high power transmission efficiency by transmitting power through the contact of the gear pair. When evaluating the strength and fatigue life of a gearbox using a design load or an equivalent load, there is a possibility that the results will be very different from the actual ones. Therefore, in this study, the load duration distribution (LDD) constructed based on the actual workload was used to evaluate the strength and fatigue life of the gearbox reliably. As a result of evaluating the strength and fatigue life of the gearbox using LDD, it was confirmed that the existing gearbox did not satisfy the target lifespan in the operating environment. Therefore, the reasons for these results were analyzed, and design modification was performed based on the analyzed results. As a result of design modification, shaft deflection decreased by rearrangement of the bearings, from an overhung type to a straddle type, thereby improving the fatigue life of gears and bearings. Finally, the load distribution acting on the gear tooth surface was improved through micro-geometry modification of the gears.

Feasibility and acceptability of a digital tele-guided intervention targeting components of the addictive appetite model for bulimia nervosa and binge-eating disorder in Korea.

OBJECTIVE: We aimed to evaluate the feasibility, acceptability, and potential impact of a tele-guided digital-based intervention based on the addictive appetite model of recurrent binge eating. METHOD: Female college students with bulimia nervosa (BN) or binge-eating disorder (BED) (n = 22) received a 6-week guided intervention targeting addictive processes and emotion regulation. The feasibility of the intervention was evaluated, and the outcomes were assessed at baseline, the end of the intervention, and 1-month follow-up. RESULTS: Of the participants, 86.4% (n = 19) completed the intervention. The self-help materials were viewed 6.03 ± 3.06 times per week, and the duration of using the self-help materials was 113.16 ± 160.19 min/week. The intervention group experienced a significant reduction with a moderate effect on binge eating at the end of the intervention (Hedges' g = 0.58), and the effects lasted through follow-up (Hedges' g = 0.82). DISCUSSION: The results suggest that the digital intervention targeting a maintenance mechanism of recurrent binge eating was feasible and acceptable for patients with BN and BED, proving the potential for symptom improvement. PUBLIC SIGNIFICANCE: The addictive appetite model provides the framework for new interventions to improve treatments for BN and BED. This study found that the digital intervention based on the model was feasible and acceptable for patients with BN and BED.

Evaluation of (CNT@CIP)-Embedded Magneto-Resistive Sensor Based on Carbon Nanotube and Carbonyl Iron Powder Polymer Composites.

The conductive polymeric composites incorporating carbon nanotube (CNT) and carbonyl iron powder (CIP) have attracted much attention for various sensor applications. In this paper, a comprehensive study of the magneto-sensing property of a CNT-CIP embedded polymer composite is conducted to implement the composite as magneto-sensors. Thus, this study experimentally investigated the magneto-sensing performances of CNT-doped polymeric composites with the addition of CIP in terms of electrical conductivity, sensitivity, repeatability, and response time. First, the CNT-CIP clusters were manufactured and their interactions were analyzed with the zeta potential measurement and SEM observation. Then, the CNT-CIP clusters were embedded into the polymeric composites for the magneto-sensing evaluations. Experiments showed that the CNT contents in the range of percolation threshold (i.e., 0.5% and 0.75%) are optimal values for sensor applications. The addition of CNT 0.5% and 0.75% resulted in a high sensitivity of 7% and a faster response time within 400 ms. Experiment evaluation confirmed a high potential of implementing CNT-CIP composite as magneto-sensors.

Up-Sampling Method for Low-Resolution LiDAR Point Cloud to Enhance 3D Object Detection in an Autonomous Driving Environment.

Automobile datasets for 3D object detection are typically obtained using expensive high-resolution rotating LiDAR with 64 or more channels (Chs). However, the research budget may be limited such that only a low-resolution LiDAR of 32-Ch or lower can be used. The lower the resolution of the point cloud, the lower the detection accuracy. This study proposes a simple and effective method to up-sample low-resolution point cloud input that enhances the 3D object detection output by reconstructing objects in the sparse point cloud data to produce more dense data. First, the 3D point cloud dataset is converted into a 2D range image with four channels: x, y, z, and intensity. The interpolation on the empty space is calculated based on both the pixel distance and range values of six neighbor points to conserve the shapes of the original object during the reconstruction process. This method solves the over-smoothing problem faced by the conventional interpolation methods, and improves the operational speed and object detection performance when compared to the recent deep-learning-based super-resolution methods. Furthermore, the effectiveness of the up-sampling method on the 3D detection was validated by applying it to baseline 32-Ch point cloud data, which were then selected as the input to a point-pillar detection model. The 3D object detection result on the KITTI dataset demonstrates that the proposed method could increase the mAP (mean average precision) of pedestrians, cyclists, and cars by 9.2%p, 6.3%p, and 5.9%p, respectively, when compared to the baseline of the low-resolution 32-Ch LiDAR input. In future works, various dataset environments apart from autonomous driving will be analyzed.

EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification.

Objective.Classification of electroencephalography (EEG)-based motor imagery (MI) is a crucial non-invasive application in brain-computer interface (BCI) research. This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based MI classification that outperforms the state-of-the-art methods.Approach.The proposed CNN model, namely EEG-inception, is built on the backbone of the inception-time network, which has showed to be highly efficient and accurate for time-series classification. Also, the proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing. Furthermore, this paper proposes a novel data augmentation method for EEG signals to enhance the accuracy, at least by 3%, and reduce overfitting with limited BCI datasets.Main results.The proposed model outperforms all state-of-the-art methods by achieving the average accuracy of 88.4% and 88.6% on the 2008 BCI Competition IV 2a (four-classes) and 2b datasets (binary-classes), respectively. Furthermore, it takes less than 0.025 s to test a sample suitable for real-time processing. Moreover, the classification standard deviation for nine different subjects achieves the lowest value of 5.5 for the 2b dataset and 7.1 for the 2a dataset, which validates that the proposed method is highly robust.Significance.From the experiment results, it can be inferred that the EEG-inception network exhibits a strong potential as a subject-independent classifier for EEG-based MI tasks.

Analysis of Electrical Resistance and Impedance Change of Magnetorheological Gels with DC and AC Voltage for Magnetometer Application.

Magnetorheological gel (MRG) is a smart material that can change its stiffness property by external magnetic field and has been applied as a smart rubber in suppressing vibration. Recent studies show that the electrical resistance of MRG also can be affected with external magnetic field. Thus, this study aimed to conduct analysis on MRG resistance variation due to external magnetic field with DC and AC input voltage. With an DC input voltage, the resistance change due to magnetic field was modeled. In addition, the capacitance variation of the material was observed. The impedance of MRG due to AC input voltage was analyzed and was observed that the impedance of MRG was affected by both the magnetic field and the input frequency. With the experiment data, the impedance modeling of MRG in frequency domain was derived. Based on experiment results, the performance and limitation of MRG as a magnetometer sensor are discussed.

Estimating Clothing Thermal Insulation Using an Infrared Camera.

In this paper, a novel algorithm for estimating clothing insulation is proposed to assess thermal comfort, based on the non-contact and real-time measurements of the face and clothing temperatures by an infrared camera. The proposed method can accurately measure the clothing insulation of various garments under different clothing fit and sitting postures. The proposed estimation method is investigated to be effective to measure its clothing insulation significantly in different seasonal clothing conditions using a paired t-test in 99% confidence interval. Temperatures simulated with the proposed estimated insulation value show closer to the values of actual temperature than those with individual clothing insulation values. Upper clothing's temperature is more accurate within 3% error and lower clothing's temperature is more accurate by 3.7%~6.2% error in indoor working scenarios. The proposed algorithm can reflect the effect of air layer which makes insulation different in the calculation to estimate clothing insulation using the temperature of the face and clothing. In future, the proposed method is expected to be applied to evaluate the customized passenger comfort effectively.

The acceptable duration between occupational exposure to hepatitis B virus and hepatitis B immunoglobulin injection: Results from a Korean nationwide, multicenter study.

BACKGROUND: Postexposure prophylaxis for occupational exposure to hepatitis B virus (HBV) plays an important role in the prevention of HBV infections in health care workers (HCWs). We examined data concerning the acceptable duration between occupational exposure and administration of a hepatitis B immunoglobulin (HBIG) injection in an occupational clinical setting. METHODS: A retrospective analysis was conducted with data from 143 cases of HCWs exposed to HBV in 15 secondary and tertiary teaching hospitals between January 2005 and June 2013. Data were taken from the infection control records of each hospital. RESULTS: Active vaccination after HBV exposure was started in 119 cases (83.2%) and postvaccination testing for hepatitis B antibody showed positive seroconversion in 93% of cases. In 98 cases (68.5%), HBIG was administered within 24 hours after HBV exposure; however, 45 HCWs (31.5%) received an HBIG injection more than 24 hours postexposure and 2 among the 45 received an injection after 7 days. Although 31.5% received an HBIG injection more than 24 hours postexposure, no cases of seroconversion to hepatitis b antibody positivity occurred. CONCLUSIONS: For susceptible HCWs, HBIG administered between 24 hours and 7 days postexposure may be as effective as administration within 24 hours in preventing occupational HBV infection.

Note: Vibration suppression using tunable vibration absorber based on stiffness variable magneto-rheological gel.

This paper proposes a novel adaptive tunable vibration absorber (TVA) based on a smart material the magnetorheological gel (MRG) to achieve a wide range of tonal vibration suppression on the primary system to protect any connected sensitive device. The vibration suppression performance of the MRG TVA was analyzed by conducting a modal test of the primary system under different magnetic fields. The experiment verified that the proposed MRG TVA can be controlled to produce 379% of stiffness change or 115% of tuning frequency under just 200 mT magnetic field. The proposed MRG TVA was found to possess a wider tuning frequency range than the TVA based on other smart material such as magnetorheological elastomer.

Note: Reliable and non-contact 6D motion tracking system based on 2D laser scanners for cargo transportation.

Maritime transportation demands an accurate measurement system to track the motion of oscillating container boxes in real time. However, it is a challenge to design a sensor system that can provide both reliable and non-contact methods of 6-DOF motion measurements of a remote object for outdoor applications. In the paper, a sensor system based on two 2D laser scanners is proposed for detecting the relative 6-DOF motion of a crane load in real time. Even without implementing a camera, the proposed system can detect the motion of a remote object using four laser beam points. Because it is a laser-based sensor, the system is expected to be highly robust to sea weather conditions.

Design improvement of the three-beam detector towards a precise long-range 6-degree of freedom motion sensor system.

This paper presents performance enhancement of the three beam detector through design improvement based on the error analysis. The three-beam detector is a novel sensor system that measures six motions (i.e., 3 translations and 3 rotations) of a remote object with high accuracy by implementing three laser distance sensors and a vision sensor. In this paper, finite element analysis and parameter analysis are applied to the three-beam detector to analyze its structural frames and sensor configuration, respectively, in terms of sensing error. By virtue of such a systematic, quantitative error analysis, this research has successfully improved the performance of the three-beam detector and thoroughly validated through field tests that it can detect the 6-degree of freedom motions of a remote target at 30 m with an accuracy of 1.51 mm and 0.18° and precision of 0.38 mm and 0.13°.

Fabrications of electrospun nanofibers containing inorganic fillers for dye-sensitized solar cells.

Poly(vinylidenefluoride-co-hexafluoropropylene) (PVDF-HFP) nanofibers containing inorganic fillers were fabricated by electrospinning. Dye-sensitized solar cells (DSSCs) using these nanofibers showed improved short circuit currents without degraded fill factors or open circuit voltages. The long-term stabilities of cells using electrospun PVDF-HFP/titanium isopropoxide (TIP) nanofibers were significantly improved.

Photovoltaic and thermal properties of electrolytes based on electrospun poly(vinylidene fluoride-hexafluoro propylene)/poly(methyl methacrylate) nanofibers for dye-sensitized solar cells.

We prepared electrospun polymer nanofibers by electrospnning method and investigated about their applications to dye-sensitized solar cells (DSSCs). Electrospun polymer nanofibers applied to the polymer matrix in electrolyte for DSSCs. To improve the stiffness of polymer nanofiber, poly(vinylidene fluoride-hexafluoro propylene)/Poly(methyl methacrylate) (PVDF-HFP/PMMA) blend nanofibers were prepared and examined. In the electrospun PVDF-HFP/PMMA (1:1) blend nanofibers, the best results of VOC, JSC, FF, and efficiency of the DSSC devices showed 0.71 V, 12.8 mA/cm2, 0.61, and 5.56% under AM 1.5 illumination.

Incidence and risk factors for surgical site infection after gastric surgery: a multicenter prospective cohort study.

BACKGROUND: Surgical site infection (SSI) is a potentially morbid and costly complication of surgery. While gastrointestinal surgery is relatively common in Korea, few studies have evaluated SSI in the context of gastric surgery. Thus, we performed a prospective cohort study to determine the incidence and risk factors of SSI in Korean patients undergoing gastric surgery. MATERIALS AND METHODS: A prospective cohort study of 2,091 patients who underwent gastric surgery was performed in 10 hospitals with more than 500 beds (nine tertiary hospitals and one secondary hospital). Patients were recruited from an SSI surveillance program between June 1, 2010, and August 31, 2011 and followed up for 1 month after the operation. The criteria used to define SSI and a patient's risk index category were established according to the Centers for Disease Control and Prevention and the National Nosocomial Infection Surveillance System. We collected demographic data and potential perioperative risk factors including type and duration of the operation and physical status score in patients who developed SSIs based on a previous study protocol. RESULTS: A total of 71 SSIs (3.3%) were identified, with hospital rates varying from 0.0 - 15.7%. The results of multivariate analyses indicated that prolonged operation time (P = 0.002), use of a razor for preoperative hair removal (P = 0.010), and absence of laminar flow in the operating room (P = 0.024) were independent risk factors for SSI after gastric surgery. CONCLUSIONS: Longer operation times, razor use, and absence of laminar flow in operating rooms were independently associated with significant increased SSI risk after gastric surgery.

Note: real time control of a tunable vibration absorber based on magnetorheological elastomer for suppressing tonal vibrations.

An adaptive tunable vibration absober based on magnetorheological elastomer (MRE) is designed as an intelligent device for auto-tuning itself to the time-varying harmonic disturbance force to reduce the unwanted vibration of the primary system in the steady state. The objectives of this note are to develop and implement a continuous control method for a MRE tunable vibration absorber (TVA) and to evaluate its performance in suppressing time-varying tonal vibrations. In the proposed control, the stiffness of MREs is continuously varied based on a nonlinear tuning function that relates the response of the system to the input magnetic field density. Through experiments, it will be shown that the proposed MRE TVA reduces in real time the transmission of a time-varying excited vibration of 48-55 Hz, which shows the potential applicability of the MRE in reducing unwanted vibration to precision devices.

Risk factors for surgical site infection after gastric surgery: a multicentre case-control study.

BACKGROUND: Surgical site infection (SSI) is a potentially morbid and costly complication of surgery. We conducted a multicentre case-control study to determine the risk factors for SSI in patients undergoing gastric surgery and to establish strategies to reduce the risk of SSI. METHODS: Between January 2007 and December 2008, 121 patients who developed an SSI after gastric surgery were matched with controls who had undergone surgery on the dates closest to those of the cases, at 13 centres in Korea. RESULTS: The results of multivariate analyses showed that the independent risk factors for SSI after gastric surgery were older age (p = 0.016), higher body mass index (BMI) (p = 0.033), male gender (p = 0.047), and longer duration of prophylactic antibiotic use (p < 0.001). CONCLUSION: Older age, higher BMI, male gender, and longer duration of prophylactic antibiotic use were independently associated with significant increases in the risk of SSI. Additional prospective randomized studies are required to confirm these results.

Synthesis, characterization, and photovoltaic properties of soluble TiOPc derivatives.

We have synthesized soluble TiOPc derivatives containing alkoxy groups for use as additives in dye-sensitized solar cells (DSSCs). The DSSC devices containing these TiOPc derivatives exhibited short-circuit current densities of 8.49~10.04 mA/cm(2) and power conversion efficiencies of 2.73~3.62 % under AM 1.5 illumination and 100 mW/cm(2) irradiation.