Enhancing Deep Learning-Based Segmentation Accuracy through Intensity Rendering and 3D Point Interpolation Techniques to Mitigate Sensor Variability.

In the context of LiDAR sensor-based autonomous vehicles, segmentation networks play a crucial role in accurately identifying and classifying objects. However, discrepancies between the types of LiDAR sensors used for training the network and those deployed in real-world driving environments can lead to performance degradation due to differences in the input tensor attributes, such as x, y, and z coordinates, and intensity. To address this issue, we propose novel intensity rendering and data interpolation techniques. Our study evaluates the effectiveness of these methods by applying them to object tracking in real-world scenarios. The proposed solutions aim to harmonize the differences between sensor data, thereby enhancing the performance and reliability of deep learning networks for autonomous vehicle perception systems. Additionally, our algorithms prevent performance degradation, even when different types of sensors are used for the training data and real-world applications. This approach allows for the use of publicly available open datasets without the need to spend extensive time on dataset construction and annotation using the actual sensors deployed, thus significantly saving time and resources. When applying the proposed methods, we observed an approximate 20% improvement in mIoU performance compared to scenarios without these enhancements.

Feasibility of ventilator-assisted tubeless anesthesia for video-assisted thoracoscopic surgery.

General anesthesia providing one-lung ventilation (OLV) with double-lumen endotracheal intubation has been considered inevitable for thoracic surgery. However, with the recent trend of less invasive surgical technique and enhanced recovery after surgery, tubeless anesthesia has been performed in various thoracic surgeries. The aim of this study was to establish a feasible and safe strategy of ventilator-assisted tubeless anesthesia in video-assisted thoracoscopic surgeries (VATS) based on single-institution experiences. We retrospectively reviewed the medical records of patients who underwent tubeless VATS from November 2019 to December 2021. Perioperative anesthetic and surgical variables as well as complications were reported. Seventeen patients with a median age of 29 and American Society of Anesthesiologists physical status I to II underwent video-assisted pulmonary wedge resection under monitored anesthesia care (MAC) using propofol and remifentanil. Mechanical ventilation was applied in synchronized intermittent mandatory ventilation with pressure support mode through facemask if respiratory support was required. During the operation, none of the patients showed hypoxemia or involuntary movement interfering operation. No patients were converted to general anesthesia or open thoracotomy unintentionally. All patients were discharged on median 2 days postoperatively without complications. Ventilator-assisted tubeless VATS is a feasible and safe option in low-risk patients undergoing video-assisted pulmonary wedge resection.

Hole Depth Prediction in a Femtosecond Laser Drilling Process Using Deep Learning.

In high-aspect ratio laser drilling, many laser and optical parameters can be controlled, including the high-laser beam fluence and number of drilling process cycles. Measurement of the drilled hole depth is occasionally difficult or time consuming, especially during machining processes. This study aimed to estimate the drilled hole depth in high-aspect ratio laser drilling by using captured two-dimensional (2D) hole images. The measuring conditions included light brightness, light exposure time, and gamma value. In this study, a method for predicting the depth of a machined hole by using a deep learning methodology was devised. Adjusting the laser power and the number of processing cycles for blind hole generation and image analysis yielded optimal conditions. Furthermore, to forecast the form of the machined hole, we identified the best circumstances based on changes in the exposure duration and gamma value of the microscope, which is a 2D image measurement instrument. After extracting the data frame by detecting the contrast data of the hole by using an interferometer, the hole depth was predicted using a deep neural network with a precision of within 5 µm for a hole within 100 µm.

Glass Substrate Dust Removal Using 233 fs Laser-Generated Shockwave.

Eliminating dust is gaining importance as a critical requirement in the display panel manufacturing process. The pixel resolution of display panels is increasing rapidly, which means that even small dust particles on the order of a few micrometers can affect them. Conventional surface cleaning methods such as ultrasonic cleaning (USC), CO2 cleaning, and wet cleaning may not be sufficiently efficient, economical, or environment friendly. In this study, a laser shockwave cleaning (LSC) method with a 233 fs pulsed laser was developed, which is different from the laser ablation cleaning method. To minimize thermal damage to the glass substrate, the effect of the number of pulses and the gap distance between the focused laser beam and the glass substrate were studied. The optimum number of pulses and gap distance to prevent damage to the glass substrate was inferred as 500 and 20 µm, respectively. With the optimal pulse number and gap distance, cleaning efficiency was tested at a 95% removal ratio regardless of the density of the particles. The effective cleaning area was measured using the removal ratio map and compared with the theoretical value.

Short-Term Success Rates of Smoking Cessation Support Programs and Factors Predicting Smoking Relapse: Using Data from a Smoking Cessation Clinic in a Hospital.

BACKGROUND: Although the number of medical institutions running a smoking cessation clinic is on the rise, there remains a paucity of research on the long- and short-term success rates of smoking cessation programs, as well as on smoking relapse rates, before and after project implementation. This study assessed the general characteristics of patients visiting the smoking cessation clinic, success rate of smoking cessation in the short term, and risks of relapse. METHODS: Medical records from March 2015 to April 2017 were analyzed and telephone surveys were conducted with 151 smokers who visited a hospital smoking cessation clinic from March 2015 to April 2017. RESULTS: Of the 139 smokers who were eligible for follow-up, 22 (15.8%) failed to quit smoking initially. The clinic's 6-month success rate of smoking cessation was 64.83%. Those with higher medication compliance had a lower risk of primary failure (odds ratio, 0.056; 95% confidence interval, 0.005-0.609), whereas those with higher age (hazard ratio [HR], 0.128; P=0.0252) and a greater number of visits to the clinic (HR, 0.274; P=0.0124) had a lower risk of relapsing. CONCLUSION: The risk of primary failure to quit was higher with low medication compliance, and that of relapsing was higher with lower age and fewer number of clinic visits. Various evaluation and analysis methods can be carried out in the future based on the accumulated data for maintenance of smoking cessation and relapse prevention.