• Safety and Health at Work
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• v.13 no.1
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• pp.40-50
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• 2022

Background: Underground mines have several hazards that could lead to serious consequences if they come into effect. Acquiring, evaluating, and using the real-time data from the atmospheric monitoring system and miner's positional information is crucial in deciding the best course of action. Methods: A graphical user interface-based software is developed that uses an AutoCAD-based mine map, real-time atmospheric monitoring system, and miners' positional information to guide on the shortest route to mine exit and other locations within the mine, including the refuge chamber. Several algorithms are implemented to enhance the visualization of the program and guide the miners through the shortest routes. The information relayed by the sensors and communicated by other personnel are collected, evaluated, and used by the program in proposing the best course of action. Results: The program was evaluated using two case studies involving rescue relating to elevated carbon monoxide levels and increased temperature simulating fire scenarios. The program proposed the shortest path from the miner's current location to the exit of the mine, nearest refuge chamber, and the phone location. The real-time sensor information relayed by all the sensors was collected in a comma-separated value file. Conclusion: This program presents an important tool that aggregates information relayed by sensors to propose the best rescue strategy. The visualization capability of the program allows the operator to observe all the information on a screen and monitor the rescue in real time. This program permits the incorporation of additional sensors and algorithms to further customize the tool.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.9-15
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• 2023

Metaverse is a modern new technology that is advancing quickly. The goal of this study is to investigate this technique from the perspective of computer vision as well as general perspective. A thorough analysis of computer vision related Metaverse topics has been done in this study. Its history, method, architecture, benefits, and drawbacks are all covered. The Metaverse's future and the steps that must be taken to adapt to this technology are described. The concepts of Mixed Reality (MR), Augmented Reality (AR), Extended Reality (XR) and Virtual Reality (VR) are briefly discussed. The role of computer vision and its application, advantages and disadvantages and the future research areas are discussed.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.455-462
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• 2020

Noise which is unwanted sound is a serious pollutant that can affect human health, as well as the working and living environment if exposed to humans. However, current noise management on the construction project is generally conducted after the noise exceeds the regulation standard, which increases the conflicts with inhabitants near the construction site and threats to the safety and productivity of construction workers. To overcome the limitations of the current noise management methods, the activities of construction equipment which is the main source of construction noise need to be managed throughout the construction period in real-time. Therefore, this paper proposed a framework for automatically detecting noise sources in construction sites in real-time based on convolutional neural networks (CNNs) according to the following four steps: (i) Step 1: Definition of the noise sources; (ii) Step 2: Data preparation; (iii) Step 3: Noise source classification using the audio CNN; and (iv) Step 4: Noise source detection using the visual CNN. The short-time Fourier transform (STFT) and temporal image processing are used to contain temporal features of the audio and visual data. In addition, the AlexNet and You Only Look Once v3 (YOLOv3) algorithms have been adopted to classify and detect the noise sources in real-time. As a result, the proposed framework is expected to immediately find construction activities as current noise sources on the video of the construction site. The proposed framework could be helpful for environmental construction managers to efficiently identify and control the noise by automatically detecting the noise sources among many activities carried out by various types of construction equipment. Thereby, not only conflicts between inhabitants and construction companies caused by construction noise can be prevented, but also the noise-related health risks and productivity degradation for construction workers and inhabitants near the construction site can be minimized.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.126-146
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• 2024

Caladium bicolor is one of the most popular plants in Thailand. The original species of Caladium bicolor was found a hundred years ago. Until now, there are more than 500 species through multiplication. The classification of Caladium bicolor can be done by using its color and shape. This study aims to develop a model to classify Caladium bicolor using a transfer learning technique. This work also presents a proof of concept, GUI design, and web application deployment using the user-design-center method. We also evaluated the performance of the following pre-trained models in this work, and the results are as follow: 87.29% for AlexNet, 90.68% for GoogleNet, 93.59% for XceptionNet, 93.22% for MobileNetV2, 89.83% for RestNet18, 88.98% for RestNet50, 97.46% for RestNet101, and 94.92% for InceptionResNetV2. This work was implemented using MATLAB R2023a.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1132-1141
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• 2021

Objective: To investigate changes in the median nerve, retinaculum, and carpal tunnel on ultrasound after successful endoscopic carpal tunnel release (ECTR). Materials and Methods: This prospective study involved 37 wrists in 35 patients (5 male, 30 female; mean age ± standard deviation [SD], 56.9 ± 6.7 years) with primary carpal tunnel syndrome (CTS). An in-house developed scoring system (0-3) was used to gauge the clinical improvement after ECTR. Ultrasound was performed before ECTR, and at 1, 3, and 12 months post-ECTR. Changes in the median nerve, flexor retinaculum, and carpal tunnel morphology on ultrasound after ECTR were analyzed. Ultrasound parameters for different clinical improvement groups were compared. Results: All patients improved clinically after ECTR. The average clinical improvement score ± SD at 12 months post-ECTR was 2.2 ± 0.7. The median nerve cross-sectional area proximal and distal to the tunnel decreased at all time intervals post-ECTR but remained swollen compared to normal values. Serial changes in the median nerve caliber and retinacular bowing after ECTR were more pronounced at the tunnel outlet than at the tunnel inlet. The flexor retinaculum had reformed in 25 (68%) of 37 wrists after 12 months. Conclusion: Postoperative changes in median nerve and retinaculum parameters were most pronounced at the tunnel outlet. Even in patients with clinical improvement after ECTR, nearly all ultrasound parameters remain abnormal at one year post-ECTR. These ultrasound parameters should not necessarily be relied upon to diagnose persistent CTS after ECTR.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.17-23
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• 2020

Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.

• Sleep Medicine and Psychophysiology
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• v.4 no.2
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• pp.164-171
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• 1997

Introduction : Periodic limb movement disorder (PLMD) is shown to common in patients with OSA and may become evident or worsened when treated with nasal continuous positive airway pressure (CPAP). Whether this is due to im proved sleep continuity. adverse nocturnal body positioning, uncovered by CPAP, or due to the CPAP stimulus is still debat-ed. We hypothesized that the increase in PLM activity following CPAP is associated with more supine-sleeping tendencies when being treated with CPAP. In the present work, we compared differences in the PLMD index (PLMI) and sleeping position of patients with sleep disordered breathing before and after CPAP treatment. Method : We studied 16 patients (mean age 46 yr, 9M, 7F) with OSA (11 patients) or UARS (5 patients) who either had PLMD on initial polysomnogram (baseline PSG) or on nasal CPAP trial (CPAP PSG). All periodic leg movements were scored on anterior tibialis EMG during sleep according to standard criteria (net duration; 0.5-5.0 seconds, intervals; 4-90 seconds. 4 consecutive movements). Paired t-tests compared PLMD index (PLMI), PLMD-related arousal index (PLMD-ArI), respiratory disturbance index (RDI), and supine sleeping position spent with baseline PSG and CPAP PSG. Results : Ten patients (63%) on baseline PSG and fifteen patients (94%) on CPAP PSG had documented PLMD ($PLMI{\ge}5$) respectively with significant increase on CPAP PSG(p<0.05). Ten patients showed the emergence (6/10 patients) or substantial worsening (4/10 patients) of PLMD during CPAP trial. Mean CPAP pressure was $7.6{\pm}1.8\;cmH_2O$. PLMI tended to increase from baseline PSG to CPAP PSG, and significantly increase when excluding 2 outlier (baseline PSG, $19.0{\pm}25.8/hr$ vs CPAP PSG, $29.9{\pm}12.5/hr$, p<0.1). PLMD-ArI showed no significant change, but a significant decrease was detected when excluding 2 outlier (p<0.1). There was no significant sleeping positional difference (supine vs non-supine) on baseline PSG, but significantly more supine position (supine vs non-supine, p<0.05) on CPAP PSG. There was no significant difference in PLMI during supine-sleeping and nonsupine-sleeping position on both of baseline PSG and CPAP PSG. There was also no significant difference in PLMI during supine-sleeping position between baseline PSG and CPAP PSG. With nasal CPAP, there was a highly significant reduction in the RDI (baseline PSG, $14.1{\pm}21.3/hr$ vs CPAP PSG, $2.7{\pm}3.9/hr$, p<0.05). Conclusion : This preliminary data confirms previous findings that CPAP is a very effective treatment for OSA, and that PLMD is developed or worsened with treatment by CPAP. This data also indicates that supine-sleeping position is more common when being treated with CPAP. However, there was no clear evidence that supine position is the causal factor of increased PLMD with CPAP. It is, however, suggested that the relative movement limitation induced by CPAP treatment could be a contributory factor of PLMD.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1119-1123
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• 2014

Head and neck cancers (HNC) are extremely complex disease types and it is likely that chromosomal instability is involved in the genetic mechanisms of its genesis. However, there is little information regarding the background levels of chromosome instability in these patients. In this pilot study, we examined spontaneous chromosome instability in short-term lymphocyte cultures (72 hours) from 72 study subjects - 36 newly diagnosed HNC squamous cell carcinoma patients and 36 healthy ethnic controls. We estimated chromosome instability (CIN) using chromosomal aberration (CA) analysis and nuclear level anomalies using the Cytokinesis Block Micronucleus Cytome Assay (CBMN Cyt Assay). The proliferation rates in cultures of peripheral blood lymphocytes (PBL) were assessed by calculating the Cytokinesis Block Proliferation Index (CBPI). Our results showed a significantly higher mean level of spontaneous chromosome type aberrations (CSAs), chromatid type aberration (CTAs) dicentric chromosomes (DIC) and chromosome aneuploidy (CANE UP) in patients (CSAs, $0.0294{\pm}0.0038$; CTAs, $0.0925{\pm}0.0060$; DICs, $0.0213{\pm}0.0003$; and CANE UPs, $0.0308{\pm}0.0035$) compared to controls (CSAs, $0.0005{\pm}0.0003$; CTAs, $0.0058{\pm}0.0015$; DICs, $0.0005{\pm}0.0003$; and CANEUPs, $0.0052{\pm}0.0013$) where p<0.001l. Similarly, spontaneous nuclear anomalies showed significantly higher mean level of micronuclei (MNi), nucleoplasmic bridges (NPBs) and nuclear buds (NBUDs) among cases (MNi, $0.01867{\pm}0.00108$; NPBs, $0.0156{\pm}0.00234$; NBUDs, $0.00658{\pm}0.00068$) compared with controls (MNi, $0.00027{\pm}0.00009$; NPBs, $0.00002{\pm}0.00002$; NBUDs, $0.00011{\pm}0.00007$).The evaluation of CBPI supported genomic instability in the peripheral blood lymphocytes showing a significantly lower proliferation rate in HNC patients ($1.525{\pm}0.005552$) compared to healthy subjects ($1.686{\pm}0.009520$) (p<0.0001). In conclusion, our preliminary results showed that visible spontaneous genomic instability and low rate proliferation in the cultured peripheral lymphocytes of solid tumors could be biomarkers to predict malignancy in early stages.

• Journal of Chest Surgery
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• v.55 no.2
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• pp.118-125
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• 2022

Background: A time course analysis was undertaken to evaluate how perioperative process-of-care and outcome measures evolved after implementation of an enhanced recovery after thoracic surgery (ERATS) program. Methods: Outcome and process-of-care measures were compared between patients undergoing major elective thoracic surgery during a 9-month pre-ERATS implementation period to those at 1-3, 4-6, and 7-9 months post-ERATS implementation. Outcome measures included length of stay, the 30-day readmission rate, 30-day emergency department visits, and minor and major adverse events. Process measures included first time to activity, out-of-bed, ambulation, fluid diet, diet as tolerated, as well as removal of the first and last chest tube, epidural, patient-controlled analgesia, and Foley and intravenous catheters. Results: In total, 704 patients (352 pre-ERATS, 352 post-ERATS) were included. Mobilization-related process measures, including time to first activity (16.5 vs. 6.8 hours, p<0.001), out-of-bed (17.6 vs. 8.9 hours, p<0.001), and ambulation (32.4 vs. 25.4 hours, p=0.04) saw statistically significant improvements by 1-3 months post-ERATS implementation compared to pre-ERATS. Time to Foley removal improved by 4-6 months post-ERATS (19.5 vs. 18.2 hours, p=0.003). Outcome measures, including the 30-day readmission rate and emergency department visits, steadily decreased post-ERATS. By 7-9 months post-ERATS, both minor (18.2% vs. 7.9%, p=0.009) and major (13.6% vs. 4.4%, p=0.007) adverse events demonstrated statistically significant improvements. Length of stay trended towards improvement from 6.2 days pre-ERATS to 4.8 days by 7-9 months post-ERATS (p=0.06). Conclusion: The adoption of ERATS led to improvements in multiple process-of-care measures, which may collectively and gradually achieve optimization of clinical outcomes.