• 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.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.493-498
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• 2020

The fourth industrial revolution is the first topic thrown by Klaus Schwab at the Davos World Economic Forum in January 2016, meaning the next industrial revolution led by the Internet of Things (IOT), artificial intelligence (AI), robot technology and life sciences. In addition, in our lives, humans, computers and machines are connected organically, and organic relationships are evolving and developing at a furious rate in all areas of life. Since the 1953 armistice agreement, South Korea has remained in a state of confrontation with North Korea, and there have been continued fighting by the North, including naval skirmishes in the West Sea, artillery attacks on Yeonpyeong Island, the sinking of the Cheonan warship, and unmanned aerial vehicles and ankle mines. To prepare for such a local initiative, our military is constantly preparing and will have to strengthen its combat capabilities by developing and introducing advanced military equipment. After all, the military drone industry linked to the Fourth Industrial Revolution following the development of new war should continue its research on military drones in line with accurate diagnosis and the rapid development of future science and technology and IT technologies.

• Journal of Biomedical Engineering Research
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• v.45 no.4
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• pp.139-147
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• 2024

This study enhances AI algorithms for extracting spinal regions from Whole Spine X-rays, aiming for higher accuracy while minimizing learning and detection times. Whole Spine X-rays, critical for diagnosing conditions such as scoliosis and kyphosis, necessitate precise differentiation of spinal contours. The conventional manual methodology encounters challenge due to the overlap of anatomical structures, prompting the integration of AI to overcome these limitations and enhance diagnostic precision. In this study, 1204 AP and 500 LAT Whole Spine X-ray images were meticulously labeled, spanning the third cervical to the fifth lumbar vertebrae. We based our efforts on the HR-Net algorithm, which exhibited the highest accuracy, and proceeded to simplify its network architecture and enhance the block structure for optimization. The optimized HR-Net algorithm demonstrates an improvement, increasing accuracy by 2.98% for the AP dataset and 1.59% for the LAT dataset compared to its original formulation. Additionally, the modification resulted in a substantial reduction in learning time by 70.06% for AP images and 68.43% for LAT images, along with a decrease in detection time by 47.18% for AP and 43.07% for LAT images. The time taken per image for detection was also reduced by 47.09% for AP and 43.07% for LAT images. We suggest that the application of the proposed HR-Net in this study can lead to more accurate and efficient extraction of spinal regions in Whole Spine X-ray images. This can become a crucial tool for medical professionals in the diagnosis and treatment of spinal-related conditions, and it will serve as a foundation for future research aimed at further improving the accuracy and speed of spinal region segmentation.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.526-532
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• 2023

Falls are fatal accidents that occur more than 420,000 times a year worldwide. Therefore, to study patients with falls, we found the association between extrinsic injury codes and principal diagnosis S-codes of patients with falls, and developed a prediction model to predict extrinsic injury codes based on the data of principal diagnosis S-codes of patients with falls. In this study, we received two years of data from 2020 and 2021 from Institution A, located in Gangneung City, Gangwon Special Self-Governing Province, and extracted only the data from W00 to W19 of the extrinsic injury codes related to falls, and developed a prediction model using W01, W10, W13, and W18 of the extrinsic injury codes of falls, which had enough principal diagnosis S-codes to develop a prediction model. 80% of the data were categorized as training data and 20% as testing data. The model was developed using MLP (Multi-Layer Perceptron) with 6 variables (gender, age, principal diagnosis S-code, surgery, hospitalization, and alcohol consumption) in the input layer, 2 hidden layers with 64 nodes, and an output layer with 4 nodes for W01, W10, W13, and W18 exogenous damage codes using the softmax activation function. As a result of the training, the first training had an accuracy of 31.2%, but the 30th training had an accuracy of 87.5%, which confirmed the association between the fall extrinsic code and the main diagnosis S code of the fall patient.

• Journal of Animal Reproduction and Biotechnology
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• v.38 no.4
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• pp.215-224
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• 2023

Background: Reproductive management practices play crucial roles to maximize the reproductive performance of cows, and thus contribute to farm profitability. We aimed to assess the reproductive management of cows currently practiced in the dairy farms in an urban farming system. Methods: A total of 62 dairy farms were randomly selected considering all size of farms such as small (1-5 cattle), medium (6-20 cattle) and large farms (> 20 cattle) from selected areas of Dhaka city in Bangladesh. The reproductive management-related parameters viz. estrus detection, breeding method, pregnancy diagnosis, dry cow and parturition management, vaccination and treatment of reproductive problems etc. were obtained in a pre-defined questionnaire during the farm visit. Results: The visual observation method was only used (100.0%; 62/62) for estrus detection irrespective of size of the farms; while farmers observed cows for estrus 4-5 times a day, but only for 20-60 seconds each time. Regardless of farm size, 89.0% (55/62) farms used artificial insemination (AI) for breeding the cows. Intriguingly, all farms (100.0%) routinely checked the cows for pregnancy at 35-40 days post-breeding using rectal palpation technique by registered veterinarian. However, only 6.5% (4/62) farms practiced dry cow management. Notably, all farms (100.0%) provided nutritional supplements (Vit D, Ca and P) during late gestation. However, proper hygiene and cleanliness during parturition was not practiced in 77.4% (48/62) farms; even though 96.7% (60/62) farms treated cows by registered veterinarian for parturition-related problems. Conclusions: While farmers used AI service for breeding and timely check their cows for pregnancy; however, they need to increase observation time (30 minutes/ observation, twice in a day: early morning and early night) for estrus detection, consider dry cow management and ensure hygienic parturition for maximizing production.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1382-1383
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• 2008

In this paper, we have studied for analysis of the partial discharge(PD) signal based on fuzzy algorism. Partial discharge signal detector is difficult because of partial discharge signal is very non-linear. Also, it is very difficult work that separate partial discharge signal from noise. We constructed partial discharge accumulation detection system that use Labview for detection of non-linear partial discharge signal. And analyzed Partial discharge signal that is detected by Labview system utilizing Fuzzy model.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.216-222
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• 2021

Recently, various robots are being used for the purpose of structural inspection or safety diagnosis, and their needs are also rising rapidly. Among the structural inspection using robots, a lot of researches has recently been conducted on inspection of various facilities and structures using an unmanned aerial vehicle (UAV). However, since GNSS (Global Navigation Satellite System) signals cannot be received in an environment near or below structures, the operation of UAVs has been done manually. For a stable autonomous flight without GNSS signals, additional technologies are required. This paper proposes the autonomous flight system for structural inspection consisting of simultaneous localization and mapping (SLAM), path planning, and controls. The experiments were conducted on an actual large bridge to verify the feasibility of the system, and especially the performance of the proposed SLAM algorithm was compared through comparative analysis with the state-of-the-art algorithms.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.133-138
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• 2022

Digital radiation inspection equipment is a medical device that deals with human life and requires stability and high reliability. However, this system is currently the most advanced technology and the domestic market is almost occupied by European products including Japan. Therefore, research and development are needed not only to replace domestic medical devices, which are largely dependent on expensive imported products, but also to develop more economical and user-oriented products that are easy to operate and produce devices that lead to accurate diagnosis. In particular, among the digital X-ray systems, the motor driving technology and the mechatronics technology related to the development of mechanical devices have matured to some extent in Korea. In this paper, selection of AC servomotor for digital radiation inspection suitable for imaging purpose, and application of conversion device and control method to check performance and improve problems.