• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1453-1463
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• 2021

Feeding is the most important behavior that represents the health and welfare of weanling pigs. The early detection of feed refusal is crucial for the control of disease in the initial stages and the detection of empty feeders for adding feed in a timely manner. This paper proposes a real-time technique for the detection and recognition of small pigs using a deep-leaning-based method. The proposed model focuses on detecting pigs on a feeder in a feeding position. Conventional methods detect pigs and then classify them into different behavior gestures. In contrast, in the proposed method, these two tasks are combined into a single process to detect only feeding behavior to increase the speed of detection. Considering the significant differences between pig behaviors at different sizes, adaptive adjustments are introduced into a you-only-look-once (YOLO) model, including an angle optimization strategy between the head and body for detecting a head in a feeder. According to experimental results, this method can detect the feeding behavior of pigs and screen non-feeding positions with 95.66%, 94.22%, and 96.56% average precision (AP) at an intersection over union (IoU) threshold of 0.5 for YOLOv3, YOLOv4, and an additional layer and with the proposed activation function, respectively. Drinking behavior was detected with 86.86%, 89.16%, and 86.41% AP at a 0.5 IoU threshold for YOLOv3, YOLOv4, and the proposed activation function, respectively. In terms of detection and classification, the results of our study demonstrate that the proposed method yields higher precision and recall compared to conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.77-84
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• 2022

In this paper, we propose a Label Reassignment Strategy to improve the performance of an object detection algorithm. Our approach involves two stages: an inference stage and an assignment stage. In the inference stage, we perform multi-scale inference with predefined scale sizes on a trained model and re-infer masked images to obtain robust classification results. In the assignment stage, we calculate the IoU between bounding boxes to remove duplicates. We also check box and class occurrence between the detection result and annotation label to re-assign the dominant class type. We trained the YOLOX-L model with the re-annotated dataset to validate our strategy. The model achieved a 3.9% improvement in mAP and 3x better performance on AP_S compared to the model trained with the original dataset. Our results demonstrate that the proposed Label Reassignment Strategy can effectively improve the performance of an object detection model.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Smart Media Journal
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• v.12 no.9
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• pp.28-37
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• 2023

The main factor affecting the decline in rice yield is damage caused by localized heavy rains or typhoons. The method of analyzing the rice lodging area is difficult to obtain objective results based on visual inspection and judgment based on field surveys visiting the affected area. it requires a lot of time and money. In this paper, we propose the method of estimation and diagnosis for rice lodging areas using a Vision Transformer-based Segformer for RGB images, which are captured by unmanned aerial vehicles. The proposed method estimates the lodging, normal, and background area using the Segformer model, and the lodging rate is diagnosed through the rice field inspection criteria in the seed industry Act. The diagnosis result can be used to find the distribution of the rice lodging areas, to show the trend of lodging, and to use the quality management of certified seed in government. The proposed method of rice lodging area estimation shows 98.33% of mean accuracy and 96.79% of mIoU.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.949-965
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• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.

• Electronics and Telecommunications Trends
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• v.30 no.3
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• pp.84-94
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• 2015

5G와 IoT로 인한 무선 트래픽 급증에 대응하기 위해서는, 무선망 성능 및 주파수효율 향상, 신규주파수할당, 주파수 공동사용 기술 개발 등이 복합적으로 적용되어야 달성 가능하다. 그의 일환으로, LTE를 비면허 대역에 사용하려는 LTE-U(Long Term Evolution-Unlicensed)라는 새로운 패러다임의 수평적 주파수 공동사용 기술을, 이동통신에 적용하려는 시도가 진행되고 있다. LTE-A의 주파수 집성기술을 활용하여, 1차 캐리어를 면허 대역 LTE 기반으로 하고, 2차 캐리어를 비면허 대역 LTE로 묶어서 고속으로 데이터를 전송하는 기술이다. 우선적으로 5GHz 비면허 대역에 적용을 검토하고 있는데, 기존에 사용하던 Wi-Fi 및 기상레이다 등과의 공정한 공존(fair coexistence)이 가장 중요하다. 따라서 각국의 5GHz 주파수 대역 규제 현황, 공존을 위한 LBT(Listen-Before-Talk)통신 메커니즘, 표준화 현황을 살펴본다. 또한 이해 당사자인 Wi-Fi, LTE, 이용자, 기술기준의 입장을 살펴보고, 구현이슈, 지적소유권 동향 등을 검토하고, 기술적 및 정책적 대응전략을 살펴본다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3364-3382
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• 2023

Remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi-scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC, mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.

• The Journal of Korean Physical Therapy
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• v.26 no.6
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• pp.453-458
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• 2014

Purpose: The purpose of this study was to compare the activities of the abdominal and neck flexor muscles of children with and without cerebral palsy (CP) while lifting the head in a supine position. Methods: The subjects were eight children with CP and eight children without the disease. The activities of the external abdominal oblique (EO), internal abdominal oblique (IO), rectus abdominis (RA), sternocleidomastoid (SCM), and RA/SCM muscles were collected by surface electromyography (EMG) when the children lifted their heads. A Mann-Whitney U test was used to compare the activity of each muscle during the head-lifting exercise. Statistical significance was accepted at p<0.05. Results: The activities of the EO, IO, and RA, and RA/SCM muscles differed significantly between the children with and without CP, but there was no significant between-group difference in the activity of the SCM muscle. Conclusion: These findings suggest that the abdominal muscles are not employed as much in the activities of children with CP compared to those without the disease. Additionally, those with CP were more dependent on the neck flexor muscle during the head-lifting exercise in a supine position.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.175-184
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• 2016

The purpose of the study focuses on increasing dangers of using smartphones while driving recently, and then is to come up with measures to reduce traffic accidents caused by using devices like a smartphone. This study conducted a survey of drivers using their smartphones while driving to understand risks caused by using a smartphones while operating vehicles. Results showed that a lot of activities may lead to distracted driving, such as texting, making phone calls, using GPS or road maps, game, etc. In this paper, we presented that functions of smartphone should be controlled partially while driving for safe driving performance. These results suggest that using IoT-based smart devices like a beacon and a smartphone application implemented, tentatively called "Safe driving solution", while driving can reduce traffic accidents. Thus, in order to effectively prevent dangerous driving due to the use of smartphones, a "Safe driving solution" which restricts all functions except for calls and driver assistance functions is suggested.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.719-729
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• 2017

Recently, the field of agriculture has been gaining a new leap with the integration of ICT technology in agriculture. In particular, smart farms, which incorporate the Internet of Things (IoT) technology in agriculture, are in the spotlight. Smart farm technology collects and analyzes information such as temperature and humidity of the environment where crops are cultivated in real time using sensors to automatically control the devices necessary for harvesting crops in the control device, Environment. Although smart farm technology is paying attention as if it can solve everything, most of the research focuses only on increasing crop yields. This paper focuses on the development of a system architecture that can harvest high quality crops at the optimum stage rather than increase crop yields. In this paper, we have developed an architecture using apple trees as a sample and used the color information and weight information to predict the harvest time of apple trees. The simple board that collects color information and weight information and transmits it to the server side uses Arduino and adopts model-driven development (MDD) as development methodology. We have developed an architecture to provide services to PC users in the form of Web and to provide Smart Phone users with services in the form of hybrid apps. We also developed an architecture that uses beacon technology to provide orchestration information to users in real time.