• Archives of Plastic Surgery
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• v.38 no.5
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• pp.695-698
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• 2011

Purpose: Since spinal tuberculosis is increasing in prevalence, it appears that a repair of spinal soft tissue defect as a complication of spinal tuberculosis can be a meaningful work. We report this convenient and practical reconstructive surgery which use bilateral latissimus dorsi musculocutaneous advancement flap. Methods: Before the operation, $13{\times}9.5$ cm sized skin and soft tissue defect was located on the dorsal part of a patient from T11 to L3. And dura was exposed on L2. Under the general endotrachel anesthesia, the patient was placed in prone position. After massive saline irrigation, dissection of the bilateral latissimus dorsi musculocutaneous flaps was begun just upper to the paraspinous muscles (at T11 level) by seperating the paraspinous muscles from overlying latissimus dorsi muscles. The plane between the paraspinous muscles fascia and the posterior edge of the latissimus dorsi muscle was ill-defined in the area of deformity, but it could be identified to find attachment of thoracolumbar fascia. The seperation between latissimus dorsi and external oblique muscle was identified, and submuscular plane of dissection was developed between the two muscles. The detachment from thoracolumbar fascia was done. These dissections was facilitated to advance the flap. The posterior perforating vasculature of the latissimus dorsi muscle was divided when encountered approximately 6 cm lateral to midline. Seperating the origin of the latissimus dorsi muscle from rib was done. The dissection was continued on the deep surface of the latissimus dorsi muscle until bilateral latissimus dorsi musculocutaneous flaps were enough to advance for closure. Once this dissection was completely bilateraly, the bipedicled erector spinae muscle was advanced to the midline and was repaired 3-0 nylon to cover the exposed vertebrae. And two musculocutaneous units were advanced to the midline for closure. Three 400 cc hemovacs were inserted beneath bilateral latissimus dorsi musculocutaneous flaps and above exposed vertebra. The flap was sutured with 3-0 & 4-0 nylon & 4-0 vicryl. Results: The patient was kept in prone and lateral position. Suture site was stitched out on POD14 without wound dehiscence. According to observative findings, suture site was stable on POD55 without wound problem. Conclusion: Bilateral latissimus dorsi musculocutaneous advancement flap was one of the useful methods in repairing of large spinal soft tissue defect resulting from spinal tuberculosis.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.1
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• pp.11-17
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• 2013

'Multi-scale mass-deployable cooperative robots' is a next generation robotics paradigm where a large number of robots that vary in size cooperate in a hierarchical fashion to collect information in various environments. While this paradigm can exhibit the effective solution for exploration of the wide area consisting of various types of terrain, its technical maturity is still in its infant state and many technical hurdles should be resolved to realize this paradigm. In this paper, we propose to develop new design and manufacturing methodologies for the multi-scale mass-deployable cooperative robots. In doing so, we present various fundamental technologies in four different research fields. (1) Adaptable design methods consist of compliant mechanisms and hierarchical structures which provide robots with a unified way to overcome various and irregular terrains. (2) Soft composite materials realize the compliancy in these structures. (3) Multi-scale integrative manufacturing techniques are convergence of traditional methods for producing various sized robots assembled by such materials. Finally, (4) the control and communication techniques for the massive swarm robot systems enable multiple functionally simple robots to accomplish the complex job by effective job distribution.

• Pediatric Infection and Vaccine
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• v.26 no.2
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• pp.112-117
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• 2019

Eikenella corrodens rarely causes invasive head and neck infections in immunocompetent children. We report a case of epidural abscess caused by E. corrodens in a previously healthy 13-year-old boy who presented with fever, headache, and vomiting. On physical examination upon admission, there was no neck stiffness, but discharge from the right ear was observed. Brain magnetic resonance imaging (MRI) revealed approximately 4.5-cm-sized epidural empyema on the right temporal lobe as well as bilateral ethmoid and sphenoid sinusitis, right mastoiditis, and right otitis media. During treatment with vancomycin and cefotaxime, purulent ear discharge aggravated, and on follow-up brain MRI, the empyema size increased to $5.6{\times}3.4cm$ with interval development of an abscess at the right sphenoid sinus. Burr hole trephination was performed, and foul-smelling pus was aspirated from the epidural abscess near the right temporal lobe. Pus culture yielded E. corrodens. Endoscopic sphenoidotomy was also performed with massive pus drainage, and the same organism was grown. The patient was treated with intravenous cefotaxime for 3 weeks and recovered well with no other complications. Therefore, E. corrodens can cause serious complications in children with untreated sinusitis.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.29-41
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• 2022

Recently, with the development of deep learning technology, a variety of huge models with excellent performance have been devised by pre-training massive amounts of text data. However, in order for such a model to be applied to real-life services, the inference speed must be fast and the amount of computation must be low, so the technology for model compression is attracting attention. Knowledge distillation, a representative model compression, is attracting attention as it can be used in a variety of ways as a method of transferring the knowledge already learned by the teacher model to a relatively small-sized student model. However, knowledge distillation has a limitation in that it is difficult to solve problems with low similarity to previously learned data because only knowledge necessary for solving a given problem is learned in a teacher model and knowledge distillation to a student model is performed from the same point of view. Therefore, we propose a heterogeneous knowledge distillation method in which the teacher model learns a higher-level concept rather than the knowledge required for the task that the student model needs to solve, and the teacher model distills this knowledge to the student model. In addition, through classification experiments on about 18,000 documents, we confirmed that the heterogeneous knowledge distillation method showed superior performance in all aspects of learning efficiency and accuracy compared to the traditional knowledge distillation.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.