• Economic Analysis
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• v.25 no.1
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• pp.66-122
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• 2019

As the Korean economy grew, employment expanded steadily, with the number of economically active people increasing and the employment-to-population rate also increasing. However, the working age population started to decline in 2017, and the employment of women and young people has been sluggish. The proportion of non-salaried workers in Korea is much higher than in other OECD countries, and is also excessive, considering Korea's income levels. In addition, the proportion of non-regular workers and the proportion of workers employed at small companies are particularly high among salaried workers. In light of these characteristics of Korean employment, the urgent problems facing the employment structure can be summarized by the deepening dual structure of the labor market, the increase in youth unemployment, sluggish female employment figures, and an excessive share of self-employment. Overall, it is seen that labor market duality is the main structural factor of the employment problems in Korea. Therefore, in order to fundamentally address this employment problem, it is necessary to concentrate policy efforts on alleviating labor market duality.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.663-670
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• 2023

In this study, we aimed to perform a comparative evaluation using quantitative factors between a region-growing based segmentation with noise reduction algorithms and a U-Net based segmentation. Initially, we applied median filter, median modified Wiener filter, and fast non-local means algorithm to computed tomography (CT) images, followed by region-growing based segmentation. Additionally, we trained a U-Net based segmentation model to perform segmentation. Subsequently, to compare and evaluate the segmentation performance of cases with noise reduction algorithms and cases with U-Net, we measured root mean square error (RMSE) and peak signal to noise ratio (PSNR), universal quality image index (UQI), and dice similarity coefficient (DSC). The results showed that using U-Net for segmentation yielded the most improved performance. The values of RMSE, PSNR, UQI, and DSC were measured as 0.063, 72.11, 0.841, and 0.982 respectively, which indicated improvements of 1.97, 1.09, 5.30, and 1.99 times compared to noisy images. In conclusion, U-Net proved to be effective in enhancing segmentation performance compared to noise reduction algorithms in CT images.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.35-49
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• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.4
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• pp.244-252
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• 2023

The timeliness and accuracy of test results are crucial factors for clinicians to decide and promptly administer effective and targeted antimicrobial therapy, especially in life-threatening infections or when vital organs and functions, such as sight, are at risk. Further research is needed to refine and optimize matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based assays to obtain accurate and reliable results in the shortest time possible. MALDI-TOF MS-based bacterial identification focuses primarily on techniques for isolating and purifying pathogens from clinical samples, the expansion of spectral libraries, and the upgrading of software. As technology advances, many MALDI-based microbial identification databases and systems have been licensed and put into clinical use. Nevertheless, it is still necessary to develop MALDI-TOF MS-based antimicrobial-resistance analysis for comprehensive clinical microbiology characterization. The important applications of MALDI-TOF MS in clinical research include specific application categories, common analytes, main methods, limitations, and solutions. In order to utilize clinical microbiology laboratories, it is essential to secure expertise through education and training of clinical laboratory scientists, and database construction and experience must be maximized. In the future, MALDI-TOF mass spectrometry is expected to be applied in various fields through the use of more powerful databases.

• Journal of Dental Rehabilitation and Applied Science
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• v.39 no.4
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• pp.237-249
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• 2023

Orofacial dystonia is a neuromotor disorder that causes irregular or repetitive movements of the face, lips, tongue, and jaw involuntarily, also called tic disorder. Edentulous patients with these symptoms experience functional and aesthetic problems, including difficulty using complete dentures, speech and swallowing difficulties, and orofacial pain. In this case, for a patient with orofacial dystonia who experienced complete edentulism at a relatively young age, restorative treatment was performed with a maxillary complete denture with bilateral posterior zirconia occlusal surfaces and a mandibular implant-supported fixed prosthesis, and continuous smile training was performed. The aim was to improve the aesthetics of facial muscles. As a result of the treatment, the patient was very satisfied with not only improved chewing function and aesthetics, but also regained psychological stability and was able to lead a normal daily life, so we would like to report this.

• Journal of The Korean Institute of Defense Technology
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• v.5 no.3
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• pp.8-17
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• 2023

The current weapon system is operated as a complex weapon system with various standards and protocols applied, so there is a risk of failure in smooth information exchange during combined and joint operations on the battlefield. The interoperability of weapon systems to carry out precise strikes on key targets through rapid situational judgment between weapon systems is a key element in the conduct of war. Since the Korean military went into service, there has been a need to change the configuration and improve performance of a large number of software and hardware, but there is no verification system for the impact on interoperability, and there are no related test tools and facilities. In addition, during combined and joint training, errors frequently occur during use after arbitrarily changing the detailed operation method and software of the weapon/power support system. Therefore, periodic verification of interoperability between weapon systems is necessary. To solve this problem, rather than having people schedule an evaluation period and conduct the evaluation once, AI should continuously evaluate the interoperability between weapons and power support systems 24 hours a day to advance warfighting capabilities. To solve these problems, To this end, preliminary research was conducted to improve defense interoperability capabilities by applying natural language processing techniques (①Word2Vec model, ②FastText model, ③Swivel model) (using published algorithms and source code). Based on the results of this experiment, we would like to present a methodology (automated evaluation of interoperability requirements evaluation / level measurement through natural language processing model) to implement an automated defense interoperability evaluation tool without relying on humans.

• The Journal of Bigdata
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• v.8 no.2
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• pp.73-82
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• 2023

With 78% of current fisheries workers being elderly, there's a pressing need to address labor shortages. Consequently, active research on smart aquaculture technologies, centered on object detection and tracking algorithms, is underway. These technologies allow for fish size analysis and behavior pattern forecasting, facilitating the development of real-time monitoring and automated systems. Our study utilized video data from cameras outside aquaculture facilities and implemented fish detection and tracking algorithms. We aimed to tackle high maintenance costs due to underwater conditions and camera corrosion from ammonia and pH levels. We evaluated the performance of a real-time system using YOLOv7 for fish detection and the SORT algorithm for movement tracking. YOLOv7 results demonstrated a trade-off between Recall and Precision, minimizing false detections from lighting, water currents, and shadows. Effective tracking was ascertained through re-identification. This research holds promise for enhancing smart aquaculture's operational efficiency and improving fishery facility management.

• Journal of Food Hygiene and Safety
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• v.30 no.1
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• pp.13-27
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• 2015

A systemic review on the factors affecting food catering hygiene was conducted to provide information for risk management of food catering in Korea. In total 47 keywords relating to food catering and food hygiene were searched for published journals in the DBpia for the last decade (2005-2014). As a result, 1,178 published papers were searched and 142 articles were collected by the expert review. To find the major factors affecting food catering and microbial safety, an analysis based on organization and stakeholder were conducted. School catering (64 papers) was a major target rather than industry (5 pagers) or hospitals (3 papers) in the selected articles. The factors affecting school catering were "system/facility/equipment (15 papers)", "hygiene education (12 papers)", "production/delivery company (6 papers)", food materials (4 papers)" and "any combination of the above factors (9 papers)". The major problems are follow. 1) The problems of "system/facility/equipment" were improper space division/separation, lack of mass cooking utensil, lack of hygiene control equipment, difficulty in temperature and humidity control, and lack of cooperation in the HACCP team (dietitian's position), poor hygienic classroom in the case of class dining (students'), hard workload/intensity of labor, poor condition of cook's safety (cook's) and lack of parents' monitoring activity (parents'). 2) The problem of "hygiene education' were related to formal and perfunctory hygiene education, lack of HACCP education, lack of compliance of hygiene practice (cook's), lack of personal hygiene education and little effect of education (students'). 3) The problems of "production/delivery company" were related to hygiene of delivery truck and temperature control, hygiene of employee in the supplying company and control of non-accredited HACCP company. 4) The area of "food materials" cited were distrust of safety regarding to raw materials, fresh cut produces, and pre-treated food materials. 5) In addition, job stability/the salary can affect the occupational satisfaction and job commitment. And job stress can affect the performance and the hygiene practice. It is necessary for the government to allocate budget for facility and equipment, conduct field survey, improve hygiene training program and inspection, prepare certification system, improve working condition of employees, and introducing hygiene and layout consulting by experts. The results from this study can be used to prepare education programs and develop technology for improving food catering hygiene and providing information.

• Proceedings of KOSOMES biannual meeting
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• 2004.05b
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• pp.127-150
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• 2004

목포해양대학교 현행 4개 학부의 교육과정은 1997년도에 전면 개편되어 학부 단위로 무난히 실시되고 있지만, 해사계열의 특수성과 해양공학계열의 학부별 전공 구성의 차이점 등으로 인하여 일률적 학부운영방침을 대학 전체에 동일하게 적용하는 것은 다소무리가 있었다. 해양계 특성화라는 대학의 목표를 향하여 각 학부의 교육목표와 전공특성을 살리면서 사회와 관련산업계의 시대적인 요구를 만족시키기 위하여 학부운영을 효율화$\cdot$극대화할 수 있는 체제 정립 방안을 연구하고 있다. 1차 년도의 연구결과에 의하면, 해상운송시스템학부는 효율적인 학부운영체제의 개선 및 운영시스템 개발을 위하여 학부 특성 및 전공 구성에 대한 분석을 통하여 문제점을 발견하고 해결방안을 모색함과 동시에 학생들의 전공 선택권을 최대한 보장할 수 있는 효율적인 학부 운영 체제를 구축하는 방안을 제시한다. 기관시스템공학부는 현행 의무복수전공제도의 문제점 분석과 해결 방안 강구를 위하여 전공들간의 인계성을 강화하며 활성화할 수 있는 방안을 모색하고 해양경찰학 전공의 운영 및 지도 방안을 연구한다. 또한 해기품질관리 관련 규정 등의 분석을 통해 교육 및 훈련에 대한 질적 향상을 꾀하고 해기품질 향상을 위한 교육평가시스템을 개발하고 구축하고자 한다. 해양전자$\cdot$통신공학부는 전공간의 연계성 구축과 효율적 운영방안을 모색하기 위하여 학부제 및 복수전공제와 관련하여 설문조사 문항을 개발$\cdot$분석하고, 해양전자공학 전공 교과목 정비 및 교재 개발을 시도하고 있다. 해양시스템공학부는 전공구성의 특성을 고려한 탐색과목의 설치 및 산업체 실습과 연계한 학점인정과목의 검토를 위하여 현행의 전공소개 프로그램을 분석하고, 졸업생의 취업을 분석하며 산업체의 요구사항을 조사하고 있다.산 알고리즘의 정당성을 보였다. 맞이하고 있음을 볼 수 있다. 국내광업이 21C 급변하는 산업환경에 적응하여 생존하기 위해서는 각종 첨단산업에서 요구하는 소량 다품종의 원료광물을 적기에 공급 할 수 있는 전문화된 기술력을 하루속히 확보해야 하며, 이를 위해 고품위의 원료광물 확보를 위한 탐사 및 개발을 적극 추진하고 가공기술의 선진화를 위해 선진국과의 기술제휴 등 자원산업 글로벌화 정책이 절실히 요구되고 있음을 알 수 있다. 또한 삶의 질을 향상시키려는 현대인의 가치관에 부합하기 위해서는 각종 소비제품의 원료를 제공하는 광업의 본래 목적 이외에도 자연환경 훼손을 최소화하며 개발 할 수밖에 없는 구조적인 어려움에 직면할 수밖에 없다. 이처럼 국내광업이 안고 있는 여러 가지 난제들을 극복하기 위해서는 업계와 정부가 합심하여 국내광업 육성의 중요성을 재인식하고 새로운 마음가짐으로 관련 정책을 수립 일관성 있게 추진해 나가야 할 것으로 보인다.의 연구 결과를 요약하면 다음과 같다. 첫째, 브랜드 이미지와 서비스 품질과의 관계에서 브랜드이미지는 서비스 품질의 선행변수가 될 수 있음을 증명하였으며 4개 요인의 이미지 중 사풍이미지를 제외한 영업 이미지, 제품 이미지, 마케팅 이미지가 서비스 품질에 영향을 미치고 있음을 알 수 있다. 둘째, 지각된 서비스 품질과 가격 수용성과의 관계에서, 서비스 품질은 최소 가격에 신뢰서비스 요인에서 정의 영향을 미치고 있으나 부가서비스, 환경서비스에서는 역의 영향을 미침을 알수 있고, 최대 가격에 있어서는 욕구서비스 요인은 정의 영향을 미치지만 부가서비스의 경우에는 역의 영향을 미치고 있음을 알 수 있다. 셋째, 서비스품질과 재 방문 의도와의 관계에 있어서 서비스품질은 재 방문 의도에 영향을 미침을 알 수 있다. 따라서 브랜드 이미지는 서비스품질의 선행변수가 될 수 있으며, 서비스품