• Korean Journal of Ecology and Environment
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• v.55 no.2
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• pp.175-183
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• 2022

In this study, 46 reservoirs in South Korea were characterized based on heavy metal concentration in sediments. We analyzed the relationship between heavy metal concentrations, physicochemical water quality and hydromorphological factors in each reservoir. Study reservoirs were classified into five groups of reservoirs, by hierarchical cluster analysis based on the similarities of heavy metal concentration. Group 1 had the most severe sediment heavy metal contamination among the groups, whereas Groups 2 and 3 showed low levels of heavy metal contamination. Group 4 displayed high value of Ni, and Group 5 showed high contamination of Pb, Cu, Cr, Ni, and Hg. Groups 1 and 5, which had high concentration of heavy metals in sediments, showed a high density of mines in the catchment of reservoirs. Heavy metal concentration was high in reservoirs with large capacity or the ones located at higher elevation, and also highly related with number of mines in the catchment of reservoir. This study can contribute to the systematic management of sediment heavy metals in reservoirs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.225-227
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• 2022

Now In Seoul, about 75,000 CCTVs are installed in 25 district offices. Each ward office in Seoul has built a control center for CCTV control and is building information such as people, vehicle types, license plate recognition and color classification into big data through 24-hour artificial intelligence intelligent image analysis. Seoul Metropolitan Government has signed MOUs with the Ministry of Land, Infrastructure and Transport, the National Police Agency, the Fire Service, the Ministry of Justice, and the military base to enable rapid response to emergency/emergency situations. In other words, we are building a smart city that is safe and can prevent disasters by providing CCTV images of each ward office. In this paper, the CCTV image is designed to extract the characteristics of the vehicle and personnel when an incident occurs through artificial intelligence, and based on this, predict the escape route and enable continuous tracking. It is designed so that the AI automatically selects and displays the CCTV image of the route. It is designed to expand the smart city integration platform by providing image information and extracted information to the adjacent ward office when the escape route of a person or vehicle related to an incident is expected to an area other than the relevant jurisdiction. This paper will contribute as basic data to the development of smart city integrated platform research.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.159-172
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• 2023

In general, social problem-solving research aims to create important social value by offering meaningful answers to various social pending issues using scientific technologies. Not surprisingly, however, although numerous and extensive research attempts have been made to alleviate the social problems and issues in nation-wide, we still have many important social challenges and works to be done. In order to facilitate the entire process of the social problem-solving research and maximize its efficacy, it is vital to clearly identify and grasp the important and pressing problems to be focused upon. It is understandable for the problem discovery step to be drastically improved if current social issues can be automatically identified from existing R&D resources such as technical reports and articles. This paper introduces a comprehensive dataset which is essential to build a machine learning model for automatically detecting the social problems and solutions in various national research reports. Initially, we collected a total of 700 research reports regarding social problems and issues. Through intensive annotation process, we built totally 24,022 sentences each of which possesses its own category or label closely related to social problem-solving such as problems, purposes, solutions, effects and so on. Furthermore, we implemented four sentence classification models based on various neural language models and conducted a series of performance experiments using our dataset. As a result of the experiment, the model fine-tuned to the KLUE-BERT pre-trained language model showed the best performance with an accuracy of 75.853% and an F1 score of 63.503%.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Journal of Oriental Neuropsychiatry
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• v.34 no.2
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• pp.95-113
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• 2023

Objectives: To review studies evaluating effects of acupuncture on pain and depressive symptoms in fibromyalgia. Methods: Quantitative evidences (RCTs) were systematically reviewed. Literature were searched for a combination of fibromyalgia and depression (The Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, medline (via PubMed), Kmbase, KISS, ScienceON, OASIS, CiNii, CNKI). Quantitative research findings were critically appraised by Cochrane risk of bias (RoB) tool and pooled. Meta-analysis was then conducted using Review Manager (RevMan) 5.4. Results: Eighteen studies were selected. American College of Rheumatology (ACR) classification criteria for Fibromyalgia Syndrome was most frequently used as diagnostic criteria for fibromyalgia. As for outcome measurement, Hamilton Rating Scale for Depression (HAMD), Visual Analog Scale (VAS), and Total Effective Rate (TER) were used most commonly. Meta-analysis of ten studies revealed that both Depression and VAS scores of the Acupuncture+Western Medicine group were significantly lower than those of Western Medicine group (Depression: SMD, -0.94, 95% CI, -1.17 to -0.70; VAS: MD, -1.51, 95% CI, -1.83 to -1.19). Also, TERs of both Acupuncture group and Acupuncture+Western Acupuncture+Western Medicine group were significantly higher than those of the Western Medicine group (OR: 2.38, 95% CI: 1.29 to 4.41; and OR: 7.40, 95% CI: 3.41 to 16.07). There was no significant difference in Depression or VAS score between the Acupuncture Group and the Western Medicine Group. Conclusions: Acupuncture might be an effective option for pain and depressive symptoms of fibromyalgia when it is combined with Western Medicine treatment. For more accurate results, more types of Korean medicine treatment should be conducted.

• The Korean Journal of Nuclear Medicine Technology
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• v.27 no.1
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• pp.42-46
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• 2023

Purpose The reference range described in Adrenocorticotropic Hormone reagent used in our laboratory is 10-60 pg/mL at 8 a.m. to 10 a.m., and 6-30 pg/mL at 8 p.m. to 10 p.m. However, in the case of outpatients, blood is mainly collected between 10 a.m. and 6 p.m., accounting for 57.8% of the total. Therefore, This study is intended to help make a more accurate diagnosis by reevaluating the reference range provided by the manufacturer of the Adrenocorticotropic Hormone reagent and setting split-timed reference range. Materials and Methods The patients collected blood before 10 a.m. were group A (68 people), and the patients collected blood after 10 a.m. were set to group B (80 people). A T-test was performed between groups to test their significance. And it was confirmed whether it was necessary to set the gender classification as a subgroup. The method of setting the reference range was calculated by the Bayesian's method and the Hoffmann's method. Results The reference range of Group A was 8.6 to 60.6 pg/mL by the Bayesian's method, and the Hoffmann's method was 3.6 to 61.3 pg/mL. The reference range of Group B was 6.9 to 50.5 pg/mL when applying the Bayesian's method, and the Hoffmann method's was 2.3 to 48.9 pg/mL. Conclusion This study was concluded that it was necessary to set the split-timed reference range. Through this study, the later the blood collection time, the lower the level of Adrenocorticotropic Hormone, indicating that blood collection time is important for patients with clinical significance. If a large number of subjects are selected and supplemented in the future, it is believed that systematic and accurate reference range can be set.

• Journal of Appropriate Technology
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• v.5 no.2
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• pp.70-81
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• 2019

The garment industry is one of the most labor-intensive manufacturing industries, with its sewing process relying almost entirely on manual labor. Its costs highly depend on the efficiency of this production line and thus is crucial to determine the production rate in real-time for line balancing. However, current production tracking methods are costly and make it difficult for many Small and Medium-sized Enterprises (SMEs) to implement them. As a result, their reliance on manual counting of finished products is both time consuming and prone to error, leading to high manufacturing costs and inefficiencies. In this paper, a production tracking system that uses the sewing machines' energy consumption data to track and count the total number of sewing tasks completed through Convolutional Neural Network (CNN) classifiers is proposed. This system was tested on two target sewing tasks, with a resulting maximum classification accuracy of 98.6%; all sewing tasks were detected. In the developing countries, the garment sewing industry is a very important industry, but the use of a lot of capital is very limited, such as applying expensive high technology to solve the above problem. Applied with the appropriate technology, this system is expected to be of great help to the garment industry in developing countries.

• Journal of the Korean Orthopaedic Association
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• v.52 no.1
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• pp.59-64
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• 2017

Purpose: To analyze the result of a repeat discectomy for ipsilateral recurrent lumbar disc herniation and to investigate the potential factors that influenced the outcomes for this surgery. Materials and Methods: Fifty-nine patients, who underwent reoperation after lumbar discectomy with a minimum follow-up period of 2 years, were reviewed. The surgical outcome was assessed using the visual analogue scale (VAS) and Macnab classification, and the recovery rate was calculated in accordance with VAS. A statistical analysis was carried out by SPSS to evaluate the possible factors that may have influenced the outcomes of the reoperation. Results: The rate of reoperation after lumbar disc surgery due to the recurrent disc herniation was 6.0% (59/983 cases). The average recovery rate of VAS from the 1st operation was approximately 77%, and from the 2nd operation was 71%. According to the Macnab criteria, the results were "excellent" or "good" in 96% of cases. Statistical analysis revealed that there was no difference of the average recovery rate (p<0.05). There is no additional instability after repeat discectomy. Factors, such as smoking, precipitating traumatic events, and diabetes mellitus did not have much influence on the average recovery rate after repeat discectomy for ipsilateral recurrent lumbar disc herniation. Conclusion: The outcomes of repeat discectomy were satisfactory. Moreover, factors, smoking, trauma history and diabetic mellitus, only had a minor impact on the outcomes of a repeat discectomy.

• Journal of the Korean Orthopaedic Association
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• v.52 no.1
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• pp.65-72
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• 2017

Purpose: The purpose of this study was to evaluate the radiologic features of juxtafacet cyst and determine the correlation between these features and clinical outcome. Materials and Methods: We analyzed a total of 23 patients. The degree of facet joint degeneration was classified using the Fujiwara method. The facet joint angles were measured with an magnetic resonance imaging to determine whether there was a difference between the cystic lesion that was occupied and the cystic lesion that was not occupied. Disc degeneration was measured by the Pfirrmann classification method. The clinical result was evaluated using the Oswestry disability index score and visual analogue scale. Results: The L4-5 level of juxtafacet cyst was mostly affected, as found in previous studies. Facet joint arthritis was more severe within the side with the cystic lesion. Significant correlation was found between disc degeneration and juxtafacet joint cyst. All patients underwent wide decompression and fusion. Clinical result was excellent. No patients had signs of recurrence during the follow-up periods. Conclusion: Juxtafacet cyst has a significant correlation with facet joint degeneration. Therefore, aggressive surgical treatment-not just simple cyst excision-should be considered as the treatment option for juxtafacet cyst associated with degenerative lumbar disease.