• Journal of Periodontal and Implant Science
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• 제52권3호
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• pp.220-229
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• 2022

Purpose: The aim of this study was to evaluate and compare the accuracy performance of dental professionals in the classification of different types of dental implant systems (DISs) using panoramic radiographic images with and without the assistance of a deep learning (DL) algorithm. Methods: Using a self-reported questionnaire, the classification accuracy of dental professionals (including 5 board-certified periodontists, 8 periodontology residents, and 31 dentists not specialized in implantology working at 3 dental hospitals) with and without the assistance of an automated DL algorithm were determined and compared. The accuracy, sensitivity, specificity, confusion matrix, receiver operating characteristic (ROC) curves, and area under the ROC curves were calculated to evaluate the classification performance of the DL algorithm and dental professionals. Results: Using the DL algorithm led to a statistically significant improvement in the average classification accuracy of DISs (mean accuracy: 78.88%) compared to that without the assistance of the DL algorithm (mean accuracy: 63.13%, P<0.05). In particular, when assisted by the DL algorithm, board-certified periodontists (mean accuracy: 88.56%) showed higher average accuracy than did the DL algorithm, and dentists not specialized in implantology (mean accuracy: 77.83%) showed the largest improvement, reaching an average accuracy similar to that of the algorithm (mean accuracy: 80.56%). Conclusions: The automated DL algorithm classified DISs with accuracy and performance comparable to those of board-certified periodontists, and it may be useful for dental professionals for the classification of various types of DISs encountered in clinical practice.

• 대한의용생체공학회:의공학회지
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• 제42권5호
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• pp.241-249
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• 2021

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.

• 농촌계획
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• 제28권1호
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• 정보관리학회지
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• 제40권2호
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• pp.137-156
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• 2023

사회·정치적 패러다임의 변화에 따라 공공기관의 기관업무 및 직제는 시시각각 신설되거나 통합 또는 폐지된다. 효과적인 기록관리 관점에서는 이러한 변화를 반영하여 이전에 구축된 기록물 분류체계와 현행 업무 맥락이 적정한지 검토할 필요가 있다. 그러나 대부분 기관에서는 분류체계 재정비 과정이 실무담당자나 기관 기록물 담당자의 실무 경험적 판단에 의존한 수작업으로 진행되고 있어, 기업의 변화가 적시에 반영되거나 전체 큰 맥락을 통합적으로 파악하기가 어렵다. 이에 본 연구는 이러한 문제를 보완하고 나아가 기록의 효율적인 관리를 위해 자동화 및 지능화 기술을 활용한 기록물 분류체계 재정비 방안을 제안한다. 또한 제안된 방법론을 실제 공공기관에 적용하고, 도출된 결과물을 기관의 기능분류 담당 실무자와 면담을 수행하여 그 실효성과 한계점을 검증하였다. 이를 통해 재정비한 기록물 분류체계의 정확도와 신뢰도를 높여 기록물 관리의 표준화 실현을 도모하고자 한다.

• 한국입자에어로졸학회지
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• 제4권2호
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• pp.37-50
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• 2008

Concerns on centrifugal classifiers, of which have cut sizes are below few micrometers, have been increased and it is prospected to be used in extensive industries, such as manufacturing the fine minerals, cosmetics, advanced electric materials, and life science. This paper reviews the recent progress of research and development on the centrifugal classifiers. General categorization of classifiers for feeds was assessed and separation mechanism of the classifiers was followed. History of centrifugal classifiers was explored and some points to be improved were briefly indicated. Fundamental theory of the classification by centrifugal classifiers was pearly studied, and advanced and further understandings on factors affecting the separation or grading efficiency are described. Factors determining the classification precision and efficiency of centrifugal classifiers, such as geometry, rotational speed and inclined angle of rotating vanes, feed and air flow rates, and rotor dimensions are reviewed.

• 말소리와 음성과학
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• 제4권2호
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• pp.95-104
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• 2012

Thyroidectomy patients may have vocal paralysis or paresis, resulting in a breathy voice. The aim of this study was to investigate the aerodynamic and acoustic characteristics of a breathy voice in thyroidectomy patients. Thirty-five subjects who have vocal paralysis after thyroidectomy participated in this study. According to perceptual judgements by three speech pathologists and one phonetic scholar, subjects were divided into two groups: breathy voice group (n = 21) and non-breathy voice group (n = 14). Aerodynamic analysis was conducted by three tasks (Voicing Efficiency, Maximum Sustained Phonation, Vital Capacity) and acoustic analysis was measured during Maximum Sustained Phonation task. The breathy voice group had significantly higher subglottal pressure and more pathological voice characteristics than the non breathy voice group. Showing 94.1% classification accuracy in result logistic regression of aerodynamic analysis, the predictor parameters for breathiness were maximum sound pressure level, sound pressure level range, phonation time of Maximum Sustained Phonation task and Pitch range, peak air pressure, and mean peak air pressure of Voicing Efficiency task. Classification accuracy of acoustic logistic regression was 88.6%, and five frequency perturbation parameters were shown as predictors. Vocal paralysis creates air turbulence at the glottis. It fluctuates frequency-related parameters and increases aspiration in high frequency areas. These changes determine perceptual breathiness.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.163-172
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• 2022

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.319-325
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• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• 한국물환경학회지
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• 제23권6호
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• pp.881-888
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• 2007

In order to investigate the characteristics of the non-biodegradable material, the $BOD_5/COD_{Cr}$ ratio was used. The average ratio of industrial complex's influent wastewater was 2.29~2.96, the effluent ratio was 4.29~19.0. The removal efficiency of $UV_{254}$ by physicochemical treatment was 22.8~94.7% and 5.3~77.2% by biological treatment, respectively. Of the wastewater removal efficiency for each of the items, the $BOD_5$ treatment efficiency was the greatest at 97.3% and the color & TN treatment efficiency was 40~70%. The study of the economical assessment showed that the complex as well as the individual companies spent 722~1,298 won for each ton of treated wastewater. All of the wastewater treatment facilities spent the most money on chemicals needed to treat the wastewater. The total cost for Nylon manufacturing wastewater treatment plant was the greatest while the total cost for cotton manufacturing wastewater treatment plant turned out to the lowest. As respects of removal efficiency and economocal assessment, Polyester A and Cotton manufacturing wastewater treatment plants were better effective than a dyeing industrial complex wastewater treatment plant.

• 한국프랜차이즈경영연구
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• 제6권1호
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• pp.1-20
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• 2015

Home Meal Replacement (HMR) products are ready-to-eat or pre-cooked food products that are consumed at daily home. HMR market has grown rapidly due to societal changes: increases in female social activities, silver population, and one-person households. Consumption channels of HMR can be classified into take-out, delivery, and retail. In Korean HMR market, retail sector is largely growing, but companies are focusing their business on the home delivery sector. Moreover, franchise companies are expanding their areal coverage in the HMR market based on their multi-unit strategy. However, more research on the HMR market is needed as existing studies are limited in conceptualization, classification, and processed food from malls or home-shopping channels. Therefore, we conducted the efficiency analysis on Gukseonsaeng, one of franchises that applied the take-out channel, using DEA method. According to the research on 29 franchisees of Gukseonsaeng, 77.9% of input appeared inefficient for technical efficiency, while 53.3% of input appeared inefficient for scale efficiency. Thus, we found that franchises of Gukseonsaeng are structured in increasing returns to scale (IRS), so enhancing efficiency by expanding scales need to be implemented.