• Clinics in Shoulder and Elbow
• /
• v.21 no.2
• /
• pp.82-86
• /
• 2018

Background: Although a common shoulder disease, there are no accepted classification criteria for frozen shoulder (FS). This study therefore aimed to evaluate the accuracy of the conventionally used FS classification system. Methods: Primary FS patients (n=168) who visited our clinic from January 2010 to July 2015 were included in the study. After confirming restrictions of the glenohumeral joint motion and absence of history of systemic disease, trauma, shoulder surgery, shoulder muscle weakness, or specific x-ray abnormalities, the Zuckerman and Rokito's classification was employed for diagnosing primary FS. Following clinical diagnosis, each patient underwent a shoulder magnetic resonance imaging (MRI) and blood tests (lipid profile, glucose, hemoglobin A1c, and thyroid function). Based on the results of the blood tests and MRIs, the patients were reclassified, using the criteria proposed by Zuckerman and Rokito. Results: New diagnoses were ascertained including blood test results (16 patients with diabetes, 43 with thyroid abnormalities, and 149 with dyslipidemia), and MRI revealed intra-articular lesions in 81 patients (48.2%). After re-categorization based on the above findings, only 5 patients (3.0%) were classified having primary FS. The remaining 163 patients (97.0%) had either undiagnosed systemic or intrinsic abnormalities (89 patients), whereas 74 patients had both. Conclusions: These findings demonstrate that most patients clinically diagnosed with primary FS had undiagnosed systemic abnormalities and/or intra-articular pathologies. Therefore, a modification of the Zuckerman and Rokito's classification system for FS may be required to include the frequent combinations, rather than having a separate representation of systemic abnormalities and intrinsic causes.

• The Journal of Korean Medicine
• /
• v.26 no.1 s.61
• /
• pp.93-102
• /
• 2005

Objective : This research was conducted to evaluate the method of sasangin classification by voice analysis, The 2 pilot tests were thus designed to solve the following problems: 'What are the conditions at classification for sasangin by the voice analysis?' and 'What are the important variances of /a/ parameter?'. Methods: 122 volunteers Were examined to make a diagnosis of sasangin by QSCC II and they were disease-free and healthy, First, they said /a/ three times for 2 seconds in their usual voice, Second, they said /a/ for 2 seconds by the different ways of high tone, mid tone, and low tone. The sounds were collected by a recording program (cooledit 2000) through a Sony microphone (ecm-26l). We analyzed the voices by maltlab, the simulation tool. Results: There were no differences and were correlations when one said /a/ three times for 2 seconds in the usual voice. There were some things to correlate when one said /a/ three times for 2 seconds by the different ways of high speech, usual speech, and low speech. Others were nothing to correlate. We evaluated the value of sasangin classification method by only /a/ voice analysis. The hit ratio was average $66.3\%\;:\;soyangin\;67.9\%,\;taeumin\;68.0\%,\;soeumin\;63.9\%$. Conclusion: We must set up the conditions to use the method of sasangin classification by voice analysis. The value of sasangin classification method by only fa! voice analysis was a hit ratio of $66.3\%$.

• Journal of Periodontal and Implant Science
• /
• v.52 no.3
• /
• pp.220-229
• /
• 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.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.12 no.5
• /
• pp.277-286
• /
• 2017

This paper introduces mathematical modelling and control algorithm of expendable mine disposal vehicle. This vehicle has two longitudinal thrusters, one vertical thruster and internal mass moving system which can control pitch rate. Also, the vehicle has an optical camera and forward looking sonar for underwater mine detection and classification. The vehicle is controlled via an optical cable connected with operating console on the mother ship. We describe the vehicle's 6DOF dynamic model and controller which can track the desired trajectory for the way-point tracking. These simulation results shows guidance and maneuvering performance which has other sensor data or not.

• Review of KIISC
• /
• v.27 no.3
• /
• pp.33-41
• /
• 2017

기계 학습(machine-learning) 분야의 분류 알고리즘(classification algorithms)은 의료 진단, 유전자 정보 해석, 스팸 탐지, 얼굴 인식 및 신용 평가와 같은 다양한 응용 서비스에서 사용되고 있다. 이와 같은 응용 서비스에서의 분류 알고리즘은 사용자의 민감한 정보를 포함하는 데이터를 이용하여 학습을 수행하는 경우가 많으며, 분류 결과도 사용자의 프라이버시와 연관된 경우가 많다. 따라서 학습에 필요한 데이터의 소유자, 응용 서비스 사용자, 그리고 서비스 제공자가 서로 다른 보안 도메인에 존재할 경우, 프라이버시 보호 문제가 발생할 수 있다. 본 논문에서는 이러한 문제를 해결하면서도 분류 서비스를 제공할 수 있도록 도와주는 프라이버시 보존 분류 프로토콜(privacy-preserving classification protocol: PPCP) 에 대해 소개한다. 구체적으로 PPCP의 프라이버시 보호 요구사항을 분석하고, 기존의 연구들이 프라이버시 보호를 위해 사용하는 암호학적 기본 도구(cryptographic primitive)들에 대해 소개한다. 최종적으로 그러한 암호학적 기본 도구를 사용하여 설계된 프라이버시 보존 분류 프로토콜에 대한 기존 연구들을 소개하고 분석한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.65-66
• /
• 2021

본 논문에서는 Smart Factory의 자동 공정에서 결함의 분류를 실시간으로 시도하여 자동 공정 제어를 위한 결함 분류 딥러닝 기법을 제안하고, Pooling 종류에 따른 분류 성능을 비교한다. Smart Factory 구축에 있어서 CNN을 이용한 공정 제어를 통해 제품 생산에 있어서 생산량의 증가와 불량률의 감소를 이루어내는 것이 가능하다. Smart Factory는 자동화 공정이므로 결함의 분류 속도가 중요하지만, 생산량의 증가와 불량률의 감소를 위해서는 정확하게 결함의 종류를 분류하여 Smart Factory의 공정을 제어하는 것이 더욱 중요하다. 본 논문에서는 Pooling을 Max Pooling과 Averrage Pooling을 복합적으로 설정하였을 때 높은 성능을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2024.01a
• /
• pp.145-146
• /
• 2024

본 논문에서는 최근 몇 년 동안 스마트시티 인프라 투자가 크게 성장하였으며 글로벌 스마트 쓰레기통 시장은 성장 가능성이 높을 것으로 예상된다. 본 논문에서는 이에 발맞추어 CNN과 MQTT를 활용한 스마트 쓰레기통을 제작하였다. 쓰레기의 종류를 구별하고 해당되는 쓰레기통의 뚜껑을 골라 여는 것은 현대인의 생활에서 비효율을 야기한다. 이러한 문제를 해결하고자 CNN을 통한 효율적인 분류와 MQTT를 통한 통신, 센서들을 활용한 더 나은 쓰레기 수거 방식을 제공한다. 스마트 쓰레기통으로 일상을 더욱 편하고 효율적이게 만드는 데 기여하고자 한다.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.49 no.3
• /
• pp.15-26
• /
• 2012

Recently, due to applicability increase of vector data based digital map for geographic information and evolution of geographic measurement techniques, large volumed GIS(geographic information service) services having high resolution and large volumed data are flowing actively. This paper proposed an efficient vector map compression technique using the SEC(spatial energy compaction) based on classified bins for the vector map having 1cm detail and hugh range. We encoded polygon and polyline that are the main objects to express geographic information in the vector map. First, we classified 3 types of bins and allocated the number of bits for each bin using adjacencies among the objects. and then about each classified bin, energy compaction and or pre-defined VLC(variable length coding) were performed according to characteristics of classified bins. Finally, for same target map, while a vector simplification algorithm had about 13%, compression ratio in 1m resolution we confirmed our method having more than 80% encoding efficiencies about original vector map in the 1cm resolution. Also it has not only higher compression ratio but also faster computing speed than present SEC based compression algorithm through experimental results. Moreover, our algorithm presented much more high performances about accuracy and computing power than vector approximation algorithm on same data volume sizes.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.793-794
• /
• 2006

본 논문에서는 인공 신경망을 이용한 동작 패턴 분류 시스템을 개발하였다. 이 시스템은 자이로 센서와 가속도 센서를 사용하며 3축의 자이로(각속도) 및 가속도를 측정할 수 있는 센서 모듈과 측정된 데이터를 이용해서 동작 패턴을 분류해 주는 신경망 알고리즘으로 구성된다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.11
• /
• pp.5594-5615
• /
• 2019

Malicious social robots, which are disseminators of malicious information on social networks, seriously affect information security and network environments. The detection of malicious social robots is a hot topic and a significant concern for researchers. A method based on classification has been widely used for social robot detection. However, this method of classification is limited by an unbalanced data set in which legitimate, negative samples outnumber malicious robots (positive samples), which leads to unsatisfactory detection results. This paper proposes the use of generative adversarial networks (GANs) to extend the unbalanced data sets before training classifiers to improve the detection of social robots. Five popular oversampling algorithms were compared in the experiments, and the effects of imbalance degree and the expansion ratio of the original data on oversampling were studied. The experimental results showed that the proposed method achieved better detection performance compared with other algorithms in terms of the F1 measure. The GAN method also performed well when the imbalance degree was smaller than 15%.