• The Journal of the Acoustical Society of Korea
• /
• v.42 no.4
• /
• pp.329-344
• /
• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• Phonetics and Speech Sciences
• /
• v.15 no.4
• /
• pp.71-80
• /
• 2023

This study primarily aimed to develop an automated stuttering identification and classification method using artificial intelligence technology. In particular, this study aimed to develop a deep learning-based identification model utilizing the convolutional neural networks (CNNs) algorithm for Korean speakers who stutter. To this aim, speech data were collected from 9 adults who stutter and 9 normally-fluent speakers. The data were automatically segmented at the phrasal level using Google Cloud speech-to-text (STT), and labels such as 'fluent', 'blockage', prolongation', and 'repetition' were assigned to them. Mel frequency cepstral coefficients (MFCCs) and the CNN-based classifier were also used for detecting and classifying each type of the stuttered disfluency. However, in the case of prolongation, five results were found and, therefore, excluded from the classifier model. Results showed that the accuracy of the CNN classifier was 0.96, and the F1-score for classification performance was as follows: 'fluent' 1.00, 'blockage' 0.67, and 'repetition' 0.74. Although the effectiveness of the automatic classification identifier was validated using CNNs to detect the stuttered disfluencies, the performance was found to be inadequate especially for the blockage and prolongation types. Consequently, the establishment of a big speech database for collecting data based on the types of stuttered disfluencies was identified as a necessary foundation for improving classification performance.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.1
• /
• pp.35-43
• /
• 2010

1D PCA utilized in the face appearance-based face recognition methods such as eigenface-based face recognition method may lead to less face representative power and more computational cost due to the resulting 1D face appearance data vector of high dimensionality. To resolve such problems of 1D PCA, 2D PCA-based face recognition methods had been developed. However, the face representation model obtained by direct application of 2D PCA to a face image set includes both face common features and face distinctive identity features. Face common features not only prevent face recognizability but also cause more computational cost. In this paper, we first develope a model of a face distinctive identity feature subspace separated from the effects of face common features in the face feature space obtained by application of 2D PCA analysis. Then, a novel robust face recognition based on the face distinctive identity feature subspace model is proposed. The proposed face recognition method based on the face distinctive identity feature subspace shows better performance than the conventional PCA-based methods (1D PCA-based one and 2D PCA-based one) with respect to recognition rate and processing time since it depends only on the face distinctive identity features. This is verified through various experiments using Yale A and IMM face database consisting of face images with various face poses under various illumination conditions.

• Journal of KIISE:Computing Practices and Letters
• /
• v.9 no.4
• /
• pp.381-392
• /
• 2003

Component-based Development(CBD) based on the software reuse has been more attractive from software companies that want to enhance software productivity. However, since component identification process is mainly dependent on domain expert´s intuition and experience, it was very difficult to develop tools for supporting the component identification process. In this paper, we propose a systematic procedure of identifying reusable component by using object dependencies and object usages and provide a design and implementation of its supporting tool. In object-oriented domain models. there exists several diagrams which are described in different viewpoints. From these diagrams, object dependency and object usages are extracted and merged into an object dependency network, which is a basis for performing a comfonent identification algorithm. Finally, through a case study of internet banking system, we evaluate the applicability of the proposed identification process and tool.

• Phonetics and Speech Sciences
• /
• v.14 no.4
• /
• pp.35-43
• /
• 2022

This paper studies how to improve the identification rate of laryngeal disability speech data by convolutional neural network (CNN) and machine learning ensemble learning methods. In general, the number of laryngeal dysfunction speech data is small, so even if identifiers are constructed by statistical methods, the phenomenon caused by overfitting depending on the training method can lead to a decrease the identification rate when exposed to external data. In this work, we try to combine results derived from CNN models and machine learning models with various accuracy in a multi-voting manner to ensure improved classification efficiency compared to the original trained models. The Pusan National University Hospital (PNUH) dataset was used to train and validate algorithms. The dataset contains normal voice and voice data of benign and malignant tumors. In the experiment, an attempt was made to distinguish between normal and benign tumors and malignant tumors. As a result of the experiment, the random forest method was found to be the best ensemble method and showed an identification rate of 85%.

• Convergence Security Journal
• /
• v.22 no.5
• /
• pp.11-20
• /
• 2022

As the Internet and web technology develop around mobile devices, image data contains various types of sensitive information such as people, text, and space. In addition to these characteristics, as the use of SNS increases, the amount of damage caused by exposure and abuse of personal information online is increasing. However, research on de-identification technology based on multi-type object detection for personal information protection is insufficient. Therefore, this paper proposes an artificial intelligence model that detects and de-identifies multiple types of objects using existing single-type object detection models in parallel. Through cutmix, an image in which person and text objects exist together are created and composed of training data, and detection and de-identification of objects with different characteristics of person and text was performed. The proposed model achieves a precision of 0.724 and mAP@.5 of 0.745 when two objects are present at the same time. In addition, after de-identification, mAP@.5 was 0.224 for all objects, showing a decrease of 0.4 or more.

• Proceedings of the Korea Contents Association Conference
• /
• 2010.05a
• /
• pp.542-544
• /
• 2010

본 논문은 유전 알고리즘과 신경망 알고리즘을 결합하여 내용기반 영상 식별을 하는 연구 방법을 제시한다. 특징벡터로는 색상 정보와 질감 정보를 사용하였다. 추출된 특징벡터의 집합을 제안한 모델을 통해 최적의 유효 특징벡터의 집합을 찾아 영상을 식별하고자 한다.

• Proceedings of the Korean Information Science Society Conference
• /
• 1999.10a
• /
• pp.460-462
• /
• 1999

소프트웨어의 경제성, 시장 경쟁력 확보를 위한 소프트웨어의 재사용은 소프트웨어 공학의 주요 이슈가 되고 있다. 그중 컴포넌트와 컴포넌트 기반의 소프트웨어 개발은 재사용성을 확보할 수 있는 가장 주목 받는 방안으로 제시되고 있으며 많은 기법이나 지침들이 제안되고 있다. 본 논문에서는 컴포넌트 개발에서 UML에 기반하여 컴포넌트의 인터페이스를 추출하는 기법을 제시하고자 한다. 분석 단계에서 컴포넌트의 식별이 이루어졌다고 가정하고 분석 단계에서 나온 산출물 중, Use Case 모델과 클래스 다이어그램을 이용하여 컴포넌트의 메소드들을 식별하고, 인터페이스로 정의한다. 그리고 사용자요구사항에 근거하여 Hot Spot을 식별한 후, 컴포넌트의 커스터마이즈를 위한 메소드와 인터페이스를 정의한다.

• Journal of the korean academy of Pediatric Dentistry
• /
• v.48 no.2
• /
• pp.221-228
• /
• 2021

The aim of this study was to evaluate the use of easily accessible machine learning application to identify mesiodens, and to compare the ability to identify mesiodens between trained model and human. A total of 1604 panoramic images (805 images with mesiodens, 799 images without mesiodens) of patients aged 5 - 7 years were used for this study. The model used for machine learning was Google's teachable machine. Data set 1 was used to train model and to verify the model. Data set 2 was used to compare the ability between the learning model and human group. As a result of data set 1, the average accuracy of the model was 0.82. After testing data set 2, the accuracy of the model was 0.78. From the resident group and the student group, the accuracy was 0.82, 0.69. This study developed a model for identifying mesiodens using panoramic radiographs of children in primary and early mixed dentition. The classification accuracy of the model was lower than that of the resident group. However, the classification accuracy (0.78) was higher than that of dental students (0.69), so it could be used to assist the diagnosis of mesiodens for non-expert students or general dentists.

• Proceedings of the Korean Information Science Society Conference
• /
• 2005.11a
• /
• pp.202-204
• /
• 2005

통신 네트워크 발전 추세에 따라 데이터와 음성회선을 통합하고 PSTN을 통한 전화 서비스보다 저렴한 통신비용과 다양한 부가서비스 구현 등의 여러 장점을 가진 VoIP서비스로 전환되고 있다. 하지만 VoIP서비스의 긴급서비스에서 위치식별 대한 문제점이 제기되었다. VoIP는 인터넷망을 이용하으로 부여된 발신전화번호 정보를 송출하지 않아 발신자의 위치 및 해당지역 PSAP의 식별이 불가능하다. 이에 따라 건물 내 LAN 환경에서 VoIP 단말기와 연결하는 실내의 모든 이더넷 포트에 대한 정보를 관리하여 이를 지정된 VoIP 단말기와 연계시킴으로써 위치를 확인하는 방법 등이 제안되었으나 단말기 이동시마다 위치정보를 수정해야 하는 문제가 제기되었다. 따라서 본 논문에서는 VoIP서비스에 서 RFID를 이용하여 발신자 단말기 위치를 식별하는 긴급서비스 지원 모델을 제안하고, 이를 위해 가입자 위치정보 획득 및 관리, 호 라우팅 등에 관한 구체적인 방안을 제시하였다. 그 결과 VoIP 망에서 단말기 이동에 대한 위치 확인 문제도 해결이 가능하다.