• 한국비블리아학회지
• /
• 제34권3호
• /
• pp.205-225
• /
• 2023

이 연구는 광주대표도서관 건립의 핵심 요소인 장서개발정책 수립을 위해 정보자원 인식을 조사하였다. 조사대상은 장서개발의 이해관계자인 사서·이용자·도서관 3개 집단으로, 각 68명, 98명, 20개관이 조사에 응답하였다. 조사분야는 정보자원의 유형, 정보자원의 주제, 정보자원의 수준 3개 분야이다. 연구 결과는 다음과 같다. 첫째, 사서 집단은 이용자 집단에 비해서 선호도와 필요도의 상관계수가 낮게 나타났다. 둘째, 특정대상 지원자료는 낮은 선호도와 낮은 필요도를 보였지만, 실제 이용률은 상위권으로 나타났다. 셋째, 사서들은 고문서 및 고서, 박물, 기술보고서, 학술논문을 대표도서관으로 이관하기를 원했다. 넷째, 철학 정보 자원은 선호도 중위권, 필요도 최하위권, 이관 필요성 최하위권이었다. 다섯째, 향토자료와 시정자료는 이용률 하위권과 이관 필요성 최상위권이었다. 여섯째, 현재 광주 지역 공공도서관의 정보자원 수준은 1.837, 대표도서관 요구 정보자원 수준은 3.325로 나타났다.

• 센서학회지
• /
• 제30권2호
• /
• pp.76-81
• /
• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권2호
• /
• pp.929-944
• /
• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권10호
• /
• pp.4098-4116
• /
• 2020

Mobile devices such as smartphones are very attractive targets for augmented reality (AR) services, but their limited resources make it difficult to increase the number of objects to be recognized. When the recognition process is scaled to a large number of objects, it typically requires significant computation time and memory. Therefore, most large-scale mobile AR systems rely on a server to outsource recognition process to a high-performance PC, but this limits the scenarios available in the AR services. As a part of realizing large-scale standalone mobile AR, this paper presents a solution to the problem of accuracy, memory, and speed for large-scale object recognition. To this end, we design our own basic feature and realize spatial locality, selective feature extraction, rough pose estimation, and selective feature matching. Experiments are performed to verify the appropriateness of the proposed method for realizing large-scale standalone mobile AR in terms of efficiency and accuracy.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2019년도 춘계학술발표대회
• /
• pp.398-400
• /
• 2019

Faced with the increasing demand for image recognition on mobile devices, how to run convolutional neural network (CNN) models on mobile devices with limited computing power and limited storage resources encourages people to study efficient model design. In recent years, many effective architectures have been proposed, such as mobilenet_v1, mobilenet_v2 and PeleeNet. However, in the process of feature selection, all these models neglect some information of shallow features, which reduces the capture of shallow feature location and semantics. In this study, we propose an effective framework based on Feature Pyramid Networks to improve the recognition accuracy of deep and shallow images while guaranteeing the recognition speed of PeleeNet structured images. Compared with PeleeNet, the accuracy of structure recognition on CIFA-10 data set increased by 4.0%.

• 센서학회지
• /
• 제31권1호
• /
• pp.16-23
• /
• 2022

Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.

• 한국도서관정보학회지
• /
• 제46권1호
• /
• pp.177-202
• /
• 2015

본 연구는 교육연구기관에서 생산된 지식정보자원이 기관의 지식관리시스템으로 역할을 수행하는지 살펴보고, 국가지식관리시스템의 방안 제시를 위하여 시도되었다. 홈페이지에서 제공되는 지식정보자원을 '결과의 지식정보자원', '과정의 지식정보자원', '소통의 지식정보자원'으로 분류하고, 내용분석법을 사용하여 메타데이터의 적용 현황, 검색시스템의 구성 현황을 분석하였다. 연구결과, 홈페이지 지식정보자원 관리의 개선을 위하여 표준화된 유형별 메타데이터와 분류체계의 개발, 홈페이지가 기관 생산지식정보자원의 대외 유통 창구라는 인식 고취, 홈페이지 지식정보자원에 대한 다양한 검색기능과 지식지도 제공, 원문 획득을 위한 연계정보 제공, 조직 운영자와 홈페이지 관리자의 기관 홈페이지에 대한 지식관리시스템으로서의 인식, 모바일 환경에 적합한 데이터 크기 분할과 제공 단위의 다양화 작업, 내부 전자결재시스템과 홈페이지의 유기적인 연계 기능을 제시하였다.

• Earthquakes and Structures
• /
• 제14권2호
• /
• pp.143-153
• /
• 2018

Correlation among different factors must be considered for selection of influencing factors in safety monitoring of high dam including positive correlation of variables. Therefore, a new factor selection method was constructed based on Copula entropy and mutual information theory, which was deduced and optimized. Considering the small sample size in high dam monitoring and distribution of daily monitoring samples, a computing method that avoids causality of structure as much as possible is needed. The two-dimensional normal information diffusion and fuzzy reasoning of pattern recognition field are based on the weight theory, which avoids complicated causes of the studying structure. Hence, it is used to dam safety monitoring field and simplified, which increases sample information appropriately. Next, a complete system integrating high dam monitoring and uncertainty prediction method was established by combining Copula entropy theory and information diffusion theory. Finally, the proposed method was applied in seepage monitoring of Nuozhadu clay core-wall rockfill dam. Its selection of influencing factors and processing of sample data were compared with different models. Results demonstrated that the proposed method increases the prediction accuracy to some extent.

• Journal of Information Processing Systems
• /
• 제11권3호
• /
• pp.438-449
• /
• 2015

Recognition systems for scanned or printed music scores that have been implemented on personal computers have received attention from numerous scientists and have achieved significant results over many years. A modern trend with music scores being captured and played directly on mobile devices has become more interesting to researchers. The limitation of resources and the effects of illumination, distortion, and inclination on input images are still challenges to these recognition systems. In this paper, we introduce a novel approach for recognizing music scores captured by mobile cameras. To reduce the complexity, as well as the computational time of the system, we grouped all of the symbols extracted from music scores into ten main classes. We then applied each major class to SVM to classify the musical symbols separately. The experimental results showed that our proposed method could be applied to real time applications and that its performance is competitive with other methods.

• International Journal of Contents
• /
• 제10권4호
• /
• pp.11-17
• /
• 2014

The influence of acquisition environment on music score images captured by a camera has not yet been seriously examined. All existing Optical Music Recognition (OMR) systems attempt to recognize music score images captured by a scanner under ideal conditions. Therefore, when such systems process images under the influence of distortion, different viewpoints or suboptimal illumination effects, the performance, in terms of recognition accuracy and processing time, is unacceptable for deployment in practice. In this paper, a novel, lightweight but effective approach for dealing with the issues caused by camera based music scores is proposed. Based on the staff line information, musical rules, run length code, and projection, all regions of interest are determined. Templates created from inverse filter are then used to recognize the music symbols. Therefore, all fragmentation and deformation problems, as well as missed recognition, can be overcome using the developed method. The system was evaluated on a dataset consisting of real images captured by a smartphone. The achieved recognition rate and processing time were relatively competitive with state of the art works. In addition, the system was designed to be lightweight compared with the other approaches, which mostly adopted machine learning algorithms, to allow further deployment on portable devices with limited computing resources.