• Current Optics and Photonics
• /
• v.8 no.4
• /
• pp.327-344
• /
• 2024

Optical phantoms are essential in optical imaging and measurement instruments for performance evaluation, calibration, and quality control. They enable precise measurement of image resolution, accuracy, sensitivity, and contrast, which are crucial for both research and clinical diagnostics. This paper reviews the recent advancements and challenges in phantoms for optical coherence tomography, photoacoustic imaging, digital holographic microscopy, optical diffraction tomography, and oximetry tools. We explore the fundamental principles of each technology, the key factors in phantom development, and the evaluation criteria. Additionally, we discuss the application of phantoms used for enhancing optical-image quality. This investigation includes the development of realistic biological and clinical tissue-mimicking phantoms, emphasizing their role in improving the accuracy and reliability of optical imaging and measurement instruments in biomedical and clinical research.

• Korean Journal of Artificial Intelligence
• /
• v.12 no.3
• /
• pp.33-39
• /
• 2024

In this paper, we explore the application of Kodaly hand signs in enhancing children's music education, performances, and auditory assistance technologies. This research focuses on improving the recognition rate of Multilayer Perceptron (MLP) models in identifying Kodaly hand sign scales through the integration of Artificial Neural Networks (ANN). We developed an enhanced MLP model by augmenting it with additional parameters and optimizing the number of hidden layers, aiming to substantially increase the model's accuracy and efficiency. The augmented model demonstrated a significant improvement in recognizing complex hand sign sequences, achieving a higher accuracy compared to previous methods. These advancements suggest that our approach can greatly benefit music education and the development of auditory assistance technologies by providing more reliable and precise recognition of Kodaly hand signs. This study confirms the potential of parameter augmentation and hidden layers optimization in refining the capabilities of neural network models for practical applications.

• Journal of Positioning, Navigation, and Timing
• /
• v.2 no.1
• /
• pp.9-17
• /
• 2013

In this paper, a pseudorange correction regeneration algorithm was developed to improve the positioning accuracy of DGPS using multi-reference stations, and the optimal minimum number of reference sites was determined by trying out different numbers of reference. This research was conducted using from two to five sites, and positioning errors of less than 1 m were obtained when pseudorange corrections are collected from at least four reference stations and interpolated as the pseudorange correction at the rover. After determining the optimal minimum number of reference stations, the pseudorange correction regeneration algorithm developed was tested by comparison with the performance of other algorithms. Our approach was developed based on an exponential model. If pseudorange corrections are regenerated using an exponential model, the effect of a small difference in the baseline distance can be enlarged. Therefore, weights can be applied sensitively even when the baseline distance differs by a small amount. Also weights on the baseline distance were applied differently by assigning weights depending on the difference of the longest and shortest baselines. Through this method, the positioning accuracy improved by 19% compared to the result of previous studies.

• Journal of Korea Spatial Information System Society
• /
• v.11 no.2
• /
• pp.1-6
• /
• 2009

Localization of nodes is a key technology for application of wireless sensor network. Having a GPS receiver on every sensor node is costly. In the past, several approaches, including range-based and range-free, have been proposed to calculate positions for randomly deployed sensor nodes. Most of them use some special nodes, called anchor nodes, which are assumed to know their own locations. Other sensors compute their locations based on the information provided by these anchor nodes. This paper uses a single mobile anchor node to move in the sensing field and broadcast its current position periodically. We provide a weighted centroid localization algorithm that uses coefficients, which are decided by the influence of mobile anchor node to unknown nodes, to prompt localization accuracy. We also suggest a criterion which is used to select mobile anchor node which involve in computing the position of nodes for improving localization accuracy. Weighted centroid localization algorithm is simple, and no communication is needed while locating. The localization accuracy of weighted centroid localization algorithm is better than maximum likelihood estimation which is used very often. It can be applied to many applications.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.29 no.5 s.236
• /
• pp.647-654
• /
• 2005

This research Proposes a Process design of injection molded microcellular plastic gears for enhancing the fatigue strength/durability and accuracy of the gears applying thermodynamic instability to microcellular foaming process. To develop the injection molded plastic gears by way of microceliular process, it is absolutely necessary the following two process design. The first is microcellular forming process for enhancing the strength/durability of plastic gears. To be microcellular process succeeded, based on the microcellular principle, mechanical apparatus is designed where nucleation and cell growth are to be generated renewably. The second is the counter pressure process which is mainly fur improving the tooth surface roughness and the accuracy of microcellular gears. For the former process, screw, nozzle and gas equipment are newly designed, and for the latter, counter pressure by nitrogen gas is intentionally brought about into mold cavity when injecting plastic gears. Based on the proposed process design, using gear mold, experiments of injection molding show that, in internal space of plastic gears, microcellular nuclear cells less than 5 lim in diameter have been generated homogeneously via electron microscope photos.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.18 no.6
• /
• pp.1938-1944
• /
• 2004

In this study, when we make a diagnosis of constitution using QSCC Ⅱ(Questionnaire of Sasang Constitution Classification). data mining techniques are applied to seek the classification function for improving the accuracy. Data used in the analysis are the questionnaires of 1051 patients who had been treated in Dong Eui Oriental Medical Hospital and Kyung Hee Oriental Medical Hospital. The criteria for data cleansing are the response pattern in the opposite questionnaires and the positive proportion of specific questionnaires in each constitution. And the criteria for variable selection are the test of homogeneity in frequency analysis and the coefficients in the linear discriminant function. Discriminant analysis model and decision tree model are applied to seek the classification function into Sasang constitution. The accuracy in learning sample is similar in two models, the higher accuracy in test sample is obtained in discriminant analysis model.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.6
• /
• pp.77-85
• /
• 2002

An analysis method which calculates corrective machining information for improving the motion accuracy of linear motion guide Is proposed in this paper. The method is composed of two algorithms. One is the algorithm fur prediction of the motion errors from rail form error. The other is the algorithm for prediction of rail form error from the motion errors of table. Transfer function is utilized in each algorithm, which represents the ratio of bearing reaction force variation to unit magnitude of spatial frequencies of raid from error. As the corrective machining information is acquired from the measured motion errors of table, the method has a merit not to measure rail form error directly. Validity of the method is verified both theoretically and experimentally. By applying the method, linear motion error of test equipment is reduced from 5.97$\mu$m to 0.58$\mu$m, and reduced from 32.78arcsec to 6.21 arcsec in case of angular motion error. From the results, it is confirmed that the method is very effective to improve the motion accuracy of linear motion guide.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.4
• /
• pp.925-932
• /
• 2015

In this paper, by utilizing the iBeacon is BLE (BlueTooth Low Energy) protocol devices that are supported by over the Bluetooth 4.0, and have implemented a system to improve the accuracy in the system for measuring the position of the user. After measuring through the system according to the state of the iBeacon, the interference factors are analyzed through analysis of the collected data, and applying the extended Kalman filter for calibration. Compared with the data after applying a filter with existing data, it was confirmed an increase in accuracy. Improvement techniques for providing the less complexity to the actual implementation, is effective in improving the accuracy of vulnerable services basic iBeacon.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.2
• /
• pp.670-675
• /
• 2015

Medical imaging is one of the most powerful tools for gaining information about internal organs and tissues. It is a challenging task to develop sophisticated image analysis methods in order to improve the accuracy of diagnosis. The objective of this paper is to develop a Computer Aided Diagnostics (CAD) scheme for Brain Tumour detection from Magnetic Resonance Image (MRI) using active contour models and to investigate with several approaches for improving CAD performances. The problem in clinical medicine is the automatic detection of brain Tumours with maximum accuracy and in less time. This work involves the following steps: i) Segmentation performed by Fuzzy Clustering with Level Set Method (FCMLSM) and performance is compared with snake models based on Balloon force and Gradient Vector Force (GVF), Distance Regularized Level Set Method (DRLSE). ii) Feature extraction done by Shape and Texture based features. iii) Brain Tumour detection performed by various tree classifiers. Based on investigation FCMLSM is well suited segmentation method and Random Forest is the most optimum classifier for this problem. This method gives accuracy of 97% and with minimum classification error. The time taken to detect Tumour is approximately 2 mins for an examination (30 slices).

• The Journal of Korea Robotics Society
• /
• v.15 no.4
• /
• pp.385-391
• /
• 2020

As deep learning technology has been developed and applied to various fields, it is gradually changing from an existing single image based application to a video based application having a time base in order to recognize human behavior. However, unlike 2D CNN in a single image, 3D CNN in a video has a very high amount of computation and parameter increase due to the addition of a time axis, so improving accuracy in action recognition technology is more difficult than in a single image. To solve this problem, we investigate and analyze various techniques to improve performance in 3D CNN-based image recognition without additional training time and parameter increase. We propose a time base ensemble using the time axis that exists only in the videos and an ensemble in the input frame. We have achieved an accuracy improvement of up to 7.1% compared to the existing performance with a combination of techniques. It also revealed the trade-off relationship between computational and accuracy.