• Design & Manufacturing
• /
• v.18 no.2
• /
• pp.41-50
• /
• 2024

In this study, experimental design methods were used to derive optimal process conditions for improving the thickness uniformity of a 0.40 mm, 3.5 inch light guide panel. Process mapping and expert group analysis were used to identify factors that influence the thickness of injection molded products. The key factors identified were mold temperature, mold temperature, injection speed, packing pressure, packing time, clamp force, and flash time. Considering the resin manufacturer's recommended process conditions and the process conditions for similar light guide plates, a three-level range was selected for the identified influencing factors. L27 orthogonal array process conditions were generated using the Taguchi method. Injection molding was performed using these L27 orthogonal array to mold the 3.5 inch light guide plates. Thickness measurements were then taken, and the results were analyzed using the signal-to-noise ratio to maximize the CpK value, leading to the determination of the optimal process conditions. The thickness uniformity of the product was analyzed by applying the derived optimum process conditions. The results showed a 97.5% improvement in the Cpk value of 3.22 compared to the process conditions used for similar light guide plates.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.10
• /
• pp.633-639
• /
• 2018

High-resolution images using remote sensing (RS) is importance to secure for spatial classification depending on the characteristics of the complex and various factors that make up the river environment. The purpose of this study is to evaluate the accuracy of the classification results and to suggest the possibility of applying the high resolution hyperspectral images obtained by using the drone to perform spatial classification. Hyperspectral images obtained from study area were reduced the dimensionality with PCA and MNF transformation to remove effects of noise. Spatial classification was performed by supervised classifications such as MLC(Maximum Likelihood Classification), SVM(Support Vector Machine) and SAM(Spectral Angle Mapping). In overall, the highest classification accuracy was showed when the MLC supervised classification was used by MNF transformed image. However, it was confirmed that the misclassification was mainly found in the boundary of some classes including water body and the shadowing area. The results of this study can be used as basic data for remote sensing using drone and hyperspectral sensor, and it is expected that it can be applied to a wider range of river environments through the development of additional algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.3
• /
• pp.59-66
• /
• 2021

Humans mainly recognize surrounding objects using visual and auditory information among the five senses (sight, hearing, smell, touch, taste). Major research related to the latest object recognition mainly focuses on analysis using image sensor information. In this paper, after emitting various chirp audio signals into the observation space, collecting echoes through a 2-channel receiving sensor, converting them into spectral images, an object recognition experiment in 3D space was conducted using an image learning algorithm based on deep learning. Through this experiment, the experiment was conducted in a situation where there is noise and echo generated in a general indoor environment, not in the ideal condition of an anechoic room, and the object recognition through echo was able to estimate the position of the object with 83% accuracy. In addition, it was possible to obtain visual information through sound through learning of 3D sound by mapping the inference result to the observation space and the 3D sound spatial signal and outputting it as sound. This means that the use of various echo information along with image information is required for object recognition research, and it is thought that this technology can be used for augmented reality through 3D sound.

• The KIPS Transactions:PartB
• /
• v.18B no.3
• /
• pp.117-130
• /
• 2011

The purpose of this study is to produce a better way to detect crossroad accidents, which involves an efficient method to produce background images in consideration of object movement and preserve/demonstrate the candidate accident region. One of the prior studies proposed an employment of traffic signal interval within crossroad to detect accidents on crossroad, but it may cause a failure to detect unwanted accidents if any object is covered on an accident site. This study adopted inverse perspective mapping to control the scale of object, and proposed different ways such as producing robust background images enough to resist surrounding noise, generating candidate accident regions through information on object movement, and by using edge information to preserve and delete the candidate accident region. In order to measure the performance of proposed algorithm, a variety of traffic images were saved and used for experiment (e.g. recorded images on rush hours via DVR installed on crossroad, different accident images recorded in day and night rainy days, and recorded images including surrounding noise of lighting and shades). As a result, it was found that there were all 20 experiment cases of accident detected and actual effective rate of accident detection amounted to 76.9% on average. In addition, the image processing rate ranged from 10~14 frame/sec depending on the area of detection region. Thus, it is concluded that there will be no problem in real-time image processing.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.2
• /
• pp.369-377
• /
• 2008

Though the Hough transform(HT) is a well-known method for detecting analytical shape represented by a number of free parameters, the basic property of the HT, the one-to-many mapping from an image spare to a Hough space, causes the innate problem, the sensitivity to noise. This basic problem also deteriorates the quality of detected lines and makes the detected line deviated from the real one or generates some bogus, multiple lines where only one real line exists. The size of Hough space also affects the quality of detected lines. In this paper, we analyzed the line distortions in the traditional Hough transform and showed that the distortions are relieved in the edge strength Hough transform(ESHT), which is a modified HT. However the usage of expanded edge and edge strength in ESHT can cause some new line distortions which do not exist in the HT. These new ones can be solved by a proper setting of decreasing and broadening parameter values and the optimal values can be determined only by some pre-determined values. We also illustrated several examples to show the distortion-decreasing property of ESHT.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1709-1722
• /
• 2022

The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model's input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.

• Journal of Biomedical Engineering Research
• /
• v.28 no.2
• /
• pp.258-264
• /
• 2007

Spatiotemporal changes of brain rhythmic activity at a certain frequency have been usually monitored in real time using scalp potential maps of multi-channel electroencephalography(EEG) or magnetic field maps of magnetoencephalography(MEG). In the present study, we investigate if it is possible to implement a real-time brain activity monitoring system which can monitor spatiotemporal changes of cortical rhythmic activity on a subject's cortical surface, neither on a sensor plane nor on a standard brain model, with a high temporal resolution. In the suggested system, a frequency domain inverse operator is preliminarily constructed, considering the individual subject's anatomical information, noise level, and sensor configurations. Spectral current power at each cortical vertex is then calculated for the Fourier transforms of successive sections of continuous data, when a single frequency or particular frequency band is given. An offline study which perfectly simulated the suggested system demonstrates that cortical rhythmic source changes can be monitored at the cortical level with a maximal delay time of about 200 ms, when 18 channel EEG data are analyzed under Pentium4 3.4GHz environment. Two sets of artifact-free, eye closed, resting EEG data acquired from a dementia patient and a normal male subject were used to show the feasibility of the suggested system. Factors influencing the computational delay are investigated and possible applications of the system are discussed as well.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.3
• /
• pp.1224-1242
• /
• 2018

Aiming at motion analysis and compensation, it is essential to conduct motion detection with images. However, motion detection and tracking from low-altitude images obtained from an unmanned aerial system may pose many challenges due to degraded image quality caused by platform motion, image instability and illumination fluctuation. This research tackles these challenges by proposing a modified joint transform correlation algorithm which includes two preprocessing strategies. In spatial domain, a modified fuzzy edge detection method is proposed for preprocessing the input images. In frequency domain, to eliminate the disturbance of self-correlation items, the cross-correlation items are extracted from joint power spectrum output plane. The effectiveness and accuracy of the algorithm has been tested and evaluated by both simulation and real datasets in this research. The simulation experiments show that the proposed approach can derive satisfactory peaks of cross-correlation and achieve detection accuracy of displacement vectors with no more than 0.03pixel for image pairs with displacement smaller than 20pixels, when addition of image motion blurring in the range of 0~10pixel and 0.002variance of additive Gaussian noise. Moreover,this paper proposes quantitative analysis approach using tri-image pairs from real datasets and the experimental results show that detection accuracy can be achieved with sub-pixel level even if the sampling frequency can only attain 50 frames per second.

• Journal of KSNVE
• /
• v.9 no.1
• /
• pp.196-205
• /
• 1999

The existence. bifurcation. and the orbital stability of periodic motions, which is called nonlinear normal mode, in a nonlinear dual mass Hamiltonian system. which has 6th order homogeneous polynomial as a nonlinear term. are studied in this paper. By direct integration of the equations of motion. Poincare Map. which is a mapping of a phase trajectory onto 2 dimensional surface in 4 dimensional phase space. is obtained. And via the Birkhoff-Gustavson canonical transformation, the analytic expression of the invariant curves in the Poincare Map is derived for small value of energy. It is found that the nonlinear system. which is considered in this paper. has 2 or 4 nonlinear normal modes depending on the value of nonlinear parameter. The Poincare Map clearly shows that the bifurcation modes are stable while the mode from which they bifurcated out changes from stable to unstable.

• Journal of KSNVE
• /
• v.9 no.1
• /
• pp.206-213
• /
• 1999

In the beam-like ship vibration analysis. three-dimensional correction factor(J-factor) can be calculated by considering the three-dimensional effect of the two-dimensional added mass. However, existing method is time-consuming with low accuracy in respect of global vibration analyses for vessels with large breadth. In this paper, to improve the demerit of the previous method, a new method of the beam-like ship vibration analysis is introduced In this method. the three-dimensional fluid added mass of surrounding water is calculated directly by solving the velocity potential problem using the Boundary Element Method (BEM). Then the three-dimensional added mass is evaluated as the lumped mass for each strip. Also, the beam-like ship vibration analysis for the structural beam model if performed with the lumped mass considered. It was verified that this new method is useful for the beam-like ship vibration analysis by comparing results obtained from both the existing method and the new method with experimental measurements for the open top container model.