• Food Engineering Progress
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• v.13 no.2
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• pp.117-121
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• 2009

Detection of spoilage odors from beef during storage was investigated using sensory evaluation with R-index, and microbial assay for Pseudomonas. Beef samples were tested to measure the flavor changes, which were converted to R-index, and the Pseudomonas levels during storage. There was a steep rise in R-index until 36 hr after storage at 25$^{\circ}C$, and then a gentle rise from 48 hr, whereas, there was a steady rise in R-index in the whole range of storage at 5$^{\circ}C$. Detection time of spoilage odors according to R-index was statistically analyzed at $\alpha$=5% to be at 30.92${\pm}$3.47 hr and 169.80${\pm}$11.27 hr for 25 and 5$^{\circ}C$ storage, respectively, and analyzed at $\alpha$=1% to be 34.80${\pm}$4.01 and 176.41${\pm}$9.89 hr for 25 and 5$^{\circ}C$ storage, respectively. At the detection times of spoilage odors, the Pseudomonas levels were found to be almost the same, but less than 6-7 log CFU/g generally known as a standard level at occurrence of spoilage odors in beef. This indicated that some other factors than the Pseudomonas reactions could be associated with generation of spoilage odors.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.237.1-237.1
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• 2014

Etching is one of the most important steps in semiconductor manufacturing. In etch process control a critical task is to stop the etch process when the layer to be etched has been removed. If the etch process is allowed to continue beyond this time, the material gets over-etched and the lower layer is partially removed. On the other hand if the etch process is stopped too early, part of the layer to be etched still remains, called under-etched. Endpoint detection (EPD) is used to detect the most accurate time to stop the etch process in order to avoid over or under etch. The goal of this research is to develop a hardware and software system for EPD. The hardware consists of an Optical Plasma Monitor (OPM) sensor which is used to continuously monitor the plasma optical emission intensity during the etch process. The OPM software was developed to acquire and analyze the data to perform EPD. Our EPD algorithm is based on the following theory. As the etch process starts the plasma generated in the vacuum is added with the by-products from the etch reactions on the layer being etched. As the endpoint reaches and the layer gets completely removed the plasma constituents change gradually changing the optical intensity of the plasma. Although the change in optical intensity is not apparent, the difference in the plasma constituents when the endpoint has reached leaves a unique signature in the data gathered. Though not detectable in time domain, this signature could be obscured in the frequency spectrum of the data. By filtering and analysis of the changes in the frequency spectrum before and after the endpoint we could extract this signature. In order to do that, first, the EPD algorithm converts the time series signal into frequency domain. Next the noise in the frequency spectrum is removed to look for the useful frequency constituents of the data. Once these useful frequencies have been selected, they are monitored continuously in time and using a sub-algorithm the endpoint is detected when significant changes are observed in those signals. The experiment consisted of three kinds of etch processes; ashing, SiO2 on Si etch and metal on Si etch to develop and evaluate the EPD system.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.10
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• pp.17-26
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• 2010

This is concerned with the technology to monitor the vehicle operation, failure and disorder by using WiBro portable device. More precisely, the technology makes it possible that the information collection device is connected to both ECU(Electronic Control Unit) which is the device for controlling engine, transmission, brake, air-bag, etc that are connected to in-vehicle network and OBD-II connector that is for data collection from various sensors. In addition, with a WiBro portable device (cell phone, PDA, PMP, UMPC, etc). equipped with a vehicle diagnostic programs, information for operation, failure and malfunction can be obtained and analyzed in real-time, and alarm is alerted when the vehicle is in abnormal status, which makes the early reactions to the status. Furthermore, the collected data can be sent through WiBro network to the server managed by the company specialized in managing the vehicles, thus the technology could help the drivers who have less knowledge about their auto-vehicles have safe and economic driving. There is always a possibility of malfunction due to various types of noise that are caused by wring-harness when the device is wired-connected. In this research, in order to overcome this problem, we propose a system configuration that can do monitoring and diagnosis with a device for collecting data from vehicle and a personal WiBro device. Also, we performed research on data acquisition and interlock for the system defined by the definition for information and data sharing platform.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.2
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• pp.74-79
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• 2017

Purpose Proton therapy can deliver an optimal dose to tumor while reducing unnecessary dose to normal tissue as compared the conventional photon therapy. As proton beams are irradiated into tissue, various positron emitters are produced via nuclear fragmentation reactions. These positron emitters could be used for the dose verification by using PET. However, the short half-life of the radioisotopes makes it hard to obtain the enough amounts of events. The aim of this study is to investigate the effect of off-line PET imaging scan time on the PET image quality. Materials and Methods The various diameters of spheres (D=37, 28, 22 mm) filled with distilled water were inserted in a 2001 IEC body phantom. Then proton beams (100 MU) were irradiated into the center of the each sphere using the wobbling technique with the gantry angle of $0^{\circ}$. The modulation widths of the spread out bragg peak were 16.4, 14.7 and 9.3 cm for the spheres of 37, 28 and 22 mm in diameters respectively. After 5 min of the proton irradiation, the PET images of the IEC body phantom were obtained for 50 min. The PET images with different time courses (0-10 min, 11-20 min, 21-30 min, 31-40 min and 41-50 min) were obtained by dividing the frame with a duration of 10 min. In order to evaluate the off-line PET image quality with the different time courses, the contrast-to-noise ratio (CNR) of the PET image calculated for each sphere. Results The CNRs of the sphere (D=37 mm) were 0.43, 0.42, 0.40, 0.31 and 0.21 for the time courses of 0-10 min, 11-20 min, 21-30 min, 31-40 min and 41-50 min respectively. The CNRs of the sphere (D=28 mm) were 0.36, 0.32, 0.27, 0.19 and 0.09 for the time courses of 0-10 min, 11-20 min, 21-30 min, 31-40 min and 41-50 min respectively. The CNR of 37 mm sphere was decreased rapidly after 30 min of the proton irradiation. In case of the spheres of 28 mm and 22 mm, the CNR was decreased drastically after 20 min of the irradiation. Conclusion The off-line PET imaging time is an important factor for the monitoring of the proton therapy. In case of the lesion diameter of 22 mm, the off-line PET image should be obtained within 25 min after the proton irradiation. When it comes to small size of tumor, the long PET imaging time will be beneficial for the proton therapy treatment monitoring.