• Investigative Magnetic Resonance Imaging
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• v.18 no.1
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• pp.1-6
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• 2014

Purpose : $T_2{^*}$ relaxation time which includes susceptibility information represents unique feature of tissue. The objective of this study was to investigate $T_2{^*}$ relaxation times of the normal glandular tissue and fat of breast using a 3T MRI system. Materials and Methods: Seven-echo MR Images were acquired from 52 female subjects (age $49{\pm}12 $years; range, 25 to 75) using a three-dimensional (3D) gradient-echo sequence. Echo times were between 2.28 ms to 25.72 ms in 3.91 ms steps. Voxel-based $T_2{^*}$ relaxation times and $R_2{^*}$ relaxation rate maps were calculated by using the linear curve fitting for each subject. The 3D regions-of-interest (ROI) of the normal glandular tissue and fat were drawn on the longest echo-time image to obtain $T_2{^*}$ and $R_2{^*}$ values. Mean values of those parameters were calculated over all subjects. Results: The 3D ROI sizes were $4818{\pm}4679$ voxels and $1455{\pm}785$ voxels for the normal glandular tissue and fat, respectively. The mean $T_2{^*}$ values were $22.40{\pm}5.61ms$ and $36.36{\pm}8.77ms$ for normal glandular tissue and fat, respectively. The mean $R_2{^*}$ values were $0.0524{\pm}0.0134/ms$ and $0.0297{\pm}0.0069/ms$ for the normal glandular tissue and fat, respectively. Conclusion: $T_2{^*}$ and $R_2{^*}$ values were measured from human breast tissues. $T_2{^*}$ of the normal glandular tissue was shorter than that of fat. Measurement of $T_2{^*}$ relaxation time could be important to understand susceptibility effects in the breast cancer and the normal tissue.

• Investigative Magnetic Resonance Imaging
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• v.18 no.1
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• pp.17-24
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• 2014

Purpose : To determine whether high-resolution contrast-enhanced three dimensional imaging with spoiled gradient-recalled sequence (HR-CE 3D-SPGR) plays a meaningful role in the assessment of intracranial vertebral artery (ICVA) and posterior inferior cerebellar artery (PICA) in lateral medullary infarction (LMI). Materials and Methods: Twenty-five patients confirmed with LMI were retrospectively enrolled with approval by the IRB of our institute, and 3T MRI with HR-CE 3D-SPGR and contrast-enhanced magnetic resonance angiography (CE-MRA) were performed. Two radiologists who were blinded to clinical information and other brain MR images including diffusion weighted image independently evaluated arterial lesions in ICVA and PICA. The demographic characteristics, the area of LMI and cerebellar involvement were analyzed and compared between patients with arterial lesion in ICVA only and patients with arterial lesions in both ICVA and PICA on HR-CE 3D-SPGR. Results: Twenty-two of twenty-five LMI patients had arterial lesions in ICVA or PICA on HR-CE 3D SPGR. However twelve arterial lesions in PICA were not shown on CE-MRA. Concurrent cerebellar involvement appeared more in LMI patients with arterial lesion in ICVA and PICA than those with arterial lesion in ICVA alone (p = 0.069). Conclusion: HR-CE 3D-SPGR can help evaluate arterial lesions in ICVA and PICA for LMI patients.

• Science of Emotion and Sensibility
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• v.19 no.4
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• pp.21-32
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• 2016

Studies have observed various phenomena regarding the effect of the interaction between type, price, and brand image of a product on consumers' purchase intent and product quality perception. Yet, few have studied the effect of the interaction between product type and manufacturing method on these factors. However, the advent of three-dimensional (3D) printers added a new manufacturing method, 3D printing, to the traditional methods of handicraft and automated machine-based production, and research is necessary since this new framework might affect consumers' purchase intent and product quality perception. Therefore, this study aimed to verify the effects of the interaction between product type and manufacturing method on purchase intent and product quality perception. To achieve this, in our experiment 1, we selected product types with different characteristics (drone vs. violin vs. cup), and measured whether consumers preferred different manufacturing methods for each product type. The results showed that consumers preferred the 3D printing method for technologically advanced products such as drones, the handmade method for violins, and the automated machine-based manufacturing method, which allows mass production, for cups. Experiment 2 attempted to verify the effects of the differences in manufacturing method preferences for each product type on consumers' purchase intent and product quality perception. Our findings are as follows: for drones, the purchase intent was highest when 3D printing was used; for violins, the purchase intent was highest when the violins were handmade; for cups, the purchase intent was highest when machine-based manufacturing was used. Moreover, whereas the product quality perception for drones did not differ across different manufacturing methods, consumers perceived that handmade violins had the highest quality and that cups manufactured with 3D printing had the lowest quality (the purchase intent for cups was also lowest when 3D printing was used). This study is anticipated to provide a wide range of implications in various areas, including consumer psychology, marketing, and advertising.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• The Journal of Korean Society for Radiation Therapy
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• v.17 no.1
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• pp.1-8
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• 2005

Purpose : An effect of a packing to uterine treatment of a cervical cancer using a dose-volume histogram for a point dose and a volume dose of the bladder and the rectum was analyzed by establishing a three-dimensional treatment plan using a CT image. Materials and methods : Reference points of the bladder and the rectum were marked, respectively at a treatment plan device (plato brachytherapy V14.2.4) by photographing CT(marconi, USA) when the packing was used and removed under the same condition and a treatment plan was performed to Apoint depending on ICRU38. However, in case of the rectum, a maximum point was looked up and compared with the above point because the point presented from the ICRU is not proper as a representative value of a rectum point dose. Further, the volume dose depending on volume of $50\%,\;80\%,\;and\;100\%$ point doses of the rectum and the bladder was measured. The measured values were used to analyze the effect of the packing through a Wilcoxon Signed Rank Test (a SAS statistical analysis process program). Result : The reference points at the bladder and rectum doses when the packing was removed were $116.94\;35.42\%$ and $117.59\;21.08\%$, respectively. The points when the packing was used were $107.08\;38.12\%$ and $95.19\;21.32\%$, respectively. After the packing was used, the reference points at the bladder and the rectum were decreased by $9.86\%$ and $22.4\%$, respectively. When the packing was removed, the maximum points at the bladder and the rectum were $164.51\;50.89\%,\;128.81\;33.05\%$, respectively. When the packing was used, the maximum points at the bladder and the rectum were $142.31\;44.79,\;110.08\;37.03\%$, respectively. After the packing was used, the maximum points at the bladder and the rectum were decreased by $22.2\%$ and $18.73\%$, respectively. When the packing was removed, the bladder volume at $50\%,\;80\%,\;and\;100\%$ point doses of the rectum and the bladder were $48.62{\pm}18.09\%,\;16.12{\pm}11.15\%,\;and\;7.51{\pm}6.63\%$, respectively and its rectum volume were $23.41{\pm}14.44\%,\;6.27{\pm}4.28\%,\;2.79{\pm}2.27\%$, respectively. When the packing was used, the bladder volume at $50\%,\;80\%,\;and\;100\%$ point doses of the rectum and the bladder were $40.33{\pm}16.72,\;11.63{\pm}8.72,\;and\;4.87{\pm}4.75\%$, respectively and its rectum volume were $18.96{\pm}8.37\%,\;4.75{\pm}2.58\%,\;and\;1.58{\pm}1.06\%$, respectively. After the packing was used, the bladder volume at $50\%,\;80\%,\;and\;100\%$ point doses of the rectum and the bladder were decreased by $8.29\%,\;4.49\%,\;and\;2.64\%$, respectively and its bladder volume were decreased by $4.45\%,\;1.52\%,\;and\;1.21\%$, respectively. Conclusion : Values at Reference point doses of the bladder and the rectum recommended from the ICRU 38 were 0.0781 and 0.0781, respectively and values of their maximum point doses were 0.0156 and 0.0156, respectively, as a result of which an effect of the packing using at the uterine intracavitary treatment of an uterine cervical cancer through the three-dimensional treatment plan used CT were measured. That is, the values at reference point doses and the values at maximum point doses show similar difference. However, P value was 0.15 at over $50\%,\;80\%,\;and\;100\%$ volume doses and the value shows no similar difference. In other words, the effect of the packing looks like having a difference at the point dose, but actually shows no difference at the volume dose. The reason is that the volume of the bladder and the rectum are wide but the volume of the packing is only a portion. Therefore, the effect of decreasing the point dose was not great. Further, the farer the distance is, the more weak the intensity of radiation is because the intensity of radiation is proportional to inverse square of a distance. Therefore, the effort to minimize an obstacle of the bladder and the rectum by using the packing should be made.