• Journal of Korean Society of Transportation
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• v.19 no.1
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• pp.101-114
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• 2001

Transportation enterprises should maintain constant and qualitative operation. Thus, in short period, transportation enterprises don't change supply in accordance with demand. In the result, transportation enterprises don't reduce operation in spite of management deficit at will. In freight transportation type, less-than-truckload(LTL) has more relation with above transportation feature than truckload(TL) does. Because freight transportation supply of TL is more flexible than that of LTL in correspondence of freight transportation demand. Relating to above mention, it appears that shortage of road and freight terminal of LTL is larger than that of TL. Especially in road and freight terminal comparison, shortage of freight terminal is larger than that of road. Shortage of road is the largest in 1990, and improved after-ward. But shortage of freight terminal is serious lately. So freight terminal needs more expansion than road, and shows better investment condition than road. Freight terminal expansion brings road expansion in LTL, on the contrary, freight terminal expansion substitutes freight terminal for road in TL. In transportation revenue, freight terminal's contribution to LTL is larger than that to TL. However, when we adjust quasi-fixed factor - road and freight terminal - to optimal level in the long run, in TL, diseconomies of scale becomes large, but in LTL, economies of scale becomes large. Consequently, it is necessary for TL to make counterplans to activate management of small size enterprises and owner drivers. And LTL should make use of economies of scale by solving the problem, such as nonprofit route, excess of rental freight handling of office, insufficiency of freight terminal, shortage of driver, and unpreparedness of freight insurance.

• The Korean Journal of Nuclear Medicine
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• v.36 no.6
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• pp.317-324
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• 2002

Purpose: The use of statistical parametric mapping (SPM) program has increased for the analysis of brain PET and SPECT images. Montreal Neurological Institute (MNI) coordinate is used in SPM program as a standard anatomical framework. While the most researchers look up Talairach atlas to report the localization of the activations detected in SPM program, there is significant disparity between MNI templates and Talairach atlas. That disparity between Talairach and MNI coordinates makes the interpretation of SPM result time consuming, subjective and inaccurate. The purpose of this study was to develop a program to provide objective anatomical information of each x-y-z position in ICBM coordinate. Materials and Methods: Program was designed to provide the anatomical information for the given x-y-z position in MNI coordinate based on the Statistical Probabilistic Anatomical Map (SPAM) images of ICBM. When x-y-z position was given to the program, names of the anatomical structures with non-zero probability and the probabilities that the given position belongs to the structures were tabulated. The program was coded using IDL and JAVA language for 4he easy transplantation to any operating system or platform. Utility of this program was shown by comparing the results of this program to those of SPM program. Preliminary validation study was peformed by applying this program to the analysis of PET brain activation study of human memory in which the anatomical information on the activated areas are previously known. Results: Real time retrieval of probabilistic information with 1 mm spatial resolution was archived using the programs. Validation study showed the relevance of this program: probability that the activated area for memory belonged to hippocampal formation was more than 80%. Conclusion: These programs will be useful for the result interpretation of the image analysis peformed on MNI coordinate, as done in SPM program.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.4
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• pp.267-274
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• 2008

Purpose: Transient neurological deterioration (TND) is one of the complications after extracranial-intracranial bypass surgery, and it has been assumed to be caused by postoperative transient hyperperfusion. This study was performed to evaluate the relationship between TND and preoperative and postoperative cerebral perfusion status on brain perfusion SPECT following superficial temporal artery - middle cerebral artery (STA-MCA) anastomosis surgery. Materials and Methods: A total of 60 STA-MCA anastomosis surgeries of 56 patients (mean age: $50{\pm}16$ yrs; M:F=29:27; atherosclerotic disease: 33, moyamoya disease: 27) which were done between September 2003 and July 2006 were enrolled. The resting cerebral perfusion and cerebral vascular reserve (CVR) after acetazolamide challenge were measured before and 10 days after surgery using 99mTc-ethylcysteinate dimer (ECD) SPECT. Moreover, the cerebral perfusion was measured on the third postoperative day. With the use of the statistical parametric mapping and probabilistic brain atlas, the counts for the middle cerebral artery (MCA) territory were calculated for each image, and statistical analyses were performed. Results: In 6 of 60 cases (10%), TND occurred after surgery. In all patients, the preoperative cerebral perfusion of affected MCA territory was significantly lower than that of contralateral side (p=0.002). The cerebral perfusion on the third and tenth day after surgery was significantly higher than preoperative cerebral perfusion (p=0.001, p=0.02). In TND patients, basal cerebral perfusion and CVR on preoperative SPECT were significantly lower than those of non-TND patients (p=0.01, p=0.05). Further, the increases in cerebral perfusion on the third day after surgery were significant higher than those in other patients (p=0.008). In patients with TND, the cerebral perfusion ratio of affected side to contralateral side on third postoperative day was significantly higher than that of other patients (p=0.002). However, there was no significant difference of the cerebral perfusion ratio on preoperative and tenth postoperative day between patients with TND and other patients. Conclusion: In patients with TND, relative and moderate hyperperfusion was observed in affected side after bypass surgery. These finding may help to understand the pathophysiology of TND.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.101-119
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• 2022

Existing algal bloom monitoring is based on field sampling, and there is a limit to understanding the spatial distribution of algal blooms, such as the occurrence and spread of algae, due to local investigations. In this study, algal bloom monitoring was performed using an unmanned aerial vehicle and multispectral sensor, and data on the distribution of algae were provided. For the algal bloom monitoring site, data were acquired from the Mulgeum·Mae-ri site located in the lower part of the Nakdong River, which is the areas with frequent algal bloom. The Chlorophyll-a(Chl-a) value of field-collected samples and the Chl-a estimation formula derived from the correlation between the spectral indices were comparatively analyzed. As a result, among the spectral indices, Maximum Chlorophyll Index (MCI) showed the highest statistical significance(R²=0.91, RMSE=8.1mg/m³). As a result of mapping the distribution of algae by applying MCI to the image of August 05, 2021 with the highest Chl-a concentration, the river area was 1.7km², the Warning area among the indicators of the algal bloom warning system was 1.03km²(60.56%) and the Algal Bloom area occupied 0.67km²(39.43%). In addition, as a result of calculating the number of occurrence days in the area corresponding to the "Warning" in the images during the study period (July 01, 2021~November 01, 2021), the Chl-a concentration above the "Warning" level was observed in the entire river section from 12 to 19 times. The algal bloom monitoring method proposed in this study can supplement the limitations of the existing algal bloom warning system and can be used to provide information on a point-by-point basis as well as information on a spatial range of the algal bloom warning area.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.131-141
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• 2023

Changes in consumer needs and behaviors according to lifestyle changes lead to consumption culture, affecting the automobile market. However, research and research to provide options tailored to the lifestyle of consumers in related markets are still insufficient. Focusing on pickup truck accessories applied to pickup trucks that reflect lifestyle the most among vehicle types, this study first examined the theoretical background of the aftermarket market and lifestyle of pickup trucks. Second, through image mapping, the market possibilities and opportunity factors of pickup trucks were discovered through market size analysis and possibilities, and through this, user types could be classified. Third, interviews were conducted with those representing user types, the contents were organized, and interviews were conducted centering on related groups to create a persona of a user group, and what needs each group's persona wanted. Finally, a design concept suitable for the issue keywords and insights derived for each user lifestyle type was presented. In this study, the user type was divided into ① outdoor activity type, ② hobby activity type, and ③ small-scale work type, and a design case study was conducted by applying the concept suitable for the keyword for each group. For the outdoor activity type, a variable storage structure and a living space-type accessory design were presented, and for the hobby type, a modular decktop design and a sports coupe-type hardtop design were presented. For the small business type, a partition that is easy to fix the load and a stepper design that is easy to board the cargo box were presented. It is expected that the size of the pickup truck aftermarket will be expanded by diversifying the option designs that users want by lifestyle by applying them to the development of pickup truck accessories that fit the lifestyle of pickup truck users in the automobile market, which is currently mass customized.

• 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.