• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.310-326
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• 2006

In this paper, we deal with a method of efficiently processing continuous queries in a data stream environment. We classify previous query processing methods into two dual categories - data-initiative and query-initiative - depending on whether query processing is initiated by selecting a data element or a query. This classification stems from the fact that data and queries have been treated asymmetrically. For processing continuous queries, only data-initiative methods have traditionally been employed, and thus, the performance gain that could be obtained by query-initiative methods has been overlooked. To solve this problem, we focus on an observation that data and queries can be treated symmetrically. In this paper, we propose the duality model of data and queries and, based on this model, present a new viewpoint of transforming the continuous query processing problem to a multi-dimensional spatial join problem. We also present a continuous query processing algorithm based on spatial join, named Spatial Join CQ. Spatial Join CQ processes continuous queries by finding the pairs of overlapping regions from a set of data elements and a set of queries defined as regions in the multi-dimensional space. The algorithm achieves the effects of both of the two dual methods by using the spatial join, which is a symmetric operation. Experimental results show that the proposed algorithm outperforms earlier methods by up to 36 times for simple selection continuous queries and by up to 7 times for sliding window join continuous queries.

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.468-478
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• 2004

It is essential in various application areas of data mining and bioinformatics to effectively retrieve the occurrences of interesting patterns from sequence databases. For example, let's consider a network event management system that records the types and timestamp values of events occurred in a specific network component(ex. router). The typical query to find out the temporal casual relationships among the network events is as fellows: 'Find all occurrences of CiscoDCDLinkUp that are fellowed by MLMStatusUP that are subsequently followed by TCPConnectionClose, under the constraint that the interval between the first two events is not larger than 20 seconds, and the interval between the first and third events is not larger than 40 secondsTCPConnectionClose. This paper proposes an indexing method that enables to efficiently answer such a query. Unlike the previous methods that rely on inefficient sequential scan methods or data structures not easily supported by DBMSs, the proposed method uses a multi-dimensional spatial index, which is proven to be efficient both in storage and search, to find the answers quickly without false dismissals. Given a sliding window W, the input to a multi-dimensional spatial index is a n-dimensional vector whose i-th element is the interval between the first event of W and the first occurrence of the event type Ei in W. Here, n is the number of event types that can be occurred in the system of interest. The problem of‘dimensionality curse’may happen when n is large. Therefore, we use the dimension selection or event type grouping to avoid this problem. The experimental results reveal that our proposed technique can be a few orders of magnitude faster than the sequential scan and ISO-Depth index methods.hods.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.239-245
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• 2014

Purpose : The purpose of this study is to verify the accuracy of dose delivery according to the pitch and roll rotational setup error with 6D robotic couch in Intensity Modulated Radiation Therapy (IMRT) for pelvic region in patients. Materials and Methods : Trilogy(Varian, USA) and 6D robotic couch(ProturaTM 1.4, CIVCO, USA) were used to measure and analyze the rotational setup error of 14 patients (157 setup cases) for pelvic region. The total 157 Images(CBCT 78, Radiography 79) were used to calculate the mean value and the incidence of pitch and roll rotational setup error with Microsoft Office Excel 2007. The measured data (3 mm, 3%) at the reference angle ($0^{\circ}$) without couch rotation of pitch and roll direction was compared to the others at different pitch and roll angles ($1^{\circ}$, $1.5^{\circ}$, $2^{\circ}$, $2.5^{\circ}$) to verify the accuracy of dose delivery by using 2D array ionization chamber (I'mRT Matrixx, IBA Dosimetry, Germany) and MultiCube Phantom(IBA Dosimetry, Germany). Result from the data, gamma index was evaluated. Results : The mean values of pitch and roll rotational setup error were $0.9^{\circ}{\pm}0.7$, $0.5^{\circ}{\pm}0.6$. The maximum values of them were $2.8^{\circ}$, $2.0^{\circ}$. All of the minimum values were zero. The mean values of gamma pass rate at four different pitch angles ($1^{\circ}$, $1.5^{\circ}$, $2^{\circ}$, $2.5^{\circ}$) were 97.75%, 96.65%, 94.38% and 90.91%. The mean values of gamma pass rate at four different roll angles ($1^{\circ}$, $1.5^{\circ}$, $2^{\circ}$, $2.5^{\circ}$) were 93.68%, 93.05%, 87.77% and 84.96%. when the same angles ($1^{\circ}$, $1.5^{\circ}$, $2^{\circ}$) of pitch and roll were applied simultaneously, The mean values of each angle were 94.90%, 92.37% and 87.88%, respectively. Conclusion : As a result of this study, it was able to recognize that the accuracy of dose delivered is lowered gradually as pitch and roll increases. In order to increase the accuracy of delivered dose, therefore, it is recommended to perform IGRT or correct patient's position in the pitch and roll direction, to improve the quality of treatment.

• Progress in Medical Physics
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• v.22 no.2
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• pp.99-105
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• 2011

The purpose of this study is to evaluate the accuracy of IMRT in our clinic from based on TG119 procedure and establish action level. Five IMRT test cases were described in TG119: multi-target, head&neck, prostate, and two C-shapes (easy&hard). There were used and delivered to water-equivalent solid phantom for IMRT. Absolute dose for points in target and OAR was measured by using an ion chamber (CC13, IBA). EBT2 film was utilized to compare the measured two-dimensional dose distribution with the calculated one by treatment planning system. All collected data were analyzed using the TG119 specifications to determine the confidence limit. The mean of relative error (%) between measured and calculated value was $1.2{\pm}1.1%$ and $1.2{\pm}0.7%$ for target and OAR, respectively. The resulting confidence limits were 3.4% and 2.6%. In EBT2 film dosimetry, the average percentage of points passing the gamma criteria (3%/3 mm) was $97.7{\pm}0.8%$. Confidence limit values determined by EBT2 film analysis was 3.9%. This study has focused on IMRT commissioning and quality assurance based on TG119 guideline. It is concluded that action level were ${\pm}4%$ and ${\pm}3%$ for target and OAR and 97% for film measurement, respectively. It is expected that TG119-based procedure can be used as reference to evaluate the accuracy of IMRT for each institution.

• 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.