• 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.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• 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 Korean Elementary Science Education
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• v.24 no.3
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• pp.259-267
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• 2005

This study was to investigate the elementary students' preconception on the brightness of electric bulb and degree of consistency on their preconceptions. Participants were 160 students of fifth graders in Seoul area. They had already teamed about the brightness of series circuit and parallel circuit of batteries. After they solved six problems in the same context, we provided them a pair of circuit which was an anomalous situation. And then they conducted CCLT (Cognitive Conflict Level Test). Elementary school students showed various preconceptions when they explained the light of bulb of two Simple electric Circuits. Many Students Consistently Showed the Scientific misconceptions like 'the light of bulb of two simple electric circuits was that the more batteries and the fewer bulbs were brighter.' The level of consistency that students presented scientific misconceptions was grouped all of four, such as 'high, middle, low, and nothing.' Therefore the higher scientific achievement they have, the higher consistency they have. As the students had high consistency level, they revealed high cognitive conflict level significantly. This high consistency will help them to change their preconception on the brightness of electric bulb and their cognitive conflict.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.6
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• pp.256-268
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• 2016

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2015. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering were carried out in the areas of flow, heat and mass transfer, cooling and heating, and air-conditioning, the renewable energy system and the flow inside building rooms. Research issues dealing with air-conditioning machines and fire and exhausting smoke were reduced. CFD seems to be spreading to more research areas. (2) Research works on heat transfer area were carried out in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the economic analysis of GHG emission, micro channel heat exchanger, effect of rib angle on thermal performance, the airside performance of fin-and-tube heat exchangers, theoretical analysis of a rotary heat exchanger, heat exchanger in a cryogenic environment, the performance of a cross-flow-type, indirect evaporative cooler made of paper/plastic film. In the area of pool boiling and condensing, the bubble jet loop heat pipe was studied. In the area of industrial heat exchangers, researches were performed on fin-tube heat exchanger, KSTAR PFC and vacuum vessel at baking phase, the performance of small-sized dehumidification rotor, design of gas-injection port of an asymmetric scroll compressor, effect of slot discharge-angle change on exhaust efficiency of range hood system with air curtain. (3) In the field of refrigeration, various studies were carried in the categories of refrigeration cycle, alternative refrigeration/energy system, system control. In the refrigeration cycle category, a cold-climate heat pump system, $CO_2$ cascade systems, ejector cycles and a PCM-based continuous heating system were investigated. In the alternative refrigeration/energy system category, a polymer adsorption heat pump, an alcohol absorption heat pump and a desiccant-based hybrid refrigeration system were investigated. In the system control category, turbo-refrigerator capacity controls and an absorption chiller fault diagnostics were investigated. (4) In building mechanical system research fields, eighteen studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, and renewable energies, piping in the buildings. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which can improve the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the user and location awareness technology applied dimming lighting control system, the lighting performance evaluation for light-shelves, the improvement evaluation of air quality through analysis of ventilation efficiency and the evaluation of airtightness of sliding and LS window systems. The subjects of building energy were worked on the energy saving estimation of existing buildings, the developing model to predict heating energy usage in domestic city area and the performance evaluation of cooling applied with economizer control. The studies were also performed related to the experimental measurement of weight variation and thermal conductivity in polyurethane foam, the development of flame spread prevention system for sandwich panels, the utilization of heat from waste-incineration facility in large-scale horticultural facilities.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.