• The Journal of Engineering Geology
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• v.31 no.4
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• pp.701-718
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• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.167-174
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• 2015

Purpose : The pre-treatment QA using Portal dosimetry for Volumetric Arc Therapy To analyze whether maintaining the reproducibility depending on various factors. Materials and Methods : Test was used for TrueBeam STx$^{TM}$ (Ver.1.5, Varian, USA). Varian Eclipse Treatment planning system(TPS) was used for planning with total of seven patients include head and neck cancer, lung cancer, prostate cancer, and cervical cancer was established for a Portal dosimetry QA plan. In order to measure these plans, Portal Dosimetry application (Ver.10) (Varian) and Portal Vision aS1000 Imager was used. Each Points of QA was determined by dividing, before and after morning treatment, and the after afternoon treatment ended (after 4 hours). Calibration of EPID(Dark field correction, Flood field correction, Dose normalization) was implemented before Every QA measure points. MLC initialize was implemented after each QA points and QA was retried. Also before QA measurements, Beam Ouput at the each of QA points was measured using the Water Phantom and Ionization chamber(IBA dosimetry, Germany). Results : The mean values of the Gamma pass rate(GPR, 3%, 3mm) for every patients between morning, afternoon and evening was 97.3%, 96.1%, 95.4% and the patient's showing maximum difference was 95.7%, 94.2% 93.7%. The mean value of GPR before and after EPID calibration were 95.94%, 96.01%. The mean value of Beam Output were 100.45%, 100.46%, 100.59% at each QA points. The mean value of GPR before and after MLC initialization were 95.83%, 96.40%. Conclusion : Maintain the reproducibility of the Portal Dosimetry as a VMAT QA tool required management of the various factors that can affect the dosimetry.

• Journal of radiological science and technology
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• v.33 no.3
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• pp.277-282
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• 2010

The study is to verify non-uniform dose distribution in Field-In-Field (FIF) technique using two-dimensional ionization chamber (MatriXX, Wellhofer Dosimetrie, Germany) for breast tangential irradiation. The MatriXX and an inverse planning system (Eclipse, ver 6.5, Varian, Palo Alto, USA) were used. Hybrid plans were made from the original twenty patients plans. To verify the non-uniform dose distribution in FIF technique, each portal prescribed doses (90 cGy) was delivered to the MatriXX. The measured doses on the MatriXX were compared to the planned doses. The quantitative analyses were done with a commercial analyzing tool (OmniPro IMRT, ver. 1.4, Wellhofer Dosimetrie, Germany). The delivered doses at the normalization points were different to average 1.6% between the calculated and the measured. In analysis of line profiles, there were some differences of 1.3-5.5% (Avg: 2.4%), 0.9-3.9% (Avg: 2.5%) in longitudinal and transverse planes respectively. For the gamma index (criteria: 3 mm, 3%) analyses, there were shown that 90.23-99.69% (avg: 95.11%, std: 2.81) for acceptable range ($\gamma$-index $\geq$ 1) through the twenty patients cases. In conclusion, through our study, we have confirmed the availability of the FIF technique by comparing the calculated with the measured using MatriXX. In the future, various clinical applications of the FIF techniques would be good trials for better treatment results.

• 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.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.529-542
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• 2000

Recent years, the use of multimedia information is rapidly increasing, and the video media is the most rising one than any others, and this field Integrates all the media into a single data stream. Though the availability of digital video is raised largely, it is very difficult for users to make the effective video access, due to its length and unstructured video format. Thus, the minimal interaction of users and the explicit definition of video structure is a key requirement in the lately developing image and video management systems. This paper defines the terms and hierarchical video structure, and presents the system, which construct the clustering-based video hierarchy, which facilitate users by browsing the summary and do a random access to the video content. Instead of using a single feature and domain-specific thresholds, we use multiple features that have complementary relationship for each other and clustering-based methods that use normalization so as to interact with users minimally. The stage of shot boundary detection extracts multiple features, performs the adaptive filtering process for each features to enhance the performance by eliminating the false factors, and does k-means clustering with two classes. The shot list of a result after the proposed procedure is represented as the video hierarchy by the intelligent unsupervised clustering technique. We experimented the static and the dynamic movie videos that represent characteristics of various video types. In the result of shot boundary detection, we had almost more than 95% good performance, and had also rood result in the video hierarchy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.64-70
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• 2016

The recent development of modern weapon system by SOS (System of System) has required users to have more exact decision making. It is possible to achieve the control of complex weapon system more efficiently and effectively by increasing usability. Accordingly, many studies on graphical display have been conducted for several years in the field of HCI (Human Computer Interaction) and GUI (Graphic User Interface), starting from its design stage. Therefore, this paper focuses on evaluating the system GUI usability and analyzing several important points based on performance model, which is a tool for the evaluation and the improvement of service quality. Performance Model, the main focus of this study, reflects user expectations (which is defined as user importance in this paper). The study consists of four steps. First, 34 checklists are drawn from the existing studies related to GUI usability evaluation by using a heuristic method, and then the checklists are matched with 11 weapon system design factors. Next, the study evaluates the importance of GUI element and the usability of weapon system "A" with the checklists twice respectively. Third, the performance of user importance ($P_i$) and the performance of usability ($P_u$) are calculated by modifying a numerical formula for normalization in this step. Finally, the study compares the approach it takes and the existing usability evaluation method, demonstrating that there is a significant difference between the two methods as a result. In addition, 4 improvement factors are suggested for weapon system "A" as "Shortcut" and "Description of Abbreviation," and so on. Although it is necessary to conduct more studies for higher reliability and validity of the results, this study is meaningful considering it takes a new point of view.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2003.09a
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• pp.78-78
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• 2003

Purpose: This event related fMRI study was to further our understanding about how different brain regions could contribute to effective access of specific information stored in long term memory. This experiment has allowed us to determine the brain regions involved in recognition of familiar faces among non familiar faces. Materials and Methods: Twelve right handed normal, healthy volunteer adults participated in face recognition experiment. The paradigm consists of two 40 familiar faces, 40 unfamiliar faces and control base with scrambled faces in a randomized order, with null events. Volunteers were instructed to press on one of two possible buttons of a response box to indicate whether a face was familiar or not. Incorrect answers were ignored. A 1.5T MRI system(GMENS) was employed to evaluate brain activity by using blood oxygen level dependent (BOLD) contrast. Gradient Echo EPI sequence with TR/TE= 2250/40 msec was used for 17 contiguous axial slices of 7mm thickness, covering the whole brain volume (240mm Field of view, 64 ${\times}$ 64 in plane resolution). The acquired data were applied to SPM99 for the processing such as realignment, normalization, smoothing, statistical ANOVA and statistical preference. Results/Disscusion: The comparison of familiar faces vs unfamiliar faces yielded significant activations in the medial temporal regions, the occipito temporal regions and in frontal regions. These results suggest that when volunteers are asked to recognize familiar faces among unfamiliar faces they tend to activate several regions frequently involved in face perception. The medial temporal regions are also activated for familiar and unfamiliar faces. This interesting result suggests a contribution of this structure in the attempt to match perceived faces with pre existing semantic representations stored in long term memory.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.4
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• pp.200-209
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• 2003

Distribution coefficient ($K_d$) is an universal parameter estimating cadmium partition for a soil-water-crop system in agricultural lands. This study was performed to find some factors affecting soil-water partition coefficients for cadmium in some Korean soils. The distribution coefficients ($K_d$) of cadmium for the 15 series of agricultural soils were measured at quasi-steady state in the pH ranges from 2 to 11. The adsorption data of the selected soils showed a linear relationship between log $K_d$ and pH, which was well agreed with theoretically expected results ; $log\;K_d=0.6339pH+0.5532(r^2=0.70^{**})$. Normalization of the partition coefficients were performed in a range of pH 3.5 ~ 8.5 to minimize adverse effects of Al dissolution, cationic competition, and organic matter dissolution. The $K_d$-om, partition coefficients normalized for organic matter, improved this linearity to the pH of soils. The values of $K_d$-om measured from the field samples were significantly correlated with those of $K_d$ predicted from the sorption-edge experimental data ($r^2=0.68^{**}$).

• Resources Recycling
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• v.26 no.1
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• pp.69-77
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• 2017

Used small household appliances(small e-waste) consists of a variety of complex materials and components. The small e-waste is mainly composed of plastics and an important potential source of waste plastic. The black plastics, particularly are very difficult to separate by resin type and therefore these are mainly recycled in the form of a mixtures. In the present study, the sorting technologies such as gravity and electro static separation, near-infrared ray(NIR) and IR/Raman optical sorting separation on mixture of black plastics were analyzed and their limitations on sorting process were also investigated. The Laser Induced Breakdown Spectroscopy(LIBS) spectrum of each black plastics was used for identification of black plastics by resin type, and after analyzing the normalization operation, Principal Component Analysis(PCA) was carried out. The spectrum data was optimized through PCA process. In order to improve the identification accuracy and sorting efficiency of black plastics, it is necessary to design a classifier with high efficiency and to improve the performance and reliability of the classifier by applying the field of intelligent algorithms.

• Journal of Wetlands Research
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• v.15 no.3
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• pp.367-380
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• 2013

In this study, sub-indicators, and thematic mid-indexes to evaluate the water use characteristics were selected through historical data analysis and factor analysis, and consisted of the subject approach framework. And the integrated index was developed to evaluate water use characteristics of the watershed. Using developed index, the water use characteristics were assessed for 812 standard basins with the exception for North Korea using data of 1990 to 2007 from the relevant agencies. A sensitivity analysis is conducted for this study to determine the proper way through various normalization and weighting methods. To increase the objectivity of developed index, the history of the damage indicators are excluded in the analysis. In addition, in order to ensure its reliability, results from index with and without consideration of the damage history were compared. Also, the index is also applied to real data for 2008 Gangwon region to verify its field applicability. Through the validation process this index confirmed the adequacy for the indicators selection and calculation method. The results of this study were analyzed based on the spatial and time vulnerability of the basin's water use, which can be applied to various parts such as priority decision-making for water business or policy, mitigations for the vulnerable components of the basin, and supporting measures to establishment by providing relevant information about it.