• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.6
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• pp.670-680
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• 2010

A low power single-chip CMOS receiver for 60 GHz mobile application are proposed in this paper. The single-chip receiver consists of a 4-stage current re-use LNA with under 4 dB NF, Cgs compensating resistive mixer with -9.4 dB conversion gain, Ka-band low phase noise VCO with -113 dBc/Hz phase noise at 1 MHz offset from 26.89 GHz, high-suppression frequency doubler with -0.45 dB conversion gain, and 2-stage current re-use drive amplifier. The size of the fabricated receiver using a standard 0.13 ${\mu}m$ CMOS technology is 2.67 mm$\times$0.75 mm including probing pads. An RF bandwidth is 6.2 GHz, from 55 to 61.2 GHz and an LO tuning range is 7.14 GHz, from 48.45 GHz to 55.59 GHz. The If bandwidth is 5.25 GHz(4.75~10 GHz) The conversion gain and input P1 dB are -9.5 dB and -12.5 dBm, respectively, at RF frequency of 59 GHz. The proposed single-chip receiver describes very good noise performances and linearity with very low DC power consumption of only 21.9 mW.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.1
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• pp.23-30
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• 2015

Purpose : External markers respiratory movement measuring device (RPM; Real-time Position Management, Varian Medical System, USA) Liver Cancer Radiation Therapy Respiratory gated with respiratory signal with irradiation time and the actual research by analyzing the respiratory phase with the breathing motion measurement device respiratory tuning evaluate the accuracy of radiation therapy Materials and Methods : May-September 2014 Novalis Tx. (Varian Medical System, USA) and liver cancer radiotherapy using respiratory gated RPM (Duty Cycle 20%, Gating window 40% ~ 60%) of 16 patients who underwent total when recording the analyzed respiratory movement. After the breathing motion of the external markers recorded on the RPM was reconstructed by breathing through the acts phase analysis, for Beam-on Time and Duty Cycle recorded by using the reconstructed phase breathing breathing with RPM gated the prediction accuracy of the radiation treatment analysis and analyzed the correlation between prediction accuracy and Duty Cycle in accordance with the reproducibility of the respiratory movement. Results : Treatment of 16 patients with respiratory cycle during the actual treatment plan was analyzed with an average difference -0.03 seconds (range -0.50 seconds to 0.09 seconds) could not be confirmed statistically significant difference between the two breathing (p = 0.472). The average respiratory period when treatment is 4.02 sec (${\pm}0.71sec$), the average value of the respiratory cycle of the treatment was characterized by a standard deviation 7.43% (range 2.57 to 19.20%). Duty Cycle is that the actual average 16.05% (range 13.78 to 17.41%), average 56.05 got through the acts of the show and then analyzed% (range 39.23 to 75.10%) is planned in respiratory research phase (40% to 60%) in was confirmed. The investigation on the correlation between the ratio Duty Cycle and planned respiratory phase and the standard deviation of the respiratory cycle was analyzed in each -0.156 (p = 0.282) and -0.385 (p = 0.070). Conclusion : This study is to analyze the acts after the breathing motion of the external markers recorded during the actual treatment was confirmed in a reproducible ratios of actual treatment of breathing motion during treatment, and Duty Cycle, planned respiratory gated window. Minimizing an error of the treatment plan using 4DCT and enhance the respiratory training and respiratory signal monitoring for effective treatment it is determined to be necessary.

• Journal of the Korean Geographical Society
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• v.36 no.4
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• pp.474-482
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• 2001

The purpose of this study is to investigate the formation and development processes of beach resort by fabric construction in a island environment. The results are as follows. (1) The research area(Tong-ri beach, Bokil-myon, Chollanam-do)has been transformed to belch by sedimentary environmental change since latter half of 1800's. (2) The mean slope of beach face is 0.96°, and the difference of attitude between beach and mud flat face is 75cm. (3) The mean particle size of beach surface sediment is 3.53$\Phi$. This value is very finer than that of any other beach in Korea peninsula. But its value is coarser than that of mud flat surface sediment. (4) The particle size distribution of core sediment is become changed to fine particle in 70cm depth. This value is corresponded to difference of altitude between beach face and mud flat face. (5) The analysis of aerial photographs after 1970 indicates that sedimentation process was not brisked since 1970's. Consequently, the research ares has been developed by sedimentary environmental change for sea-level rise effect and wave height energy rise effect.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.2
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• pp.21-31
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• 2010

This paper presents a design of PLL and SSCG for reducing the EMI effect at the electronic machinery and tools for DisplayPort application. This system is composed of the essential element of PLL and Charge-Pump2 and Reference Clock Divider to implement the SSCG operation. In this paper, 270MHz/162MHz dual-mode PLL that can provide 10-phase and 1.35GHz/810MHz PLL that can reduce the jitter are designed for 2.7Gbps/162Gbps DisplayPort application. The jitter can be reduced drastically by combining 270MHz/162MHz PLL with 2-stage 5 to 1 serializer and 1.35GHz PLL with 2 to 1 serializer. This paper propose the frequency divider topology which can share the divider between modes and guarantee the 50% duty ratio. And, the output current mismatch can be reduced by using the proposed charge-pump topology. It is implemented using 0.13 um CMOS process and die areas of 270MHz/162MHz PLL and 1.35GHz/810MHz PLL are $650um\;{\times}\;500um$ and $600um\;{\times}\;500um$, respectively. The VCO tuning range of 270 MHz/162 MHz PLL is 330 MHz and the phase noise is -114 dBc/Hz at 1 MHz offset. The measured SSCG down spread amplitude is 0.5% and modulation frequency is 31kHz. The total power consumption is 48mW.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of The Korean Association For Science Education
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• v.22 no.4
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• pp.796-805
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• 2002

This study examined the knowledge structure constructed by children before formal instruction, and successive changes in the structural complexity of knowledge during and after the learning of 'Structure and Function of Plant' unit. It also investigated how those changes were affected by children's learning modes. The researchers made the 5th graders draw the first draft of their concept map to see the pre-existing knowledge structure concerned with the unit and four more concept maps after completing every fourth lesson. And to see how long their knowledge structures were preserved, the researchers made children draw additional concept maps in 3 days, 3 months, and 7 months after completing the unit. Children drew their current concept maps on the basis of the previous one while learning the unit and without the previous one after completing the unit. Each concept map drawn by children showed the degree of their current understanding on the structures and functions of plants. The results revealed that only two levels of hierarchy and five relationships among the components of the first concept map(relationship, hierarchy, cross link and example) were proven to be valid in terms of conceptual relevance. Growth in the structural complexity of knowledge took place progressively throughout the unit and the effects of learning mode on the growth were favorably reflected in concept map scores of meaningful learners over time(relationship, cross link, example: p<.01, hierarchy: p<.05). Although there were some differences on the concept map scores between two types of learners, they commonly showed that knowledge restructuring had occurred apparently in the early periods from the 1st to the 6th lesson and had not occurred at all in the last period of the unit. The frequency of tuning was higher in rote learners than in meaningful learners throughout the unit, but the frequency of accretion was reverse. Concept map scores of rote learners constructed in the course of learning of the unit decreased little by little gradually in all the categories after completing the unit. However, the average total map score of meaningful learners increased a little more in 7 months than in 3 months after completing the unit. Therefore it can be inferred that meaningful learners construct more stable and well-differentiated knowledge structures than the rote learners.