• 한국노년학
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• v.28 no.4
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• pp.1147-1164
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• 2008

There has been a sharp increase in the number of grandmothers raising grandchildren in contemporary Korean society. However, little is known about the complexity of the experiences of the custodial grandparenting. Most of the studies mainly examined burden/cost and have paid little attention to the positive aspects of custodial grandparenting. In order to overcome this limitation of previous research, this study aims 1) to examine not only the costs but also the rewards of custodial grandparenting and to explore related factors, 2) to develop the typology based on relative rewards-burden perception of grandmothers about custodial grandparenting and explore the group differences. The data were gathered from 449 grandmothers raising their grandchildren as a primary caregiver, using a structured questionnaire. The data were analyzed using descriptive statistics, correlation, hierarchical multiple regression and ANOVA, with SPSS WIN 12.0 program. Main findings are as follows: First, custodial grandmothers report not only care-giving burden such as physical burnout and economic burden but also various rewards such as joy of watching their grandchildren grow and feeling good about themselves to be a help with their adult children, i.e. grandchildren's father or mother. Second, factors related to the level of perceived cost of grandparenting are different from the factors affecting the positive aspects of grandparenting. Third, results of the two by two cross-tab analysis based on the level of rewards and burden show that about 32 percent of the grandparents belong to Type II group(high rewards-low cost). This result is quite contrary to the assumption of previous research focusing mostly on cost and burden of custodial grandparenting. Fourth, four groups were different in terms of grandmother & grandchildren's characteristics. Implications of these results are discussed.

• Journal of Korea Water Resources Association
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• v.52 no.8
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• pp.545-554
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• 2019

The most drought assessments are based on a drought index, which depends on univariate variables such as precipitation and soil moisture. However, there is a limitation in representing the drought conditions with single variables due to their complexity. It has been acknowledged that a multivariate drought index can more effectively describe the complex drought state. In this context, this study propose a Copula-based drought index that can jointly consider precipitation and soil moisture. Unlike precipitation data, long-term soil moisture data is not readily available so that this study utilized a Gaussian Mixture Non-Homogeneous Hidden Markov chain Model (GM-NHMM) model to simulate the soil moisture using the observed precipitation and temperature ranging from 1973 to 2014. The GM-NHMM model showed a better performance in terms of reproducing key statistics of soil moisture, compared to a multiple regression model. Finally, a bivariate frequency analysis was performed for the drought duration and severity, and it was confirmed that the recent droughts over Jeollabuk-do in 2015 have a 20-year return period.

• International Area Studies Review
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• v.21 no.1
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• pp.25-57
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• 2017

In this study, we analyzed the information asymmetry and the agency problem in major affiliated companies on the basis of the R&D investment. As a result of comparing how the R&D investment effects on major affiliated companies and the independent companies, even the achievement of R&D investment effects in a positive way to the firm value, the positive effect appears much lower on major affiliated companies comparing independent companies. In order to analyze the case, we investigated in a separate way according to the shareholding ratio and the affiliated market using the sample of the independent company and the group affiliated company. As a result of such analysis, the cause of this comes from the agency problem in major affiliated company, not the asymmetry information of affiliated company. After we analyzed the sample of the research depending on the affiliation market, we could observe there is a little impact of the asymmetry information in the outcome of the R&D investment of the major affiliated companies. In contrast, the companies which rated lower in the ratio of the shareholding appears much less in the positive effect of R&D investment compared to the companies which rated at a higher level. This phenomenon was also consistently observed when changing the research method or further subdividing the sample of companies belonging to the group based on the ownership share of major shareholders.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.2
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• pp.63-74
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• 2009

In 2005, Wang et al. discovered devastating collision attacks on the main hash functions from the MD4 family. After the discovery of Wang, many analysis results on the security of existing hash-based cryptographic schemes are presented. At CRYPTO'07, Fouque, Leurent and Nguyen presented full key-recovery attacks on HMAC/NMAC-MD4 and NMAC-MD5[4]. Such attacks are based on collision attacks on the underlying hash function, and the most expensive stage is the recovery of the outer key. At EUROCRYPT'08, Wang, Ohta and Kunihiro presented improved outer key recovery attack on HMAC/NMAC-MD4, by using a new near collision path with a high probability[2]. This improves the complexity of the full key-recovery attack on HMAC/NMAC-MD4 which proposed by Fouque, Leurent and Nguyen at CRYPTO'07: The MAC queries decreases from $2^{88}$ to $2^{72}$, and the number of MD4 computations decreases from $2^{95}$ to $2^{77}$. In this paper, we propose improved outer key-recovery attack on HMAC/NMAC-MD4 with $2^{77.1246}$ MAC queries and $2^{37}$ MD4 computations, by using divide and conquer paradigm.

• Korean Journal of Environmental Biology
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• v.39 no.1
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• pp.88-99
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• 2021

In this study, a land-based marine closed mesocosm (LMCM) experiment was performed to objectively assess the initial stability of an artificial ecosystem experiment against biological and non-biological factors when evaluating ecosystem risk assessment. Changes in the CV (coefficient of value) amplitude were used as data to analyze the stability of the experimental system. The CV of the experimental variables in the LMCM groups (200, 400, 600, and 1,000 L) was maintained within the range of 20-30% for the abiotic variables in this study. However, the difference in CV amplitude in biological factors such as chlorophyll-a, phytoplankton, and zooplankton was high in the 600 L and 1,000 L LMCM groups. This result was interpreted as occurring due to the lack of control over biological variables at the beginning of the experiment. In addition, according to the ANOVA results, significant differences were found in biological contents such as COD (chemical oxygen demand), chlorophyll-a, phosphate, and zooplankton in the CV values between the LMCM groups(p<0.05). In this study, the stabilization of biological variables was necessary to to control and maintain the rate of changes in initial biological variables except for controllable water quality and nutrients. However, given the complexity of the eco-physiological activities of large-scale LMCMs and organisms in the experimental group, it was difficult to do. In conclusion, artificial ecosystem experiments as a scientific tool can distinguish biological and non-biological factors and compare and analyze clear endpoints. Therefore, it is deemed necessary to establish research objectives, select content that can maintain stability, and introduce standardized analysis techniques that can objectively interpret the experimental results.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1633-1641
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• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.36-41
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• 2021

Although the technology for prediction or analysis using artificial intelligence is constantly developing, a black-box problem does not interpret the decision-making process. Therefore, the decision process of the AI model can not be interpreted from the user's point of view, which leads to unreliable results. We investigated the problems of artificial intelligence and explainable artificial intelligence using Blockchain to solve them. Data from the decision-making process of artificial intelligence models, which can be explained with Blockchain, are stored in Blockchain with time stamps, among other things. Blockchain provides anti-counterfeiting of the stored data, and due to the nature of Blockchain, it allows free access to data such as decision processes stored in blocks. The difficulty of creating explainable artificial intelligence models is a large part of the complexity of existing models. Therefore, using the point cloud to increase the efficiency of 3D data processing and the processing procedures will shorten the decision-making process to facilitate an explainable artificial intelligence model. To solve the oracle problem, which may lead to data falsification or corruption when storing data in the Blockchain, a blockchain artificial intelligence problem was solved by proposing a blockchain-based explainable artificial intelligence model that passes through an intermediary in the storage process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.1100-1118
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• 2021

In a wide range of ML applications, the training data contains privacy-sensitive information that should be kept secure. Training the ML systems by privacy-sensitive data makes the ML model inherent to the data. As the structure of the model has been fine-tuned by training data, the model can be abused for accessing the data by the estimation in a reverse process called model inversion attack (MIA). Although, MIA has been applied to shallow neural network models of recognizers in literature and its threat in privacy violation has been approved, in the case of a deep learning (DL) model, its efficiency was under question. It was due to the complexity of a DL model structure, big number of DL model parameters, the huge size of training data, big number of registered users to a DL model and thereof big number of class labels. This research work first analyses the possibility of MIA on a deep learning model of a recognition system, namely a face recognizer. Second, despite the conventional MIA under the white box scenario of having partial access to the users' non-sensitive information in addition to the model structure, the MIA is implemented on a deep face recognition system by just having the model structure and parameters but not any user information. In this aspect, it is under a semi-white box scenario or in other words a gray-box scenario. The experimental results in targeting five registered users of a CNN-based face recognition system approve the possibility of regeneration of users' face images even for a deep model by MIA under a gray box scenario. Although, for some images the evaluation recognition score is low and the generated images are not easily recognizable, but for some other images the score is high and facial features of the targeted identities are observable. The objective and subjective evaluations demonstrate that privacy cyber-attack by MIA on a deep recognition system not only is feasible but also is a serious threat with increasing alert state in the future as there is considerable potential for integration more advanced ML techniques to MIA.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.453-460
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• 2022

As high-performance quantum computers are expected to be developed, studies are being actively conducted to build a post-quantum security system that is safe from potential quantum computer attacks. When the Grover's algorithm, a representative quantum algorithm, is used to search for a secret key in a symmetric key cryptography, there may be a safety problem in that the security strength of the cipher is reduced to the square root. NIST presents the post-quantum security strength estimated based on the cost of the Grover's algorithm required for an attack of the cryptographic algorithm as a post-quantum security requirement for symmetric key cryptography. The estimated cost of Grover's algorithm for the attack of symmetric key cryptography is determined by the quantum circuit complexity of the corresponding encryption algorithm. In this paper, the quantum circuit of the SCHWAEMM algorithm, AEAD family of SPARKLE, which was a finalist in NIST's lightweight cryptography competition, is efficiently implemented, and the quantum cost to apply the Grover's algorithm is analyzed. At this time, the cost according to the CDKM ripple-carry adder and the unbounded Fan-Out adder is compared together. Finally, we evaluate the post-quantum security strength of the lightweight cryptography SPARKLE SCHWAEMM algorithm based on the analyzed cost and NIST's post-quantum security requirements. A quantum programming tool, ProjectQ, is used to implement the quantum circuit and analyze its cost.