• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.73-80
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• 2010

A wireless sensor network which is deployed in hostile environment can be easily compromised by attackers. The selective forwarding attack can jam the packet or drop a sensitive packet such as the movement of the enemy on data flow path through the compromised node. Xiao, Yu and Gao proposed the checkpoint-based multi-hop acknowledgement scheme(CHEMAS). In CHEMAS, each path node enable to be the checkpoint node according to the pre-defined probability and then can detect the area where the selective forwarding attacks is generated through the checkpoint nodes. In this scheme, the number of hops is very important because this parameter may trade off between energy conservation and detection capacity. In this paper, we used the fuzzy rule system to determine adaptive threshold value which is the number of hops for the ACK packets. In every period, the base station determines threshold value while using fuzzy logic. The energy level, the number of compromised node, and the distance to each node from base station are used to determine threshold value in fuzzy logic.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.33-40
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• 2009

The critical lag of crosswalk pedestrians is an important parameter in analyzing traffic operation at unsignalized crosswalks, however there is few research in this field in Korea. The purpose of this study is to develop a model to estimate the critical lag. Among the elements which influence the critical lag, the age of pedestrians and the length of crosswalks, which have fuzzy characteristics, and the each lag which is rejected or accepted are collected on crosswalks of which lengths range from 3.5 m to 10.5 m. The values of the critical lag range from 2.56 sec. to 5.56 sec. The age and the length are divided to the 3 fuzzy variables each, and the critical lag of each case is estimated according to Raff's technique, so a total of 9 fuzzy rules are established. Based on the rules, an ANFIS (Adaptive Neuro-Fuzzy Inference System) model to estimate the critical lag is built. The predictability of the model is evaluated comparing the observed with the estimated critical lags by the model. Statistics of $R^2$, MAE, MSE are 0.96, 0.097, 0.015 respectively. Therefore, the model is evaluated to explain the result well. During this study, it is found that the critical lag increases rapidly over the pedestrian's age of 40 years.

• 한국노년학
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• v.39 no.2
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• pp.325-345
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• 2019

The purpose of this study is a exploratory study for understanding the process of the social isolation of the socially isolated elderly through the approach to their life history. The research was analyzed by one of the methods of qualitative research on life history, the conceptual framework of 'Dimensions, turning, and adaptation' of Mandelbaum(1973). According to the results of this study, the socially isolated elderly people were found to be socially isolated by experiencing complex difficulties such as family disconnection, poverty, poor job and health deterioration. Specifically, in the area of life, there was experience of poor relationship with parent, absence of family, poverty of family and unfavorable relationship with surrounding people in life with original family before isolation. They had bad jobs in the labor market, such as hard labor, delivery, business, and chores. In the area of turning point, we experienced family break due to the separation of the original family and the spouse due to various reasons such as financial crisis, parental divorce and death, spouse affair, economic difficulty. In a transitional stage in the life, many reasons such as the financial crisis, the death of parents, the extramarital affair and economic difficulties led to the disconnection from their original family and their spouses. In an adaptive phase, participants accepted the changed life at each turning point in their lives, carrying out their roles, compromising and trying to adapt properly. He said that their current life, which has entered the social safety net system of the people's basic recipients, has led him to live a more stable life and is adapting to personal hobbies and vicarious satisfaction through networks. This result is somewhat different from previous studies in which isolated elderly people were severely exposed to the risk of depression and loneliness. However, we should also consider the characteristics of this study that interviewed elderly people with relatively low isolation. Based on the results of this research, he presented various practical policy implications.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.394-407
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• 2023

Ecological restoration is an ecological technology that diagnoses problematic ecological spaces and restores the damaged ecosystem to a healthy appearance similar to its original appearance based on reference information obtained by analyzing intact nature. To achieve successful restoration, the project must be carried out in respect of a series of procedures. However, in Korea, restoration projects are usually actively promoted regardless of diagnostic evaluation, which wastes cost and energy, and the effect is not significant. As the reference information is not utilized, ecological restoration to return the damaged nature makes features different greatly from the appearance of nature, causing another damage. As the restoration effect is not evaluated, it is impossible to determine whether it is successful or not, and as a result, even if the project continues, there is no development and no effect. However, advanced societies have not only made academic progress by respecting these procedures but also have great economic effects along with the improvement of environmental conditions as ecological restoration has become an industry. Therefore, the international society recognizes ecological restoration as an important means of solving environmental problems at the global level, including climate change, and international organizations are actively promoting projects to treat the injured planet. However, most of the restoration projects promoted in Korea were evaluated below the level as a result of the evaluation of the effect. Nevertheless, those who have led low-quality projects are blocking plans to establish ecological restoration as a new industry that can contribute significantly to improving these levels, and thus the problem is expected to worsen. To solve this problem, it is necessary to filter out defective businesses by introducing a strict and correct project evaluation system by dividing it into before and after. Furthermore, it is necessary to establish ecological restoration as an industry and leave the process in the principles of the market.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.633-640
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• 2023

With the recent development of static and dynamic modulated brachytherapy methods in brachytherapy, which use radiation shielding to modulate the dose distribution to deliver the dose, the amount of parameters and data required for dose calculation in inverse treatment planning and treatment plan optimization algorithms suitable for new directional beam intensity modulated brachytherapy is increasing. Although intensity-modulated brachytherapy enables accurate dose delivery of radiation, the increased amount of parameters and data increases the elapsed time required for dose calculation. In this study, a GPU-based CUDA-accelerated dose calculation algorithm was constructed to reduce the increase in dose calculation elapsed time. The acceleration of the calculation process was achieved by parallelizing the calculation of the system matrix of the volume of interest and the dose calculation. The developed algorithms were all performed in the same computing environment with an Intel (3.7 GHz, 6-core) CPU and a single NVIDIA GTX 1080ti graphics card, and the dose calculation time was evaluated by measuring only the dose calculation time, excluding the additional time required for loading data from disk and preprocessing operations. The results showed that the accelerated algorithm reduced the dose calculation time by about 30 times compared to the CPU-only calculation. The accelerated dose calculation algorithm can be expected to speed up treatment planning when new treatment plans need to be created to account for daily variations in applicator movement, such as in adaptive radiotherapy, or when dose calculation needs to account for changing parameters, such as in dynamically modulated brachytherapy.

• Journal of Korean Society of Forest Science
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• v.113 no.2
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• pp.198-209
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• 2024

This study aimed to investigate the impact of light intensity, manipulated through different shading levels, on the growth and physiological responses of Thermopsis lupinoides. To assess the effects of shading treatments, we examined leaf mass per area, chlorophyll content, chlorophyll fluorescence response, and photosynthetic characteristics. T. lupinoidesexhibited adaptive responses under low light conditions (50% shading), showing increased leaf area and decreased leaf mass per area as shading levels increased. These changes indicate morpho-physiological adaptations to reduced light availability. At 50% shading, the physiological and ecological responses were favorable, with optimal photosynthetic functions including chlorophyll content, photosynthesis saturation point, photosynthetic rate, carbon fixation efficiency, stomatal conductance, transpiration rate, and water use efficiency. However, at 95% shading, the essential light conditions for growth were not met, significantly impairing photosynthetic functions. Consequently, 50% shading was determined to be the most optimal condition for T. lupinoides growth. These findings provide valuable insights for effective ex-situconservation practices and site selection for T. lupinoides, serving as foundational data for habitat restoration efforts.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.1
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• pp.187-204
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• 2024

This study was conducted to examine the current status of record management in parliamentary offices and identify areas of improvement. For this, in-depth interviews were conducted primarily with parliamentary aides to investigate their perceptions and needs. The research revealed that although the responsibility for record management in parliamentary offices lies with the aides, systematic record management is lacking because of inadequate awareness. While some aides recognize the importance of record management, there is still a need for a change in perception and practice. Furthermore, the study found that there is a lack of systematic education and support for effective implementation. The perceptions of aides were classified into three types: proactive (type A), pragmatically adaptive (type B), and those emphasizing the specificity of parliamentary records (type C). In particular, the change in perception of aides in types B and C is crucial, considering their pivotal role in parliamentary office record management. In response, this study suggests education and awareness improvement programs for record management, the introduction of an integrated record management system, and the establishment of policy and institutional support as key tasks.

• IMMUNE NETWORK
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• v.22 no.2
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• pp.16.1-16.20
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• 2022

The gastrointestinal tract is the first organ directly affected by fasting. However, little is known about how fasting influences the intestinal immune system. Intestinal dendritic cells (DCs) capture antigens, migrate to secondary lymphoid organs, and provoke adaptive immune responses. We evaluated the changes of intestinal DCs in mice with short-term fasting and their effects on protective immunity against Listeria monocytogenes (LM). Fasting induced an increased number of CD103⁺CD11b^- DCs in both small intestinal lamina propria (SILP) and mesenteric lymph nodes (mLN). The SILP CD103⁺CD11b^- DCs showed proliferation and migration, coincident with increased levels of GM-CSF and C-C chemokine receptor type 7, respectively. At 24 h post-infection with LM, there was a significant reduction in the bacterial burden in the spleen, liver, and mLN of the short-term-fasted mice compared to those fed ad libitum. Also, short-term-fasted mice showed increased survival after LM infection compared with ad libitum-fed mice. It could be that significantly high TGF-β2 and Aldh1a2 expression in CD103⁺CD11b^- DCs in mice infected with LM might affect to increase of Foxp3⁺ regulatory T cells. Changes of major subset of DCs from CD103⁺ to CD103^- may induce the increase of IFN-γ-producing cells with forming Th1-biased environment. Therefore, the short-term fasting affects protection against LM infection by changing major subset of intestinal DCs from tolerogenic to Th1 immunogenic.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.