• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.411-426
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• 2011

The construction of composite indicators should be normalized and weighted to render them comparable and evaluable variables in the field, which undergoes absence of a distinct methodology and where the application of universally popular method is common. Constructing of indices does not compare and analyze applying various normalizing and weighting, but constructer generally use chosen method and develops indicators and indices in most research. In this study, indices are applied various normalization and weighting methods, thereby analyzing how much impact the index and identifying individual characteristics derive a more reasonable way to help other research in the future. 5 different methods of normalization and 4 different types of weights were compared and analyzed. There are different results depending applied normalized methods and Z-score method best reflects the characteristics of the variables. According to weighting methods, the calculated results show little difference, but the ranking results of indices did not changed significantly. It might be better to provide constructors with a set of normalization and weighting methods to reflect their characteristics in order to build flood indices through the result of this study.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.137-146
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• 2014

We have developed the Forest Carbon Sink Index on afforestation and reforestation activities, a regulation stated in article 26 of the 'Law on the maintenance and enhancement of carbon sink (Carbon Sink Law)', which took effect on March, 2013. According to the legal purpose to evaluate the performance of individual forest carbon offset projects and to compare each other at a certain point, values of the forest carbon sink index were calculated by the scoring method. Three criteria were established based on the Carbon Sink Law: 'Carbon' (real greenhouse gas reduction), 'Human' (socio-economic effect) and 'Nature' (environmental effect). Continuously, 9 indicators from the three criteria were selected by top-down approach; the adequacy of each criteria and indicators were reviewed through on-line Delphi survey; and finally weighted value of each criteria and indicators were assigned. To reflect the characteristics of the domestic forest carbon offset projects, which focus on corporate social responsibility-typed projects, we applied the score weighting method to minimize gaps among criteria and ones among indicators. After applying our newly developed forest carbon sink index to five domestic forest carbon offset projects, we could confirm that the criteria of 'Human' and 'Nature', which criteria are in relatively low weight, can play a role as an actual incentive to reduce negative socio-economic and environmental impacts. Based on performance evaluation of the five forest carbon offset project by the forest carbon sink index, the best or good performance project developers could be rewarded, and further the performance evaluation would work as an incentive to stimulate the involvement of domestic project developers in the field of forest carbon offset project.

• Annals of Clinical Neurophysiology
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• v.4 no.2
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• pp.114-118
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• 2002

Backgrounds : Carpal tunnel syndrome (CTS) is a common condition that is usually diagnosed by electrophysiologic studies. However, CTS provide limited information to determine the causes of CTS and to choose the treatment method. We evaluated diagnostic sensitivity of MR imaging and treatment decisions by MR imaging in electrodiagnosed CTS. Methods : 14 patients (26 wrists) with electrodiagnosed CTS were studied using MR imaging. In 26 wrists for which axial T1 & T2 weighted images were obtained at 1.5T with a decided wrist coil. Previously described MR imaging of CTS such as increased median nerve signal, flattening of median nerve, reticular bowing, tenosynovitis and space occupying lesions were retrospectively evaluated. Degree of improvement was evaluated by global symptom score (GSS). The GSS rated symptoms from 0 (no symptoms) to 10 (severe) in each of five categories: pain, numbness, paresthesia, weakness/clumsiness, and nocturnal awakening. Subjects' GSS was recorded at baseline, 2 weeks, 1 month, 6 months after treatment. We decided to medical treatment that showed mainly inflammatory sign such as increased median nerve signal, tenosinovitis and to surgical treatment such as space occupying lesion, high canal pressure sign. Results : MR imaging showed that increased median nerve signal were in 20 wrists (77%), flattening of median nerve were in 6 wrists (23%), reticular bowing were in 3 wrists (12%), tenosynovitis were in 8 wrists (32%), decreased canal size in 2 wrists (7.6%), space occupying lesion were in 1 wrist (4%). A good outcome was revealed in 21 wrists by medical treatment that showed mainly increased median nerve signal, tenosynovitis. The mean GSS were 27.7 at baseline, 11.2 at 2 weeks, 11.0 at 6 months in medical treatment group. Another 5 wrist had surgical treatment shown by ganglion and high canal pressure sign such as median nerve flattening, reticular bowing, decreased canal size: 3 wrists had good prognosis, but 2 wrists (one patient) had no significant improvement due to small carpal tunnel size. Conclusions : Our results are in agreement with most previously described MR imaging signs of CTS. MR imaging plays an important role in several cases and especially in the assessment of failure of surgical treatment. Knowledge of MR findings may permit more rational choice of treatment.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.15 no.3
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• pp.231-237
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• 2017

The present study researched and analyzed decontamination technology for decommissioning a nuclear power plant. The decision-making technique (EXPERT-CHOICE) was used to evaluate and select the optimal decontamination technology. In principle, this evaluation method is generally performed by a group of experts in the relevant field. The results of the weights were calculated by multiplying the weights with regard to each criterion and evaluation score. The evaluation scores were categorized into 3 ranges (high, medium, and low), and each range was weighted for differentiation. The level of the technology analysis was improved by additionally quantifying the weights with regard to each criterion and subdividing criteria into subcriteria. The basic assumption of the evaluation was that the weight values would decided on in an expert survey and assigned to each criterion. The evaluation criteria followed high weight for the 'High' range. Accordingly, H, M, and L were assigned weights of 10:5:1, respectively. This was based on the EXPERT-CHOICE optimal analysis. The minimum and maximum values were excluded, and the average value was used as the evaluation value for each scenario.

• Cartoon and Animation Studies
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• s.39
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• pp.393-412
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• 2015

See and feel the emotion recognition is the image of a person variously changed according to the environment, personal disposition. Thus, the image recognition has been focused on the emotional sensibilities computer you want to control the number studies. However, existing emotional computing model is numbered and the objective is clearly insufficient measurement conditions. Thus, through quantifiable image Emotion Recognition and emotion computing, is a study of the situation requires an objective assessment scheme. In this paper, the sensitivity was represented by numbered sizes quantified according to the image recognition calculation emotion. So apply the principal attributes of the color image emotion recognition as a configuration parameter. In addition, in calculating the color sensitivity by applying a digital computing focused research. Image color emotion computing research approach is the color of emotion attribute, brightness, and saturation reflects the weighted according to importance to the emotional scores. And free-degree by applying the sensitivity point to the image sensitivity formula (X), the tone (Y-axis) is calculated as a number system. There pleasure degree (X-axis), the tension and position the position of the image point that the sensitivity of the emotional coordinate crossing (Y-axis). Image color coordinates by applying the core emotional effect of Russell (Core Affect) is based on the 16 main representatives emotion. Thus, the image recognition sensitivity and compares the number size. Depending on the magnitude of the sensitivity scores demonstrate this sensitivity must change. Compare the way the images are divided up the top five of emotion recognition emotion emotions associated with 16 representatives, and representatives analyzed the concentrated emotion sizes. Future studies are needed emotional computing method of calculation to be more similar sensibility and human emotion recognition.

• Journal of Animal Science and Technology
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• v.66 no.4
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• pp.682-701
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• 2024

This study estimated the heritabilities (h²) and genetic and phenotypic correlations between reproductive traits, including calving interval (CI), age at first calving (AFC), gestation length (GL), number of artificial inseminations per conception (NAIPC), and carcass traits, including carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) in Korean Hanwoo cows. In addition, the accuracy of genomic predictions of breeding values was evaluated by applying the genomic best linear unbiased prediction (GBLUP) and the weighted GBLUP (WGBLUP) method. The phenotypic data for reproductive and carcass traits were collected from 1,544 Hanwoo cows, and all animals were genotyped using Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The genetic parameters were estimated using a multi-trait animal model using the MTG2 program. The estimated h² for CI, AFC, GL, NAIPC, CWT, EMA, BF, and MS were 0.10, 0.13, 0.17, 0.11, 0.37, 0.35, 0.27, and 0.45, respectively, according to the GBLUP model. The GBLUP accuracy estimates ranged from 0.51 to 0.74, while the WGBLUP accuracy estimates for the traits under study ranged from 0.51 to 0.79. Strong and favorable genetic correlations were observed between GL and NAIPC (0.61), CWT and EMA (0.60), NAIPC and CWT (0.49), AFC and CWT (0.48), CI and GL (0.36), BF and MS (0.35), NAIPC and EMA (0.35), CI and BF (0.30), EMA and MS (0.28), CI and AFC (0.26), AFC and EMA (0.24), and AFC and BF (0.21). The present study identified low to moderate positive genetic correlations between reproductive and CWT traits, suggesting that a heavier body weight may lead to a longer CI, AFC, GL, and NAIPC. The moderately positive genetic correlation between CWT and AFC, and NAIPC, with a phenotypic correlation of nearly zero, suggesting that the genotype-environment interactions are more likely to be responsible for the phenotypic manifestation of these traits. As a result, the inclusion of these traits by breeders as selection criteria may present a good opportunity for developing a selection index to increase the response to the selection and identification of candidate animals, which can result in significantly increased profitability of production systems.

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.114-118
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• 1997

Purpose: Functional MR imaging is the method of demonstrating changes in regional cerebral blood flow produced by sensory, motor, and any other tasks. Functional MR of visual cortex is performed as a patient stares a photic stimulation, so adaptable photic stimulation is necessary. The purpose of this study is to evaluate whether the size of photic stimulator can affect the degree of visual cortex activation. Materials and Methods: Functional MR imaging was performed in 5 volunteers with normal visual acuity. Photic stimulator was made by 39 light-emitting diodes on a plate, operating at 8Hz. The sizes of photic stimulator were full field, half field and focal central field. The MR imager was Siemens 1.5-T Magnetom Vision system, using standard head coil. Functional MRI utilized EPI sequence (TR/TE= 1.0/51. Omsec, matrix $No.=98{\times}128$, slice thickness=8mm) with 3sets of 6 imaging during stimulation and 6 imaging during rest, all 36 scannings were obtained. Activation images were obtained using postprocessing software(statistical analysis by Z-score), and these images were combined with T-1 weighted anatomical images. The activated signals were quantified by numbering the activated pixels, and activation a index was obtained by dividing the pixel number of each stimulator size with the sum of the pixel number of 3 study using 3 kinds of stimulators. The correlation between the activation index and the stimulator size was analysed. Results: Mean increase of signal intensities on the activation area using full field photic stimulator was about 9.6%. The activation index was greatest on full field, second on half field and smallest on focal central field in 4. The index of half field was greater than that of full field in 1. The ranges of activation index were full field 43-73%(mean 55%), half field 22-40 %(mean 32%), and focal central field 5-24%(mean 13%). Conclusion: The degree of visual cortex activation increases with the size of photic stimulator.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.2
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• pp.47-61
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• 2000

Agroclimatic zoning was done for paddy rice culture in North Korea based on a simulation experiment. Daily weather data for the experiment were generated by 3 steps consisting of spatial interpolation based on topoclimatological relationships, zonal summarization of grid cell values, and conversion of monthly climate data to daily weather data. Regression models for monthly climatological temperature estimation were derived from a statistical procedure using monthly averages of 51 standard weather stations in South and North Korea (1981-1994) and their spatial variables such as latitude, altitude, distance from the coast, sloping angle, and aspect-dependent field of view (openness). Selected models (0.4 to 1.6$^{\circ}C$ RMSE) were applied to the generation of monthly temperature surface over the entire North Korean territory on 1 km$\times$l km grid spacing. Monthly precipitation data were prepared by a procedure described in Yun (2000). Solar radiation data for 27 North Korean stations were reproduced by applying a relationship found in South Korea ([Solar Radiation, MJ m$^{-2}$ day$^{-1}$ ] =0.344 + 0.4756 [Extraterrestrial Solar Irradiance) + 0.0299 [Openness toward south, 0 - 255) - 1.307 [Cloud amount, 0 - 10) - 0.01 [Relative humidity, %), $r^2$=0.92, RMSE = 0.95 ). Monthly solar irradiance data of 27 points calculated from the reproduced data set were converted to 1 km$\times$1 km grid data by inverse distance weighted interpolation. The grid cell values of monthly temperature, solar radiation, and precipitation were summed up to represent corresponding county, which will serve as a land unit for the growth simulation. Finally, we randomly generated daily maximum and minimum temperature, solar irradiance and precipitation data for 30 years from the monthly climatic data for each county based on a statistical method suggested by Pickering et a1. (1994). CERES-rice, a rice growth simulation model, was tuned to accommodate agronomic characteristics of major North Korean cultivars based on observed phenological and yield data at two sites in South Korea during 1995~1998. Daily weather data were fed into the model to simulate the crop status at 183 counties in North Korea for 30 years. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to score the suitability of the county for paddy rice culture.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.