• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.6
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• pp.306-318
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• 2018

In order to evaluate the seismic capacity of massive vertical type breakwaters which have intensively been deployed along the coast of South Korea over the last two decades, we carry out the preliminary numerical simulation against the PoHang, GyeongJu, Hachinohe 1, Hachinohe 2, Ofunato, and artificial seismic waves based on the measured time series of ground acceleration. Numerical result shows that significant sliding can be resulted in once non-negligible portion of seismic energy is shifted toward the longer period during its propagation process toward the ground surface in a form of shear wave. It is well known that during these propagation process, shear waves due to the seismic activity would be amplified, and non-negligible portion of seismic energy be shifted toward the longer period. Among these, the shift of seismic energy toward the longer period is induced by the viscosity and internal friction intrinsic in the soil. On the other hand, the amplification of shear waves can be attributed to the fact that the shear modulus is getting smaller toward the ground surface following the descending effective stress toward the ground surface. And the weakened intensity of soil as the number of attacking shear waves are accumulated can also contribute these phenomenon (Das, 1993). In this rationale, we constitute the numerical model using the model by Hardin and Drnevich (1972) for the weakened shear modulus as shear waves go on, and shear wave equation, in the numerical integration of which $Newmark-{\beta}$ method and Modified Newton-Raphson method are evoked to take nonlinear stress-strain relationship into account. It is shown that the numerical model proposed in this study could duplicate the well known features of seismic shear waves such as that a great deal of probability mass is shifted toward the larger amplitude and longer period when shear waves propagate toward the ground surface.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.431-438
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• 2021

To prepare more stringent regulations for USCG Phase II ballast water management, this study investigated the staining efficiency of SYBR Green I(SGI) and SYBR Gold(SG) on the virus-like particle (VLP). A dye with high staining efficiency was applied to the treated water that was passed through the ballast water treatment system (BWTS). VLP staining was observed most clearly under the 100-fold and 200-fold dilution of the stock solution when the volume of filtered samples was 0.5 mL to 2 mL. The staining efficiency of SGI and SG did not show a significant difference. On the other hand, the green fluorescence of viruses in the sample stained with SGI was more pronounced than in the samples stained with SG (expressed yellow fluorescence), making it easier to observe. The abundance of VLP in the test water and control water treatments that did not pass through the two types of BWTS (electrolysis type, UV + electrolysis type) was approximately 109 - 1010 VLP 100 mL-1. In contrast, no stained VLP was observed in the treated water treatments. Moreover, SGI was confirmed to be effectively stained under various salinity conditions, including seawater, brackish water, and freshwater. Further verification tests and development of staining methods under various BWTS are required, but the SGI staining method is believed to be a good alternative to the VLP live/dead determination of the USCG Phase II type approval test.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5A
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• pp.407-415
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• 2010

In Korea, more than 90% of the total number of steel bridges built for 40~70 m span length is a steel box-girder bridge type. A steel box-girder bridge is suitable for long span or curved bridges with outstanding flexural and torsional rigidity as well as good constructability and safety. However, a steel box-girder bridge is uneconomical, requiring many secondary members and workmanship such as stiffeners and ribs requiring welding attachments to flanges or webs. Therefore, in US and Japan, a plate girder bridge, which is relatively cheap and easy to construct is generally used. One type of the plate girder bridge is the two- or three-main girder plate bridge, which is a composite plate girder bridge that minimizes the number of required main girders by increasing the distance between the adjacent girders. Also, for the simplification of girder section, the stiffener which requires attachment to the web is not required. The two-main steel girder plate bridge is a representative type of plate girder bridges, which is suitable for bridges with 10 m effective width and has been developed in the early 1960s in France. To ensure greater safety of two- or three-main girder plate bridges, a larger steel section is used in the bridge domestically than in Europe or Japan. Also, the total number of two- or three-main girder plate bridge constructed in Korea is significantly less than the steel box girder bridge due to a lack of designers' familiarity with more complex design detailing of the bridge compare to that of a steel box girder bridge design. In this study, a new construction method called Turn Over method is proposed to minimize the steel section size used in a two- or three-main girder plate bridge by applying prestressing force to the member using confining concrete section's weight to reduce construction cost. Also, a full scale 20 m Turn Over girder specimen and a Turn Over girder bridge specimen were tested to evaluate constructability and structural safety of the members constructed using Turn Over process.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Korean Journal of Agricultural Science
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• v.2 no.2
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• pp.357-373
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• 1975

This study was conducted to evaluate the winter active polycross progenies of 10 genotypes selected at the hot and dry climate of the Southern Oregon in their performance in the progeny test comparing with a high yielding variety, 'Fawn', and a winter active variety, 'TFM', as the control varieties at Daejon, Korea. Various plant and leaf characteristics, especially which related to photosynthesis, and forage production during the first summer after their establishment, were examined. The important conclusions of this study are summarized as follows: 1. The winter active genotypes and variety had less leaf fresh weight and dry weight per leaf than variety 'Fawn'. Variations among polycross progenies of genotypes for these characteristics were great. 2. The winter active genotypes and variety had less leaf area per leaf than variety 'Fawn'. Leaf area among polycross progenies of genotypes deviated greatly and poly cross progenies of 'genotype-16' had the same average leaf area as 'Fawn'. 3. Differences of specific leaf weight (S. L. W.) in the winter active genotypes and variety were not significant. Probably the genetic diversity for S. L. W were not big and were narrowed down already in this genetic population. It was suggested that the photosynthate production within the population might not be different and there might be differences in the photosynthate production-translocation balance. Further study for the diurnal change in S. L. W. within the population might be useful. 4. The winter active variety and genotypes had less leaf width than 'Fawn' does. Leaf width among polycross progenies of genotypes deviated significantly. 5. Differences among controls and polycross progeny group in the initial plant height were significant and variety 'Fawn' was taller than the winter active genotypes and variety. But the differences were not significant in the regrowth of plant height after the first forage harvest. On the contrary. the differences among polycross progenies of genotypes were not significant in the initial plant but the differences in their polycross progeny performance became obvious and great in the regrowth ability which is an improtent agronomic characteristics for forage crops produced in the pasture and for hay and silage. 6. Plant width of the winter active genotypes and variety was lesser than 'Fawn' variety. 7. Differences of tiller number became evident and variety 'Fawn' had higher tiller number than the winter active genotypes and variety after the first forage cutting. There, deviations among polycross progenies of genotypes were great for this characteristic. It was obvious that the genetic differences became more evident in the second measurement after the first cutting of forage probably because this characteristic were stimulated by defoliation in the cartain genotypes and variety. 8. The winter active genotypes and variety on the initial growth. the regrowth ability andtotal yield had lesser forage yield than variety 'Fawn'. Deviation of forage yield among polycross progenies of genotypes were great and gave basis for selection according to their polycross progeny performance improving the forage yield of these winter active tall fescue population during summer. 9. It was concluded that the winter active variety and genotypes in this study was poorer than variety 'Fawn' for the most of leaf and plant characteristics including forage yield. For these measurements, the variations among polycross progenies of genotypes were great. and plant breeding might able to improve further this winter active tall fescue through the polycross progeny testing method for the higher forage production during summer in Korea. 10. The result of the associations among various characteristics under study were quite agreeable with the results of the analysis of variance and woul be useful in the selection of desirable genotypes for the development of a new variety.

• Journal of Popular Narrative
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• v.26 no.3
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• pp.45-86
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• 2020

In this article, the "modernization of the tradition" constructed on the cultural politics and the way in which it appropriated in the korean theatre in the 1970s were analyzed. It is trying to reveal its implications. It is also a work to critically review the aspects of self-censorship in the korean theatre in the 70s. To that end, we looked at the theatre company Minye Theatre, which preoccupied the traditional discussions in the 1970s by creating national dramas. Until now, the evaluation of the theatre company Minye Theatre in the 1970s has focused on the achievement on the directing of Heo Gyu, who promoted the succession and transformation of tradition. However, the traditional ideology constructed in the state-led cultural politics in the 70s and the way in which it was operated cannot be evaluated only in terms of artistic achievement. The ideology of tradition is selected according to the selective criteria of the subject to appropriate tradition. What's important is that certain objects are excluded, discarded, re-elected, re-interpreted and re-recognized in the selection process of selected traditional ideology. This is the situation in the '70s, when tradition was constantly re-recognized amid differences between the decadent and the disorder that were then designated as non-cultural, and led to a new way of appropriate. The nation-led traditional discussion of the '70s legalized the tradition with stable values, one of the its way was the national literary and artistic support. Under the banner of modernization of tradition, theatre company Minye preoccupied the discussions on the tradition and presented folk drama as a new theatre. As an alternative to the crisis of korean theatre at the time, the Minye chose the method of inheriting and transforming tradition. It is noteworthy that Heo Gyu, the representative director of the theatre company Minye, recognized the succession and transformation of traditional performance as both a calling and an experiment. For Heo Gyu, tradition was accepted as an irresistible stable value and an unquestionable calling, and as a result, his performance, filled with excessive traditional practices, became overambitious, especially when it failed to reflect the present-here reality, the repeated use of traditional expression tools resulted in skilled craftsmanship, not artistic creation. The traditional ideology of the 70s unfolds in a new aspect of appropriation in the 80s. In 1986, Son Jin-Cheok, Kim Seong-nyeo, and Yoon Mun-sik, who were key members of the theatre company Minye Theatre, left the theatre to create the theatre company Michu, and secured popularity through Madangnori(popular folk yard theatre). Son Jin-Cheok's Madangnori is overbearing through satire and humor. It gained popularity by criticizing and mocking state power. On the other hand, not only the form of traditional performance, but also the university-centered Madanggeuk movement, which appropriated on the spirit of resistance from the people to its traditional values, has rapidly grown. In the field of traditional discussions of the 70s, Madanggeuk was self-born through appropriation in which the spirit of resistance of the people is used as a traditional value. Madanggeuk as well as Michu that achieved the popularization of Madangnori cannot be discussed solely by the artistic achievement of the modernization of tradition. Critics of korean theatre in response to state-led traditional discussions in the 70s was focused only on the qualitative achievement of performing arts based on artistry. I am very sorry for that. As a result, the popular resistance of the Madanggeuk and the Madangnori were established in the 'difference' with the traditions of the theatre company Minye Theatre. Theatre company Minye Theatre was an opportunity for the modernization of tradition, but the fact that it did not continuously produce significant differences. This is the meaning and limitation of the "tradition" of the theatre company Minye Theatre in the history of korean theatre in the 1970s.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Tuberculosis and Respiratory Diseases
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• v.42 no.6
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• pp.900-912
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• 1995

Background: Although graded exercise stress tests are widely used for the evaluation of cardiorespiratory performance, normal standards on respiratory gas exchange and ventilatory functions at maximal exercise in Koreans have not been well established. The purpose of this study is to provide reference values on these by sex and age, along with derivation of some of their prediction equations. Method: Symptom-limited maximal exercise test was carried out by Bruce protocol in 1,000 healthy adults consisting of 603 males and 397 females, aged 20~66 years. Among them VC, $FEV_1$ and MVV were also determined in 885 cases. All the subjects were members of a health center, excluding athletes. During the exercise, subjects were allowed to hold on to front hand rail of the treadmill for safety purpose. Results: The $VO_2\;max/m^2$, $VCO_2\;max/m^2$ and $V_E\;max/m^2$ were greater in males than in females and decreased with age. The RR max in men and women was similar but decreased slightly with age. The $V_T$ max was markedly greater in men but showed no significant changes with age in either gender. The mean of $V_T$ max/VC, $V_E$ max/MVV and BR revealed that there were considerable ventilatory reserves at maximal exercise even in older females. The regression equations of the cardinal parameters obtained using exercise time(ET, min), age(A, yr), height(Ht, cm), weight(W, kg), sex(S, 0=male; 1=female), VC(L), $FEV_1$(L) and $V_E$ max(L) as variables are as follows: $VO_2\;max/m^2$(L/min)=1.449+0.073 ET-0.007A+0.010W-0.006Ht-0.209S, $VCO_2\;max/m^2$(L/min)=1.672+0.063ET-0.008A+0.010W-0.005Ht-0.319S, VE max/$m^2$(L/min)=58.161+1.503ET-0.315A-9.871S or VE max/$m^2$(L/min)=47.873+6.548 $FEV_1$-5.715 S, and VT max(L)=1.497+0.223VC-0.493S. Conclusion: Respiratory gas exchange and ventilatory variables at maximal exercise were studied in 1,000 non-athletes by Bruce protocol. During exercise, the subjects were allowed to hold on to hand rail of the treadmill for safety purpose. We feel that our results would provide ideal target values for patients and healthy individuals to be achieved, since our study subjects were members of a health center whose physical fitness levels were presumably higher than ordinary population.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.5
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• pp.80-88
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• 2020

With the issuance of one-week fine dust emergency reduction measures in March 2019, the public's anxiety about fine dust is increasingly growing. In order to assess the application of air purifying plant-based bio-filters to public facilities, this study presented a method for measuring pollutant reduction effects by creating an indoor environment for continuous discharge of particle pollutants and conducted basic studies to verify whether indoor air quality has improved through the system. In this study conducted in a lecture room in spring, the background concentration was created by using mosquito repellent incense as a pollutant one hour before monitoring. Then, according to the schedule, the fine dust reduction capacity was monitored by irrigating for two hours and venting air for one hour. PM₁₀, PM_2.5, and temperature & humidity sensors were installed two meters front of the bio-filters, and velocity probes were installed at the center of the three air vents to conduct time-series monitoring. The average face velocity of three air vents set up in the bio-filter was 0.38±0.16 m/s. Total air-conditioning air volume was calculated at 776.89±320.16㎥/h by applying an air vent area of 0.29m×0.65m after deducing damper area. With the system in operation, average temperature and average relative humidity were maintained at 21.5-22.3℃, and 63.79-73.6%, respectively, which indicates that it satisfies temperature and humidity range of various conditions of preceding studies. When the effects of raising relatively humidity rapidly by operating system's air-conditioning function are used efficiently, it would be possible to reduce indoor fine dust and maintain appropriate relative humidity seasonally. Concentration of fine dust increased the same in all cycles before operating the bio-filter system. After operating the system, in cycle 1 blast section (C-1, β=-3.83, β=-2.45), particulate matters (PM₁₀) were lowered by up to 28.8% or 560.3㎍/㎥ and fine particulate matters (PM_2.5) were reduced by up to 28.0% or 350.0㎍/㎥. Then, the concentration of find dust (PM₁₀, PM_2.5) was reduced by up to 32.6% or 647.0㎍/㎥ and 32.4% or 401.3㎍/㎥ respectively through reduction in cycle 2 blast section (C-2, β=-5.50, β=-3.30) and up to 30.8% or 732.7㎍/㎥ and 31.0% or 459.3㎍/㎥ respectively through reduction in cycle 3 blast section (C-3, β=5.48, β=-3.51). By referring to standards and regulations related to the installation of vegetation bio-filters in public facilities, this study provided plans on how to set up objective performance evaluation environment. By doing so, it was possible to create monitoring infrastructure more objective than a regular lecture room environment and secure relatively reliable data.

• 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.