• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.683-694
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• 2003

We present X-tree Diff, a change detection algorithm for tree-structured data. Our work is motivated by need to monitor massive volume of web documents and detect suspicious changes, called defacement attack on web sites. From this context, our algorithm should be very efficient in speed and use of memory space. X-tree Diff uses a special ordered labeled tree, X-tree, to represent XML/HTML documents. X-tree nodes have a special field, tMD, which stores a 128-bit hash value representing the structure and data of subtrees, so match identical subtrees form the old and new versions. During this process, X-tree Diff uses the Rule of Delaying Ambiguous Matchings, implying that it perform exact matching where a node in the old version has one-to one corrspondence with the corresponding node in the new, by delaying all the others. It drastically reduces the possibility of wrong matchings. X-tree Diff propagates such exact matchings upwards in Step 2, and obtain more matchings downwsards from roots in Step 3. In step 4, nodes to ve inserted or deleted are decided, We aldo show thst X-tree Diff runs on O(n), woere n is the number of noses in X-trees, in worst case as well as in average case, This result is even better than that of BULD Diff algorithm, which is O(n log(n)) in worst case, We experimented X-tree Diff on reat data, which are about 11,000 home pages from about 20 wev sites, instead of synthetic documets manipulated for experimented for ex[erimentation. Currently, X-treeDiff algorithm is being used in a commeercial hacking detection system, called the WIDS(Web-Document Intrusion Detection System), which is to find changes occured in registered websites, and report suspicious changes to users.

• Journal of Gifted/Talented Education
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• v.12 no.2
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• pp.1-15
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• 2002

The purpose of this study was to investigate the characteristics of preschoolers' giftedness by their parents' perception. Total 3 groups of 148 subjects from age 30 months to 6 years 10 months old young gifted children's parents participated. The major findings were as follows : (1) There were critical characteristics of preschoolers' giftedness by parents' perception, which were 'good memory', 'high curiosity', 'read and understand of math', 'enjoy of learning and high motivation', 'high concentration', reading books', 'verbal ability', 'creativity', 'questions', and 'independency', (2) These characteristics of preschoolers' giftedness showed more strong and intense as they got older, and (3) Some characteristics revealed more, but the other characteristics revealed less as they got older. These findings suggested the consideration of child's age as the reliable identification process of young gifted children.

• (The)Study of the Eastern Classic
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• no.69
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• pp.451-479
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• 2017

The objective of this study is to examine the tasks for listing martial arts of "Muyedobotongji" on the UNESCO Representative List of Intangible Cultural Heritage of Humanity. The conclusions are like below. First, "Muyedobotongji" was published in 1790(14th year of King Jeongjo). The 24 martial arts of "Muyedobotongji" were basically divided into three types like stabbing, chopping & cutting, and hitting. Second, the value of martial arts of "Muyedobotongji" is highly evaluated because it has systematically put together the martial arts of three countries like Korea, China, and Japan of the 18th century, suitable for the actual status of Joseon Dynasty, in the new perspective. The value of "Muyedobotongji" as a Memory of the World is the martial arts book emphasizing the practicality, so that everyone including officers and soldiers could easily learn. Third, the procedure of registering martial arts of "Muyedobotongji" in the UNESCO Representative List of Intangible Cultural Heritage of Humanity has three stages including preparation/submission, screening, and decision, which takes two years. Especially, the screening assistance organization, as an organization under the Intangible Cultural Heritage Convention Intergovernmental Committee is composed of total six countries(one for each area) out of 24 member countries. Fourth, the tasks for listing martial arts of "Muyedobotongji" in the UNESCO Representative List of Intangible Cultural Heritage of Humanity are like following. (1) It would be necessary to conduct a total inspection of the collection of "Muyedobotongji". (2) It would be necessary to designate the martial arts of "Muyedobotongji" as the municipal/provincial/national intangible cultural heritage. (3) It would be needed to standardize the practical martial arts technique/movement of "Muyedobotongji". (4) The historical evidence of martial arts costumes/weapons of "Muyedobotongji" should be studied. (5) A committee for the registration of martial arts of "Muyedobotongji" in the UNESCO Representative List of Intangible Cultural Heritage of Humanity should be organized. (6) There should be a close cooperation system between relevant departments like the World Heritage Team of Cultural Heritage Administration and the Ministry of Foreign Affairs. (7) Domestic/foreign data related to martial arts of "Muyedobotongji" should be comprehensively collected to meet the registration standard of UNESCO. (8) The registration type of Intangible Cultural Heritage of Humanity should be prepared.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

• Journal of the Korean earth science society
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• v.23 no.4
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• pp.357-368
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• 2002

In this study we have tried to explain the barrel-shaped morphology for young supernova remnants considering the dynamical effects of the ejecta. We consider the magnetic field amplification resulting from the Rayleigh-Taylor instability near the contact discontinuity. We can generate the synthetic radio image assuming the cosmic-ray pressure and calculate the azimuthal intensity ratio (A) to enable a quantitative comparison with observations. The postshock magnetic field are amplified by shearing, stretching, and compressing at the R-T finger boundary. The evolution of the instability strongly depends on the deceleration of the ejecta and the evolutionary stage of the remnant. the strength of the magnetic field increases in the initial phase and decreases after the reverse shock passes the constant density region of the ejecta. However, some memory of the earlier phases of amplification is retained in the interior even when the outer regions turn into a blast wave. The ratio of the averaged magnetic field strength at the equator to the one at the pole in the turbulent region can amount to 7.5 at the peak. The magnetic field amplification can make the large azimuthal intensity ratio (A=15). The magnitude of the amplification is sensitive to numerical resolution. This mens the magnetic field amplification can explain the barrel-shaped morphology of young supernova remnant without the dependence of the efficiency of the cosmic-ray acceleration on the magnetic field configuration. In order for this mechanism to be effective, the surrounding magnetic field must be well-ordered. The small number of barrel-shaped remnants may indicate that this condition rarely occurs.

• The Magazine of the IEIE
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• v.29 no.6
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• pp.76-80
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• 2002

'Liquid Crystal의 상전이(相轉移)와 광학적 이방성(異方性)이 1888년과 1889년 F. Reinitzer와 O. Lehmann에 의해 Monatsch Chem.과 Z.Physikal.Chem.에 각각 보고된 후 부터 제2차 세계대전이 끝난 뒤인 1950년대 까지는 Liquid Crystal을 단지실험실에서의 기초학문 차원의 연구 대상으로만 다루어 왔다. 1963년 Williams가 Liquid Crystal Device로는 최초로 특허 출원을 하였으며, 1968년 RCA사의 Heilmeier등은 Nematic 액정(液晶)에 저주파(低周波) 전압(電壓)을 인가하면 투명한 액정이 혼탁(混濁)상태로 변화하는 '동적산란(動的散亂)'(Dynamic Scattering) 현상을 이용하여 최초의 DSM(Dynamic Scattering Mode) LCD(Liquid Crystal Display)를 발명하였다. 비록 150V 이상의 높은 구동전압과 과소비전력의 특성 때문에 실용화에는 실패하였지만 Guest-Host효과와 Memory효과 등을 발견하였다. 1970년대에 이르러 실온에서 안정되게 사용 가능한 액정물질들이 합성되고(H. Kelker에 의해 MBBA, G. Gray에 의한 Cyano-Biphenyl 액정의 합성), CMOS 트랜지스터의 발명, 투명도전막(ITO), 수은전지등의 주변기술들의 발전으로 인하여 LCD의 상품화가 본격적으로 이루어지게 되었다. 1971년에는 M. Shadt, W. Helfrich, J.L. Fergason등이 TN(Twisted Nematic) LCD를 발명하여 전자 계산기와 손목시계에 응용되었고, 1970년대 말에는 Sharp에서 Dot Matrix형의 휴대형 컴퓨터를 발매하였다. 이러한 단순 구동형의 TN LCD는 그래픽 정보를 표시하는 데에는 품질의 한계가 있어 1979년 영국의 Le Comber에 의해 a-Si TFT(amorphous Silicon Thin Film Transistor) LCD의 연구가 시작되었고, 1983년 T.J. Scheffer, J. Nehring, G. Waters에 의해 STN(Super Twisted Nematic) LCD가 창안되었고, 1980년 N. Clark, S. Lagerwall 및 1983년 K.Yossino에 의해 Ferroelectric LCD가 등장하여 LCD의 정보 표시량 증대에 크게 기여하였다. Color화의 진전은 1972년 A.G. Ficher의 셀 외부에 RGB(Red, Green, Blue) filter를 부착하는 방안과, 1981년 T. Uchida 등에 의한 셀 내부에 RGB filter를 부착하는 방법에 의해 상품화가 되었다. 1985년에는 J.L. Fergason에 의해 Polymer Dispersed LCD가 발명되었고, 1980년대 중반에 이르러 동화상(動畵像) 표시가 가능한 a-Si TFT LCD의 시제품(試製品) 개발이 이루어지고 1990년부터는 본격적인 양산 시대에 접어들게 되었다. 1990년대 초에는 STN LCD의 Color화 및 대형화(大型化) 고(高)품위화에 힘입어 Note-Book PC에 LCD가 본격적으로 적용이 되었고, 1990년대 후반에는TFT LCD의 표시품질 대비 가격경쟁력 확보로 인하여 Note-Book PC 시장을 독점하기에 이르렀다. 이후로는 TFT LCD의 대형화가 중요한 쟁점으로 부각되고 있고, 1995년 삼성전자는 당시 세계최대 크기의 22' TFT LCD를 개발하였다. 또한 LCD의 고정세(高情細)화를 위해 Poly Si TFT LCD의 개발이 이루어졌고, 디지타이져 일체형 LCD의 상품화가 그 응용의 폭을 넓혔으며, LCD의 대형화를 위해 1994년 Canon에 의해 14.8', 21' 등의 FLCD가 개발되었다. 대형화 방안으로 Tiled LCD 기술이 개발되고 있으며, 1995년에 Sharp에 의해 21' 두장의 Panel을 이어 붙인 28' TFT LCD가 전시되었고 1996년에는 21' 4장의 Panel을 이어 붙인 40'급 까지의 개발이 시도 되었으며 현재는 LCD의 특성향상과 생산설비의 성능개선과 안정적인 공정관리기술을 바탕으로 삼성전자에서 단패널 40' TFT LCD가 최근에 개발되었다. Projection용 디스플레이로는 Poly-Si TFT LCD를 이용하여 $25'{\sim}100'$사이의 배면투사형과 전면투사형 까지 개발되어 대형 TV시장을 주도하고 있다. 21세기 디지털방송 시대를 맞아 플라즈마디스플레이패널(PDP) TV, 액정표시장치 (LCD)TV, 강유전성액정(FLCD) TV 등 2005년에 약 1500만대 규모의 거대 시장을 형성할 것으로 예상되는 이른바 '벽걸이TV'로 불리는 차세대 초박형 TV 시장을 선점하기 위하여 세계 가전업계들이 양산에 총력을 기울이고 있다. 벽걸이TV 시장이 본격적으로 형성되더라도 PDP TV와 LCD TV가 직접적으로 시장에서 경쟁을 벌이는 일은 별로 없을 것으로 보인다. 향후 디지털TV 시장이 본격적으로 열리면 40인치 이하의 중대형 시장은 LCD TV가 주도하고 40인치 이상 대화면 시장은 PDP TV가 주도할 것으로 보는 시각이 지배적이기 때문이다. 그러나 이러한 직시형 중대형(重大型)디스플레이는 그 가격이 너무 높아서 현재의 브라운관 TV를 대체(代替)하기에는 시일이 많이 소요될 것으로 추정되고 있다. 그 대안(代案)으로는 비교적 저가격(低價格)이면서도 고품질의 디지털 화상구현이 가능한 고해상도 프로젝션 TV가 유력시되고 있다. 이러한 고해상도 프로젝션 TV용으로 DMD(Digital Micro-mirror Display), Poly-Si TFT LCD와 LCOS(Liquid Crystals on Silicon) 등의 상품화가 진행되고 있다. 인터넷과 정보통신 기술의 발달로 휴대형 디스플레이의 시장이 예상 외로 급성장하고 있으며, 요구되는 디스플레이의 품질도 단순한 문자표시에서 그치지 않고 고해상도의 그래픽 동화상 표시와 칼라 표시 및 3차원 화상표시까지 점차로 그 영역이 넓어지고 있다. <표 1>에서 보여주는 바와 같이 LCD의 시장규모는 적용분야 별로 지속적인 성장이 예상되며, 새로운 응용분야의 시장도 성장성을 어느 정도 예측할 수 있다. 따라서 LCD기술의 연구개발 방향은 크게 두가지로 분류할 수 있으며 첫째로는, 현재 양산되고 있는 LCD 상품의 경쟁력강화를 위하여 원가(原價) 절감(節減)과 표시품질을 향상시키는 것이며 둘째로는, 새로운 타입의 LCD를 개발하여 기존 상품을 대체하거나 새로운 시장을 창출하는 분야로 나눌 수 있다. 이와 같은 관점에서 현재 진행되고 있는 LCD기술개발은 다음과 같이 분류할 수 있다. 1) 원가 절감 2) 특성 향상 3) New Type LCD 개발.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.3
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• pp.106-115
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• 2011

This study, based on (edited in 18th century), analysed the landscape system and cultural landscape elements of Yuhoidang(Hageowon 何去園) Garden in Musu-dong, Daejeon, and the findings are as in the following. YuHoidang(Gwon Yijin 權以鎭) managed Hageowon Garden in Musu-dong, located on the southern branch of Mt. Bomun, to realize his utopia. The completion of Hageowon Garden was only possible due to his installation of a variety of facilities in family gravesite on the hill behind his house: Shimyoso(Samgeunjeongsa 三近精舍, in 1707), Naboji(納汚池, in 1713), Banhwanwon(in 1714) and expended exterior space(in 1727). With regard to the landscape system of the village, the main range of mountains consists of Mt. Daedun, Mt. Odae and Mt. Bomun. The main high mountain of the three is Mt. Bomun, where 'Blue Dragon' hill branches off on the east side(Eungbong), 'White Tiger' in the west(Cheongeun and Sajeong) and Ansan(inner mountain) in the south. The landscape system is featured by 'mountains in back and rivers in front'. The river in the south-west, with its source in Mt. Juryun is called as the 'Stream of outer perfect spot', while the 'Stream of inner perfect spot' rises from Eungbong, passing through the east part of the village into the south-western direction. Banhwanwon Garden(盤桓園) was created with the stream in the east and natural bedrocks, and its landscape elements includes Naboji, Hwalsudam, Gosudae, Sumi Waterfall, Dogyeong(path of peach trees), Odeeokdae(platform with persimmon trees), Maeryong(Japanese apricot tree), springs and observatories. An expanded version of Banhwanwon was Hageowon garden, where a series of 'water-trees-stone' including streams, four ponds, five observation platforms, three bamboo forests and Chukgyeongwon(縮景園) of an artificial hill gives the origin forest a scenic atmosphere. When it comes to semantics landscape elements, there are (1) Yuhoidang to cherish the memory of a deceased parents, (2) Naboji for family unification, (3) Gosudae to keep fidelity, (4) Odeokdae to collect virtue and wisdom, (5) Sumi Waterfall to aspire to be a man of noble character, (6) Yocheondae for auspicious life, (7) Sumanheon and Gigungjae to be in pursuit of hermitic life, (8) Hwalsudam for development of family and study, (9) Mongjeong to repay favor of ancestors, (10) Seokgasan, a symbol of secluded life, (11) Hageowon to enjoy guarding graves in retired life. The spatial composition of Hageowon was realized through (1) Yuhoidang's inside gardens(Naboji, Jucheondang, Odeokdae, Dogyeong, Back yard garden and others) (2) Sumanheon(收漫軒) Byeolup or Yuhoidang's back yard gardens (Seokyeonji, Yocheondae, Sumanheon, Baegyeongdae, Amseokwon and others) (3) Chukgyeongwon of the artificial hill(which is also the east garden of Sumanheon, being composed of Hwalsudam, Sumi Waterfall and Gasan or 12 mountaintops) (4) the scenic spots for unifying Confucianism, Buddhism and Taoism are Cemetry garden in the back hill of the village, the temple of Yeogyeongam, Sansinkak(ancestral ritual place of folk religion) and Geoeopjae(family school). On top of that, Chagyeongwon Garden(借景園) commands a panoramic distant view of nature's changing beauty through the seasons.

• 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.