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Memory Organization for a Fuzzy Controller.

  • Jee, K.D.S.;Poluzzi, R.;Russo, B.
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.1041-1043
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    • 1993
  • Fuzzy logic based Control Theory has gained much interest in the industrial world, thanks to its ability to formalize and solve in a very natural way many problems that are very difficult to quantify at an analytical level. This paper shows a solution for treating membership function inside hardware circuits. The proposed hardware structure optimizes the memoried size by using particular form of the vectorial representation. The process of memorizing fuzzy sets, i.e. their membership function, has always been one of the more problematic issues for the hardware implementation, due to the quite large memory space that is needed. To simplify such an implementation, it is commonly [1,2,8,9,10,11] used to limit the membership functions either to those having triangular or trapezoidal shape, or pre-definite shape. These kinds of functions are able to cover a large spectrum of applications with a limited usage of memory, since they can be memorized by specifying very few parameters ( ight, base, critical points, etc.). This however results in a loss of computational power due to computation on the medium points. A solution to this problem is obtained by discretizing the universe of discourse U, i.e. by fixing a finite number of points and memorizing the value of the membership functions on such points [3,10,14,15]. Such a solution provides a satisfying computational speed, a very high precision of definitions and gives the users the opportunity to choose membership functions of any shape. However, a significant memory waste can as well be registered. It is indeed possible that for each of the given fuzzy sets many elements of the universe of discourse have a membership value equal to zero. It has also been noticed that almost in all cases common points among fuzzy sets, i.e. points with non null membership values are very few. More specifically, in many applications, for each element u of U, there exists at most three fuzzy sets for which the membership value is ot null [3,5,6,7,12,13]. Our proposal is based on such hypotheses. Moreover, we use a technique that even though it does not restrict the shapes of membership functions, it reduces strongly the computational time for the membership values and optimizes the function memorization. In figure 1 it is represented a term set whose characteristics are common for fuzzy controllers and to which we will refer in the following. The above term set has a universe of discourse with 128 elements (so to have a good resolution), 8 fuzzy sets that describe the term set, 32 levels of discretization for the membership values. Clearly, the number of bits necessary for the given specifications are 5 for 32 truth levels, 3 for 8 membership functions and 7 for 128 levels of resolution. The memory depth is given by the dimension of the universe of the discourse (128 in our case) and it will be represented by the memory rows. The length of a world of memory is defined by: Length = nem (dm(m)+dm(fm) Where: fm is the maximum number of non null values in every element of the universe of the discourse, dm(m) is the dimension of the values of the membership function m, dm(fm) is the dimension of the word to represent the index of the highest membership function. In our case then Length=24. The memory dimension is therefore 128*24 bits. If we had chosen to memorize all values of the membership functions we would have needed to memorize on each memory row the membership value of each element. Fuzzy sets word dimension is 8*5 bits. Therefore, the dimension of the memory would have been 128*40 bits. Coherently with our hypothesis, in fig. 1 each element of universe of the discourse has a non null membership value on at most three fuzzy sets. Focusing on the elements 32,64,96 of the universe of discourse, they will be memorized as follows: The computation of the rule weights is done by comparing those bits that represent the index of the membership function, with the word of the program memor . The output bus of the Program Memory (μCOD), is given as input a comparator (Combinatory Net). If the index is equal to the bus value then one of the non null weight derives from the rule and it is produced as output, otherwise the output is zero (fig. 2). It is clear, that the memory dimension of the antecedent is in this way reduced since only non null values are memorized. Moreover, the time performance of the system is equivalent to the performance of a system using vectorial memorization of all weights. The dimensioning of the word is influenced by some parameters of the input variable. The most important parameter is the maximum number membership functions (nfm) having a non null value in each element of the universe of discourse. From our study in the field of fuzzy system, we see that typically nfm 3 and there are at most 16 membership function. At any rate, such a value can be increased up to the physical dimensional limit of the antecedent memory. A less important role n the optimization process of the word dimension is played by the number of membership functions defined for each linguistic term. The table below shows the request word dimension as a function of such parameters and compares our proposed method with the method of vectorial memorization[10]. Summing up, the characteristics of our method are: Users are not restricted to membership functions with specific shapes. The number of the fuzzy sets and the resolution of the vertical axis have a very small influence in increasing memory space. Weight computations are done by combinatorial network and therefore the time performance of the system is equivalent to the one of the vectorial method. The number of non null membership values on any element of the universe of discourse is limited. Such a constraint is usually non very restrictive since many controllers obtain a good precision with only three non null weights. The method here briefly described has been adopted by our group in the design of an optimized version of the coprocessor described in [10].

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Bankruptcy Forecasting Model using AdaBoost: A Focus on Construction Companies (적응형 부스팅을 이용한 파산 예측 모형: 건설업을 중심으로)

  • Heo, Junyoung;Yang, Jin Yong
    • Journal of Intelligence and Information Systems
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    • v.20 no.1
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    • pp.35-48
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    • 2014
  • According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

The Establishment of Seongjusa Temple and the Production of Iron Buddhas (성주사 창건과 철불 조성 연구)

  • Kang Kunwoo
    • MISULJARYO - National Museum of Korea Art Journal
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    • v.104
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    • pp.10-39
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    • 2023
  • Seongjusa Temple was founded in Boryeong in Chungcheongnam-do Province by Monk Muyeom (800-888), better known as Nanghye Hwasang. After returning from studying in China, Muyeom stayed in the Silla capital city of Gyeongju for a period. He later settled in a temple that was managed by the descendants of Kim In-mun (629-694). He then restored a burned-out temple and opened it in 847 as a Seon (Zen) temple named Seongjusa. It prospered and grew to become a large-scale temple with several halls within its domains. The influence of Seongjusa in the region can be seen in the Historical Record of Seongjusa Temple on Sungamsan Mountain, which relates that there were seventy-three rooms within the domains of the temple. What is most notable in the record is that the temple is referred to as "栴檀林九間," which means either "a structure with nine rooms built with Chinese juniper wood" or "a place that houses Chinese juniper wood and has nine rooms." Regardless of the interpretation, Seongjusa Temple had a large amount of juniper wood. Around this time, the term "juniper" referred to the olibanum tree (Boswellia sacra) native to the islands of Java and Sumatra in Southeast Asia. It is presumed that at some point after the death of Jang Bogo, the maritime forces that controlled the southwestern coast of Korea may have acquired a large amount of Southeast Asian olibanum wood and offered it to Seongjusa Temple. During the reign of King Munseong, Kim Yang (808-857) patronized Seongjusa Temple and its head monk Muyeom, who enjoyed a lofty reputation in the region. He sought to strengthen his own position as a member of the royal lineage of King Muyeol and create a bridge between the royal family and Seongjusan Buddhist sect. The court of King Wonseong designated Seongjusa Temple as a regional base for the support of royal authority in an area where anti-royal sentiment remained strong. Monk Muyeom is believed to have created an iron Buddha to protect the temple, enlighten the people, and promote regional stability. Given that the Seongjusa community had expanded to include more than 2,000 followers, the iron Buddha at Seongjusa Temple would have been perceived as an image that rallied the local residents. It is assumed that there were two iron Buddhas at Seongjusa Temple. The surviving parts of these Buddhas and the size of their pedestals suggest that they were respectively enshrined in the Geumdang Main Hall and the Samcheonbuljeon Hall of Three Thousand Buddhas. It is presumed that the first iron Buddha in Geumdang was a large statue over two meters in height and the second one was medium-sized with the height over one meter. The Historical Record of Seongjusa Temple on Sungamsan Mountain contains the phrase "改創選法堂五層重閣" which indicates that a multistoried Geumdang was newly built to enshrine a large Buddha sculpture like the first iron Buddha when Seongjusa Temple was founded. Also, according to the Stele of Seongjusa Temple and the surviving finger fragments, the first Buddha was making the fear-not and wish-granting (abhayavarada) mudras. The main Buddha of Seongjusa Temple is possibly Nosana Buddha, just like the main Buddhas at the contemporaneous temples Silsangsa, Borimsa, and Samhwasa. Given that Monk Muyeom studied Hwaeom teachings in his early years and received royal patronage upon his return, it is believed that the retro tendencies of the Hwaeom school, centered on the royal family of the Silla Dynasty, were reflected in Seongjusa temple.

A Study on the Meaning and Cultural Properties Value of Rock-Go-Board from the Viewpoint of Site and Location Characteristics (입지와 장소 특성으로 본 암각바둑판의 의미와 문화재적 가치)

  • Park, Joo Sung;Rho, Jae Hyun;Sim, Woo Kyung
    • Korean Journal of Heritage: History & Science
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    • v.44 no.4
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    • pp.172-205
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    • 2011
  • Go bears significant meanings in terms of cultural and entertaining functions in Asia Eastern such as China and Japan. Beyond the mere entertaining level, it produces philosophical and mythic discourse as well. As a part of effort to seek an identity of Korean traditional garden culture, this study traced back to find meanings of rock-go-board and taste for the arts which ancestors pursued in playing Go game, through analysis and interpretation of correlation among origin of place name, nearby scenery, carved letters and vicinal handed-down place name. At the same time, their position, shape and location types were interpreted through comprehensive research and analysis of stone-go-boards including rock-go-board. Particularly, it focused on the rock names related to Sundoism(仙道) Ideal world, fixed due to a connection between traces of Sundoism and places in a folk etymology. Series of this work is to highlight features of the immortal sceneries, one of traditional landscaping ideals, by understanding place identity and scenic features of where the rock-go-boards are carved. These works are expected to become foundation for promotion and preservation of the traditional landscaping remains. The contents of this study could be summarized as follows; First, round stone and square board for round sky and angled land, black and white color for harmony of yin and yang and 361paths for rotating sky are symbols projecting order of universe. Sayings of Gyuljungjirak(橘中之樂), Sangsansaho(商山四皓), Nangagosa(爛柯故事) formed based on the idea of eternity stand for union of sky and sun. It indicates Go game which matches life and nature spatiotemporally and elegant taste for arts pursuing beauty and leisure. Second, the stone-go-boards found through this research, are 18 in total. 3 of those(16.1%), Gangjin Weolnamsaji, Yangsan Sohanjeong and Banryongdae ones were classified into movable Seokguk and 15(83.9%) including Banghakdong were turned out to be non-movable rock-go-boards carved on natural rocks. Third, upon the result of materializing location types of rock-go-boards, 15 are mountain stream type(83.9%) and 3 are rock peak type(16.1%). Among those, the one at Sobaeksam Sinseonbong is located at the highest place(1,389m). Considering the fact that all of 15 rock-go-boards were found at mountainous areas lower than 500m, it is recognizable that where the Go-boards are the parts of the living space, not far from secular world. Fourth, there are 7 Sunjang(巡將) Go with 17 Hwajeoms(花點), which is a traditional Go board type, but their existences, numbers and shapes of Hwajeom appear variously. Based on the fact, it is recognizable that culture of making go-board had been handed down for an extended period of time. Among the studied rock-goboards, the biggest one was Muju Sasunam[$80(82)cm{\times}80(82)cm$] while the smallest one was Yangsan Sohandjeong Seokguk ($40cm{\times}40cm$). The dimension of length and breadth are both $49cm{\times}48cm$ on average, which is realistic size for actual Go play. Fifth, the biggest bed rock, an under-masonry with carved Go-board on it, was one in Muju Sasunam[$8.7m{\times}7.5m(65.25m^2)$], followed by ones in Hoengseong Chuiseok[$7.8m{\times}6.3m(49.14m^2$] and Goisan Sungukam[$6.7m{\times}5.7m(37.14m^2)$]. Meanwhile, the smallest rock-go-board was turned out to be one in Seoul Banghak-dong. There was no consistency in directions of the Go-boards, which gives a hint that geographical features and sceneries of locations were considered first and then these were carved toward an optimal direction corresponding to the conditions. Sixth, rock-go-boards were all located in valleys and peaks of mountains with breathtaking scenery. It seems closely related to ancestors' taste for arts. Particularly, rock-go-boards are apprehended as facilities related to taste for arts for having leisure in many mountains and big streams under the idea of union of sky and human as a primitive communal line. Go became a medium of hermits, which is a traditional image of Go-game, and symbol of amusement and entertainment with the idea that Go is an essence of scholar culture enabling to reach the Tao of turning back to nature. Seventh, the further ancient time going back to, the more dreamlike the Go-boards are. It is an evident for that Sundoism, which used to be unacceptable once, became more visible and realistic. Considering the high relation between rock-go-boards and Sundoism relevant names such as Sundoism peak in Danyang Sobaeksan, 4 hermits rock in Muju and Sundoism hermit rock in Jangsu, Sundoism hermit rocks and rock-go-boards are sceneries and observation spots to express a communication of worship and longing for Sundoism. Eighth, 3 elements-physical environment such as location type of the rock-go-boards, human activities concentrated on 8 sceneries and Dongcheongugok(洞天九曲) setup and relevancy to Confucian scholars, as well as 'Sangsansaho' motif and 'Nangagosa' symbolic meaning were used as interpretation tools in order to judge the place identity. Upon the result, spatial investigation is required with respect to Sunyoodongcheon(仙遊洞天) concept based on enjoyment to unify with the nature rather than Dongcheongugok concept of neo-Confucian, for Dongcheon and Dongmoon(洞門) motives carved around the rock-go-boards. Generally, places where mountain stream type rock-go-boards were formed were hermit spaces of Confucianism or Sundoism. They are considered to have compromised one other with the change of times. Particularly, in the rock-go-board at the mountain peak, sublimity-oriented advent of Sundoism is considered as a significant factor to control place identity. Ninth, including where the rock-go-boards were established, the vicinal areas are well-known as parts of Dongcheongugok and Palkyung(八景) mostly. In addition, many of Sundoism relevant expressions were discovered even in the neighboring carvings written by scholars and nobility, which means sophisticated taste based on longing for Sundoism world played a significant role in making go-board. The rock-go-board is an integration of cultural phenomena naturally managed by seclusion of scholars in the Joseon Dynasty as well as remains and essence of Korean traditional landscaping. Some rock-go-boards out of 17 discovered in South Korea, including ones in Sobaeksan Sinsunbong, Banghak-dong, Chungju Gongili, Muju Sasunam, Yangsan Eogokdong Banryongdae Seokguk, are damaged such as cracks in rocks or fainted lines by hardships of time and hand stains. Worse yet, in case of Eunyang Bangudae Jipcheongjeong board, it is very difficult to identify the shape due to being buried. Rock-go-boards are valuable sculptures in terms of cultural asset and artwork since they reflect ancestors' love for nature and longing for Sundoism world. Therefore, they should be maintained properly with right preservation method. Not only rock-boards itself but also peripheral places are excellent cultural heritages and crucial cultural assets. In addition, vicinal sceneries of where rock-goboards and pavilion spots are the representative remains of embracing prototype of Korean traditional landscaping and major parts of cultural properties.