• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.7 no.1
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• pp.31-39
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• 2004

Purpose: Although acute nonspecific mesenteric lymphadenitis (ANML) is probably common cause of abdominal pain in children, which can be severe enough to be an abdominal emergency, the clinical features of mesenteric lymphadenitis are not clear. Also, a differential diagnosis with acute appendicitis (APPE) is indispensable to avoid serious complications. The clinical features of ANML were determined, and the risk factors for differential diagnosis with APPE were analyzed. Methods: Between November 2000 and May 2001, data from 26 patients (aged 1 to 11 years) with ANML and 21 patients (aged 2 to 13 years) with APPE were reviewed. ANML was defined as a cluster of five or more lymph nodes measuring 10 mm or greater in their longitudinal diameter in the right lower quadrant (RLQ) without an identifiable specific inflammatory process on the ultrasonographic examination. There were risk factors on patient's history, physical examination, and laboratory examination; the location of abdominal pain, abdominal rigidity, rebound tenderness, fever, nocturnal pain, the vomiting intensity, the diarrhea intensity, the symptom duration, and the peripheral blood leukocytes count. Results: Of the 26 ANML patients and 21 APPE patients, abdominal pain was noted on periumbilical (76.9% vs 14.2%), on RLQ (11.5% vs 71.4%), with abdomen rigidity (7.6% vs 80.9%), with rebound tenderness (0.0% vs 76.1%)(p<0.05), in the lower abdomen (11.5% vs 14.2%), and at night (80.8% vs 100.0%) (p>0.05). The clinical symptoms were vomiting (38.4% vs 90.4%), the vomiting intensity ($1.5{\pm}0.7$ [1~3]/day vs $4.5{\pm}2.9$ [1~10]/day), diarrhea (65.3% vs 28.5%) (p<0.05), and fever (61.5% vs 76.2%)(p>0.05). The period to the subsidence of abdominal pain in the ANMA patients was $2.5{\pm}0.5$ (2~3) days. The laboratory data showed a significant difference in the peripheral blood leukocytes count ($8,403{\pm}1,737[5,900{\sim}12,300]/mm^3\;vs\;15,471{\pm}3,749[5,400{\sim}20,800]/mm^3$)(p<0.05). Discriminant analysis between ANML and APPE showed that the independent discriminant factors were a vomiting intensity and the peripheral blood leukocytes count and the discriminant power was 95.7%. Conclusion: The clinical characteristics of ANML were abrupt onset of periumbilical pain without rigidity or rebound tenderness, a mild vomiting intensity, normal peripheral leukocytes count, and relatively short clinical course. If the abdominal pain persist for more than 3 days, and/or the vomiting intensity is more than 3 times/day, and/or the peripheral leukocytes count is over $13,500/mm^3$, abdominal ultrasonography is recommended to rule out APPE.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.1
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• pp.62-66
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• 2013

Purpose: I-131 is a radioisotope widely used for thyroid gland treatments. The physical half life is 8.01 and characterized by emitting beta and gamma rays which is used in clinical practice for the purpose of acquiring treatment and images. In order to reduce the recurrence rate after surgery in high-risk thyroid cancer patients, the remaining thyroid tissue is either removed or the I-131 is used for treatment during relapse. In cases of using a high dosage of radioactive iodine requiring hospitalization, the patient is administered dosage in the hospital isolation ward over a certain period of time preventing I-131 exposure to others. By checking the radiation amount emitted from patients before discharge, the patients are discharged after checking whether they meet the legal standards (50 uSv/h). After patients are discharged from the hospital, the contamination level is checked in many parts of the ward before the next patients are hospitalized and when necessary, decontamination operations are performed. It is expected that there is exposure to radiation when measuring the ward contamination level and dose check emitted from patients at the time of discharge whereby the radiation exposure by health workers that come from the patients in this process is the main factor. This study analyzed the correlation between discharge dose of patients and ward contamination level through a variety of factors such as renal functions, gender, age, dosage, etc.). Materials and Method: The study was conducted on 151 patients who received high-dosage radioactive iodine treatment at Soon Chun Hyang University Hospital during the period between 8/1/2011~5/31/2012 (Male: Female: 31:120, $47.5{\pm}11.9$, average dosage of $138{\pm}22.4$ mCi). As various factors expected to influence the patient discharge dose & ward contamination such as the beds, floors, bathroom floors, and washbasins, the patient renal function (GFR), age, gender, dosage, and the correlation between the expected Tg & Tg-Tb expected to reflect the remaining tissue in patients were analyzed. Results: In terms of the discharge dose and GFR, a low correlation was shown in the patient discharge dose as the GFR was higher (p < 0.0001). When comparing the group with a dosage of over 150mCi and the group with a lower dosage, the lower dosage group showed a significantly lower discharge dose ($24{\pm}10.4uSv/h$ vs $28.7{\pm}11.8uSv/h$, p<0.05). Age, gender, Tg, Tg-Tb did not show a significant relationship with discharge dose (p> 0.05). The contamination level in each spot of the treatment ward showed no significant relationship with GFR, Tg, Tg-Tb, age, gender, and dosage (p>0.05 ). Conclusion: This study says that discharge of the dose in the patient's body is low in GFR higher and Dosage 150mCi under lower. There was no case of contamination of the treatment ward, depending on the dose and renal association. This suggests that patients' lifestyles or be affected by a variety of other factors.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.1
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• pp.44-49
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• 2017

Purpose Radiation exposure management has been strictly regulated for the radiation workers, but there are only a few studies on potential risk of radiation exposure to non-radiation workers, especially nurses in a general ward. The present study aimed to estimate the exact total exposure of the nurse in a general ward by close contact with the patient undergoing nuclear medicine examinations. Materials and Methods Radiation exposure rate was determined by using thermoluminescent dosimeter (TLD) and optical simulated luminescence (OSL) in 14 nurses in a general ward from October 2015 to June 2016. External radiation rate was measured immediately after injection and examination at skin surface, and 50 cm and 1 m distance from 50 patients (PET/CT 20 pts; Bone scan 20 pts; Myocardial SPECT 10 pts). After measurement, effective half-life, and total radiation exposure expected in nurses were calculated. Then, expected total exposure was compared with total exposures actually measured in nurses by TLD and OSL. Results Mean and maximum amount of radiation exposure of 14 nurses in a general ward were 0.01 and 0.02 mSv, respectively in each measuring period. External radiation rate after injection at skin surface, 0.5 m and 1 m distance from patients was as following; $376.0{\pm}25.2$, $88.1{\pm}8.2$ and $29.0{\pm}5.8{\mu}Sv/hr$, respectively in PET/CT; $206.7{\pm}56.6$, $23.1{\pm}4.4$ and $10.1{\pm}1.4{\mu}Sv/hr$, respectively in bone scan; $22.5{\pm}2.6$, $2.4{\pm}0.7$ and $0.9{\pm}0.2{\mu}Sv/hr$, respectively in myocardial SPECT. After examination, external radiation rate at skin surface, 0.5 m and 1 m distance from patients was decreased as following; $165.3{\pm}22.1$, $38.7{\pm}5.9$ and $12.4{\pm}2.5{\mu}Sv/hr$, respectively in PET/CT; $32.1{\pm}8.7$, $6.2{\pm}1.1$, $2.8{\pm}0.6$, respectively in bone scan; $14.0{\pm}1.2$, $2.1{\pm}0.3$, $0.8{\pm}0.2{\mu}Sv/hr$, respectively in myocardial SPECT. Based upon the results, an effective half-life was calculated, and at 30 minutes after examination the time to reach normal dose limit in 'Nuclear Safety Act' was calculated conservatively without considering a half-life. In oder of distance (at skin surface, 0.5 m and 1 m distance from patients), it was 7.9, 34.1 and 106.8 hr, respectively in PET/CT; 40.4, 199.5 and 451.1 hr, respectively in bone scan, 62.5, 519.3 and 1313.6 hr, respectively in myocardial SPECT. Conclusion Radiation exposure rate may differ slightly depending on the work process and the environment in a general ward. Exposure rate was measured at step in the general examination procedure and it made our results more reliable. Our results clearly showed that total amount of radiation exposure caused by residual radioactive isotope in the patient body was neglectable, even comparing with the natural radiation exposure. In conclusion, nurses in a general ward were much less exposed than the normal dose limit, and the effects of exposure by contacting patients undergoing nuclear medicine examination was ignorable.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.77-97
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• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• Asia Marketing Journal
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• v.12 no.4
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• pp.137-164
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• 2011

In the past, retailers secured customer loyalty by offering convenient locations, unique assortments of goods, better services than competitors, and good credit policy. All this has changed. Goods assortments among stores have become more alike as national-brand manufacturers place their goods in more and more retail stores. Service differentiation also has eroded. Many department stores have trimmed services, and many discount stores have increased theirs. Customers have become smarter shoppers. They don't pay more for identical brands, especially when service differences have diminished. In the face of increased competition from discount storess and specialty stores, department stores are waging a comeback war. Growth of intertype competition, competition between store-based and non-store-based retailing and growing investment in technology are changing the way consumers shop and retailers sell. Different types of stores-discount stores, catalog showrooms, department stores-all compete for the same consumers by carrying the same type of merchandise. The biggest winners are retailers that have helped shoppers to be economically cautious, simplified their increasingly busy and complicated lives, and provided an emotional connection. The growth of e-retailers has forced traditional brick-and-mortar retailers to respond. Basically brick-and-mortar retailers utilize their natural advantages, such as products that shoppers can actually see, touch, and test, real-life customer service, and no delivery lag time for small-sized purchases. They also provide a shopping experience as a strong differentiator. They are adopting practices as calling each shopper a "guest". The store atmosphere should match the basic motivations of the shopper. If target consumers are more likely to be in a task-oriented and functional mindset, then a simpler, more restrained in-store environment may be better. Consistent with this reasoning, some retailers of experiential products are creating in-store entertainment to attract customers who want fun and excitement. The retail experience must deliver value to turn a one-time visitor into a loyal customer. Retailers need a tool that measures the full range of components that define experience-based value. This study uses an experiential value scale(EVS) developed by Mathwick, Malhotra and Rigdon(2001) which reflects the benefits derived from perceptions of playfulness, aesthetics, customer "return on investment" and service excellence. EVS is useful to predict differences in shopping preferences and patronage behavior of customers. EVS consists of items measuring efficiency, economic value, visual appeal, entertainment value, service excellence, escapism, and intrinsic enjoyment, which are subscales of experiencial value. Efficiency, economic value, service excellence are linked to the utilitarian shopping value. And visual appeal, entertainment value, escapism and intrinsic enjoyment are linked to hedonic shopping value. It has been found that consumers value hedonic experiences activated from escapism and attractiveness of shopping environment as much as the product quality, price, and the convenient location. As a result, many department stores, discount stores, and other retailers are introducing differential marketing strategy based on emotional/hedonic values. Many researches suggest that consumers go shopping not only for buying products but also for various shopping experiences. In other words, they seek the practical, rational value as well as social, recreational values in the shopping process(Babin et al, 1994; Bloch et al, 1994). Retailers may enhance buyer's loyalty to store by providing excellent emotional/hedonic value such as the excitement from shopping, not just the practical value of buying good products efficiently. We investigate the effect of perceived shopping values on the emotional experience and store loyalty based on the EVS(Experiential Value Scales) developed by Holbrook(1994), Mathwick, Malhotra and Rigdon(2001). This study assumes that the relative effect of shopping value dimensions on the responses of shoppers will differ according to types of stores and analyzes the moderating effect of store type(department store VS. discount store) on the causal relationship between shopping value dimensions and store loyalty. Emprical results show that utilitarian values of shopping experience and hedonic value of shipping experience give the positive effect on the emotional response of consumers and store loyalty. We also found the moderating effect of store types. The effect of utilitarian shopping values on the attitude toward discount store is higher than the effect of utilitarian shopping values on the attitude toword department store. And the effect of hedonic shopping value on the emotional response to discount store is higher than on the emotional response to department store. The empirical results reflect on the recent trend that discount stores try to fulfill the hedonic needs of consumers as well as utilitarian needs(i.e, low price) that discount stores traditionally have focused on

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.157-166
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• 2015

Purpose : Cyber-Knife tumor tracking system, based on the correlation relationship between the position of a tumor which moves in response to the real time respiratory cycle signal and respiration was obtained by the LED marker attached to the outside of the patient, the location of the tumor to predict in advance, the movement of the tumor in synchronization with the therapeutic device to track real-time tumor, is a system for treating. The purpose of this study, in the cyber knife tumor tracking radiation therapy, trying to evaluate the accuracy of tumor tracking radiation therapy system due to the change in the form of unpredictable sudden breathing due to cough and sleep. Materials and Methods : Breathing Log files that were used in the study, based on the Respiratory gating radiotherapy and Cyber-knife tracking radiosurgery breathing Log files of patients who received herein, measured using the Log files in the form of a Sinusoidal pattern and Sudden change pattern. it has been reconstituted as possible. Enter the reconstructed respiratory Log file cyber knife dynamic chest Phantom, so that it is possible to implement a motion due to respiration, add manufacturing the driving apparatus of the existing dynamic chest Phantom, Phantom the form of respiration we have developed a program that can be applied to. Movement of the phantom inside the target (Ball cube target) was driven by the displacement of three sizes of according to the size of the respiratory vertical (Superior-Inferior) direction to the 5 mm, 10 mm, 20 mm. Insert crosses two EBT3 films in phantom inside the target in response to changes in the target movement, the End-to-End (E2E) test provided in Cyber-Knife manufacturer depending on the form of the breathing five times each. It was determined by carrying. Accuracy of tumor tracking system is indicated by the target error by analyzing the inserted film, additional E2E test is analyzed by measuring the correlation error while being advanced. Results : If the target error is a sine curve breathing form, the size of the target of the movement is in response to the 5 mm, 10 mm, 20 mm, respectively, of the average $1.14{\pm}0.13mm$, $1.05{\pm}0.20mm$, with $2.37{\pm}0.17mm$, suddenly for it is variations in breathing, respective average $1.87{\pm}0.19mm$, $2.15{\pm}0.21mm$, and analyzed with $2.44{\pm}0.26mm$. If the correlation error can be defined by the length of the displacement vector in the target track is a sinusoidal breathing mode, the size of the target of the movement in response to 5 mm, 10 mm, 20 mm, respective average $0.84{\pm}0.01mm$, $0.70{\pm}0.13mm$, with $1.63{\pm}0.10mm$, if it is a variant of sudden breathing respective average $0.97{\pm}0.06mm$, $1.44{\pm}0.11mm$, and analyzed with $1.98{\pm}0.10mm$. The larger the correlation error values in both the both the respiratory form, the target error value is large. If the motion size of the target of the sine curve breathing form is greater than or equal to 20 mm, was measured at 1.5 mm or more is a recommendation value of both cyber knife manufacturer of both error value. Conclusion : There is a tendency that the correlation error value between about target error value magnitude of the target motion is large is increased, the error value becomes large in variation of rapid respiration than breathing the form of a sine curve. The more the shape of the breathing large movements regular shape of sine curves target accuracy of the tumor tracking system can be judged to be reduced. Using the algorithm of Cyber-Knife tumor tracking system, when there is a change in the sudden unpredictable respiratory due patient coughing during treatment enforcement is to stop the treatment, it is assumed to carry out the internal target validation process again, it is necessary to readjust the form of respiration. Patients under treatment is determined to be able to improve the treatment of accuracy to induce the observed form of regular breathing and put like to see the goggles monitor capable of the respiratory form of the person.