• Journal of the Korean earth science society
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• v.42 no.3
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• pp.247-263
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• 2021

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.2028-2042
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• 1994

Properly defining signal and noise in a self-organizing system like ART(Adaptive Resonance Theory) neural network model raises a number of subtle issues. Pattern context must enter the definition so that input features, treated as irrelevant noise when they are embedded in a given input pattern, may be treated as informative signals when they are embedded in a different input pattern. The ATR automatically self-scales their computational units to embody context and learning dependent definitions of a signal and noise and there is no problem in categorizing input pattern that have features similar in nature. However, when we have imput patterns that have features that are different in size and nature, the use of only one vigilance parameter is not enough to differentiate a signal from noise for a good categorization. For example, if the value fo vigilance parameter is large, then noise may be processed as an informative signal and unnecessary categories are generated: and if the value of vigilance parameter is small, an informative signal may be ignored and treated as noise. Hence it is no easy to achieve a good pattern categorization. To overcome such problems, a Coupled-ART neural network capable of modularized categorization of patterns is proposed. The Coupled-ART has two layer of tightly coupled modules. the upper and the lower. The lower layer processes the global features of a pattern and the structural features, separately in parallel. The upper layer combines the categorized outputs from the lower layer and categorizes the combined output, Hence, due to the modularized categorization of patterns, the Coupled-ART classifies patterns more efficiently than the ART1 model.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• 대한공업교육학회지
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• v.43 no.2
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• pp.79-96
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• 2018

In this study, we look at the effect that the achievement goal of specialized high school students has on academic self-efficacy and the difference in academic self-efficacy depending on achievement goal orientation. The purpose of this research is to help students to efficiently increase their academic self-efficacy, develop research and study life guidance measures to improve negative factors, and select professors and learning methods. To achieve the purpose, survey was conducted with achievement goal orientation measurement tools(26 questions) and academic self-efficacy measurement tools(28 questions) for 745 students of 18 specialized technical high school students in 5 districts. The results of this study are as follows. First, preference to task difficulty and self-controlling efficacy have highly positive correlations with mastery goal orientation and confidence and mastery avoidance goal orientation have highly negative correlations each other. Second, achievement goal orientation form of specialized high school students were divided into 5 forms; 'execution avoidance(34.8%)', 'mastery orientation(20.8%)', 'approach(17%)', 'avoidance competition(14.9%)', and'mastery avoidance(12.5%)'. In preference to task difficulty, 'approach'group showed the highest average point and 'mastery avoidance'showed the lowest average point. The average point of 'approach' group was higher than other groups in confidence, but 'mastery orientation' group showed the highest average point. Through the results of this study, academic self-efficacy makes an effect by a certain direction in accordance with achievement goal orientation and it's necessary to access academic problems differently according to student's goal directivity. Therefore, it's necessary to provide educational method by student type based on explanation about academic self-efficacy of achievement goal orientation of specialized high school students and analysis on achievement goal orientation form.

• Journal of The Korean Association For Science Education
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• v.24 no.5
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• pp.851-868
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• 2004

The main purposes of this study is to develop a web-based STS biology instruction program (WB-STS) for the scientifically gifted students. The specific main research questions were as follows; 1. How can the WB-STS for biology education be developed and what are the primary components involved in it? 2. Is there any proper validity for developed the WB-STS in biology education? To solve the above mentioned problems, several procedures were applied. First, in order to develop WB-STS for the scientifically gifted students, NCISE, Renzulli' s Enrichment Triad Model and the Iowa Chautauqua program's main characteristics were analyzed systematically and the principles and general process for constructing WB-STS were examined. Additionally, the needs of students and the goals of Biology education were identified thoroughly. And then all these ideas were embodied in an agenda for constructing WB-STS. Second, to analyse the validity and utility of developing WB-STS, a questionnaire was developed and submitted to seven specialists and a group of twenty students who would participate in the experiment later. The main results of study are summarized below: First, WB-STS appeared to be successfully constructed based on Renzulli' s Enrichment Triad Model and the Iowa Chautauqua program. Its main features are that it was made emphasizing a learner-centered approach and constructive learning. It is composed of five steps: Scientific theme selection -${\rightarrow}$Exploration ${\rightarrow}$ Concept & Principle Check ${\rightarrow}$ Finding Solution ${\rightarrow}$ Action. Second, seven specialists and a group of students assessed the developed WB-STS's validity and utility with a questionnaire, the results appeared satisfactory. Students showed high interest in WB-STS and gave a positive evaluation of WB-STS.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Journal of Korean Academy of Nursing
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• v.22 no.1
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• pp.81-115
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• 1992

The problem addressed by this study was to determine the effect of nurse - patient agreement on the means to achieve nursing goals in the early postpartum period of primiparous mothers. It was hypothesized that the experimental treatment would result in hegher self-confidence and satisfaction in maternal role performance. This purpose was to contribute to the planning of nursing care to enhance self- confidence and satisfaction in maternal role performance and to the development of relevant nursing theory. Especially, the early postpartum period is crucial toward in recovery from childbirth and attainment of the maternal role. Maternal role attaintment is a complex social and cognitive process of stimulus -response accomplished by learning. Most women attain the maternal role sucessfully. But, some primiparous mothers experience difficultites in attainment of the maternal role due to lack of experience and knowledge. Self-confidence and satisfaction in maternal role performance are important factors in attainment and adjustment to the maternal role (Mercer, 1981a, 1981b ; Lederman, Weigarten, and Lederman, 1981 :Bobak and Jensen, 1985). Nursing is defined as behaviors of nurses add patients that attain nursing goals through action, reaction, interaction, and transaction. For attainment of nursing goals, active participating transactions must occur by agreement on the means to achieve those goals through nurse -patient mutual goal setting and establishment of their active relationships(King, 1981, Ha, 1977). Based on King's theory of goal attainment (1981), this stuy was planned as a non-equivalent control group, non -synchronized quasi -experimental design using agreement on the means to achieve nursing goals in early postpartum as the experimental treatment. The data were collected from July 20 to Sep. 1, 1991 by questionnaires with 60 primiparous mothers planing to breast feed after normal deliveries at W hospital in Pusan, Korea. The subjects were divided into a control group(conventional group) -those admitted from July 20 to Aug. 12, and an experimental group(agreement group) - those admitted from Aug. 13 to Sep. 1. The instument for agreement on the means to nursing goals in the early postpartum period included five steps - identification of disturbances of problems through action, reaction, and interaction with primiparous mothers : mutual early postpartal nursing goal setting : exploration of the means to achieve goals ; agreement on the means (self- care, ealry maternal -infant contact, performance of mothering behavior, and communicating about the infant's behavior and health condition) : implementation of the means. This instrument was developed on the basis of King's elements that lead to transactions in nurse-patient interactions. Lederman et al's (1981) scale for Confidence in ability to cope with tasks of motherhood and Lederman et al's(1981) scale for Mother's satisfaction with motherhood and infant care were used to measure self-confidence and satisfaction in maternal role performance ·with the subjects immediately after admission and on the day of discharge. Self-care performance in the experimental group was measured by self -evaluation tool developed by the investigator from the literature concerned. The tools to measure Pelf-confidence and satisfaction in maternal role performance, and the tool to measure self-evaluation of self-care performance were tested for internal reliability. Cronbach's Alphas were 0.94, 0.94, and 0.63. The data were analysed by using in S.P.S.S. computerized program and included percentage, x²-test, t-test, ANOVA, and Pearson Correlation Coefficient. The conclusions obtained from this study are summerized as follows : 1. The degree of self-confidence in maternal role performance of the total subjects group measured before the experimental treatment was above average with a mean score of 2.77(range 2.14-3.64). Out of 14 items, those with relatively high mean scores were ‘I would like to be a better mother than I am’(3.95), and ‘I have my doubts about whether I am a good mother’(2.87). Those with low mean scores were ‘I know that my baby wants most of the times’(2.28), ‘When the baby cries, I can tell what she /he wants’(2.37), and ‘I have confidence in my ability to care for the baby’(2;50). That is, the self - confidence of Primiparous mothers was considerably high in mothering, but rather low in activities concerning the infant care and understanding of the infant behavior. The degree of satisfaction in maternal role performance of the total subjects group measured before the experimental treatment was high with a mean score of 3.18(range 1.92-3.92). Out of 13 items, those with relatively high mean scores were ‘I am glad 1 had this baby now’(3.75), ‘I play with the baby between feedings when s/he is awake and quiet’(3.67), and ‘I enjoy being a mother’(3.27). Those with low mean scores were ‘I am upset about having too many responsibilities as a mother’(2.78), ‘It bothers me to get up for the baby at night’(2.82), and ‘I get annoyed if the baby frequently interrupts my activities’.(2.82), That is, the satisfaction of primiparous mothers was considerably high in mothering and infant care, but rather low in restraints in time or on the mother's self accomplishment and development. 2. Agreement on the means to achieve nursing goals in the early postpartum period included process of mutual goal setting, exploration of the means to achieve goals, and ahreement in concert means to achieve goals based on the mothers' condition, concerns, self-perception of the nurse - patient interactions. In the process of agreement, there was agreement that the means to achieve goals should be through trust and establishment of active relationships with the nurse through identification of problems according to planned nursing goals and active interaction, such as explanations, teaching, changing of opinions, acceptance or rejection of explanations, and proposing of questions. Therefore agreement on the means to achieve nursing goals in the early postpartum period appears to be an effective nursing intervention for primiparous mothers. 3. The degree of self- confidence in maternal role performance of the exprimental group was higher than that of the control group(t＝3.95, p＜0.01). Out of 14 items, those with higher score in the experimental group were ‘I would like to be a better mother than I am’(t＝1.93, p＜0.05), ‘I know that my baby wants most of the times’(t＝2.75, p＜0.01), ‘When the baby cries, 1 can tell what she/he wants’(t＝2.10, p＜0.05), ‘I have confidence in my ability to care for the baby’(t＝3.72, p＜0.01), ‘I trust my own judement in deciding how to care for the baby’(t＝1.96, p＜0.05), ‘I feel that I know my baby and what to do for him /her’(t＝2.44, p＜0.01), ‘I am concerned about being able to meet the baby's needs’(t＝2.87, p＜0.01), ‘I know what my baby likes and dislikes’(t＝3.26, p＜0.01), ‘I don't know to care for the baby as well as I should’(t＝2.07, p＜0.05), and ‘I am unsure about whether I give enough attention to the baby’(t＝3.04, p＜0.01), That is, the degree of self-confidence in mothering, activities concerning infant care, and understanding of infant behavior of the experimental group was higher than that of the control group. Therefore, the first hypothesis, that the degree of self-confidence in maternal role performance of the experimental group would be higher than that of the control group, was supported(t＝3.95, p＜0.01). 4. The degree of satisfaction in the maternal role performance of the exprimental group was higer than that or the control group(t＝2.31, p＜0.05). Out of 13 items, those with higher score in the experimental group were ‘I am glad I had this baby now’(t＝2.29, p＜0.05), ‘I enjoy taking care of the baby’(t＝2.4g, p＜0.01), ‘It is boring for me to care for the baby and do the same thing over and over’(t＝2.87, P＜0.01), ‘I am unhappy with the amount of time I have for activities other than childcare’(t＝2.51, p＜0.01), and ‘When bathing and diapering the baby, I would like to be doing something else’(t＝2.43, p＜0.01). That is, the degree of satisfaction in mothering, infant care, and restraints in time of on the mother's self accomplishment and development in the experimental group was higher than that of the control group. Therefore, the second hypothesis, that the degree of satisfaction in maternal role performance of the experimental group would be higher than that of the control group, was supported(t＝2.31, p＜0.05). 5. The third hypothesis, that the higher the degree of satisfaction in materenal role performance, the higher the degree of self-confidence in materenal role performance in the experimental group, was supported (r＝0.57, p＜0.01)

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.10 no.2
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• pp.145-157
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• 1999

This study was performed by the children and adolescents who were abused or neglected physically, emotionally that were selected in child & adolescents psychiatric ward. We investigated the number of these case in admitted children & adolescents, and also observed characteristics of symptoms, developmental history, characteristics of abuse style, characteristics of abusers, family dynamics and psychopathology. We hypothesized that all kinds of abuse will influnced to emotional, behavioral problems, developmental courses on victims, interactive effects on family dynamics and psychopathology. That subjects were 22 persons of victims who be determined by clinical observation and clinical note. The results of the study were as follows：1) Demographic characteristics of victims：ratio of sex was 1：6.3(male：female), mean age was $11.1{\pm}2.5$. According to birth order, lst was 12(54.5%), 2nd was 5(23%), 3rd was 2(9%) and only child was 3(13.5%). 2) Characteristics of family：According to socioeconomic status, middle to high class was 3(13.5%), middle one was 9(41.% ), middle to low one was 9(41%), low one was 1(0.5%). according to number of family, under the 3 person was 3(13.5%), 4-5 was 17(77.5%), 6-7 was 2(9%). according to marital status of parents, divorce or seperation were 5(23%), remarriage 2(9%), severe marital discord was 19(86.5%). In father, antisocial behavior was 7(32%), alcohol dependence was 10(45.5%). In mother, alcohol abuse was 5(23%), depression was 17(77.3%), history of psychiatric management was 6(27%). 3) Characteristics of abuse：Physical abuse was 18(81.8%), physical and emotional abuse and neglect were 4(18.2%). according to onset of abuse, before 3 years was 15(54.5%), 3-6 years was 5(27.5%), schooler was 1(15%). Only father offender was 2(19%), only mother offender was 8(35.4%), both offender was 8(35.4%), accompaning with spouse abuse was 7(27%), and accompaning with other sibling abuse was 4(18.2%). 4) General characteristics and developmental history of victims：Unwanted baby was 12(54.5%), developmental delay before abuse was9(41%), comorbid developmental disorder was 15(68%). there were 6(27.5%) who didn‘t show definite sign of developmental delay before abuse. 5) Main diagnosis and comorbid diagnosis：According to main diagnosis, conduct disorder 6(27.3%), borderline child 5(23%), depression4(18%), attention deficit hyperactivity disorder(ADHD) 4(18%), pervasive developmental disorder not otherwise specified 2(9%), selective mutism 1(5%). According to comorbid diagnosis, ADHD, borderline intelligence, mental retardation, learning disorder, developmental language disorder, oppositional defiant disorder, chronic tic disorder, functional enuresis and encoporesis, anxiety disorder, dissociative disorder, personality disorder due to medical condition. 5) Course of treatment：A mean duration of admission was $2.4{\pm}1.5$ months. 11(15%) showed improvement of symtoms, however 11(50%) was not changed of symtoms.