• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.119-125
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• 2015

A concentration meter is widely used at purification plants, sewage treatment plants and waste water treatment plants to sort and transfer high concentration sludge and to control the amount of chemical dosage. When the strange substance is contained in the sludge, however, the attenuation of ultrasonic wave could be increased or not be transmitted to the receiver. At that case, the value of concentration meter is higher than the actual density value or vibrated up and down. It has also been difficult to automate the residuals treatment process according to the problems as sludge attachment or damage of a sensor. Multi-beam ultrasonic concentration meter has been developed to solve these problems, but the failure of the ultrasonic beam of a specific concentration measurement value degrade the performance of the entire system. This paper proposes the method to improve the accuracy of sludge concentration rate by choosing reliable sensor values and learning them by proposed algorithm. The prediction algorithm is chosen as neuro-fuzzy model, which is tested by the various experiments.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.41-49
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• 2021

All application programs, including malware, call the Application Programming Interface (API) upon execution. Recently, using those characteristics, attempts to detect and classify malware based on API Call information have been actively studied. However, datasets containing API Call information require a large amount of computational cost and processing time. In addition, information that does not significantly affect the classification of malware may affect the classification accuracy of the learning model. Therefore, in this paper, we propose a method of extracting a essential feature set after reducing the dimensionality of API Call information by applying various feature selection methods. We used CICAndMal2020, a recently announced Android malware dataset, for the experiment. After extracting the essential feature set through various feature selection methods, Android malware classification was conducted using CNN (Convolutional Neural Network) and the results were analyzed. The results showed that the selected feature set or weight priority varies according to the feature selection methods. And, in the case of binary classification, malware was classified with 97% accuracy even if the feature set was reduced to 15% of the total size. In the case of multiclass classification, an average accuracy of 83% was achieved while reducing the feature set to 8% of the total size.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.19-30
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• 2018

Visual information understanding is known as one of the most difficult and challenging problems in the realization of machine intelligence. This paper proposes deriving visual verb and construction of ActionNet database as a video database for video semantic understanding. Even though development AI (artificial intelligence) algorithms have contributed to the large part of modern advances in AI technologies, huge amount of database for algorithm development and test plays a great role as well. As the performance of object recognition algorithms in still images are surpassing human's ability, research interests shifting to semantic understanding of video contents. This paper proposes candidates of visual verb requiring in the construction of ActionNet as a learning and test database for video understanding. In order to this, we first investigate verb taxonomy in linguistics, and then propose candidates of visual verb from video description database and frequency of verbs. Based on the derived visual verb candidates, we have defined and constructed ActionNet schema and database. According to expanding usability of ActionNet database on open environment, we expect to contribute in the development of video understanding technologies.

• (The)Korea Educational Review
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• v.24 no.1
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• pp.199-225
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• 2018

Using the 3rd to the 6th wave data from the Korean Children and Youth Panel Survey(KCYPS), we examined the effects of academic characteristics and social relations on the trajectories of the life satisfaction among adolescents. OLS results showed that male students were more satisfied with their lives than female students in the 3rd grade of middle school. As academic characteristics, academic achievement and the level of class adjustment improved life satisfaction but the amount of learning time spent had a negative effect on life satisfaction. However, the effect of academic achievement lost its statistical significance after including variables of social relations. Relationships with parents, teachers, and friends had positive effects on life satisfaction. The longitudinal analysis using the fixed effect estimation also showed a similar result of the associations among the variables to that in the OLS analysis. The life satisfaction gap between male and female students narrowed over time from the 3rd grade of middle school to the 2nd grade of high school. The effects of relationships with parents and friends showed significant effects on both female and male students, but the relationship with their teachers was significant only for female students. Based on the results, we discussed the issues of Korean education related to the life satisfaction among adolescents.

• The Journal of Korean Academic Society of Nursing Education
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• v.4 no.2
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• pp.236-248
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• 1998

This survey study was attempted for two purposes : 1) to grasp Korea National Open University(KNOU) students' changing aspects for their education need through comparison analyses with 1996 data ; 2) to establish foundation of the systematic network formation by investgating students' opinion about network framework. Among randomly assigned 4,500 students, 1,505 KNOU nursing students who allowed to participate in the study completed the questionnaires. The data were collected by mail. For the comparison 1996 data were also used. The data were analyzed using descriptive statistics, chi-square test, and t-test. Results of this study were as follows 1. The admission purposes of the KNOU nursing students were 'in order to get a bachelor's degree (70.7%)', 'to do studying and working simultaneously(43.0%)', or 'to be admitted for the graduate school (41%)' etc. Comparing the admission purposes by age, the investigator found 4 items which are 'small amount of tuition', 'graduate school admission', 'aspiration for the university', 'promotion or commencement of work' showed statistically significant differences. These 4 items were also found to show significant differences by marital status. 2. In relation to the learning media, the study showed most students(74%) got effective informations from the school newspaper(36.5%) or peer group(37.7%). The result showed that few students (0.7%) used the computer for communication. The research indicated that KNOU nursing students have tendency to rely on printed materials more than on broadcasting media. This is almost the same result as that of 1996. 3. The results revealed that 12.4% of the respondents had ever experienced unregistration or temporary withdrawal. The most common reason for the unregistration was 'due to family affairs or their job (71.3%)'. There were no change for this aspects with 1996. 4. As for the professors-students network formation. The result revealed that 38.5% students among respondents had heard of the network formation. 78.7% of respondents, however, positively responed that they would willingly participate in the networking if it is made. Especially the students showed much interest in 'the improvement for the understanding of study' and 'strengthening of the relations between professors and students'.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.21-31
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• 2018

Cyber penetration attacks can not only damage cyber space but can attack entire infrastructure such as electricity, gas, water, and nuclear power, which can cause enormous damage to the lives of the people. Also, cyber space has already been defined as the fifth battlefield, and strategic responses are very important. Most of recent cyber attacks are caused by malicious code, and since the number is more than 1.6 million per day, automated analysis technology to cope with a large amount of malicious code is very important. However, it is difficult to deal with malicious code encryption, obfuscation and packing, and the dynamic analysis technique is not limited to the performance requirements of dynamic analysis but also to the virtual There is a limit in coping with environment avoiding technology. In this paper, we propose a machine learning based malicious code analysis technique which improve the weakness of the detection performance of existing analysis technology while maintaining the light and high-speed analysis performance applicable to commercial endpoints. The results of this study show that 99.13% accuracy, 99.26% precision and 99.09% recall analysis performance of 71,000 normal file and malicious code in commercial environment and analysis time in PC environment can be analyzed more than 5 per second, and it can be operated independently in the endpoint environment and it is considered that it works in complementary form in operation in conjunction with existing antivirus technology and static and dynamic analysis technology. It is also expected to be used as a core element of EDR technology and malware variant analysis.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.103-108
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• 2022

Various studies have been conducted on cloud observation using all-sky images acquired with a wide-angle camera system since the early 21st century, but it is judged that an automatic observation system that can completely replace the eye observation has not been obtained. In this study, to verify the quantification of cloud observation, which is the final step of the algorithm proposed to automate the observation, the cloud distribution of the all-sky image and the corrected image were compared and analyzed. The reason is that clouds are formed at a certain height depending on the type, but like the retina image, the center of the lens is enlarged and the edges are reduced, but the effect of human learning ability and spatial awareness on cloud observation is unknown. As a result of this study, the average cloud observation error of the all-sky image and the corrected image was 1.23%. Therefore, when compared with the eye observation in the decile, the error due to correction is 1.23% of the observed amount, which is very less than the allowable error of the eye observation, and it does not include human error, so it is possible to collect accurately quantified data. Since the change in cloudiness due to the correction is insignificant, it was confirmed that accurate observations can be obtained even by omitting the unnecessary correction step and observing the cloudiness in the pre-correction image.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.53-66
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• 2023

Recently, there has been a significant interest in the development of autonomous driving simulation environment based on digital twin. In the development of such digital twin-based simulation environment, many researches has been conducted not only performance and functionality validation of autonomous driving, but also generation of virtual training data for deep learning. However, such digital twin-based autonomous driving simulation system has the problem of requiring a significant amount of time and cost for the system development and the data construction. Therefore, in this research, we aim to propose a method for rapidly designing and implementing a digital twin-based autonomous driving simulation system, using only the existing 3D models and high-definition map. Specifically, we propose a method for integrating 3D model of FBX and NGII HD Map for the Busan EDC area into CARLA, and a method for adding and modifying CARLA functions. The results of this research show that it is possible to rapidly design and implement the simulation system at a low cost by using the existing 3D models and NGII HD map. Also, the results show that our system can support various functions such as simulation scenario configuration, user-defined driving, and real-time simulation of traffic light states. We expect that usability of the system will be significantly improved when it is applied to broader geographical area in the future.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.455-467
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• 2017

When performing target detection using hyperspectral imagery, a feature extraction process is necessary to solve the problem of redundancy of adjacent spectral bands and the problem of a large amount of calculation due to high dimensional data. This study proposes a new band selection method using the $L_{2,1}$-norm regression model to apply the feature selection technique in the machine learning field to the hyperspectral band selection. In order to analyze the performance of the proposed band selection technique, we collected the hyperspectral imagery and these were used to analyze the performance of target detection with band selection. The Adaptive Cosine Estimator (ACE) detection performance is maintained or improved when the number of bands is reduced from 164 to about 30 to 40 bands in the 350 nm to 2500 nm wavelength band. Experimental results show that the proposed band selection technique extracts bands that are effective for detection in hyperspectral images and can reduce the size of the data without reducing the performance, which can help improve the processing speed of real-time target detection system in the future.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.2 no.1
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• pp.102-115
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• 1991

This study reviewed the stuttering literature and reported the clinical experiment in stuttering intervention. There is still no single answer as to the cause of stuttering or to the most effective therapy for stutterers despite the vast amount of research. One certain thing is that we have come closer to a better understanding of the stuttering and to more effective therapy. There have been three main statements about the origins of stuttering ; biologic origins ; psychodynamic origins ; environmental-learning origins. There also have been various methods of the treatment of stuttering. Broadly, two major treatment approaches are attentive ; stuttering modification therapy and fluency shaping therapy. In this experiment, the researcher attempted to investigate complex elements that each child might have and to use an integrative approach rather than to keep the specific one. Individual subjects were evaluated by a multidisciplinary team. Initially, the subjects received individual therapy. They then were placed in group therapy. The purpose of the group therapy was to raise their fluencies to the higher communicative situation and to maintain improved fluency over time. All three subjects improved their fluencies in reading and in conversation and showed the better(SSI)scores in total stuttering behaviors. It was also discussed that it is necessary to have sensitive assessment tools to investigate each element of stuttering ; and to develop a therapy program reflecting current advanced stuttering theories.