• Computers and Concrete
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• v.22 no.2
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• pp.239-248
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• 2018

TBM penetration rate is a function of intact rock properties, rock mass conditions and TBM operational parameters. Machine rate of penetrationcan be predicted by knowledge of the ground conditions and its effects on machine performance. The variation of TBM operational parameters such as penetration rate and thrust plays an important role in its performance. This study presents the results of the analysis on the TBM penetration rates in schistose rock types present along the alignment of Golab tunnel based on the analysis of a TBM performance database established for every stroke through different schistose rock types. The results of the analysis are compared to the results of some empirical and theoretical predictive models such as NTH and QTBM. Additional analysis was performed to find the optimum thrust and revolution per minute values for different schistose rock types.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.216-220
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• 2007

Camera calibration in machine vision is the process of determining the intrinsic camera parameters and the three-dimensional (3D) position and orientation of the camera frame relative to a certain world coordinate system. On the other hand, Takagi-Sugeno-Kang (TSK) fuzzy system is a very popular fuzzy system and approximates any nonlinear function to arbitrary accuracy with only a small number of fuzzy rules. It demonstrates not only nonlinear behavior but also transparent structure. In this paper, we present a novel and simple technique for camera calibration for machine vision using TSK fuzzy model. The proposed method divides the world into some regions according to camera view and uses the clustered 3D geometric knowledge. TSK fuzzy system is employed to estimate the camera parameters by combining partial information into complete 3D information. The experiments are performed to verify the proposed camera calibration.

• Journal of the Semiconductor & Display Technology
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• v.8 no.3
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• pp.39-44
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• 2009

Local Binary Pattern (LBP) is becoming a popular tool for various machine vision applications such as face recognition, classification and background subtraction. In this paper, we propose a new extension of LBP, called the Diagonal LBP (DLBP), to handle the image-based gender classification problem arise in interactive display systems. Instead of comparing neighbor pixels with the center pixel, DLBP generates codes by comparing a neighbor pixel with the diagonal pixel (the neighbor pixel in the opposite side). It can reduce by half the code length of LBP and consequently, can improve the computation complexity. The Support Vector Machine is utilized as the gender classifier, and the texture profile based on DLBP is adopted as the feature vector. Experimental results revealed that our approach based on the diagonal LPB is very efficient and can be utilized in various real-time pattern classification applications.

• Journal of Korean Medical classics
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• v.36 no.2
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• pp.23-34
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• 2023

Objectives : We propose a method to disassemble Traditional East Asian Medicine herbal formulas using machine learning. Methods : After creating a model using Byte Pair Encoding(BPE) and G-Score, the model was trained with training data. Afterwards, the learned model was applied to the test data, of which the results were compared with expert opinion. Results : The results acquired through the model were not significantly different from those of modern expert opinions. However, there were cases where the meaning was partially unclear, while there were cases where new knowledge could be obtained through the disassembling process. Conclusions : It is expected that disassembling herbal formulas through the proposed method in this study will help save resources required to understand complex ones.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.3
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• pp.76-83
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• 2000

인공지능(artificial intelligence)은 컴퓨터를 보다 지능적으로 만들기 위한 추론과 학습 방법에 관해 연구하는 컴퓨터 과학의 한 분야다. 특히 기계학습(machine learning)은 지식을 자동으로 획득하기 위한 원리와 기법을 개발하는 인공지능의 한 분야로서 생물정보학의 많은 중요한 문제 해결을 위한 매우 유용한 도구가 되고 있다.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.492-497
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• 1999

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skillful engineers. Grinding operations include a large number of functional parameters, since there are several ways of coping with grinding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workshop, the other is the quantitative method which utilizes the experimental data obtained by sensor. But, they are all difficult to accomplish from the grinding trouble-shooting system. The reason is that grinding troubles are not easily controlled in the quantitative method, and therefore, trouble-shooting has mainly relied on the knowledge of skilful engineers. Thus, there is an important issue of how a grinding trouble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper, basic strategy to develop the grinding database of rule-based rule, which is strongly depended upon experience and intuition, is described.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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• pp.230-238
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• 2003

The configuration design of a mechanical product can be efficiently performed when it is based on the functional modeling. There are methodologies, which decompose function from the abstract level to the concrete level and match the functions to physical parts. But it is difficult to carry out an innovative design when the function is matched only to a pre-detined part. This paper describes the configuration design process of a mechanical product with a design expert system, which uses function taxonomy and TRIZ theory. The expert system can propose a functional modeling of a new part. which is not in the existing parts list. The abstraction levels of design knowledge are introduced, which describe the operation of mechanical product in the levels of abstraction. This is the theoretical background of using knowledge of function and TRIZ for configuration design. The expert system is adequate to control this design knowledge. which expresses knowledge of functional modeling, mapping rules between functions and parts, selection of parts, and TRIZ theory. The hierarchy of functions and machine parts are properly expressed by classes and objects in the expert system. A design expert system has been implemented for the configuration design of a train bogie, and a new brake system of the bogie is introduced with the aid of TRIZ's 30 function groups.

• The Journal of Advanced Prosthodontics
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• v.10 no.6
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• pp.395-400
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• 2018

PURPOSE. This study tried to find the most significant factors predicting implant prognosis using machine learning methods. MATERIALS AND METHODS. The data used in this study was based on a systematic search of chart files at Seoul National University Bundang Hospital for one year. In this period, oral and maxillofacial surgeons inserted 667 implants in 198 patients after consultation with a prosthodontist. The traditional statistical methods were inappropriate in this study, which analyzed the data of a small sample size to find a factor affecting the prognosis. The machine learning methods were used in this study, since these methods have analyzing power for a small sample size and are able to find a new factor that has been unknown to have an effect on the result. A decision tree model and a support vector machine were used for the analysis. RESULTS. The results identified mesio-distal position of the inserted implant as the most significant factor determining its prognosis. Both of the machine learning methods, the decision tree model and support vector machine, yielded the similar results. CONCLUSION. Dental clinicians should be careful in locating implants in the patient's mouths, especially mesio-distally, to minimize the negative complications against implant survival.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.47-50
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• 1991

This paper discusses a knowledge-based system being developed by KAIST and KEPCO to assist planning the annual maintenance schedule of power units. To meet users' requirements, we have designed the system with several features: man-machine interaction, catalog system, user-friendliness, the hybrid-system of math-model and knowledge-base. In this paper, we introduce the outline of our system.