• Wind and Structures
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• v.36 no.6
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• pp.355-366
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• 2023

Although machine learning (ML) techniques have been widely used in various fields of engineering practice, their applications in the field of wind engineering are still at the initial stage. In order to evaluate the feasibility of machine learning algorithms for prediction of wind loads on high-rise buildings, this study took the exposure category type, wind direction and the height of local wind force as the input features and adopted four different machine learning algorithms including k-nearest neighbor (KNN), support vector machine (SVM), gradient boosting regression tree (GBRT) and extreme gradient (XG) boosting to predict wind force coefficients of CAARC standard tall building model. All the hyper-parameters of four ML algorithms are optimized by tree-structured Parzen estimator (TPE). The result shows that mean drag force coefficients and RMS lift force coefficients can be well predicted by the GBRT algorithm model while the RMS drag force coefficients can be forecasted preferably by the XG boosting algorithm model. The proposed machine learning based algorithms for wind loads prediction can be an alternative of traditional wind tunnel tests and computational fluid dynamic simulations.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1195-1204
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• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.86-94
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• 2012

In this paper we propose a hardware architecture of high-speed CGH (computer generated hologram) generation processor, which particularly reduces the number of memory access times to avoid the bottle-neck in the memory access operation. For this, we use three main schemes. The first is pixel-by-pixel calculation rather than light source-by-source calculation. The second is parallel calculation scheme extracted by modifying the previous recursive calculation scheme. The last one is a fully pipelined calculation scheme and exactly structured timing scheduling by adjusting the hardware. The proposed hardware is structured to calculate a row of a CGH in parallel and each hologram pixel in a row is calculated independently. It consists of input interface, initial parameter calculator, hologram pixel calculators, line buffer, and memory controller. The implemented hardware to calculate a row of a $1,920{\times}1,080$ CGH in parallel uses 168,960 LUTs, 153,944 registers, and 19,212 DSP blocks in an Altera FPGA environment. It can stably operate at 198MHz. Because of the three schemes, the time to access the external memory is reduced to about 1/20,000 of the previous ones at the same calculation speed.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.154-159
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• 2007

Wake distribution data of stem flow fields have been accumulated systematically by model tests. If the correlation between geometrical hull information and wake distribution is grasped through the accumulated data, this correlation can be helpful to designing similar ships. In this paper, Neuro-Fuzzy system that is emerging as a new knowledge over a wide range of fields nowadays is tried to estimate the wake distribution on the propeller plan. Neuro-Fuzzy system is well known as one of prospective and representative analysis method for prediction, classification, diagnosis of real complicated world problem, and it is widely applied even in the engineering fields. For this study three-dimensional stern hull forms and nominal wake values from a model test ate structured as processing elements of input and output layer, respectively. The proposed method is proved as an useful technique in ship design by comparing measured wake distribution with predicted wake distribution.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1732-1737
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• 2004

This paper proposes Internet-based checking technique for machine-tools with variant CNC (Computerized Numerical Controller). According to the architecture of CNC, CNC is classified into two types such as CAC (Closed Architecture Controller) which is conventional CNC, and OAC (Open Architecture Controller) which is a recently introduced PC-based controller. CAC has a closed architecture and it is dependent on CNC vender specification. Because of this, it has been very difficult for users to implement an application programs in CNC domain. Therefore, an additionally special module is required for Internet-based application such as remote checking. In this case, web I/O embedded module can be efficiently applied for Internet-based checking. The module is directly attached to TCP/IP network for communication. In order to obtain the monitoring data of CNC machines, the I/O signals of the module are assigned to PLC (Programmable Logic Controller) input and output (I/O) signals within CNC domain. On the other hand, OAC has a PC-based open architecture and an additional module is not necessary for the connection with external site. Because of this, a simple DAU is just used for signal sensing and data acquisition without additional communication modules. For Internet-based remote checking of machine-tools with OAC, a user-defined daemon and application programs are implemented as the form of internal function within the PC-based controller. Internet communication is performed between the daemon program in CNC domain and web script programs in external server. Checking points defined in this research are classified into two categories such as structured point and operational point. The formal includes the vibration of bearing, temperature of spindle unit and another periodical management. And the latter includes oil checking, clamp locking/unlocking and machining on/off status.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.1-7
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• 2002

This paper presents a new PLL clock generator that can improve a jitter noise characteristics and acquisition process by designing a multi-PFD(Phase Frequency Detector) and an adaptive charge pump circuit. The conventional PLL has not only a jitter noise caused from such a demerit of the wide dead zone and duty cycle, but also a long delay interval that makes a high speed operation unable. An advanced multi-structured PFD circuit using the TSPC(True Single Phase Clocking) circuit is proposed, in which it shows an excellent functionalities in terms of the jitter noises by designing its circuit with the exact dead zone and duty cycle. Our new designed adaptive charge pump in the loop filter of a PLL can improve an acquisition characteristic by adaptively increasing of current. The Hspice simulation is done to evaluate the performance of the proposed circuit. Simulation result shows that our PLL has under 0.01ns in the dead zone, no influence from the duty cycle of input signals and under 50ns in the acquisition time. This circuit will be able to be used in develops of high-performance microprocessors and digital systems.  

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.77-84
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• 2017

Generally, a time-varying vibration signal is generated in a rotating machine system, and when there is a failure in the rotating machine, the signal contains noise. In this paper, we propose a system consisting of an adaptive predictor and a binary tree filter bank for analyzing time - varying vibration signals with noise. And the vibration signal analyzed results in this system is used for fault diagnosis of the rotating machine. The adaptive predictor of the proposed system predicts the periodic signal components, and the filter bank system decomposes the difference signal between the input signal and the predicted periodic signal into subband. Since each subband signal includes a noise signal component due to a failure, it is possible to diagnose the failure of the using rotary machine. The validity of the proposed vibration signal analysis method is shown in the simulations, where the periodic components cancelled vibrating signals are decomposed to 32 subband, and the signal characteristics related faults are analyzed.

• Journal of Engineering Education Research
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• v.10 no.2
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• pp.5-18
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• 2007

This paper presents a concept of training coursework for engineers in cooperation with the industry combining system, comparing Korea with France. The students, after first two years in a university for the foundation/basic courses, will be centered in the industry, rather than at an academic institution, where field training engineering coursework will be offered in structured or capstone design(problem based learning) formats through the industry. This study on the improvement of the concept has several advantages including the followings ; 1) Industry hiring local-area students who have the potential to be long-term employees; 2) Industry's immediate access to employees with developing engineering skills; 3) On-the-job training reduced industry training costs after graduation; 4) More effective learning through observing complex operations; 5) Students and industry input for continuous improvement of the curriculum; 6) Greater amenability on the part of industry to actively participate in research and development; 7) Increasing in the flow of real research problems for engineering. Finally, the implications for student quality, accreditation, assessment of partnership, academic freedom, and fundraising for scholarships and researches are discussed briefly.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1739-1752
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• 1996

Software reengineering involves improving the software maintenance process and improving existing systems by applying new technologies and software tools. Software reengineering can help us understand existing systems and discover software components that are common across systems. In the paper, we discuss the program analysis and environment to assist reengineering. Program analysis takesan existing program as input and generates information about structured part and object-oriented part. It is used to restructure the information by extracting code through reengineering methodology. These restructuring informations with object-oriented archilccture are mapping prolog form to query by using direct reation and summary relation.