• Knowledge Management Research
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• v.14 no.1
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• pp.121-132
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• 2013

In education, innovative ways of teaching and learning are always under development and keep being proposed with advanced concepts since the ancient times. Student-centered learning, problem-based learning and cooperative learning have been three major trends under development in secondary education research and practice more than a decade or so. Combined with advanced information and communication technologies, these trends will greatly transform the way we teach and learn in classroom environment and may change the classroom environment itself, into a more interactive and self-centered coaching type environment. In this study, a smart environment that utilizes advanced information technology devices and network is conceptualized, accommodating requirements contained and proposed in the recent trendy pedagogies. Pedagogical cases discussed in these trends are analyzed in detail, producing requirements for such a learning and coaching environment. These requirements are modeled using unified modeling language, leading to a proposal of a basic architecture for an information system supporting this environment.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.3
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• pp.272-280
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• 2022

The first step is to determine the principal dimensions of the design ship, such as length between perpendiculars, beam, draft and depth when accomplishing the design of a new vessel. To make this process easier, a database with a large amount of existing ship data and a regression analysis technique are needed. Recently, deep learning, a branch of artificial intelligence (AI) has been used in regression analysis. In this paper, deep learning neural networks are used for regression analysis to find the regression function between the input and output data. To find the neural network structure with the highest accuracy, the errors of neural network structures with varying the number of the layers and the nodes are compared. In this paper, Python TensorFlow Keras API and MATLAB Deep Learning Toolbox are used to build deep learning neural networks. Constructed DNN (deep neural networks) makes helpful in determining the principal dimension of the ship and saves much time in the ship design process.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.50-57
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• 2022

Along with the development of the 4^th Industrial Revolution technology, artificial intelligence technology is also being used in the field of systems engineering. This study analyzed the development status of artificial intelligence technology in the areas of systems engineering core processes such as stakeholder needs and requirements definition, system requirement analysis, and system architecture definition, and presented future technology development directions. In the definition of stakeholder needs and requirements, technology development is underway to compensate for the shortcomings of the existing requirement extraction methods. In the field of system requirement analysis, technology for automatically checking errors in individual requirements and technology for analyzing categories of requirements are being developed. In the field of system architecture definition, a technology for automatically generating architectures for each system sector based on requirements is being developed. In this study, these contents were summarized and future development directions were presented.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.204-214
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• 2017

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

• Journal of Ocean Engineering and Technology
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• v.34 no.4
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• pp.277-284
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• 2020

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

• Journal of Ocean Engineering and Technology
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• v.34 no.5
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• pp.371-376
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• 2020

Underwater acoustics, which is the study of the phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. Underwater acoustics is mainly applied in the field of remote sensing, wherein information on a target object is acquired indirectly from acoustic data. Presently, machine learning, which has recently been applied successfully in a variety of research fields, is being utilized extensively in remote sensing to obtain and extract information. In the earlier parts of this work, we examined the research trends involving the machine learning techniques and theories that are mainly used in underwater acoustics, as well as their applications in active/passive SONAR systems (Yang et al., 2020a; Yang et al., 2020b; Yang et al., 2020c). As a follow-up, this paper reviews machine learning applications for the inversion of ocean parameters such as sound speed profiles and sediment geoacoustic parameters.

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.227-236
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• 2020

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

• Asia pacific journal of information systems
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• v.28 no.4
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• pp.240-257
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• 2018

In today's turbulent business landscape, a firm's ability to explore new IT capabilities and exploit current ones is essential for enabling organizational agility and achieving high organizational performance. We propose IT exploration and exploitation as two critical organizational learning processes that are essential for gaining and sustaining competitive advantages. However, it remains unclear how the emerging cloud-based IT architecture affects an organization's ability to explore and exploit its IT capabilities. We conceptualize modularity and virtuality as two critical dimensions of emerging cloud-based IT architecture and investigate how they affect IT exploration and exploitation. We test our hypotheses using data obtained from our field survey of IT managers. We find that modularity is positively associated with both exploration and exploitation whereas virtuality is positively associated with exploration, but not with exploitation. We also find that the effect of modularity on exploitation is stronger than its effect on exploration.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1113-1119
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• 1989

Neural network control has many innovative potentials for intelligent adaptive control. Among many, it promises real time adaption, robustness, fault tolerance, and self-learning which can be achieved with little or no system models. In this paper, a dynamic robot controller has been developed based on a backpropagation neural network. It gradually learns the robot's dynamic properties through repetitive movements being initially trained with a PD controller. Its control performance has been tested on a simulated PUMA 560 demonstrating fast learning and convergence.

• Journal of Information Technology Applications and Management
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• v.26 no.5
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• pp.27-39
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• 2019

The Internet of Things (IoT) is a new paradigm that is revolutionizing computing. It is intended that all objects around us will be connected to the network, providing "anytime, anywhere" access to information. This study introduces IoT with Kolb's learning style in order to enhance the learning experience especially for inclusive education for primary and secondary schools where delivery of knowledge is not limited to physical, cognitive disabilities, human diversity with respect to ability, language, culture, gender, age and of other forms of human differences. The article also emphasizes the role of learning style as a discovery process that incorporates the characteristics of problem solving and learning. Kolb's Learning Style was chosen as it is widely used in research and in practical information systems applications. A consistent pattern of finding emerges by using a combination of Kolb's learning style and internet of things where specific individual differences, learning approach differences and IoT application differences are taken as a main research framework. Further several suggestions were made by using this combination to IoT architecture and smart environment of internet of things. Based on these suggestions, future research directions are proposed.