• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.1
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• pp.70-86
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• 2015

Short crested waves play an important role for planning and design of harbours. In this context a numerical simulation is carried out to evaluate wave tranquility inside a real harbour located in east coast of India. The annual offshore wave climate proximity to harbour site is established using Wave Model (WAM) hindcast wave data. The deep water waves are transformed to harbour front using a Near Shore spectral Wave model (NSW). A directional analysis is carried out to determine the probable incident wave directions towards the harbour. Most critical threshold wave height and wave period is chosen for normal operating conditions using exceedence probability analysis. Irregular random waves from various directions are generated confirming to Pierson Moskowitz spectrum at 20m water depth. Wave incident into inner harbor through harbor entrance is performed using Boussinesq Wave model (BW). Wave disturbance experienced inside the harbour and at various berths are analysed. The paper discusses the progresses took place in short wave modeling and it demonstrates application of wave climate for the evaluation of harbor tranquility using various types of wave models.

• Korean Management Science Review
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• v.23 no.1
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• pp.21-41
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• 2006

Given the growing deployment of mobile offices, we need to understand the fectors which affect their successful use and implementation. We in this paper present a parsimonious model which integrates the IS success model and the TAM. The main feature of the model is that it allows one to explore the role of mobility of mobile office systems in determining the level of system usage and dependence. Although the mobility is the key property of any mobile system, there is little research effort exerted to understand how mobility affects the traditional IS factors such as perceived ease of use and usefulness. In addition to the system usage, this model also uses dependence as the dependent variable. This is expected to provide richer understanding of the study context. We collected survey data from 1,614 field service workers of a major domestic electronic company. They use PDAs to retrieve information regarding the specifics of the customer service requests and to report what has been done for each service job. The results from SEM analysis show that mobility improves the perceived ease of use which then leads the field service workers to depend more on the PDA systems. Interestingly enough, however, the workers' satisfaction does not cause more frequent use of the system.

• ETRI Journal
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• v.27 no.6
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• pp.747-758
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• 2005

The problem of selection among competing models has been a fundamental issue in statistical data analysis. Good fits to data can be misleading since they can result from properties of the model that have nothing to do with it being a close approximation to the source distribution of interest (for example, overfitting). In this study we focus on the preference among models from a family of polynomial regressors. Three decades of research has spawned a number of plausible techniques for the selection of models, namely, Akaike's Finite Prediction Error (FPE) and Information Criterion (AIC), Schwartz's criterion (SCH), Generalized Cross Validation (GCV), Wallace's Minimum Message Length (MML), Minimum Description Length (MDL), and Vapnik's Structural Risk Minimization (SRM). The fundamental similarity between all these principles is their attempt to define an appropriate balance between the complexity of models and their ability to explain the data. This paper presents an empirical study of the above principles in the context of model selection, where the models under consideration are univariate polynomials. The paper includes a detailed empirical evaluation of the model selection methods on six target functions, with varying sample sizes and added Gaussian noise. The results from the study appear to provide strong evidence in support of the MML- and SRM- based methods over the other standard approaches (FPE, AIC, SCH and GCV).

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.2
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• pp.336-349
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• 2016

The evaluation of the after-school program depends on whether it achieves its objectives or not so far which makes that it is not easy to figure out which mechanism is attributed to the consequences of the program. This study aims at developing the logic model of the after-school program and follows the processes such as literature review, opinion survey by specialists and in-depth interview with stakeholders. The target program of the study was focused on the special ability aptitude education in the elementary schools. The initial developed theory model was validated and finalized through being reviewed by specialists and teachers in charge of target schools. Based on the framework of logic model, the models are composed of context, components, activities, output/short term outcomes, and long term outcomes. As the key factors of the after-school program, amicable communication between the stakeholders, quality of the program in itself, follow-up management, counseling and guidance for students, instructors' expertise, and quality management of the program were drawn.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1345-1360
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• 2016

In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.

• Structural Engineering and Mechanics
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• v.77 no.2
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• pp.293-304
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• 2021

Structural failure due to seismic pounding between two adjacent buildings is one of the major concerns in the context of structural damage. Pounding between adjacent structures is a commonly observed phenomenon during major earthquakes. When modelling the structural response, stiffness of impact spring elements is considered to be one of the most important parameters when the impact force during collision of adjacent buildings is calculated. Determining valid and realistic stiffness values is essential in numerical simulations of pounding forces between adjacent buildings in order to achieve reasonable results. Several impact model stiffness values have been presented by various researchers to simulate pounding forces between adjacent structures. These values were mathematically calculated or estimated. In this study, a linear spring impact element model is used to simulate the pounding forces between two adjacent structures. An experimental model reported in literature was adopted to investigate the effect of different impact element stiffness k on the force intensity and number of impacts simulated by Finite Element (FE) analysis. Several numerical analyses have been conducted using SAP2000 and the collected results were used for further mathematical evaluations. The results of this study concluded the major factors that may actualise the stiffness value for impact element models. The number of impacts and the maximum impact force were found to be the core concept for finding the optimal range of stiffness values. For the experimental model investigated, the range of optimal stiffness values has also been presented and discussed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.3
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• pp.430-440
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• 2022

Shipping companies are key components of the logistics industry, which is extremely significant in enhancing the country's comprehensive national power and promoting global trade development. In the context of the implementation of the new development pattern strategy in China and the impact of the global novel coronavirus (COVID-19), this paper takes 22 Chinese shipping listed companies as the research object and analyses the operational efficiency of them from 2011 to 2020 based on the Super-SBM DEA Model and Window DEA Model. Factors affecting the efficiency are further analyzed with the Tobit model. The research conclude that the operational efficiency of Chinese shipping companies as a whole shows a steady increase from 2011 to 2020. Although most of them are in a relatively ef ective operation state, fewer are absolutely effective companies. Besides efficiency among companies differs obviously, which indicates the potential of further improvement and promotion. What's more, factors such as current economic development level, enterprise size, human resources quality and enterprise turnover speed have significant positive correlation to the operation efficiency of Chinese shipping listed companies, which is significant to improve the operation efficiency of Chinese shipping companies.

• Journal of Sensor Science and Technology
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• v.30 no.6
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• pp.429-435
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• 2021

In this study, the creation of emotional dialogue was investigated within the process of developing a robot's natural language understanding and emotional dialogue processing. Unlike an English-based dataset, which is the mainstay of natural language processing, the Korean-based dataset has several shortcomings. Therefore, in a situation where the Korean language base is insufficient, the Korean dataset should be dealt with in detail, and in particular, the unique characteristics of the language should be considered. Hence, the first step is to base this study on a specific Korean dataset consisting of conversations on emotional topics. Subsequently, a model was built that learns to extract the continuous dialogue features from a pre-trained language model to generate sentences while maintaining the context of the dialogue. To validate the model, a chatbot system was implemented and meaningful results were obtained by collecting the external subjects and conducting experiments. As a result, the proposed model was influenced by the dataset in which the conversation topic was consultation, to facilitate free and emotional communication with users as if they were consulting with a chatbot. The results were analyzed to identify and explain the advantages and disadvantages of the current model. Finally, as a necessary element to reach the aforementioned ultimate research goal, a discussion is presented on the areas for future studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

• Journal of Distribution Science
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• v.20 no.6
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• pp.75-86
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• 2022

Purpose: This research aims to provide empirical evidence on the impact of digital literacy on behavioural intention regarding using technology for distribution of higher education. Design, Methodology, and Approach: Quantitative analysis was carried out using Covariance-Based Structural Equation Model with data collected from 901 students who fully experienced 2-year study online at different universities in Vietnam. The structural model was built with digital literacy as the primary indicator and other variables were included based on modified version of Unified Theory of Acceptance and Use of Technology (UTAUT2) by adopting performance expectancy, effort expectancy, social influence, habit, and hedonic motivation variables specifically for education sector. Self-efficacy was added to eliminate possible bias in technology acceptance. Results: From the results of model estimation, digital literacy presented positive impact on the online distribution of higher education in Vietnam. The mediating effects of various indicators such as performance expectancy, effort expectancy, social influence, habit, hedonic motivation, and self-efficacy are significantly determined by research model. Conclusion: The higher level of digital literacy of the students, the more likely that they will use technology in higher education study, especially online learning. Additionally, the mediating effects of indicators from the UTAUT2 theoretical model were also evident to be positively significant.