• Journal of Biomedical Engineering Research
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• v.41 no.5
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• pp.195-202
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• 2020

Needle detection in ultrasound images is sometimes difficult due to obstruction of fat tissues. Accurate needle detection using continuous ultrasound (CUS) images is a vital stage of treatment planning for tissue biopsy and brachytherapy. The main goal of the study is classified into two categories. First, new detection model, i.e. D-Attention Unet, is developed by combining the context information of 3D medical data and CUS images. Second, the D-Attention Unet model was compared with other models to verify its usefulness for needle detection in continuous ultrasound images. The continuous needle images taken with ultrasonic waves were converted into still images for dataset to evaluate the performance of the D-Attention Unet. The dataset was used for training and testing. Based on the results, the proposed D-Attention Unet model showed the better performance than other 3 models (Unet, D-Unet and Attention Unet), with Dice Similarity Coefficient (DSC), Recall and Precision at 71.9%, 70.6% and 73.7%, respectively. In conclusion, the D-Attention Unet model provides accurate needle detection for US-guided biopsy or brachytherapy, facilitating the clinical workflow. Especially, this kind of research is enthusiastically being performed on how to add image processing techniques to learning techniques. Thus, the proposed method is applied in this manner, it will be more effective technique than before.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.11a
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• pp.100-101
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• 2014

The generation of 3D models for freeform buildings is an important task while continuous monitoring of the related spatial information at different time phases. Realistic models of freeform building have to provide high geometric accuracy and detail at an effective data size.(Al-kheder, S. 2008) The efficiency of this image-based technique has been increased considerably by the development of digital technologies. Furthermore, 3D data collection based on laser scanning has become an high quality 3D models for construction site. Therefore, in this research, we have an effort to review construction methods to make freeform envelope of building using 3D scanning technology.

• Water for future
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• v.7 no.1
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• pp.71-77
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• 1974

This paper reviews several different single site generation models for further development of a model for generating the Synthetic sequences of streamflow in the continuous streams like main streams in Korea. Initially the historical time series is looked using a time series technique, that is correlograms, to determine whether a lag one Markov model will satisfactorily represent the historical data. The single site models which were examined include an empirical model using the historical probability distribution of the random component, the linear autoregressive model(Markov model, or Thomas-Fiering model) using both logarithms of the data and Matala's log-normal transformation equations, and finally gamma distribution model.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.74-79
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• 1996

This paper describes a methodology for the development of models of discrete event system(DES). The methodology is based on transformation of continuous state space into discrete one to homomorphically represent dynamics of continuous processes in discrete events. This paper proposes a formal structure which can couple DES models within a framework. The structure employs the DEVS formalism for the DES models. The proposed formal structure has been applied to develop a DEVS model for the human cardiovascular system. For this, the cardiac cycle is partitioned into a set of phases based on events identified through VisSim simulation in the CS of the electrical analog model. VisSim is the simulation tool of visual environment for developing continuous, discrete, and hybrid system models and performing dynamic simulation. For each phase, a CS of the electrical analog model for the cardiovascular system has been simulated by VisSim 2.0. To validate this model, first develop the DEVS model, then simulate the model in the DEVSIM++ environment. It has same simulation results for the data obtained from the CS simulation using VisSim. The comparison shows that the DEVS model represents dynamics of the human heart system at each phase of cardiac cycle.

• Asia pacific journal of information systems
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• v.32 no.1
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• pp.125-150
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• 2022

Technology companies launched their intelligent personal assistants (IPAs). IPAs not only provide individuals with a convenient way to interact with technology but also offer them the opportunity to interact with AI in a useful and meaningful form. Therefore, the global IPAs have experienced tremendous growth over the past decade. But maintaining continuous usage intention is still a massive challenge for developers and marketers and previous technology adoption models are not enough to explain continuous usage intention of IPAs. Thus, we adopted the bi-dimensional perspectives of utilitarian and hedonic value in this research model, and investigated how three characteristics of IPAs - anthropomorphism, autonomy, and intelligence - affect utilitarian value and hedonic value, which in turn continuous usage intentions. 227 data were collected from IPA users. The results showed that IPAs' continuous usage intention is significantly determined by both utilitarian and hedonic value, with the hedonic value being more prominent. In addition, the results showed that anthropomorphism and intelligence are the most important antecedents of utilitarian and hedonistic value. The results also illustrated that autonomy is a crucial predictor of utilitarian value rather than hedonistic value. Our work contributes to current research by widening the theoretical understanding of the effect of IPA characteristics on continuous usage intention through bi-dimensional values. Our paper also provides IPAs' developer and marketer guidelines for enhancing continuous usage intention.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.13-19
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• 1996

In this paper, we study and compare two types of methods for classification when both continuous and categorical variables are used to describe each individual. One is neural network(NN) method using backpropagation learning(BPL). The other is logistic model(LM) method. Both the NN and LM are based on projections of the data in directions determined from interconnection weights.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.593-601
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• 2013

We often need to include categorial variables as explanatory variables in regression models. The categorial variables in regression models can be quantified through dummy variables. In this study, we provide an education tool using Excel VBA for displaying regression lines along with test results for regression models with a continuous explanatory variable and one or two categorical explanatory variables. The regression lines with test results are provided step by step for the model(s) with interaction(s), the model(s) without interaction(s) but with dummy variables, and the model without dummy variable(s). With this tool, we can easily understand the meaning of dummy variables and interaction effect through graphics and further decide which model is more suited to the data on hand.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3923-3942
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• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• Proceedings of the Membrane Society of Korea Conference
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• 1996.04a
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• pp.34-35
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• 1996

The diffusional behavior of many non-solvents in glassy or semicrystalline polymers cannot be adequately described by a concentration-dependent form of Fick's law, especially when mass transfer is coupled with structural changes. Many mathematical models have been devised to interprete non-Fickian diffusion dominated by relaxation kinetics. In formulation of non-Fickian diffusion mathematics, therefore, the most important factor to consider is how relaxation effects can influence the governing constitutive equation and boundary conditions. That is, relaxation parameters can be accommodated by variable boundary conditions or a modified continuity equation, or both, depending on specific systems and conditions (Frish, 1980). Accoring to Astarita and Nicolais (1983), the model equations can be broadly categorized as continuous or discontinuous. Continuous model equations encompass phenomena where the structural change takes place gradually over the whole volume of the polymer sample (Crank, 1953; Long and Richman, 1961; Berens and Hopfenberg, 1978). On the other hand, discontinuous model equations deal with the phenomena where the morphological change appears to be abrupt (Li, 1984). Four mathematical models with different relaxation parameters were applied to fit the anomalous sorption data observed in fluoropolymers (PVDF, ECTFE). The fitted result for PVDF-benzene sorption data is shown in Fig. 1.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.