• Journal of Navigation and Port Research
• /
• v.42 no.6
• /
• pp.539-545
• /
• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.20 no.6
• /
• pp.1049-1060
• /
• 2018

Soil conditioning is one of the key factors for successfull tunnel excavations utilizing the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) by increasing the tunnel face stability and extraction efficiency of excavated soils. In this study, conditioning agents are mixed with the weathered granite soils which are abundant in the Korean peninsula and the workability of the resulting mixture is evaluated through the slump tests to derive and propose the most suitable conditioning agent as well as the most appropriate agent mix ratios. However, since it is cumbersome to perform the slump tests all the time either in the laboratory or in-situ, a simpler test may be needed instead of the slump test; the fall cone test was proposed as a substitute. In this paper, the correlation between the slump value obtained from the slump test and the cone penetration depth obtained from the proposed fall cone test was obtained. Test results showed that a very good correlation between two was observed; it means that the simpler fall cone test can be used to assess the suitability of the conditioned soils instead of the more cumbersome slump test.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.23 no.1
• /
• pp.54-58
• /
• 2019

Purpose At now, there are many kind of dedicated heart SPECT machine in clinical nuclear medicine. Among those, the fixed focusing type SPECT can make a good quality, quantity image because a detectors of this SPECT arranged forward a special ROI and didn't rotate around of body. So, in this paper, we will evaluate a spatial uniformity about resolution and sensitivity at a same plane of a fixed focusing type SPECT. Materials and Methods We used D-SPECT as a fixed focusing type SPECT and Cario MD as a rotated parallel type SPECT to comparing each other. We injected $^{99m}Tc(14.8MBq/1cc)$ to 10 capillary tube (diameter=1mm), and we set those line sources a tfield of view of each SPECT. And then we acquired SPECT date, we applied are construction by recommended methods. By using two tomography images, we calculated a full width of half maximum as a resolution and total counts as a sensitivity, and we compared a CV (coefficientofvariation) values between two images as a spatial uniformity. Results In case of D-SPECT, a CV of resolution and sensitivity are 7.45%, 12.34%. In case of Cario MD, an CV of resolution and sensitivity are 12.49%, 21.84% Conclusion As a results, CV of resolution and sensitivity of a fixed focusing type SPECT is 67.75%, 77.00% higher than ones of a rotated parallel type SPECT. It means that a fixed focusing type SPECT is more uniformed, because this new SPECT can reduce a motion blur artifact by rotating detector around body, also all of detector that made by semiconductor arrange forward a special FOV like heart.

• Journal of Radiation Protection and Research
• /
• v.44 no.3
• /
• pp.103-109
• /
• 2019

Background: Four-dimensional computed tomographic (4DCT) images are increasingly used in clinic with the growing need to account for the respiratory motion of the patient during radiation treatment. One of the reason s that makes the dose evaluation using 4DCT inaccurate is a change of the patient respiration during the treatment session, i.e., intrafractional uncertainty. Especially, when the amplitude of the patient respiration is greater than the respiration range during the 4DCT acquisition, such an organ motion from the larger respiration is difficult to be represented with the 4DCT. In this paper, the method to generate images expecting the organ motion from a respiration with extended amplitude was proposed and examined. Materials and Methods: We propose a method to generate extra-phase images from a given set of the 4DCT images using deformable image registration (DIR) and linear extrapolation. Deformation vector fields (DVF) are calculated from the given set of images, then extrapolated according to respiratory surrogate. The extra-phase images are generated by applying the extrapolated DVFs to the existing 4DCT images. The proposed method was tested with the 4DCT of a physical 4D phantom. Results and Discussion: The tumor position in the generated extra-phase image was in a good agreement with that in the gold-standard image which is separately acquired, using the same 4DCT machine, with a larger range of respiration. It was also found that we can generate the best quality extra-phase image by using the maximum inhalation phase (T0) and maximum exhalation phase (T50) images for extrapolation. Conclusion: In the present study, a method to construct extra-phase images that represent expanded respiratory motion of the patient has been proposed and tested. The movement of organs from a larger respiration amplitude can be predicted by the proposed method. We believe the method may be utilized for realistic simulation of radiation therapy.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.23 no.1
• /
• pp.102-112
• /
• 2019

This paper studies impact fracture behavior under temperature variation and compressive flexural strength of cement composites using VAE(vinyl acetate ethylene) powder polymer and PVA(polyvinyl alcohol) fiber. Impact test were conducted in the temperature range selected for the $-35^{\circ}C$, $0^{\circ}C$ and $35^{\circ}C$. In this experimental study, impact test were carried out using a drop impact testing machine (Ceast 9350) to obtain such as displacement, time, and impact fracture energy of normal specimen and and cement composites specimen. As test results, the use of VAE powder polymer and PVA fiber were observed to enhance the flexural strength of mortar. The compressive strength of PVA fibers reinforced cement composites was slightly decreased at 28 days, but the flexural strength was observed to increase 24.4% of normal mortar strength. As a result of the drop impact tests, PVA fiber reinforced cement composites specimens showed microcracks due to energy dispersion and crack prevention with bridge effect of the fibers, and scabbing or perforation by impact was suppressed. On the other hand, the normal mortar and VAE powder polymer cement composites specimens were carried out to the perforation and macro crack. Most of normal mortar and the cement composites subjected to impact load on specimens shows mostly local brittle failure. The impact resistant performance of the specimen with PVA fiber was greatly improved due to the increase of flexure performance.

• The Journal of Society for e-Business Studies
• /
• v.24 no.1
• /
• pp.1-16
• /
• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.21 no.2
• /
• pp.227-242
• /
• 2019

This paper presents a method to predict ground types ahead of a tunnel face utilizing operational data of the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) when running through soil ground. The time series analysis model which was applicable to predict the mixed ground composed of soils and rocks was modified to be applicable to soil tunnels. Using the modified model, the feasibility on the choice of the soil conditioning materials dependent upon soil types was studied. To do this, a self-organizing map (SOM) clustering was performed. Firstly, it was confirmed that the ground types should be classified based on the percentage of 35% passing through the #200 sieve. Then, the possibility of predicting the ground types by employing the modified model, in which the TBM operational data were analyzed, was studied. The efficacy of the modified model is demonstrated by its 98% accuracy in predicting ground types ten rings ahead of the tunnel face. Especially, the average prediction accuracy was approximately 93% in areas where ground type variations occur.

• Journal of Internet Computing and Services
• /
• v.22 no.1
• /
• pp.23-30
• /
• 2021

This paper proposes a noise-tolerant image classification system using multiple autoencoders. The development of deep learning technology has dramatically improved the performance of image classifiers. However, if the images are contaminated by noise, the performance degrades rapidly. Noise added to the image is inevitably generated in the process of obtaining and transmitting the image. Therefore, in order to use the classifier in a real environment, we have to deal with the noise. On the other hand, the autoencoder is an artificial neural network model that is trained to have similar input and output values. If the input data is similar to the training data, the error between the input data and output data of the autoencoder will be small. However, if the input data is not similar to the training data, the error will be large. The proposed system uses the relationship between the input data and the output data of the autoencoder, and it has two phases to classify the images. In the first phase, the classes with the highest likelihood of classification are selected and subject to the procedure again in the second phase. For the performance analysis of the proposed system, classification accuracy was tested on a Gaussian noise-contaminated MNIST dataset. As a result of the experiment, it was confirmed that the proposed system in the noisy environment has higher accuracy than the CNN-based classification technique.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.6
• /
• pp.461-469
• /
• 2020

In this paper, we propose a PCA-based feature feedback method for extracting important areas of handwritten numeric data sets and face data sets. A PCA-based feature feedback method is proposed by extending the previous LDA-based feature feedback method. In the proposed method, the data is reduced to important feature dimensions by applying the PCA technique, one of the dimension reduction machine learning algorithms. Through the weights derived during the dimensional reduction process, the important points of data in each reduced dimensional axis are identified. Each dimension axis has a different weight in the total data according to the size of the eigenvalue of the axis. Accordingly, a weight proportional to the size of the eigenvalues of each dimension axis is given, and an operation process is performed to add important points of data in each dimension axis. The critical area of the data is calculated by applying a threshold to the data obtained through the calculation process. After that, induces reverse mapping to the original data in the important area of the derived data, and selects the important area in the original data space. The results of the experiment on the MNIST dataset are checked, and the effectiveness and possibility of the pattern recognition method based on PCA-based feature feedback are verified by comparing the results with the existing LDA-based feature feedback method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.3
• /
• pp.201-207
• /
• 2021

Deep learning, a type of machine learning, performs learning while changing the weights as it progresses through each learning process. Tensor Flow and Keras provide the results of the end of the learning in graph form. Thus, If an error occurs, the result must be discarded. Consequently, existing technologies provide a function to roll back learning results, but the rollback function is limited to results up to five times. Moreover, they applied the concept of MLOps to track the deep learning process, but no rollback capability is provided. In this paper, we construct a system that manages the intermediate value of the learning process by blockchain to record the intermediate learning process and can rollback in the event of an error. To perform the functions of blockchain, the deep learning process and the rollback of learning results are designed to work by writing Smart Contracts. Performance evaluation shows that, when evaluating the rollback function of the existing deep learning method, the proposed method has a 100% recovery rate, compared to the existing technique, which reduces the recovery rate after 6 times, down to 10% when 50 times. In addition, when using Smart Contract in Ethereum blockchain, it is confirmed that 1.57 million won is continuously consumed per block creation.