We optimally designed the VCO(voltage-controlled oscillator) with spiral inductor using the MOSIS HP 0.5
A forecasting method using deep learning does not have consistent results due to the differences in the characteristics of the dataset, even though they have the same forecasting models and parameters. For example, the forecasting model X optimized with dataset A would not produce the optimized result with another dataset B. The forecasting model with the characteristics of the dataset needs to be optimized to increase the accuracy of the forecasting model. Therefore, this paper proposes novel optimization steps for outlier removal, dataset classification, and a CNN-LSTM-based hyperparameter tuning process to forecast the daily power usage of a university campus based on the hourly interval. The proposing model produces high forecasting accuracy with a 2% of MAPE with a single power input variable. The proposing model can be used in EMS to suggest improved strategies to users and consequently to improve the power efficiency.
The ultra-long stroke engine was developed to generate greater power at lower speeds than previous designs to enhance the propulsion efficiency. The torsional exciting force, on the other hand, was increased significantly. Therefore, it is possible to control the torsional vibration of its shaft system equipped with the fuel efficient ultra-long stroke engine by adopting a damper although the torsional vibration could be controlled adequately by applying tuning and turning wheels on the engine previously. In this paper, the dynamic characteristics of a viscous-spring damper used to control the torsional vibration of the corresponding shaft system are reviewed and then examined to determine what vibration characteristics might be used to optimize the viscous-spring damper. In some cases, operators of eco-ships have recently experienced the problem of delayed RPM acceleration. It has been suggested that the proper measures for controlling the torsional vibration in the shaft system should involve adjusting the design parameters of its damper determined by the optimum damper design theory to avoid the fatigue damage of shafts.
Recently, many state-of-art spectroscopy techniques are used to unravel the mysteries of condensed matters. And numerous heterostructures have provided a new avenue to search for new emergent phenomena. Especially, near the interface, various forms of symmetry-breaking can appear, which induces many novel phenomena. Although these intriguing phenomena can be emerged at the interface, by using conventional measurement techniques, the experimental investigations have been limited due to the buried nature of interface. One of the ways to overcome this limitation is in situ investigation of the layer-by-layer evolution of the electronic structure with increasing of the thickness. Namely, with very thin layer, we can measure the electronic structure strongly affected by the interface effect, but with thick layer, the bulk property becomes strong. Angle-resolved photoemission spectroscopy (ARPES) is powerful tool to directly obtain electronic structure, and it is very surface sensitive. Thus, the layer-by-layer evolution of the electronic structure in oxide heterostructure can be investigated by using in situ ARPES. LaNiO3 (LNO) heterostructures have recently attracted much attention due to theoretical predictions for many intriguing quantum phenomena. The theories suggest that, by tuning external parameters such as misfit strain and dimensionality in LNO heterostructure, the latent orders, which is absent in bulk, including charge disproportionation, spin-density-wave order and Mott insulator, could be emerged in LNO heterostructure. Here, we performed in situ ARPES studies on LNO films with varying the misfit strain and thickness. (1) By using LaAlO3 (-1.3%), NdGaO3 (+0.3%), and SrTiO3 (+1.7%) substrates, we could obtain LNO films under compressive strain, nearly strain-free, and tensile strain, respectively. As strain state changes from compressive to tensile, the Ni eg bands are rearranged and cross the Fermi level, which induces a change of Fermi surface (FS) topology. Additionally, two different FS superstructures are observed depending on strain states, which are attributed to signatures of latent charge and spin orderings in LNO films. (2) We also deposited LNO ultrathin films under tensile strain with thickness between 1 and 10 unit-cells. We found that the Fermi surface nesting effect becomes strong in two-dimensions and significantly enhances spin-density-wave order. The further details are discussed more in presentation. This work was collaborated with Hyang Keun Yoo, Seung Ill Hyun, Eli Rotenberg, Ji Hoon Shim, Young Jun Chang and Hyeong-Do Kim.
So far, the deep learning, a field of artificial intelligence, has achieved remarkable results in solving problems from unstructured data. However, it is difficult to comprehensively judge situations like humans, and did not reach the level of intelligence that deduced their relations and predicted the next situation. Recently, deep neural networks show that artificial intelligence can possess powerful relational reasoning that is core intellectual ability of human being. In this paper, to analyze and observe the performance of Relation Networks (RN) among the neural networks for relational reasoning, two types of RN-based deep neural network models were constructed and compared with the baseline model. One is a visual question answering RN model using Sort-of-CLEVR and the other is a text-based question answering RN model using bAbI task. In order to maximize the performance of the RN-based model, various performance improvement experiments such as hyper parameters tuning have been proposed and performed. The effectiveness of the proposed performance improvement methods has been verified by applying to the visual QA RN model and the text-based QA RN model, and the new domain model using the dialogue-based LL dataset. As a result of the various experiments, it is found that the initial learning rate is a key factor in determining the performance of the model in both types of RN models. We have observed that the optimal initial learning rate setting found by the proposed random search method can improve the performance of the model up to 99.8%.
In this paper, a novel small-diameter cylindrical post capacitor inserted into an evanescent-mode rectangular waveguide (EMRWG) is proposed for easier tuning. In order to feed the EMRWG, the proposed structure uses a single ridge rectangular waveguide with the same width and height as the waveguide at the input and output ends. The inserted post capacitor are made up a circular groove formed in the center of the lower part of the broad wall of the EMRWG, and a concentric cylindrical post inserted into the upper part. First, the equivalent circuit model for the proposed structure is presented. When the EMRWG and the single ridge waveguide are combined, the joint susceptance and the turns ratio of the ideal transformer are calculated by two simulations using HFSS (3d fullwave simulator, Ansoft Co.) respectively. The susceptance and resonance characteristics of the inserted post were analyzed by using the obtained parameters and the characteristics of the EMRWG. A 2-post filter with a center frequency of 4.5 GHz and a bandwidth of 170 MHz was designed using a WR-90 waveguide, and the simulation results by using the HFSS and CST, equivalent circuit model were in good agreement.
This study empirically analyzed a Korean pre-trained language models (PLMs) designed for natural language generation. The performance of two PLMs - BART and GPT - at the task of abstractive text summarization was compared. To investigate how performance depends on the characteristics of the inference data, ten different document types, containing six types of informational content and creation content, were considered. It was found that BART (which can both generate and understand natural language) performed better than GPT (which can only generate). Upon more detailed examination of the effect of inference data characteristics, the performance of GPT was found to be proportional to the length of the input text. However, even for the longest documents (with optimal GPT performance), BART still out-performed GPT, suggesting that the greatest influence on downstream performance is not the size of the training data or PLMs parameters but the structural suitability of the PLMs for the applied downstream task. The performance of different PLMs was also compared through analyzing parts of speech (POS) shares. BART's performance was inversely related to the proportion of prefixes, adjectives, adverbs and verbs but positively related to that of nouns. This result emphasizes the importance of taking the inference data's characteristics into account when fine-tuning a PLMs for its intended downstream task.
In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70