Fast and accurate power bus design (FAPUD) method for multi-layers high-speed digital boards is devised for the power supply network design tool for accurate and precise high speed board. FAPUD is constructed, based on two main algorithms of the PBEC (Path Based Equivalent Circuit) model and the network synthesis method. The PBEC model exploits simple arithmetic expressions of the lumped 1-D circuit model from the electrical parameters of a 2-D power distribution network. The circuit level design based on PBEC is carried with the proposed regional approach. The circuit level design directly calculates and determines the size of on-chip decoupling capacitors, the size and the location of off-chip decoupling capacitors, and the effective inductances of the package power bus. As a design output, a lumped circuit model and a pre-layout of the power bus including a whole decoupling capacitors are obtained after processing FAPUD. In the tuning procedure, the board re-optimization considering simultaneous switching noise (SSN) added by I/O switching can be carried out because the I/O switching effect on a power supply noise can be estimated over the operation frequency range with the lumped circuit model. Furthermore, if a design changes or needs to be tuned, FAPUD can modify design by replacing decoupling capacitors without consuming other design resources. Finally, FAPUD is accurate compared with conventional PEEC-based design tools, and its design time is 10 times faster than that of conventional PEEC-based design tools.
Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.
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.
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.
We investigate the effect of the data skew of join attributes on the performance of a pipelined multi-way hash join method, and propose two new hash join methods with load balancing capabilities. The first proposed method allocates buckets statically by round-robin fashion, and the second one allocates buckets adaptively via a frequency distribution. Using hash-based joins, multiple joins can be pipelined so that the early results from a join, before the whole join is completed, are sent to the next join processing without staying on disks. Unless the pipelining execution of multiple hash joins includes some load balancing mechanisms, the skew effect can severely deteriorate system performance. In this paper, we derive an execution model of the pipeline segment and a cost model, and develop a simulator for the study. As shown by our simulation with a wide range of parameters, join selectivities and sizes of relations deteriorate the system performance as the degree of data skew is larger. But the proposed method using a large number of buckets and a tuning technique can offer substantial robustness against a wide range of skew conditions.
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.
To resolve the problem of the temperature rise in LED or OLED lighting, until now a thick metal film has been used as a heat-sink. Conventionally, this thick metal film is made by the electroplating method and used as the heat-dissipating plate of the electronic devices. However, nowadays there is increasing need for a Cu metal film with a thickness of several hundred micrometers that can be formed by the dry deposition method. In this work, we designed and fabricated a Cu film deposition system where the heating element is separated from the ceramic crucible, which makes ultra-rapid deposition possible by preventing heat loss. In addition, the resulting induction heating also contributes to the high deposition rate. By tuning the various parameters, we obtained a
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