High propylene selectivity in methanol conversion over metal (Sm, Y, and Gd) modified HZSM-5 catalysts in the methanol to propylene process

A protonated form of Zeolite Socony Mobil-5 (H-ZSM-5) catalyst (with Si/Al = 200) was synthesized through a hydrothermal method from various sources of silica, including Ludox, silicic acid, and tetraethyl orthosilicate. We also investigated the effect of loading the catalyst with yttrium (Y), samarium (Sm), and gadolinium (Gd), respectively, on the acidic properties of the catalyst. We compared the catalytic performance of the catalysts with different loadings in methanol to hydrocarbon conversion. The catalysts were characterized using X-ray diffraction analysis, Fourier transform infrared spectroscopy, scanning electron microscopy, surface area analysis measurements, thermogravimetric analysis, and temperature-programmed desorption. Among the metal-loaded Ludox-based H-ZSM-5 catalysts (LHZs), the Sm/LHZ catalyst had the best performance with 68.8 % propylene selectivity due to its high amount of weak and intermediate acid sites at 480 degrees C, WHSV =1 h-1 in a methanol to propylene conversion. However, the Gd-LHZ catalyst significantly increased the selectivity toward ethane and propane during the conversion.

Competitive advantages of a bistable vibration isolator: Cut-off frequency and operational safety near harmful resonance

This study reveals the competitive advantages of a bistable vibration isolator over conven-tional linear and monostable ones. Generally, reducing stiffness improves vibration isolation performance with a lower cut-off frequency but for the linear vibration isolators involves a large-amplitude oscillation. Nonlinear monostable and bistable vibration isolators have been developed to resolve the contradictory issue of the linear case, reducing both the cut -off frequency and the range of oscillation. However, it has not yet been determined what advantages make a bistable vibration isolator more promising than a monostable one. In this study, equivalent linear, monostable, and bistable vibration isolators were found, and their numerical and theoretical dynamic responses compared directly. First, the cut-off frequency tended to be lower in the order of the bistable, equivalent linear, and monostable vibration isolators. In other words, the bistable system provides a wider vibration isolation bandwidth than the equivalent linear one, whereas the monostable one makes it narrower. Second, the bistable vibration isolator enhanced operational safety in the amplification region thanks to its small force transmissibility, while the monostable one exacerbated the risk. Bifurcation analyses of the nonlinear steady-state oscillations of the bistable and monostable vibration isolators were also performed to identify and investigate the dangerous and safe amplification regions. The results of the analyses clearly demonstrated the superior operational safety of the bistable vibration isolator, particularly in terms of a larger safety margin in the amplification region.

Optimal stabilizing rates of switched linear control systems under arbitrary known switchings

The problem of stabilizing discrete-time switched linear control systems using continuous control input under arbitrary mode switchings is studied. It is assumed that at each time instance the switching mode can be arbitrarily chosen but is always known by the controller designing the continuous control input; thus the continuous controller is of the general form of an ensemble of mode-dependent state feedback controllers. Under this setting, the fastest (worst-case) stabilizing rate is proposed as a quantitative metric of the systems’ stabilizability. Conditions are derived on when this stabilizing rate can be exactly achieved by an admissible control policy and a counter example is given to show that the stabilizing rate may not always be attained by a mode-dependent linear state feedback control policy. Bounds on the stabilizing rate are derived using (semi)norms. When such bounds are tight, the corresponding extremal norms are characterized geometrically. Numerical algorithms based on ellipsoid and polytope norms are developed for computing bounds of the stabilizing rate and illustrated through examples.

Machine learning modeling to forecast uncertainty between capital sudden stop and boom

In emerging economies, sudden stops of capital inflows boost the collapse of stock and exchange rate markets and plunge countries into unemployment, loss of production, and diminishing exports, leading to a financial crisis. Recently, the nonlinear relationship-based machine learning (ML) model for analyzing the complexity and uncertainty of financial and economic systems has been in the spotlight, but they are still poorly used for predicting sudden stops of capital. Because there is no verified tool that elaborately measures the indicia of rapid suspension or expansion of capital for prediction purposes, ML models learned from domains with inadequately defined economic characteristics of capital movements can suffer from poor predictive power and reliability. In addition, many economists do not trust the ML model due to the lack of interpretability caused by the black box structure of ML models. In this study, three approaches are proposed for better prediction and decision-making. First, using data for 37 emerging economies from 1990 to 2019, we apply various ML techniques such as extreme gradient boost (XGB), which is well known for the latest ensemble learning technology. Second, we analyze the causal relationship to the outcomes of our models using SHAP (SHapley Additive exPlanations) methods, a powerful technique for ML interpretation. Particularly, from the perspective of post-COVID-19, our model predicts an increased probability of sudden stops in countries where a sharp decline in real interest rates and exports is evident. Finally, we propose a tool to measure capital flows and assess excessive levels for forecasting purposes. Particularly, our tool extracts credit boom events that are highly correlated with sudden stops, and the ML models with credit boom events have significantly improved predictive performance. Specifically, in the forecast of a sudden stop after one year, the prediction accuracy of the models with a credit boom event is improved by 11.2% on average compared to models without credit boom information. In addition, the gap between the predicted hit and the miss rate in the proposed models was reduced to −16.2% on average compared to the original, improving the balance of classification.

See, caption, cluster: Large-scale image analysis using captioning and topic modeling

Owing to the widespread use of smartphones and mobile devices and the prevalence of image-sharing social network services, the amount of image data available on the Web is soaring. Various tasks, such as image classification, detection, and segmentation, use tremendous amounts of image data to train machine learning models. Using these trained models, a visual feature representation vector can be extracted from individual images and subsequently be used in several applications, such as image retrieval, object detection, and clustering. However, despite the increasing demand for such analyses, few studies have analyzed the information summarized by such image datasets, especially for extracting topics, trends, and opinions from images generated by online communities. Therefore, we propose a novel approach to image topic modeling, which accounts for visual content as well as semantic information by leveraging the image captioning model. In addition, we propose an image-caption scoring model that measures the semantic similarity between an image and its generated caption in order to filter noisy data that obstruct analysis by obscuring the semantic meaning of topics extracted from the dataset. The results show that our proposed method assists in analyzing large-scale image datasets without the need to manually check individual images. Further experimental results show that our methods are particularly beneficial for applications such as data visualization, image retrieval, and image tag recommendation in the realm of large-scale image dataset analysis.

A novel sampling method for adaptive gradient-enhanced Kriging

This paper presents a novel infill-sampling strategy for adaptive gradient-enhanced Kriging (AGEK) that delivers superior results on a limited budget. The primary innovation of this method is the adaptive use of gradient information, blurring the line between Kriging and gradient-enhanced Kriging. To construct a flexible AGEK model that automatically determines whether to incorporate gradients, our proposed method unfolds in three stages: (1) primary infill-sampling, (2) secondary infill-sampling, and (3) modeling time stages. In the first stage, the primary infill-sampling technique identifies potential sample point sites. In the second stage, the secondary infill-sampling process decides whether to obtain only the response or both the response and gradient at the selected sample point. During this stage, a newly defined pseudo expected improvement reduction, pseudo integrated uncertainty reduction, and weight functions are incorporated into the secondary infill-sampling criteria. In the third stage, we propose a strategy to manage instances where training time becomes overly demanding. Benchmark test results validate the excellent performance of the proposed method. Finally, in application to an engineering problem, our method outperforms conventional approaches by producing more accurate results within a limited computational budget.

Attention-based automatic editing of virtual lectures for reduced production labor and effective learning experience

Recently there has been a surge in demand for online video-based learning, and the importance of high-quality educational videos is ever-growing. However, a uniform format of videos that neglects individual differences and the labor-intensive process of editing are major setbacks in producing effective educational videos. This study aims to resolve the issues by proposing an automatic lecture video editing pipeline based on each individual's attention pattern. In this pipeline, the eye-tracking data are obtained while each individual watches virtual lectures, which later go through multiple filters to define the viewer's locus of attention and to select the appropriate shot at each time point to create personalized videos. To assess the effectiveness of the proposed method, video characteristics, subjective evaluations of the learning experience, and objective eye-movement features were compared between differently edited videos (attention-based, randomly edited, professionally edited). The results showed that our method dramatically reduced the editing time, with similar video characteristics to those of professionally edited versions. Attention-based versions were also evaluated to be significantly better than randomly edited ones, and as effective as professionally edited ones. Eye-tracking results indicated that attention-based videos have the potential to decrease the cognitive load of learners. These results suggest that attention-based automatic editing can be a viable or even a better alternative to the human expert-dependent approach, and individually-tailored videos have the potential to heighten the learning experience and effect.

Metal matrix composite with superior ductility at 800 °C: 3D printed In718+ZrB2 by laser powder bed fusion

We investigated the microstructural and mechanical properties of ZrB2 fortified Inconel 718 (In718+ZrB2) superalloy metal matrix composite (MMC), which was produced via Laser Powder Bed Fusion (LPBF). 2 vol% ZrB2 nano powders (below 100 nm in diameter) were decorated on the surfaces of Inconel 718 alloy powders by high-speed blender. Microstructural analysis of the as-printed specimens showed that the ZrB2 decomposed during LPBF, which promoted the formation of homogeneously distributed (Zr, Ni)-based intermetallic and (Nb, Mo, Cr)-based boride nanoparticles in the matrix. The 3D printed In718+ZrB2 has remarkably lower porosity and smaller grain size compared to 3D printed In718 fabricated under the same LPBF conditions. The mechanical performance of the as-printed and heat-treated In718+ZrB2 showed significantly higher room temperature (RT) hardness, RT yield strength (σYS), and RT ultimate tensile strength (σUTS) compared to In718. High-temperature tensile tests at 800 °C showed that In718+ZrB2 has ∼10× tensile ductility with higher σYS (10 %) and σUTS (8 %) than pure In718.

악천후 환경에서도 견고한 박쥐 모방 시청각 융합 신경망 개발

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2023.2,[iv, 23 p. :]This paper deals with a neural network that mimics a bat and provides 3D information by tracking a moving object even in severe weather such as sudden fog or rain. In the case of an ultrasonic sensor, it is possible to obtain the location of an object because it is robust even in bad weather, but it is impossible to accurately predict the size of the object. In contrast, a visual sensor such as a camera can obtain the location and size of an object, but has a disadvantage in that it does not operate properly in harsh environment. We implemented a network that can provide 3D information of objects even in bad weather conditions by mapping information obtained from images and ultrasound. We demonstrated the performance of the network through intersection over union (IoU) values, and these experimental results showed that objects can be tracked even in severe weather through the mutual complement of ultrasound and camera sensors.한국과학기술원 :전기및전자공학부

Double c-VAE model을 통한 semiconductor process target ouput input parameter 예측

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2023.2,[iii, 26 p. :]This paper deals with the prediction of optimal parameters between linked processes by applying a deep learning CVAE model to the current semiconductor development system. In the process of semiconductor development, which requires a lot of time and money for one experiment, we learned the relationship between the target specification and the experimental input value to satisfy the specification with CVAE model to predict the optimal experimental variable input value and result value in advance. Experiments were conducted on predicting the optimal target value for each unit process of the spacer process to generate a uniform pattern and process optimization to satisfy the etching amount according to the process progress time of different materials. Finally, in order to reduce the measurement waiting and measurement time that take a long time in the semiconductor production process, the accuracy was improved by predicting the measurement value with the CVAE model through related parameters without actual measurement. The optimal input value was predicted by applying it to the actual mass production data of DRAM and testing the efficient model configuration according to the dimension of the experimental input value and target specification. A model that predicts experimental results for multiple experimental variables, a model that predicts experimental input variables to satisfy multiple experimental target results, and two CVAEs are configured to improve the target function of the two models at the same time, and an architecture for performance improvement was also experimented.한국과학기술원 :전기및전자공학부