Traveltime and amplitude calculation using a perturbation approach

Accurate amplitudes and correct traveltimes are critical factors that govern the quality of prestack migration images. Because we never know the correct velocity initially, recomputing traveltimes and amplitudes of updated velocity models can dominate the iterative prestack migration procedure. Most tomographic velocity updating techniques require the calculation of the change of traveltime due to local changes in velocity. For such locally updated velocity models, perturbation techniques can be a significantly more economic way of calculating traveltimes and amplitudes than recalculating the entire solutions from scratch. In this paper, we implement an iterative Born perturbation theory applied to the damped wave equation algorithm. Our iterative Born perturbation algorithm yields stable solutions for models having velocity contrasts of 30% about the initial velocity estimate, which is significantly more economic than recalculating the entire solution.This work was financially supported by National Research Laboratory Project of the Korea Ministry of Science and Technology, Brain Korea 21 project of the Korea Ministry of Education, grant No. R05-2000-00003 from the Basic Research Program of the Korea Science&Engineering Foundation, and grant No. PM10300 from Korea Ocean Research & Development Institute

Traveltime and amplitude calculations using the damped wave solution

Because of its computational efficiency, prestack Kirchhoff depth migration remains the method of choice for all but the most complicated geological depth structures. Further improvement in computational speed and amplitude estimation will allow us to use such technology more routinely and generate better images. To this end, we developed a new, accurate, and economical algorithm to calculate first-arrival traveltimes and amplitudes for an arbitrarily complex earth model. Our method is based on numerical solutions of the wave equation obtained by using well-established finite-difference or finite-element modeling algorithms in the Laplace domain, where a damping term is naturally incorporated in the wave equation. We show that solving the strongly damped wave equation is equivalent to solving the eikonal and transport equations simultaneously at a fixed reference frequency, which properly accounts for caustics and other problems encountered in ray theory. Using our algorithm, we can easily calculate first-arrival traveltimes for given models. We present numerical examples for 2-D acoustic models having irregular topography and complex geological structure using a finite-element modeling code.This work was financially supported by National Research Laboratory Project of the Korea Ministry of Science and Technology, Brain Korea 21 project of the Korea Ministry of Education, grant No. R05-2000-00003 from the Basic Research Program of the Korea Science&Engineering Foundation, and grant No. PM10300 from Korea Ocean Research & Development Institute

Technology Foresight Activities in Korea and in Countries Closing the Technology Gap

This article discusses technology foresight in selected countries which were politically dependent (colonial) before World War II and considered as "under-developed" in the post-war period. Most of them show considerable economic dynamism in the 1990s, which is not always based on their own scientific and technological capability. For this group of countries, national exercises in technology foresight are likely to be an important tool in planning the strategic direction for science and technology development in order to catch up economically as well as socially. In Korea, which has recently become an OECD member, comparative advantage based on factors such as low wages and protected industries are no longer effective as the economy is now wide opentp the world. Foresight is being used to look at comparative advantages based on Korea's own knowledge-creating activities. In southeast Asian countries foresigt is still in an infant stage, but most of these have medium-term planning cycles and have undertaken longer-term vision studies. In South Africa, a national foresight project is running, as is an adapting process to make the large national research organization fit. In Latin Americ, an agenda has been set up which indicatis the desire of several coutries to engage in foresight activities using different approaches

Performance Evaluation of a Novel Thermal Power Plant Process with Low-Temperature Selective Catalytic Reduction

We present the concept of a novel thermal power plant process in conjunction with low-temperature selective catalytic reduction (SCR). This process can be employed to achieve modern standards for NOx emissions and solve problems related to post-gas cleaning processes, such as thermal fatigue, catalyst damage, and an increase in differential pressure in the boiler. Therefore, this study is aimed at evaluating the performance of a novel flue-gas cleaning process for use in a thermal power plant, where a low-temperature SCR is implemented, along with the existing SCR. We developed a process model for a large-scale power plant, in which the thermal power plant was divided into a series of heat exchanger block models. The mass and energy balances were solved by considering the heat transfer interaction between the hot and cold sides to obtain the properties of each material flow. Using the process model, we performed a simulation of the new process. New optimal operating conditions were derived, and the effects that the new facilities have on the existing process were evaluated. The results show that the new process is feasible in terms of operating stability and cost, as well as showing an increase in the boiler thermal efficiency of up to 1.3%

Feasibility of Overground Gait Training Using a Joint-Torque-Assisting Wearable Exoskeletal Robot in Children with Static Brain Injury

Pediatric gait disorders are often chronic and accompanied by various complications, which challenge rehabilitation efforts. Here, we retrospectively analyzed the feasibility of overground robot-assisted gait training (RAGT) using a joint-torque-assisting wearable exoskeletal robot. In this study, 17 children with spastic cerebral palsy, cerebellar ataxia, and chronic traumatic brain injury received RAGT sessions. The Gross Motor Function Measure (GMFM), 6-min walk test (6 MWT), and 10-m walk test (10 MWT) were performed before and after intervention. The oxygen rate difference between resting and training was performed to evaluate the intensity of training in randomly selected sessions, while the Quebec User Evaluation of Satisfaction with assistive Technology 2.0 assessment was performed to evaluate its acceptability. A total of four of five items in the GMFM, gait speed on the 10 MWT, and total distance on the 6 MWT showed statistically significant improvement (p < 0.05). The oxygen rate was significantly higher during the training versus resting state. Altogether, six out of eight domains showed satisfaction scores more than four out of five points. In conclusion, overground training using a joint-torque-assisting wearable exoskeletal robot showed improvement in gross motor and gait functions after the intervention, induced intensive gait training, and achieved high satisfaction scores in children with static brain injury

Standard for the Quantification of a Sterilization Effect Using an Artificial Intelligence Disinfection Robot

Recent outbreaks and the worldwide spread of COVID-19 have challenged mankind with unprecedented difficulties. The introduction of autonomous disinfection robots appears to be indispensable as consistent sterilization is in desperate demand under limited manpower. In this study, we developed an autonomous navigation robot capable of recognizing objects and locations with a high probability of contamination and capable of providing quantified sterilization effects. In order to quantify the 99.9% sterilization effect of various bacterial strains, as representative contaminants with robots operated under different modules, the operating parameters of the moving speed, distance between the sample and the robot, and the radiation angle were determined. We anticipate that the sterilization effect data we obtained with our disinfection robot, to the best of our knowledge, for the first time, will serve as a type of stepping stone, leading to practical applications at various sites requiring disinfection

Nighttime Reflectance Generation in the Visible Band of Satellites

Visible (VIS) bands, such as the 0.675 &mu;m band in geostationary satellite remote sensing, have played an important role in monitoring and analyzing weather and climate change during the past few decades with coarse spatial and high temporal resolution. Recently, many deep learning techniques have been developed and applied in a variety of applications and research fields. In this study, we developed a deep-learning-based model to generate non-existent nighttime VIS satellite images using the Conditional Generative Adversarial Nets (CGAN) technique. For our CGAN-based model training and validation, we used the daytime image data sets of reflectance in the Communication, Ocean and Meteorological Satellite / Meteorological Imager (COMS/MI) VIS (0.675 &mu;m) band and radiance in the longwave infrared (10.8 &mu;m) band of the COMS/MI sensor over five years (2012 to 2017). Our results show high accuracy (bias = &minus;2.41 and root mean square error (RMSE) = 36.85 during summer, bias = &minus;0.21 and RMSE = 33.02 during winter) and correlation (correlation coefficient (CC) = 0.88 during summer, CC = 0.89 during winter) of values between the observed images and the CGAN-generated images for the COMS VIS band. Consequently, our CGAN-based model can be effectively used in a variety of meteorological applications, such as cloud, fog, and typhoon analyses during daytime and nighttime