Temporal Interpolation Is All You Need for Dynamic Neural Radiance Fields

Temporal interpolation often plays a crucial role to learn meaningful representations in dynamic scenes. In this paper, we propose a novel method to train spatiotemporal neural radiance fields of dynamic scenes based on temporal interpolation of feature vectors. Two feature interpolation methods are suggested depending on underlying representations, neural networks or grids. In the neural representation, we extract features from space-time inputs via multiple neural network modules and interpolate them based on time frames. The proposed multi-level feature interpolation network effectively captures features of both short-term and long-term time ranges. In the grid representation, space-time features are learned via four-dimensional hash grids, which remarkably reduces training time. The grid representation shows more than 100 times faster training speed than the previous neural-net-based methods while maintaining the rendering quality. Concatenating static and dynamic features and adding a simple smoothness term further improve the performance of our proposed models. Despite the simplicity of the model architectures, our method achieved state-of-the-art performance both in rendering quality for the neural representation and in training speed for the grid representation.Comment: CVPR 2023. Project page: https://sungheonpark.github.io/tempinterpner

Identification of Contributing Factors to Organizational Resilience in the Emergency Response Organization for Nuclear Power Plants

Resilience engineering is a new approach to safety, focused on systems for coping with complexity and balancing productivity with safety. Since the early 2000s, several studies have been conducted on the application of resilience to various industries. However, the nuclear industry has yet to harness the full potential of the resilience concept. The International Atomic Energy Agency (IAEA) gave an inkling of the use of this concept in its report on the human and organizational factors related to the Fukushima nuclear power plant (NPP) accident. Although the ability of emergency response organizations (EROs) to reduce the radiation risks to the public in the case of accidents is crucial, no method has been developed so far to evaluate ERO resilience in NPPs. This paper aims to determine the factors that contribute to the resilience of EROs in NPPs. This work commenced by providing a systematic review of the literature on resilience factors as applied in several domains within the last two decades, including general domains, healthcare, transportation, infrastructure, process plants, and business. Based on the review, and the application of additional procedures like resilience analysis grid filtering, ERO applicability assessment, and merger/reclassification, the resilience factors are determined. Fifty-two factors contributing to the resilience of EROs in NPPs are proposed. The identified contributing factors are expected to aid efforts to develop resilience strategies and to measure the resilience of EROs in NPPs

Mission Overview of Engineering Test Satellite, KITSAT-3

SaTReC has developed and operated two micro-satellites, KITSAT-1 and 2 successfully. Since middle of 1994, the third satellite, KITSAT-3, has been being developed. Its main mission is to perform onorbit engineering tests of core technologies for high performance small satellite. This includes 3- axis stabilization, high speed data transmission and solar panel deployment. In addition to engineering tests, there are 3 payloads; Earth Observation System (EOS) using a linear CCD camera with 17m resolution and 3 spectral bands, Space Environment Scientific Experiment (SENSE) and KITSAT Data Collection System (KDCS). KITSAT-3 is planned to be launched in middle of 1997. This paper briefly describes its mission, system configuration and operation plans for payloads and bus system of KITSAT-3

TELEMETRY AND TELECOMMAND SYSTEM OF LOW-EARTH-ORBIT MICROSATELLITE, KITSAT-1 AND 2

The telecommand system of KITSAT micorsatellite receives commands from ground stations or on-board computers. It decodes, validates and delivers commands to sub-system. The telemetry system is to collect, process and format satellite housekeeping and mission data for use by on-board computer and ground station. It is crucial for the telemetry and telecommand system to have high reliability since the spacecraft operation is mostly based on the function of this system. The telemetry and telecommand(TTC) systems for KITSAT-1 and 2 had been developed under the consideratin of the space environment of Low-Earth-Orbit and the limited mass, volume and power of micorsatellite. Since both satellites were launched in August 1992 and September 1993 respectively, the have shown to be working successfully as well as the TTC systems on-board both satellites

Assessment of tumor treatment response using active contrast encoding (ACE)-MRI: Comparison with conventional DCE-MRI.

PurposeTo investigate the validity of contrast kinetic parameter estimates from Active Contrast Encoding (ACE)-MRI against those from conventional Dynamic Contrast-Enhanced (DCE)-MRI for evaluation of tumor treatment response in mouse tumor models.MethodsThe ACE-MRI method that incorporates measurement of T1 and B1 into the enhancement curve washout region, was implemented on a 7T MRI scanner to measure tracer kinetic model parameters of 4T1 and GL261 tumors with treatment using bevacizumab and 5FU. A portion of the same ACE-MRI data was used for conventional DCE-MRI data analysis with a separately measured pre-contrast T1 map. Tracer kinetic model parameters, such as Ktrans (permeability area surface product) and ve (extracellular space volume fraction), estimated from ACE-MRI were compared with those from DCE-MRI, in terms of correlation and Bland-Altman analyses.ResultsA three-fold increase of the median Ktrans by treatment was observed in the flank 4T1 tumors by both ACE-MRI and DCE-MRI. In contrast, the brain tumors did not show a significant change by the treatment in either ACE-MRI or DCE-MRI. Ktrans and ve values of the tumors from ACE-MRI were strongly correlated with those from DCE-MRI methods with correlation coefficients of 0.92 and 0.78, respectively, for the median values of 17 tumors. The Bland-Altman plot analysis showed a mean difference of -0.01 min-1 for Ktrans with the 95% limits of agreement of -0.12 min-1 to 0.09 min-1, and -0.05 with -0.37 to 0.26 for ve.ConclusionThe tracer kinetic model parameters estimated from ACE-MRI and their changes by treatment closely matched those of DCE-MRI, which suggests that ACE-MRI can be used in place of conventional DCE-MRI for tumor progression monitoring and treatment response evaluation with a reduced scan time

Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function

Purpose: In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. Materials and Method: A total of 13 healthy subjects (younger ( 67 y/o): 5) were recruited and underwent 25-min DCE-MRI scans. The 25 min data were retrospectively truncated to 10 min to simulate a reduced scan time of 10 min. A deep learning network was trained to predict the CIF using simulated tissue contrast dynamics with two vascular transport models. The BBB permeability (PS) was measured using 3 methods: (i) Ca-25min, using DCE-MRI data of 25 min with individually sampled AIF (Ca); (ii) Ca-10min, using truncated 10min data with AIF (Ca); and (iii) Cp-10min, using truncated 10 min data with CIF (Cp). The PS estimates from the Ca-25min method were used as reference standard values to assess the accuracy of the Ca-10min and Cp-10min methods in estimating the PS values. Results: When compared to the reference method(Ca-25min), the Ca-10min and Cp-10min methods resulted in an overestimation of PS by 217 ± 241 % and 48.0 ± 30.2 %, respectively. The Bland Altman analysis showed that the mean difference from the reference was 8.85 ± 1.78 (x10−4 min−1) with the Ca-10min, while it was reduced to 1.63 ± 2.25 (x10−4 min−1) with the Cp-10min, resulting in an average reduction of 81%. The limits of agreement also reduced by up to 39.2% with the Cp-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. Conclusions: We demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention