Proportional-Integral-Derivative Controller in Proportional Navigation Guidance

In this thesis, a Proportional-Integral-Derivative (PID) guidance scheme is discussed to improve the miss distance accuracy and the finite time stability problem in the Proportional Navigation Guidance (PNG). The primary goal of this study is to design the PID guidance that can accurately intercept the fast maneuvering target. The PID guidance is the extended version of the PNG with the integral and derivative terms in parallel. For the understanding of the conventional PNG model, the two-dimensional (2-D) engagement model of the missile and target is analyzed. Two characteristics are found in the PNG model: (1) its’ stability is kept in the finite time but becomes unstable at the vicinity of the interception and (2) the Line-of-sight angle rate (LOSR) increases as the target acceleration magnitude increases. To regulate the LOSR, the PID guidance is derived based on the servomechanism theory. The PID guidance model replaces the proportional gain of the conventional PNG model by the PID controller. A PID controller design using the numerical method through the iterative simulation is presented. For the various missile and target initial geometries, the capture region of the PID guidance is evaluated and compared with the conventional PNG model. In the end, the PID guidance model shows the improved miss distance accuracy, the extended stable time, and extended capture region when compared with the PNG model

GALA: Generating Animatable Layered Assets from a Single Scan

We present GALA, a framework that takes as input a single-layer clothed 3D human mesh and decomposes it into complete multi-layered 3D assets. The outputs can then be combined with other assets to create novel clothed human avatars with any pose. Existing reconstruction approaches often treat clothed humans as a single-layer of geometry and overlook the inherent compositionality of humans with hairstyles, clothing, and accessories, thereby limiting the utility of the meshes for downstream applications. Decomposing a single-layer mesh into separate layers is a challenging task because it requires the synthesis of plausible geometry and texture for the severely occluded regions. Moreover, even with successful decomposition, meshes are not normalized in terms of poses and body shapes, failing coherent composition with novel identities and poses. To address these challenges, we propose to leverage the general knowledge of a pretrained 2D diffusion model as geometry and appearance prior for humans and other assets. We first separate the input mesh using the 3D surface segmentation extracted from multi-view 2D segmentations. Then we synthesize the missing geometry of different layers in both posed and canonical spaces using a novel pose-guided Score Distillation Sampling (SDS) loss. Once we complete inpainting high-fidelity 3D geometry, we also apply the same SDS loss to its texture to obtain the complete appearance including the initially occluded regions. Through a series of decomposition steps, we obtain multiple layers of 3D assets in a shared canonical space normalized in terms of poses and human shapes, hence supporting effortless composition to novel identities and reanimation with novel poses. Our experiments demonstrate the effectiveness of our approach for decomposition, canonicalization, and composition tasks compared to existing solutions.Comment: The project page is available at https://snuvclab.github.io/gala

Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic Models

We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic details, the generation of 3D human meshes has been a challenging problem. To address this, we decompose the problem into 2D normal map generation and normal map-based 3D reconstruction. Specifically, we first simultaneously generate realistic normal maps for the front and backside of a clothed human, dubbed dual normal maps, using a pose-conditional diffusion model. For 3D reconstruction, we ``carve'' the prior SMPL-X mesh to a detailed 3D mesh according to the normal maps through mesh optimization. To further enhance the high-frequency details, we present a diffusion resampling scheme on both body and facial regions, thus encouraging the generation of realistic digital avatars. We also seamlessly incorporate a recent text-to-image diffusion model to support text-based human identity control. Our method, namely, Chupa, is capable of generating realistic 3D clothed humans with better perceptual quality and identity variety

Proposed Protocols for Artificial Intelligence Imaging Database in Acute Stroke Imaging

Purpose To propose standardized and feasible imaging protocols for constructing artificial intelligence (AI) database in acute stroke by assessing the current practice at tertiary hospitals in South Korea and reviewing evolving AI models. Materials and Methods A nationwide survey on acute stroke imaging protocols was conducted using an electronic questionnaire sent to 43 registered tertiary hospitals between April and May 2021. Imaging protocols for endovascular thrombectomy (EVT) in the early and late time windows and during follow-up were assessed. Clinical applications of AI techniques in stroke imaging and required sequences for developing AI models were reviewed. Standardized and feasible imaging protocols for data curation in acute stroke were proposed. Results There was considerable heterogeneity in the imaging protocols for EVT candidates in the early and late time windows and posterior circulation stroke. Computed tomography (CT)-based protocols were adopted by 70% (30/43), and acquisition of noncontrast CT, CT angiography and CT perfusion in a single session was most commonly performed (47%, 14/30) with the preference of multiphase (70%, 21/30) over single phase CT angiography. More hospitals performed magnetic resonance imaging (MRI)-based protocols or additional MRI sequences in a late time window and posterior circulation stroke. Diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) were most commonly performed MRI sequences with considerable variation in performing other MRI sequences. AI models for diagnostic purposes required noncontrast CT, CT angiography and DWI while FLAIR, dynamic susceptibility contrast perfusion, and T1-weighted imaging (T1WI) were additionally required for prognostic AI models. Conclusion Given considerable heterogeneity in acute stroke imaging protocols at tertiary hospitals in South Korea, standardized and feasible imaging protocols are required for constructing AI database in acute stroke. The essential sequences may be noncontrast CT, DWI, CT/MR angiography and CT/MR perfusion while FLAIR and T1WI may be additionally required

Pediatric Cases of Recurrent Skull Giant Osteoma Misdiagnosed as Fibrous Dysplasia

Copyright © 2022 by Mutaz B. Habal, MD.ABSTRACT: Osteomas are benign mature bone tumors that typically arise in the skull. Osteomas larger than 3 cm in diameter are considered giant osteomas. Giant osteomas of the skull vault are very rare, especially in children; therefore, only a few cases have been reported in the literature. Although osteomas are usually asymptomatic, a large skull mass can cause headache, as well as esthetic disfigurement of the forehead. it can be misdiagnosed as other conditions, such as fibrous dysplasia, ossifying cephalhematoma, or other malignant bone tumors. Herein, the authors report 2 rare pediatric cases of giant osteomas mimicking fibrous dysplasia and their successful surgical excision. These cases showed good results without recurrence or complications on long-term follow-up after complete excision.N

Vehicle Routing Problem Considering Reconnaissance and Transportation

Troop movement involves transporting military personnel from one location to another using available means. To minimize damage from enemies, the military simultaneously uses reconnaissance and transportation units during troop movements. This paper proposes a vehicle routing problem considering reconnaissance and transportation (VRPCRT) for wartime troop movements. The VRPCRT is formulated as a mixed-integer programming model for minimizing the completion time of wartime troop movements and reconnaissance, and transportation vehicle routes were determined simultaneously in the VRPCRT. For this paper, an ant colony optimization (ACO) algorithm for the VRPCRT was also developed, and computational experiments were conducted to compare the ACO algorithm’s performance and that of the mixed-integer programming model. The performance of the ACO algorithm was shown to yield excellent results even for the real-size problem. Furthermore, a sensitivity analysis of the change in the number of reconnaissance and transportation vehicles was performed, and the effects of each type of vehicle on troop movement were analyzed

A BER-Suppressed PUF With an Amplification of Process Mismatch Effect in an Oscillator Collapse Topology

The physically unclonable function (PUF) has been implemented with circuits that perform amplification of randomly given small process mismatch by using an explicit amplifier or by making a signal path repeatedly experience the same delay skew in an oscillator. Though the amplifier approach provides a fast response, it is vulnerable to noise at the first stage of amplification. On the other hand, the oscillator-based scheme requires a longer time to develop a digital output while achieving good noise immunity. This article proposes a PUF circuit exploiting a hybrid architecture, which combines a process skew amplification scheme in an oscillator collapse topology. The proposed scheme compensates for the drawbacks of the two approaches while achieving merits of them, i.e., high sensitivity to process variation and good immunity to noise. The supply rails of an even-stage ring oscillator (RO) are alternately fed from a diode-based threshold-sampling block. An IC with an array of 128 PUF cells is fabricated in 40-nm CMOS, showing a native bit error rate (BER) of 0.027%. Processing of 7-b temporal majority voting (TMV7) with a 3.64% masking demonstrates an error-free operation in a nominal condition. It shows a BER of 0.0019% in the worst condition under a voltage range of 0.7–1.4 V and a temperature range of −40 °C to 125 °C.11Nsciescopu