Understandings of classical and incremental backstepping controllers with model uncertainties

This paper suggests closed-loop analysis results for both classical and incremental backstepping controllers considering model uncertainties. First, transfer functions with each control algorithm under the model uncertainties, are compared with the ones for the nominal case. The effects of the model uncertainties on the closed-loop systems are critically assessed via investigations on stability conditions and performance metrics. Second, closed-loop characteristics with classical and incremental backstepping controllers under the model uncertainties are directly compared using derived common metrics from their transfer functions. This comparative study clarifies how the effects of the model uncertainties to the closed-loop system become different depending on the applied control algorithm. It also enables understandings about the effects of additional measurements in the incremental algorithm. Third, case studies are conducted assuming that the uncertainty exists only in one aerodynamic derivative estimate while the other estimates have true values. This facilitates systematic interpretations on the impacts of the uncertainty on the specific aerodynamic derivative estimate to the closed-loop system

Closed-loop analysis with incremental backstepping controller considering measurement bias

In this paper, closed loop system characteristics with an incremental backstepping controller are investigated through theoretical analysis when both measurement biases and model uncertainties exist. Incremental backstepping algorithm is proposed in previous studies to reduce model dependency of classical backstepping algorithm with additional measurements about state derivatives and control surface deflection angles. This research enables to have following critical understandings especially about the effects of biases on these additional measurements to system characteristics with incremental backstepping method. First, these biases do not affect a characteristic equation, so they do not have any influence about a condition for absolute stability. Second, these biases cause a steady state error, and model uncertainty in control effectiveness information starts to have an impact to it when these biases are additionally considered

SOLiDzipper: A High Speed Encoding Method for the Next-Generation Sequencing Data

Background Next-generation sequencing (NGS) methods pose computational challenges of handling large volumes of data. Although cloud computing offers a potential solution to these challenges, transferring a large data set across the internet is the biggest obstacle, which may be overcome by efficient encoding methods. When encoding is used to facilitate data transfer to the cloud, the time factor is equally as important as the encoding efficiency. Moreover, to take advantage of parallel processing in cloud computing, a parallel technique to decode and split compressed data in the cloud is essential. Hence in this review, we present SOLiDzipper, a new encoding method for NGS data. Methods The basic strategy of SOLiDzipper is to divide and encode. NGS data files contain both the sequence and non-sequence information whose encoding efficiencies are different. In SOLiDzipper, encoded data are stored in binary data block that does not contain the characteristic information of a specific sequence platform, which means that data can be decoded according to a desired platform even in cases of Illumina, Solexa or Roche 454 data. Results The main calculation time using Crossbow was 173 minutes when 40 EC2 nodes were involved. In that case, an analysis preparation time of 464 minutes is required to encode data in the latest DNA compression method like G-SQZ and transmit it on a 183 Mbit/s bandwidth. However, it takes 194 minutes to encode and transmit data with SOLiDzipper under the same bandwidth conditions. These results indicate that the entire processing time can be reduced according to the encoding methods used, under the same network bandwidth conditions. Considering the limited network bandwidth, high-speed, high-efficiency encoding methods such as SOLiDzipper can make a significant contribution to higher productivity in labs seeking to take advantage of the cloud as an alternative to local computing. Availability http://szipper.dinfree.com . Academic/non-profit: Binary available for direct download at no cost. For-profit: Submit request for for-profit license from the web-site

Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection images

This paper presents a fully automatic registration method of dental cone-beam computed tomography (CBCT) and face scan data. It can be used for a digital platform of 3D jaw-teeth-face models in a variety of applications, including 3D digital treatment planning and orthognathic surgery. Difficulties in accurately merging facial scans and CBCT images are due to the different image acquisition methods and limited area of correspondence between the two facial surfaces. In addition, it is difficult to use machine learning techniques because they use face-related 3D medical data with radiation exposure, which are difficult to obtain for training. The proposed method addresses these problems by reusing an existing machine-learning-based 2D landmark detection algorithm in an open-source library and developing a novel mathematical algorithm that identifies paired 3D landmarks from knowledge of the corresponding 2D landmarks. A main contribution of this study is that the proposed method does not require annotated training data of facial landmarks because it uses a pre-trained facial landmark detection algorithm that is known to be robust and generalized to various 2D face image models. Note that this reduces a 3D landmark detection problem to a 2D problem of identifying the corresponding landmarks on two 2D projection images generated from two different projection angles. Here, the 3D landmarks for registration were selected from the sub-surfaces with the least geometric change under the CBCT and face scan environments. For the final fine-tuning of the registration, the Iterative Closest Point method was applied, which utilizes geometrical information around the 3D landmarks. The experimental results show that the proposed method achieved an averaged surface distance error of 0.74 mm for three pairs of CBCT and face scan datasets.Comment: 8 pages, 6 figures, 2 table

ALTERATIONS IN JOINT KINEMATICS AND KINETICS DURING DOWNHILL RUNNING

The purpose of this investigation was to find how joint kinematics and kinetics during downhill running change compared to level running. Fifteen recreational runners ran on a force plate imbedded treadmill with three different slopes (0 º, -6º, and -9º) at a controlled speed of 3.2 m/s. Ten steps on each slope were selected for analysis. Increased knee flexion with decreased ankle plantar-flexion and hip flexion was found during downhill running compared to level running. Decreased peak propulsive ground reaction force and posterior impulse were found during downhill running compared to level running. Additionally, increased extension moment with increased negative joint power at the knee and decreased plantar-flexion moment with decreased negative joint power at the ankle were found during downhill running compared to level running

Understandings of incremental backstepping controller considering measurement delay with model uncertainty

In this paper, closed loop characteristics with an incremental backstepping (IBKS) controller are investigated with consideration of measurement delays and model uncertainties. To judge absolute stability of the system, a systematic analysis framework is proposed which examines the existence of unstable poles from a derived characteristic equation with high nonlinearity due to the considered measurement delays. One of the key findings from the analysis results is that the system is stable only when a specific relationship between the measurement delays is satisfied and this stability condition is affected by the model uncertainty. Critical understandings about individual and integrated effects of the measurement delays and the model uncertainties to the system are suggested through a comparative study. Verification and validation of the obtained properties from the framework are performed through simulations

Sequential Magnetic Resonance Imaging Finding of Intramedullary Spinal Cord Abscess including Diffusion Weighted Image: a Case Report

Intramedullary spinal cord abscess (ISCA) is a rare infection of the central nervous system. We describe the magnetic resonance imaging (MRI) findings, including the diffusion-weighted imaging (DWI) findings, of ISCA in a 78-year-old man. The initial conventional MRI of the thoracic spine demonstrated a subtle enhancing nodule accompanied by significant edema. On the follow-up MRI after seven days, the nodule appeared as a ring-enhancing nodule. The non-enhancing central portion of the nodule appeared hyperintense on DWI with a decreased apparent diffusion coefficient (ADC) value on the ADC map. We performed myelotomy and surgical drainage, and thick, yellowish pus was drained

High-fidelity 3D Human Digitization from Single 2K Resolution Images

High-quality 3D human body reconstruction requires high-fidelity and large-scale training data and appropriate network design that effectively exploits the high-resolution input images. To tackle these problems, we propose a simple yet effective 3D human digitization method called 2K2K, which constructs a large-scale 2K human dataset and infers 3D human models from 2K resolution images. The proposed method separately recovers the global shape of a human and its details. The low-resolution depth network predicts the global structure from a low-resolution image, and the part-wise image-to-normal network predicts the details of the 3D human body structure. The high-resolution depth network merges the global 3D shape and the detailed structures to infer the high-resolution front and back side depth maps. Finally, an off-the-shelf mesh generator reconstructs the full 3D human model, which are available at https://github.com/SangHunHan92/2K2K. In addition, we also provide 2,050 3D human models, including texture maps, 3D joints, and SMPL parameters for research purposes. In experiments, we demonstrate competitive performance over the recent works on various datasets.Comment: code page : https://github.com/SangHunHan92/2K2K, Accepted to CVPR 2023 (Highlight

Reliable Identification of Deep Sulcal Pits: The Effects of Scan Session, Scanner, and Surface Extraction Tool

Sulcal pit analysis has been providing novel insights into brain function and development. The purpose of this study was to evaluate the reliability of sulcal pit extraction with respect to the effects of scan session, scanner, and surface extraction tool. Five subjects were scanned 4 times at 3 MRI centers and other 5 subjects were scanned 3 times at 2 MRI centers, including 1 test-retest session. Sulcal pits were extracted on the white matter surfaces reconstructed with both Montreal Neurological Institute and Freesurfer pipelines. We estimated similarity of the presence of sulcal pits having a maximum value of 1 and their spatial difference within the same subject. The tests showed high similarity of the sulcal pit presence and low spatial difference. The similarity was more than 0.90 and the spatial difference was less than 1.7 mm in most cases according to different scan sessions or scanners, and more than 0.85 and about 2.0 mm across surface extraction tools. The reliability of sulcal pit extraction was more affected by the image processing-related factors than the scan session or scanner factors. Moreover, the similarity of sulcal pit distribution appeared to be largely influenced by the presence or absence of the sulcal pits on the shallow and small folds. We suggest that our sulcal pit extraction from MRI is highly reliable and could be useful for clinical applications as an imaging biomarker