Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to ...their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to ...their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to ...their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.
Pediatric tumors of the central nervous system are the most common cause of
cancer-related death in children. The five-year survival rate for high-grade
gliomas in children is less than 20\%. Due to ...their rarity, the diagnosis of
these entities is often delayed, their treatment is mainly based on historic
treatment concepts, and clinical trials require multi-institutional
collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a
landmark community benchmark event with a successful history of 12 years of
resource creation for the segmentation and analysis of adult glioma. Here we
present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which
represents the first BraTS challenge focused on pediatric brain tumors with
data acquired across multiple international consortia dedicated to pediatric
neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on
benchmarking the development of volumentric segmentation algorithms for
pediatric brain glioma through standardized quantitative performance evaluation
metrics utilized across the BraTS 2023 cluster of challenges. Models gaining
knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training
data will be evaluated on separate validation and unseen test mpMRI dataof
high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023
challenge brings together clinicians and AI/imaging scientists to lead to
faster development of automated segmentation techniques that could benefit
clinical trials, and ultimately the care of children with brain tumors.
Renewable energy sources normally provide low inertia, which allows a little time for controlling the system. The inertia emulation has recently been introduced for photovoltaic (PV) generation to ...fight against the small inertia and improve the energy efficiency. The main purpose of this article is to analyze the stability of a virtual inertia-based three-phase two-stage PV system for smart grid applications. Dynamics of DC/DC converter, DC/AC converter, AC side filter, virtual inertia controller, and controllers of the DC/DC and DC/AC converters are considered in the modeling procedure. Eigenvalue analysis is performed to evaluate the sensitivity of the design parameters on stability of a grid-connected virtual inertia PV system. Time-domain simulations using MATLAB/Simscape Power System toolbox are used to validate the eigenvalue analysis results.
Online deployment of pulsed power load (PPL) is one of the most challenging issues in DC shipboard integrated power systems (SIPSs), which leads to a multi-objective optimal control problem subject ...to various constraints in this paper. Since traditional model-based methods face difficulties in designing the optimal control policy and are prone to model inaccuracy and parameter uncertainty, there is an urgent need for a model-free and also high-performance control approach. Thus, a deep reinforcement learning (DRL) optimal control, which employs the twin-delayed deep deterministic policy gradient (TD3) algorithm, is presented in this paper. The DRL optimal control adopts a stack-based state observation technique to enhance learning and control performance, and it uses a multi-objective reward function design to signify the overall dynamic performance. Besides achieving the safe and fast online deployment of PPL, it also fulfills the regulation of DC bus voltage and the proportional current sharing among distributed generations (DGs). Moreover, the DRL control has an advantage in handling the ramp rate constraints of SIPS. The optimal control satisfying ramp rate constraints can be obtained through a deep learning process. The performance of the proposed DRL control is validated by case studies considering different load conditions.
To accommodate the pulsed power load (PPL) in the dc shipboard power system, the charging performance of the energy storage system (ESS) specialized for the PPL needs to be guaranteed, which leads to ...a challenging power system optimal control problem due to multiple objectives, operational constraints, complex nonlinear system structure, and uncertainties. This paper addresses this problem by using a model-free optimal control method based on the deep reinforcement learning (DRL). First, a dc shipboard power system optimal control problem with three control objectives and the input constraints is formulated, where three objectives include the fast ESS charge, the dc bus voltage regulation, and the proportional load current sharing. Then, to solve this problem, a DRL control framework based on the improved twin-delayed deep deterministic policy gradient (TD3) algorithm is developed, which adopts a modified critic network predicting technique and a stack-based data sampling strategy that are suitable for this fast-dynamic power system. The proposed method links the DRL framework with the optimal control. With the reward function being properly designed, the presented DRL control can well realize three control objectives. Case studies considering various operating conditions of the power system verify its effectiveness.
In this article, the charging control of the energy storage system for the pulse power load accommodation in a shipboard integrated power system (SIPS) is formulated as an optimal control problem. ...The SIPS is an input-affine nonlinear system with randomness and fast dynamics. The improved twin-delayed deep deterministic policy gradient algorithm -one of the deep reinforcement learning (DRL) algorithms, is proposed to solve this optimal control problem. The proposed DRL-based control solution considers the issues regarding the reward function design and input and ramp rate constraints handling for control variables. The proposed approach linked the optimal control and DRL framework. Test cases demonstrated that we could utilize DRL algorithms to control the nonlinear system with fast dynamics by following the specific reward function design, data sampling, and constraints handling techniques.
•An integrated simulation model of ground source heat pump system was proposed.•The proposed model was validated with three-year data from practical engineering.•Dynamic operation characteristics and ...system accessories should be considered.•15-year performances of systems with different thermal imbalance ratios were evaluated.•The increased thermal imbalance ratio can deteriorate the system performance.
This study presents an integrated predictive model for the long-term performance assessment of ground source heat pump (GSHP) systems, which comprehensively considers the dynamic heat loads of the buildings and heat pump units (HPUs), the 3D dynamic heat transfer process in the ground heat exchangers (GHEs) and the energy consumption by the system accessories particularly the pumps. The model achieved significantly improved predictions than the conventional models and was in satisfactory agreement with the field-test data of an in-operation GSHP system, demonstrating the importance of systematic consideration of the dynamic system operational characteristics and 3D heat transfer processes in the GHEs. The model was also utilized to analyse the performance degradation of GSHP system induced by the soil thermal imbalance. It was found that increasing the thermal imbalance ratio of the accumulative building load from 35% to 76% led to a 10.7°C further increase in GHE outlet water temperature, a half-folded cooling COP of the HPU and a shorter life-span.
