Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML...
详细信息
Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting ...
详细信息
ISBN:
(数字)9798350361230
ISBN:
(纸本)9798350361247
Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting it with a laser. Despite its many advantages, large-scale production may be hampered by the large number of process parameters and the challenges associated with their optimization. We propose an automated parameter selection approach based on process signatures extracted from a parameterized simulation of the process. Specifically, we outline a rapid data-driven simulation method based on Physics-Informed Neural Network (PINN). This approach involves training a neural network to solve the partial differential equation describing the process at varying values of a parameter of interest (for example, the laser power), thus eliminating the need for repeated Finite Elements Method (FEM) simulations. Our preliminary experiments demonstrate the feasibility of our approach.
In the recent shift towards human-centric AI, the need for machines to accurately use natural language has become increasingly important. While a common approach to achieve this is to train large language models, this...
详细信息
In the field of integrated circuit (IC) testing, the detection of defects is crucial to ensure the reliability and functionality of the final product. Among the variety of fault models that can be used to target the m...
In the field of integrated circuit (IC) testing, the detection of defects is crucial to ensure the reliability and functionality of the final product. Among the variety of fault models that can be used to target the many possible defects in a circuit, delay faults (transition and path delay) have been used for many years. Lately, cell-aware testing (CAT) has been introduced as a different approach that aims to improve the detection of internal defects of standard cells: it involves using specific patterns to detect faults that could not be detected by common fault models (e.g., stuck-at and transition delay fault models). Both delay and cell-aware faults can be caused by several factors, such as manufacturing defects, environmental conditions, and aging effects. In this paper, we investigate the application of test patterns generated with the transition and path delay fault models in comparison with others developed with the cell-aware approach, in terms of fault coverage, pattern count and test generation time. Overall, the study shows that the combination of the path delay fault model and cell-aware testing can lead to improved fault coverage and lower test. The experimental results are presented over a wide range of open-source benchmarks and on a RISC-V design using a proprietary industrial technology library.
Nowadays, along with the trend of developing highly autonomous satellites, there is a strong motivation to improve real-time Precise Orbit Determination (POD), in particular for Low Earth Orbit (LEO) satellites. The d...
Nowadays, along with the trend of developing highly autonomous satellites, there is a strong motivation to improve real-time Precise Orbit Determination (POD), in particular for Low Earth Orbit (LEO) satellites. The development of Global Navigation Satellite System (GNSS) sensors allows to obtain low-noise measurements and provide a satellite with autonomous continuous tracking onboard. Following the deactivation of Selective Availability, a representative real-time positioning accuracy of 10 m is presently achieved by means of Global Positioning System (GPS) receivers on LEO satellites. The introduction of dynamical filtering methods has opened a new way to improve this accuracy by making use of measurements such as pseudorange or carrier-phase. This paper presents a Kalman filtering approach using pseudorange and pseudorange-rate measurements instead of pseudorange and carrier-phase ones, with advantages in terms of storage and processing requirements. An error of around 0.2 m and 1e-3 m/s for position and velocity is obtained, which is in line if not better w.r.t. other approaches.
This paper presents a lightweight AXI DMA controller architecture useful for embedded systems that do not require fully featured DMA controllers. Simulation is accomplished with VUnit, and implementation results are o...
This paper presents a lightweight AXI DMA controller architecture useful for embedded systems that do not require fully featured DMA controllers. Simulation is accomplished with VUnit, and implementation results are obtained on a Xilinx XC7Z010CLG400-1 FPGA. When compared with Xilinx's AXI DMA controller with the same configuration, the presented controller utilizes between 16 and 82% fewer resources with comparable speed.
The vast array of cloud providers present in today’s market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and co...
The vast array of cloud providers present in today’s market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module’s primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous Machine Learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase.
In this paper, a novel approach to visual servo control robotic systems is proposed. It is focused on developing a solution using 3D point features without recovering the rigid object’s pose. Pose-free motion is achi...
In this paper, a novel approach to visual servo control robotic systems is proposed. It is focused on developing a solution using 3D point features without recovering the rigid object’s pose. Pose-free motion is achieved using motion parameterization techniques based on dual numbers and dual vectors. Considering an imposed velocity field over the motion of the 3D point features ensemble, this work proposes a close-form solution to a visual servoing problem. The solution provides stable motion control while preserving the image features in the field of view. However, when some point features leave the field of view, their contribution to the control law is dropped without losing stability. The proposed solution is easy to tune and implement. Various scenarios are used in simulations and real experiments to show how the proposed solution overcomes classic servoing problems.
This work presents the study and development of a high-gain hybrid DC-DC converter with switched capacitor for photovoltaic energy applications. Qualitative analyzes and quantitative values of the converter are propos...
This work presents the study and development of a high-gain hybrid DC-DC converter with switched capacitor for photovoltaic energy applications. Qualitative analyzes and quantitative values of the converter are proposed. The proposed converter is based on the converter boost integrated into switched capacitive cells with the addition of a small inductor resonant. A mathematical modeling of the converter was developed to determine the value of the resonant inductance, where the converter was analyzed for inductance values of 1uH at 2uH. This proposal presented some advantages: such as extended static gain, reduction of voltage stress in semiconductors and reduction of current peaks in switches. A 200 W prototype was developed and its respective results are presented. Maximum efficiency obtained was 97.1% for a voltage gain of 10.
This paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal discharge. The PD...
详细信息
ISBN:
(数字)9798350374605
ISBN:
(纸本)9798350386165
This paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The Artificial Neural Network for the classification model was constructed. Moreover, 2×1D CNN feature extraction was utilized to reduce the curse of dimensionality in the dense layer of the proposed PD classification model. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed.
暂无评论