Analysis and design of complex technical systems in the field of electrical and thermal power engineering requires modeling of both continuous processes occurring in these systems and discrete control processes associ...
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ISBN:
(数字)9798350349818
ISBN:
(纸本)9798350349825
Analysis and design of complex technical systems in the field of electrical and thermal power engineering requires modeling of both continuous processes occurring in these systems and discrete control processes associated with switching and setting various operating modes. models describing such systems belong to the class of continuous-discrete ones. The paper considers a number of formal models. It is shown that there are no methods for modeling and analyzing continuous-discrete systems in which methods for studying discrete and continuous components would equally coexist. The article shows a method for constructing a matrix model, which is formed using a circuit graph. The basis is the adjacency matrix and the vector of functions. The proposed matrix model is used to solve differential and logical equations describing continuous and discrete processes, respectively. The problems of coupling continuous and discrete state models are shown. methods for solving high-dimensional matrix equations are considered.
Diffusion-Based Purification (DBP) is a stochasticity-based defense method that effectively mitigates adversarial attacks but suffers from high computational costs due to its iterative reverse process. To address this...
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ISBN:
(数字)9798331531225
ISBN:
(纸本)9798331531232
Diffusion-Based Purification (DBP) is a stochasticity-based defense method that effectively mitigates adversarial attacks but suffers from high computational costs due to its iterative reverse process. To address this, we propose ConsistencyPure, a novel purification framework leveraging a pre-trained consistency model distilled from diffusion models. ConsistencyPure requires only one forward and one reverse sampling step, achieving real-time purification while maintaining comparable defense performance and improving efficiency by one to two orders of magnitude. Our finding further confirms that the robustness of DBP stems primarily from randomness, independent of the diffusion framework’s iterative operations or noise prediction. Importantly, we highlight for the first time the crucial role of model representation learning in stochasticity- based defense methods. Extensive experiments validate the effectiveness and practicality of ConsistencyPure.
This work presents a simple linear policy for direct force control for quadrupedal robot locomotion. The motivation is that force control is essential for highly dynamic and agile motions. We learn a linear policy to ...
This work presents a simple linear policy for direct force control for quadrupedal robot locomotion. The motivation is that force control is essential for highly dynamic and agile motions. We learn a linear policy to generate end-foot trajectory parameters and a centroidal wrench, which is then distributed among the legs based on the foot contact information using a quadratic program (QP) to get the desired ground reaction forces. Unlike the majority of the existing works that use complex nonlinear function approximators to represent the RL policy or model predictive control (MPC) methods with many optimization variables in the order of hundred, our controller uses a simple linear function approximator to represent policy along with only a twelve variable QP for the force distribution. A centroidal dynamics-based MPC method is used to generate reference trajectory data, and then the linear policy is trained using imitation learning to minimize the deviations from the reference trajectory. We demonstrate this compute-efficient controller on our robot Stoch3 in simulation and real-world experiments on indoor and outdoor terrains with push recovery.
A key source of brittleness for robotic systems is the presence of model uncertainty and external disturbances. Most existing approaches to robust control either seek to bound the worst-case disturbance (which results...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
A key source of brittleness for robotic systems is the presence of model uncertainty and external disturbances. Most existing approaches to robust control either seek to bound the worst-case disturbance (which results in conservative behavior), or to learn a deterministic dynamics model (which is unable to capture uncertain dynamics or disturbances). This work proposes a different approach: training a state-conditioned generative model to represent the distribution of error residuals between the nominal dynamics and the actual system. In particular we introduce the Online Risk-Informed Optimization controller (ORIO), which uses Discrete-Time Control Barrier Functions, combined with a learned, generative disturbance model, to ensure the safety of the system up to some level of risk. We demonstrate our approach in simulations and hardware, and show that our method can learn a disturbance model that is accurate enough to enable risk-sensitive control of a quadrotor flying aggressively with an unmodelled slung load. We use a conditional variational autoencoder (CVAE) to learn a state-conditioned dynamics residual distribution, and find that the resulting controller can run at 100Hz on an embedded computer and exhibits less conservative behavior while retaining theoretical safety properties.
The use of AI in public spaces continually raises concerns about privacy and the protection of sensitive data. An example is the deployment of detection and recognition methods on humans, where images are provided by ...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
The use of AI in public spaces continually raises concerns about privacy and the protection of sensitive data. An example is the deployment of detection and recognition methods on humans, where images are provided by surveillance cameras. This results in the acquisition of great amounts of sensitive data, since the capture and transmission of images taken by such cameras happens unaltered, for them to be received by a server on the network. However, many applications do not explicitly require the identity of a given person in a scene; An anonymized representation containing information of the person’s position while preserving the context of them in the scene suffices. We show how using a customized loss function on region of interests (ROI) can achieve sufficient anonymization such that human faces become unrecognizable while persons are kept detectable, by training an end-to-end optimized autoencoder for learned image compression that utilizes the flexibility of the learned analysis and reconstruction transforms for the task of mutating parts of the compression result. This approach enables compression and anonymization in one step on the capture device, instead of transmitting sensitive, nonanonymized data over the network. Additionally, we evaluate how this anonymization impacts the average precision of pre-trained foundation models on detecting faces (MTCNN) and humans (YOLOv8) in comparison to non-ANN based methods, while considering compression rate and latency. The code and model weights for this approach, including usage examples are publicly available on GitHub: https://***/BRN-Hub/anon-compression
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study shows that the existing molecular gener...
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In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study shows that the existing molecular generative methods and docking scores both have lacked consideration in terms of specificity, which means that generated molecules bind to almost every protein pocket with high affinity. To address this, we introduce the Delta Score, a new metric for evaluating the specificity of molecular binding. To further incorporate this insight for generation, we develop an innovative energy-guided approach using contrastive learning, with active compounds as decoys, to direct generative models toward creating molecules with high specificity. Our empirical results show that this method not only enhances the delta score but also maintains or improves traditional docking scores, successfully bridging the gap between SBDD and real-world needs. Copyright 2024 by the author(s)
This paper addresses the linear auto-calibration problem of a pan-tilt-zoom (PTZ) camera. Unlike existing methods, we take full advantage of the offset of the camera center from the rotation center, which is usually n...
This paper addresses the linear auto-calibration problem of a pan-tilt-zoom (PTZ) camera. Unlike existing methods, we take full advantage of the offset of the camera center from the rotation center, which is usually non-negligible in bullet-type PTZ cameras. Without any prior assumption, we propose a linear method to recover all intrinsic parameters. First, we successively acquired at least four images using the zoom and rotation capabilities of the PTZ camera. Second, using the homography of two images at the same location but different scales, the principal point and zoom scalar can be linearly recovered. Finally, based on the unknown offset of the camera center and rotation center, we propose a linear method to solve the scale factor in the Kruppa equation and recover the remaining camera intrinsic parameters, namely focal lengths and skew. Synthetic and real experiments demonstrate the feasibility of our approach.
Event cameras are a novel type of sensor designed for capturing the dynamic changes of a scene. Due to factors such as trigger and transmission delays, a time offset exists in the data collected by multiple event came...
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Detecting surface defects in carbon fiber-reinforced composites (FRCs) during prepreg stacking is crucial for ensuring product quality. Traditional methods using horizontal bounding boxes (HBBs) for defect detection o...
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ISBN:
(数字)9798350373820
ISBN:
(纸本)9798350373837
Detecting surface defects in carbon fiber-reinforced composites (FRCs) during prepreg stacking is crucial for ensuring product quality. Traditional methods using horizontal bounding boxes (HBBs) for defect detection often lead to overlapping boxes and inaccurate results. This paper proposes an Aspect-Ratio and Scale Aware Detector network (ARSADet) specifically designed for rotated defect detection in FRCs. The network incorporates a Gabor convolutional network for feature extraction, an aspect-ratio and scale-aware label assignment method, and a newly established FRCs Oriented Defect Detection dataset (FODD). Evaluation on the FODD dataset demonstrates superior performance compared to existing methods.
Existing Large Multimodal models (LMMs) demonstrate excellent performance in handling visual tasks in everyday scenarios. However, they still face challenges in understanding structured images, such as flowcharts and ...
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