pwc-archive/papers-with-abstracts huggingface dataset:
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection
Authors: Yuanchun Li, Ziqi Zhang, Bingyan Liu, Ziyue Yang, Yunxin Liu
Date: 2021-06-11 00:00:00
Tasks: Deep Learning, Model Compression, Transfer Learning

The knowledge of a deep learning model may be transferred to a student model, leading to intellectual property infringement or vulnerability propagation. Detecting such knowledge reuse is nontrivial because the suspect models may not be white-box accessible and/or may serve different tasks. In this paper, we propose ModelDiff, a testing-based approach to deep learning model similarity comparison. Instead of directly comparing the weights, activations, or outputs of two models, we compare their behavioral patterns on the same set of test inputs. Specifically, the behavioral pattern of a model is represented as a decision distance vector (DDV), in which each element is the distance between the model's reactions to a pair of inputs. The knowledge similarity between two models is measured with the cosine similarity between their DDVs. To evaluate ModelDiff, we created a benchmark that contains 144 pairs of models that cover most popular model reuse methods, including transfer learning, model compression, and model stealing. Our method achieved 91.7% correctness on the benchmark, which demonstrates the effectiveness of using ModelDiff for model reuse detection. A study on mobile deep learning apps has shown the feasibility of ModelDiff on real-world models.
Mean-value exergy modeling of internal combustion engines: characterization of feasible operating regions
Authors: Gabriele Pozzato, Denise Rizzo, Simona Onori
Date: 2021-06-16 00:00:00

In this paper, a novel mean-value exergy-based modeling framework for internal combustion engines is developed. The characterization of combustion irreversibilities, thermal exchange between the in-cylinder mixture and the cylinder wall, and non-stoichiometric combustion allows for a comprehensive description of the availability transfer and destruction phenomena in the engine. The model is applicable to internal combustion engines operating both in steady-state and over a sequence of operating points and can be used to characterize the whole engine operating region, allowing to create static maps describing the exergetic behavior of the engine as a function of speed and load. The application of the proposed modeling strategy is shown for a turbocharged diesel engine. Ultimately, the static maps, while providing insightful information about inefficiencies over the whole operating field of the engine, are the enabling step for the development of exergy-based control strategies aiming at minimizing the overall operational losses of ground vehicles.
Nonlinear Trajectory-Based Region of Attraction Estimation for Aircraft Dynamics Analysis
Authors: Brian Lai, Torbjørn Cunis, Laurent Burlion
Date: 2021-06-16 00:00:00

Current flight control validation is heavily based on linear analysis and high fidelity, nonlinear simulations. Continuing developments of nonlinear analysis tools for flight control has greatly enhanced the validation process. Many analysis tools are reliant on assuming the analytical flight dynamics but this paper proposes an approach using only simulation data. First, this paper presents improvements to a method for estimating the region of attraction (ROA) of nonlinear systems governed by ordinary differential equations (ODEs) based only on trajectory measurements. Faster and more accurate convergence to the true ROA results. These improvements make the proposed algorithm feasible in higher-dimensional and more complex systems. Next, these tools are used to analyze the four-state longitudinal dynamics of NASA's Generic Transport Model (GTM) aircraft. A piecewise polynomial model of the GTM is used to simulate trajectories and the developed analysis tools are used to estimate the ROA around a trim condition based only on this trajectory data. Finally, the algorithm presented is extended to estimate the ROA of finitely many equilibrium point systems and of general equilibrium set (arbitrary equilibrium points and limit cycles) systems.
Regularization-Induced Bias and Consistency in Recursive Least Squares
Authors: Brian Lai, Syed Aseem Ul Islam, Dennis S. Bernstein
Date: 2021-06-16 00:00:00
Tasks: parameter estimation, regression

Within the context of recursive least squares (RLS) parameter estimation, the goal of the present paper is to study the effect of regularization-induced bias on the transient and asymptotic accuracy of the parameter estimates. We consider this question in three stages. First, we consider regression with random data, in which case persistency is guaranteed. Next, we apply RLS to finite-impulse-response (FIR) system identification and, finally, to infinite-impulse-response (IIR) system identification. For each case, we relate the condition number of the regressor matrix to the transient response and rate of convergence of the parameter estimates.
Reflector Antennas Characterization and Diagnostics using a Single Set of Far Field Phaseless Data and Crosswords-like Processing
Authors: R. Palmeri, G. M. Battaglia, A. F. Morabito, T. Isernia
Date: 2021-06-16 00:00:00
Tasks: Retrieval

We introduce and discuss a new approach to the phase retrieval of fields radiated by continuous aperture sources having a circular support, which is of interest in many applications including the detection of shape deformations on reflector antennas. The approach is based on a decomposition of the actual 2-D problem into a number of 1-D phase retrieval problems along diameters and concentric rings of the visible part of the spectrum. In particular, the 1-D problems are effectively solved by using the spectral factorization method, while discrimination arguments at the crossing points allows to complete the retrieval of the 2-D complex field. The proposed procedure, which just requires a single set of far field amplitudes, takes advantage from up to now unexplored field properties and it is assessed in terms of reflector aperture fields.
Persistent Excitation is Unnecessary for On-line Exponential Parameter Estimation: A New Algorithm that Overcomes this Obstacle
Authors: Marina Korotina, Jose Guadalupe Romero, Stanislav Aranovskiy, Alexey Bobtsov, Romeo Ortega
Date: 2021-06-16 00:00:00
Tasks: parameter estimation

In this paper, we prove that it is possible to estimate online the parameters of a classical vector linear regression equation $ Y=\Omega \theta$, where $ Y \in \mathbb{R}^n,\;\Omega \in \mathbb{R}^{n \times q}$ are bounded, measurable signals and $\theta \in \mathbb{R}^q$ is a constant vector of unknown parameters, even when the regressor $\Omega$ is not persistently exciting. Moreover, the convergence of the new parameter estimator is global and exponential and is given for both continuous-time and discrete-time implementations. As an illustration example, we consider the problem of parameter estimation of a linear time-invariant system, when the input signal is not sufficiently exciting, which is known to be a necessary and sufficient condition for the solution of the problem with the standard gradient or least-squares adaptation algorithms.
A Review of Lithium-Ion Battery Models in Techno-economic Analyses of Power Systems
Authors: Anton V. Vykhodtsev, Darren Jang, Qianpu Wang, Hamidreza Zareipour, William D. Rosehart
Date: 2021-06-16 00:00:00

The penetration of the lithium-ion battery energy storage system (BESS) into the power system environment occurs at a colossal rate worldwide. This is mainly because it is considered as one of the major tools to decarbonize, digitalize, and democratize the electricity grid. The economic viability and technical reliability of projects with batteries require appropriate assessment because of high capital expenditures, deterioration in charging/discharging performance and uncertainty with regulatory policies. Most of the power system economic studies employ a simple power-energy representation coupled with an empirical description of degradation to model the lithium-ion battery. This approach to modelling may result in violations of the safe operation and misleading estimates of the economic benefits. Recently, the number of publications on techno-economic analysis of BESS with more details on the lithium-ion battery performance has increased. The aim of this review paper is to explore these publications focused on the grid-scale BESS applications and to discuss the impacts of using more sophisticated modelling approaches. First, an overview of the three most popular battery models is given, followed by a review of the applications of such models. The possible directions of future research of employing detailed battery models in power systems' techno-economic studies are then explored.
MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles
Authors: WenQi Cai, Arash B. Kordabad, Hossein N. Esfahani, Anastasios M. Lekkas, Sebastien Gros
Date: 2021-06-16 00:00:00
Tasks: Model Predictive Control, Position, reinforcement-learning, Reinforcement Learning (RL)

In this work, we propose a Model Predictive Control (MPC)-based Reinforcement Learning (RL) method for Autonomous Surface Vehicles (ASVs). The objective is to find an optimal policy that minimizes the closed-loop performance of a simplified freight mission, including collision-free path following, autonomous docking, and a skillful transition between them. We use a parametrized MPC-scheme to approximate the optimal policy, which considers path-following/docking costs and states (position, velocity)/inputs (thruster force, angle) constraints. The Least Squares Temporal Difference (LSTD)-based Deterministic Policy Gradient (DPG) method is then applied to update the policy parameters. Our simulation results demonstrate that the proposed MPC-LSTD-based DPG method could improve the closed-loop performance during learning for the freight mission problem of ASV.
Minimization of Constraint Violation Probability in Model Predictive Control
Authors: Tim Brüdigam, Victor Gaßmann, Dirk Wollherr, Marion Leibold
Date: 2020-06-03 00:00:00
Tasks: Model Predictive Control

While Robust Model Predictive Control considers the worst-case system uncertainty, Stochastic Model Predictive Control, using chance constraints, provides less conservative solutions by allowing a certain constraint violation probability depending on a predefined risk parameter. However, for safety-critical systems it is not only important to bound the constraint violation probability but to reduce this probability as much as possible. Therefore, an approach is necessary that minimizes the constraint violation probability while ensuring that the Model Predictive Control optimization problem remains feasible. We propose a novel Model Predictive Control scheme that yields a solution with minimal constraint violation probability for a norm constraint in an environment with uncertainty. After minimal constraint violation is guaranteed the solution is then also optimized with respect to other control objectives. Further, it is possible to account for changes over time of the support of the uncertainty. We first present a general method and then provide an approach for uncertainties with symmetric, unimodal probability density function. Recursive feasibility and convergence of the method are proved. A simulation example demonstrates the effectiveness of the proposed method.
Collision Avoidance with Stochastic Model Predictive Control for Systems with a Twofold Uncertainty Structure
Authors: Tim Brüdigam, Jie Zhan, Dirk Wollherr, Marion Leibold
Date: 2021-06-15 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

Model Predictive Control (MPC) has shown to be a successful method for many applications that require control. Especially in the presence of prediction uncertainty, various types of MPC offer robust or efficient control system behavior. For modeling, uncertainty is most often approximated in such a way that established MPC approaches are applicable for specific uncertainty types. However, for a number of applications, especially automated vehicles, uncertainty in predicting the future behavior of other agents is more suitably modeled by a twofold description: a high-level task uncertainty and a low-level execution uncertainty of individual tasks. In this work, we present an MPC framework that is capable of dealing with this twofold uncertainty. A scenario MPC approach considers the possibility of other agents performing one of multiple tasks, with an arbitrary probability distribution, while an analytic stochastic MPC method handles execution uncertainty within a specific task, based on a Gaussian distribution. Combining both approaches allows to efficiently handle the twofold uncertainty structure of many applications. Application of the proposed MPC method is demonstrated in an automated vehicle simulation study.
Design and analysis of deployable clustered tensegrity cable domes
Authors: Shuo Ma, Muhao Chen, Xingfei Yuan, Robert E. Skelton
Date: 2021-06-15 00:00:00

This study presents the design and analysis of deployable cable domes based on the clustered tensegrity structures (CTS). In this paper, the statics and dynamics equations of the CTS are first given. Using a traditional Levy cable dome as an example, we show the approach to modify the Levy dome to a deployable CTS one. The strings to be clustered are determined by the requirement of prestress mode and global stability. The deployment trajectory is proposed by changing the deployment ratio (the ratio between the radius of the inner and outer rings of the cable dome). Then, the quasi-static and dynamic deployment of clustered tensegrity dome is studied. Results show that the proposed CTS cable dome always has one prestress mode and is globally stable in its deployment trajectory. In the deployment process analysis, the dynamics show that the system's dynamic response differs from the quasi-static simulation as the actuation speed increases. That is, for a fast deployment process, quasi-static simulation is not accurate enough. The dynamics effects of the deployment must be considered. The developed approaches can also be used for the design and analysis of various kinds of CTS.
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users
Authors: Xiaopeng Jiang, Shuai Zhao, Guy Jacobson, Rittwik Jana, Wen-Ling Hsu, Manoop Talasila, Syed Anwar Aftab, Yi Chen, Cristian Borcea
Date: 2021-06-13 00:00:00
Tasks: Federated Learning, Prediction, Transfer Learning

Fine-grained location prediction on smart phones can be used to improve app/system performance. Application scenarios include video quality adaptation as a function of the 5G network quality at predicted user locations, and augmented reality apps that speed up content rendering based on predicted user locations. Such use cases require prediction error in the same range as the GPS error, and no existing works on location prediction can achieve this level of accuracy. We present a system for fine-grained location prediction (FGLP) of mobile users, based on GPS traces collected on the phones. FGLP has two components: a federated learning framework and a prediction model. The framework runs on the phones of the users and also on a server that coordinates learning from all users in the system. FGLP represents the user location data as relative points in an abstract 2D space, which enables learning across different physical spaces. The model merges Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN), where BiLSTM learns the speed and direction of the mobile users, and CNN learns information such as user movement preferences. FGLP uses federated learning to protect user privacy and reduce bandwidth consumption. Our experimental results, using a dataset with over 600,000 users, demonstrate that FGLP outperforms baseline models in terms of prediction accuracy. We also demonstrate that FGLP works well in conjunction with transfer learning, which enables model reusability. Finally, benchmark results on several types of Android phones demonstrate FGLP's feasibility in real life.
Early fault detection with multi-target neural networks
Authors: Angela Meyer
Date: 2021-06-12 00:00:00
Tasks: Fault Detection

Wind power is seeing a strong growth around the world. At the same time, shrinking profit margins in the energy markets let wind farm managers explore options for cost reductions in the turbine operation and maintenance. Sensor-based condition monitoring facilitates remote diagnostics of turbine subsystems, enabling faster responses when unforeseen maintenance is required. Condition monitoring with data from the turbines' supervisory control and data acquisition (SCADA) systems was proposed and SCADA-based fault detection and diagnosis approaches introduced based on single-task normal operation models of turbine state variables. As the number of SCADA channels has grown strongly, thousands of independent single-target models are in place today for monitoring a single turbine. Multi-target learning was recently proposed to limit the number of models. This study applied multi-target neural networks to the task of early fault detection in drive-train components. The accuracy and delay of detecting gear bearing faults were compared to state-of-the-art single-target approaches. We found that multi-target multi-layer perceptrons (MLPs) detected faults at least as early and in many cases earlier than single-target MLPs. The multi-target MLPs could detect faults up to several days earlier than the single-target models. This can deliver a significant advantage in the planning and performance of maintenance work. At the same time, the multi-target MLPs achieved the same level of prediction stability.
A Direct Slip Ratio Estimation Method based on an Intelligent Tire and Machine Learning
Authors: Nan Xu, Zepeng Tang, Hassan Askari, Jianfeng Zhou, Amir Khajepour
Date: 2021-06-09 00:00:00
Tasks: Autonomous Vehicles, BIG-bench Machine Learning

Accurate estimation of the tire slip ratio is critical for vehicle safety, as it is necessary for vehicle control purposes. In this paper, an intelligent tire system is presented to develop a novel slip ratio estimation model using machine learning algorithms. The accelerations, generated by a triaxial accelerometer installed onto the inner liner of the tire, are varied when the tire rotates to update the contact patch. Meanwhile, the slip ratio reference value can be measured by the MTS Flat-Trac tire test platform. Then, by analyzing the variation between the accelerations and slip ratio, highly useful features are discovered, which are especially promising for assessing vertical acceleration. For these features, machine learning (ML) algorithms are trained to build the slip ratio estimation model, in which the ML algorithms include artificial neural networks (ANNs), gradient boosting machines (GBMs), random forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS errors are evaluated using 10-fold cross-validation (CV). The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%. The estimation results have high robustness to vehicle velocity and load, where the best NRMS errors can reach 4.88%. In summary, the present study with the fusion of an intelligent tire system and machine learning paves the way for the accurate estimation of the tire slip ratio under different driving conditions, which create new opportunities for autonomous vehicles, intelligent tires, and tire slip ratio estimation.
WaveNet-Based Deep Neural Networks for the Characterization of Anomalous Diffusion (WADNet)
Authors: Dezhong Li, Qiujin Yao, Zihan Huang
Date: 2021-06-14 00:00:00

Anomalous diffusion, which shows a deviation of transport dynamics from the framework of standard Brownian motion, is involved in the evolution of various physical, chemical, biological, and economic systems. The study of such random processes is of fundamental importance in unveiling the physical properties of random walkers and complex systems. However, classical methods to characterize anomalous diffusion are often disqualified for individual short trajectories, leading to the launch of the Anomalous Diffusion (AnDi) Challenge. This challenge aims at objectively assessing and comparing new approaches for single trajectory characterization, with respect to three different aspects: the inference of the anomalous diffusion exponent; the classification of the diffusion model; and the segmentation of trajectories. In this article, to address the inference and classification tasks in the challenge, we develop a WaveNet-based deep neural network (WADNet) by combining a modified WaveNet encoder with long short-term memory networks, without any prior knowledge of anomalous diffusion. As the performance of our model has surpassed the current 1st places in the challenge leaderboard on both two tasks for all dimensions (6 subtasks), WADNet could be the part of state-of-the-art techniques to decode the AnDi database. Our method presents a benchmark for future research, and could accelerate the development of a versatile tool for the characterization of anomalous diffusion.
GemNet: Universal Directional Graph Neural Networks for Molecules
Authors: Johannes Gasteiger, Florian Becker, Stephan Günnemann
Date: 2021-06-02 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Translation

Effectively predicting molecular interactions has the potential to accelerate molecular dynamics by multiple orders of magnitude and thus revolutionize chemical simulations. Graph neural networks (GNNs) have recently shown great successes for this task, overtaking classical methods based on fixed molecular kernels. However, they still appear very limited from a theoretical perspective, since regular GNNs cannot distinguish certain types of graphs. In this work we close this gap between theory and practice. We show that GNNs with directed edge embeddings and two-hop message passing are indeed universal approximators for predictions that are invariant to translation, and equivariant to permutation and rotation. We then leverage these insights and multiple structural improvements to propose the geometric message passing neural network (GemNet). We demonstrate the benefits of the proposed changes in multiple ablation studies. GemNet outperforms previous models on the COLL, MD17, and OC20 datasets by 34%, 41%, and 20%, respectively, and performs especially well on the most challenging molecules. Our implementation is available online.