pwc-archive/papers-with-abstracts huggingface dataset:
Generating Concise Patches for Newly Released Programming Assignments
Authors: Leping Li, Hui Liu, Kejun Li, Yanjie Jiang, and Rui Sun
Date: 2023-01-23 00:00:00
Proceeding: IEEE Transactions on Software Engineering 2023 1

In programming courses, providing students with concise and constructive feedback on faulty submissions (programs) is
highly desirable. However, providing feedback manually is often time-consuming and tedious. To release tutors from the manual
construction of concise feedback, researchers have proposed approaches such as CLARA and Refactoryto construct feedback
automatically. The key to such approaches is to ﬁx a faulty program by making it equivalent to one of its correct reference programs
whose overall structure is identical to that of the faulty submission. However, for a newly released assignment, it is likely that there are
no correct reference programs at all, let alone correct reference programs sharing identical structure with the faulty submission.
Therefore, in this paper, we propose AssignmentMendergenerating concise patches for newly released assignments. The key insight
of AssignmentMenderis that a faulty submission can be repaired by reusing ﬁne-grained code snippets from submissions (even when
they are faulty) for the same assignment. It automatically locates suspicious code in the faulty program and leverages static analysis to
retrieve reference code from existing submissions with a graph-based matching algorithm. Finally, it generates candidate patches by
modifying the suspicious code based on the reference code. Different from existing approaches, AssignmentMenderexploits faulty
submissions in addition to bug-free submissions to generate patches. Another advantage of AssignmentMenderis that it can leverage
submissions whose overall structures are different from those of the to-be-ﬁxed submission. Evaluation results on 128 faulty
submissions from 10 assignments show that AssignmentMenderimproves the state-of-the-art in feedback generation for newly
released assignments. A case study involving 40 students and 80 submissions further provides initial evidence showing that the
proposed approach is useful in practice.
Ensuring Solution Uniqueness in Fixed-Point-Based Harmonic Power Flow Analysis with Converter-Interfaced Resources: Ex-post Conditions
Authors: Antonio Di Pasquale, Johanna Kristin Maria Becker, Andreas Martin Kettner, Mario Paolone
Date: 2024-03-19 00:00:00

Recently, the authors of this paper proposed a method for the Harmonic Power-Flow (HPF) calculus in polyphase grids with widespread deployment of Converter-Interfaced Distributed Energy Resources (CIDERs). The HPF problem was formulated by integrating the hybrid nodal equations of the grid with a detailed representation of the CIDERs hardware, sensing, and controls as Linear Time-Periodic (LTP) systems, and solving the resulting mismatch equations using the Newton-Raphson (NR) method. This work introduces a novel problem formulation based on the fixed-point algorithm that, combined with the contraction property of the HPF problem, provides insights into the uniqueness of its solution. Notably, the effectiveness of the fixed-point formulation and the uniqueness of the solution are evaluated through numerical analyses conducted on a modified version of the CIGRE low-voltage benchmark microgrid.
Towards Particle Flow Event Reconstruction at the Future Circular Collider with GNNs
Authors: Dolores Garcia, Gregor Kržmanc, Philipp Zehetner, Jan Kieseler, Michele Selvaggi
Date: 2023-10-29 00:00:00
Proceeding: NeurIPS GLFrontiers Workshop 2023 10

Reconstructing particles properties from raw signals measured in particle physics detectors is a challenging task due to the complex shapes of the showers, variety in density and sparsity. Classical particle reconstruction algorithms in current detectors use a multi-step pipeline, but the increase in data complexity of future detectors will reduce their performance. We consider a geometric graph representation due to the sparsity and difference in density of particle showers. We introduce a dataset for particle level reconstruction at the Future Circular Collider and benchmark the performance of state-of-the-art GNN architectures on this dataset. We show that our pipeline performs with high efficiency and response and discuss how this type of data can further drive the development of novel geometric GNN approaches.
Towards Beyond Visual Line of Sight Piloting of UAVs with Ultra Reliable Low Latency Communication
Authors: Mustafa Ozger ，Michal Vondra， Cicek Cavdar
Date: 2018-12-13 00:00:00
Proceeding: IEEE 2018 12

在本文中，我们提出了一种用于无人机（UAV）远程驾驶的超视距（BVLOS）操作模型，该模型利用了移动边缘计算和增强现实等不同技术。超可靠低延迟通信 （URLLC） 是 5G 的一项关键服务，可实现安全的 BVLOS 操作。由于导航信号的报文大小是有限的，并且通信信道与高度有关，因此研究了不同高度在有限块长度制度下的可靠性和延迟。在我们的数值研究中，我们发现对于消息大小为 30 和 50 位，编码数据包大小（即块长度）应在 200 和 300 位范围内，以实现 BVLOS 操作。我们还发现，对于15 m/s的无人机速度和1.5 m至120 m的不同高度，无人机之间的最小距离应在0.2 m左右，以避免任何碰撞。根据我们的研究，URLLC可以通过提供10附近的误差概率来实现无人机的BVLOS操作-3，下行链路通信的延迟约为几毫秒，块长度为数十到数百位
Fragile Stable Matchings
Authors: Kirill Rudov
Date: 2024-03-18 00:00:00
Tasks: Blocking

We show how fragile stable matchings are in a decentralized one-to-one matching setting. The classical work of Roth and Vande Vate (1990) suggests simple decentralized dynamics in which randomly-chosen blocking pairs match successively. Such decentralized interactions guarantee convergence to a stable matching. Our first theorem shows that, under mild conditions, any unstable matching -- including a small perturbation of a stable matching -- can culminate in any stable matching through these dynamics. Our second theorem highlights another aspect of fragility: stabilization may take a long time. Even in markets with a unique stable matching, where the dynamics always converge to the same matching, decentralized interactions can require an exponentially long duration to converge. A small perturbation of a stable matching may lead the market away from stability and involve a sizable proportion of mismatched participants for extended periods. Our results hold for a broad class of dynamics.
A New Intelligent Reflecting Surface-Aided Electromagnetic Stealth Strategy
Authors: Xue Xiong, Beixiong Zheng, A. Lee Swindlehurst, Jie Tang, Wen Wu
Date: 2024-03-19 00:00:00

Electromagnetic wave absorbing material (EWAM) plays an essential role in manufacturing stealth aircraft, which can achieve the electromagnetic stealth (ES) by reducing the strength of the signal reflected back to the radar system. However, the stealth performance is limited by the coating thickness, incident wave angles, and working frequencies. To tackle these limitations, we propose a new intelligent reflecting surface (IRS)-aided ES system where an IRS is deployed at the target to synergize with EWAM for effectively mitigating the echo signal and thus reducing the radar detection probability. Considering the monotonic relationship between the detection probability and the received signal-to-noise-ratio (SNR) at the radar, we formulate an optimization problem that minimizes the SNR under the reflection constraint of each IRS element, and a semi-closed-form solution is derived by using Karush-Kuhn-Tucker (KKT) conditions. Simulation results validate the superiority of the proposed IRS-aided ES system compared to various benchmarks.
Context-aware LLM-based Safe Control Against Latent Risks
Authors: Xiyu Deng, Quan Khanh Luu, Anh Van Ho, Yorie Nakahira
Date: 2024-03-18 00:00:00
Tasks: In-Context Learning, Model Predictive Control

Autonomous control systems face significant challenges in performing complex tasks in the presence of latent risks. To address this, we propose an integrated framework that combines Large Language Models (LLMs), numerical optimization, and optimization-based control to facilitate efficient subtask learning while ensuring safety against latent risks. The framework decomposes complex tasks into a sequence of context-aware subtasks that account for latent risks. These subtasks and their parameters are then refined through a multi-time-scale process: high-layer multi-turn in-context learning, mid-layer LLM Chain-of-Thought reasoning and numerical optimization, and low-layer model predictive control. The framework iteratively improves decisions by leveraging qualitative feedback and optimized trajectory data from lower-layer optimization processes and a physics simulator. We validate the proposed framework through simulated case studies involving robot arm and autonomous vehicle scenarios. The experiments demonstrate that the proposed framework can mediate actions based on the context and latent risks and learn complex behaviors efficiently.
Reproducing the Acoustic Velocity Vectors in a Circular Listening Area
Authors: Jiarui Wang, Thushara Abhayapala, Jihui Aimee Zhang, Prasanga Samarasinghe
Date: 2024-03-19 00:00:00

Acoustic velocity vectors are important for human's localization of sound at low frequencies. This paper proposes a sound field reproduction algorithm, which matches the acoustic velocity vectors in a circular listening area. In previous work, acoustic velocity vectors are matched either at sweet spots or on the boundary of the listening area. Methods based on sweet spots experience performance degradation when the listener moves away from sweet spots, whereas measuring the acoustic velocity vectors on the boundary requires complicated measurement setup. This paper proposes the radial independent cylindrical harmonic coefficients of the acoustic velocity vectors (CHV-indR coefficients) in the circular listening area, which are calculated from the cylindrical harmonic coefficients of the pressure in the circular listening area by using the sound field translation formula. The cylindrical harmonic coefficients of the pressure can be measured by a circular microphone array, which can be bought off-the-shelf. By matching the CHV-indR coefficients, the acoustic velocity vectors are reproduced throughout the listening area. Simulations show that at low frequencies, where the acoustic velocity vectors are the dominant factor for localization, the proposed reproduction method based on matching the CHV-indR coefficients results in higher accuracy in reproduced acoustic velocity vectors when compared with traditional method based on matching the cylindrical harmonic coefficients of the pressure.
A Multi-loudspeaker Binaural Room Impulse Response Dataset with High-Resolution Translational and Rotational Head Coordinates in a Listening Room
Authors: Yue Qiao, Ryan Miguel Gonzales, Edgar Choueiri
Date: 2024-03-18 00:00:00

Data report for the 3D3A Lab Binaural Room Impulse Response (BRIR) Dataset (https://doi.org/10.34770/6gc9-5787).
State Estimation Using Single Body-Frame Bearing Measurements
Authors: Sifeddine Benahmed, Soulaimane Berkane
Date: 2024-03-19 00:00:00
Tasks: Position, State Estimation

This paper addresses the problem of simultaneous estimation of the position, linear velocity and orientation of a rigid body using single bearing measurements. We introduce a Riccati observer-based estimator that fuses measurements from a 3-axis accelerometer, a 3-axis gyroscope, a single body-frame vector observation (e.g., magnetometer), and a single bearing-to-landmark measurement to obtain the full vehicle's state (position, velocity, orientation). The proposed observer guarantees global exponential convergence under some persistency of excitation (PE) condition on the vehicle's motion. Simulation results are presented to show the effectiveness of the proposed approach.
Robust Fuel-Optimal Landing Guidance for Hazardous Terrain using Multiple Sliding Surfaces
Authors: Sheikh Zeeshan Basar, Satadal Ghosh
Date: 2024-03-19 00:00:00

In any spacecraft landing mission, fuel-efficient precision soft landing while avoiding nearby hazardous terrain is of utmost importance. Very few existing literature have attempted addressing both the problems of precision soft landing and terrain avoidance simultaneously. To this end, an optimal terrain avoidance landing guidance (OTALG) was recently developed, which showed promising performance in avoiding the terrain while consuming near-minimum fuel. However, its performance significantly degrades in the face of external disturbances, indicating lack of robustness. To mitigate this problem, in this paper, a near fuel-optimal guidance law is developed to avoid terrain and achieve precision soft landing at the desired landing site. Expanding the OTALG formulation using sliding mode control with multiple sliding surfaces (MSS), the presented guidance law, named `MSS-OTALG', improves precision soft landing accuracy. Further, the sliding parameter is designed to allow the lander to avoid terrain by leaving the trajectory enforced by the sliding mode and eventually returning to it when the terrain avoidance phase is completed. And finally, the robustness of the MSS-OTALG is established by proving practical fixed-time stability. Extensive numerical simulations are also presented to showcase its performance in terms of terrain avoidance, low fuel consumption, and accuracy of precision soft landing under bounded atmospheric perturbations, thrust deviations, and constraints. Comparative studies against existing relevant literature validate a balanced trade-off of all these performance measures achieved by the developed MSS-OTALG.
NN-ETM: Enabling safe neural network-based event-triggering mechanisms for consensus problems
Authors: Irene Perez-Salesa, Rodrigo Aldana-Lopez, Carlos Sagues
Date: 2024-03-19 00:00:00

Event-triggering mechanisms (ETM) have been developed for consensus problems to reduce communication while ensuring performance guarantees, but their design has grown increasingly complex by incorporating the agent's local and neighbor information. This typically results in ad-hoc solutions, which may only work for the consensus protocol under consideration. We aim to safely incorporate neural networks in the ETM to provide a general solution while guaranteeing performance. To decouple the stability analysis of the consensus protocol from the abstraction of the neural network, we derive design criteria for the consensus and ETM pair, allowing independent analysis of each element under mild constraints. Then, we propose NN-ETM, a novel ETM featuring a neural network, to optimize communication while preserving the stability guarantees of the consensus protocol.
Multi-mode Fault Diagnosis Datasets of Gearbox Under Variable Working Conditions
Authors: Shijin Chen, Zeyi Liu, Xiao He, Dongliang Zou, Donghua Zhou
Date: 2024-03-19 00:00:00
Tasks: Fault Diagnosis

The gearbox is a critical component of electromechanical systems. The occurrence of multiple faults can significantly impact system accuracy and service life. The vibration signal of the gearbox is an effective indicator of its operational status and fault information. However, gearboxes in real industrial settings often operate under variable working conditions, such as varying speeds and loads. It is a significant and challenging research area to complete the gearbox fault diagnosis procedure under varying operating conditions using vibration signals. This data article presents vibration datasets collected from a gearbox exhibiting various fault degrees of severity and fault types, operating under diverse speed and load conditions. These faults are manually implanted into the gears or bearings through precise machining processes, which include health, missing teeth, wear, pitting, root cracks, and broken teeth. Several kinds of actual compound faults are also encompassed. The development of these datasets facilitates testing the effectiveness and reliability of newly developed fault diagnosis methods.
Unveiling Four Key Factors for Tire Force Control Allocation in 4WID-4WIS Electric Vehicles at Handling Limits
Authors: Ao Lu, Runfeng Li, Yunchang Yu, Ziwang Lu, Guangyu Tian
Date: 2024-03-19 00:00:00

The four-wheel independent drive and four-wheel independent steering (4WID-4WIS) configurations enhance control flexibility and dynamic performance potential for more integrated electric vehicles. This paper comprehensively analyzes the impacts of four key factors on tire force control allocation: vertical load estimation, actuator dynamic characteristics, tire force constraints, and wheel steering precision at handling limits. The study demonstrates that precise vertical load estimation enhances lateral force allocation accuracy. Additionally, the self-compensating effect of lateral tire forces minimizes the impact of small deviations in vertical load estimation on tire force control allocation. A novel control allocation method considering actuator dynamics is introduced, effectively improving yaw rate response and reducing tracking errors. Considering tire-road adhesion and actuator rate constraints, an innovative method to calculate the real-time attainable tire force volume is proposed based on the tire slip ratio and slip angle. Feedforward control with bump steer compensation is implemented to improve wheel steering precision and lateral tire force control accuracy. Matlab/Simulink and Carsim co-simulation results emphasize the importance of these key factors' individual impacts and combined effects. This analysis offers valuable insights for developing advanced tire force control allocation strategies in 4WID-4WIS electric vehicles.
Probabilistic reachable sets of stochastic nonlinear systems with contextual uncertainties
Authors: Xun Shen, Ye Wang, Kazumune Hashimoto, Yuhu Wu, Sebastien Gros
Date: 2024-03-19 00:00:00
Tasks: Density Estimation

Validating and controlling safety-critical systems in uncertain environments necessitates probabilistic reachable sets of future state evolutions. The existing methods of computing probabilistic reachable sets normally assume that stochastic uncertainties are independent of system states, inputs, and other environment variables. However, this assumption falls short in many real-world applications, where the probability distribution governing uncertainties depends on these variables, referred to as contextual uncertainties. This paper addresses the challenge of computing probabilistic reachable sets of stochastic nonlinear states with contextual uncertainties by seeking minimum-volume polynomial sublevel sets with contextual chance constraints. The formulated problem cannot be solved by the existing sample-based approximation method since the existing methods do not consider conditional probability densities. To address this, we propose a consistent sample approximation of the original problem by leveraging conditional density estimation and resampling. The obtained approximate problem is a tractable optimization problem. Additionally, we prove the proposed sample-based approximation's almost uniform convergence, showing that it gives the optimal solution almost consistently with the original ones. Through a numerical example, we evaluate the effectiveness of the proposed method against existing approaches, highlighting its capability to significantly reduce the bias inherent in sample-based approximation without considering a conditional probability density.
Shortest Trajectory of a Dubins Vehicle with a Controllable Laser
Authors: Shivam Bajaj, Bhargav Jha, Shaunak D. Bopardikar, Alexander Von Moll, David W. Casbeer
Date: 2024-03-19 00:00:00
Tasks: Motion Planning, Position

We formulate a novel planar motion planning problem for a Dubins-Laser system that consists of a Dubins vehicle with an attached controllable laser. The vehicle moves with unit speed and the laser, having a finite range, can rotate in a clockwise or anti-clockwise direction with a bounded angular rate. From an arbitrary initial position and orientation, the objective is to steer the system so that a given static target is within the range of the laser and the laser is oriented at it in minimum time. We characterize multiple properties of the optimal trajectory and establish that the optimal trajectory for the Dubins-laser system is one out of a total of 16 candidates. Finally, we provide numerical insights that illustrate the properties characterized in this work.
Multiplier analysis of Lurye systems with power signals
Authors: William P. Heath, Joaquin Carrasco
Date: 2024-03-18 00:00:00

Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no guarantee the closed-loop system has finite incremental gain. It has been suggested in the literature that without this guarantee such a system may be critically sensitive to time-varying exogenous signals including noise. We show that multipliers guarantee the power gain of the system to be bounded and quantifiable. Furthermore power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee Lurye systems have low sensitivity to noise, provided other exogenous systems have constant steady state. We illustrate the analysis with an example where the exogenous signal is a power signal with non-zero mean.
Secure Synchronization of Heterogeneous Pulse-Coupled Oscillators
Authors: Jiaqi Yan, Hideaki Ishii
Date: 2024-03-18 00:00:00

In this paper, we consider the synchronization of heterogeneous pulse-coupled oscillators (PCOs), where some of the oscillators might be faulty or malicious. The oscillators interact through identical pulses at discrete instants and evolve continuously with different frequencies otherwise. Despite the presence of misbehaviors, benign oscillators aim to reach synchronization. To achieve this objective, two resilient synchronization protocols are developed in this paper by adapting the real-valued mean-subsequence reduced (MSR) algorithm to pulse-based interactions. The first protocol relies on packet-based communication to transmit absolute frequencies, while the second protocol operates purely with pulses to calculate relative frequencies. In both protocols, each normal oscillator periodically counts the received pulses to detect possible malicious behaviors. By disregarding suspicious pulses from its neighbors, the oscillator updates both its phases and frequencies. The paper establishes sufficient conditions on the initial states and graph structure under which resilient synchronization is achieved in the PCO network. Specifically, the normal oscillators can either detect the presence of malicious nodes or synchronize in both phases and frequencies. Additionally, a comparison between the two algorithms reveals a trade-off between relaxed initial conditions and reduced communication burden.
Aggregate Peak EV Charging Demand: The Impact of Segmented Network Tariffs
Authors: Nanda Kishor Panda, Na Li, Simon H. Tindemans
Date: 2024-03-18 00:00:00

Aggregate peak Electric Vehicle (EV) charging demand is a matter of growing concern for network operators as it severely limits the network's capacity, preventing its reliable operation. Various tariff schemes have been proposed to limit peak demand by incentivizing flexible asset users to shift their demand from peak periods. However, fewer studies quantify the effect of these tariff schemes on the aggregate level. In this paper, we compare the effect of a multi-level segmented network tariff with and without dynamic energy prices for individual EV users on the aggregate peak demand. Results based on real charging transactions from over 1200 public charging points in the Netherlands show that the segmented network tariff with flat energy prices results in more diverse load profiles with increasing aggregation, as compared to cost-optimized dispatch based on only dynamic day-ahead energy prices. When paired with dynamic energy prices, the segmented tariff still outperforms only dynamic energy price-based tariffs in reducing peaks. Results show that a balance between power thresholds and price per threshold is crucial in designing a suitable tariff, taking into account the needs of the power network. We also provide valuable insights to network operators by calculating the diversity factor for various peak demands per charging point.