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arXiv 2023年

作者： Chen, Weiqin Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States

Deep Reinforcement Learning (DRL) has achieved great success in solving complicated decision-making problems. Despite the successes, DRL is frequently criticized for many reasons, e.g., data inefficient, inflexible and intractable reward design. In this paper, we review two publications that investigate the mentioned issues of DRL and propose effective solutions. One designs the reward for human-robot collaboration by combining the manually designed extrinsic reward with a parameterized intrinsic reward function via the deterministic policy gradient, which improves the task performance and guarantees a stronger obstacle avoidance. The other one applies selective attention and particle filters to rapidly and flexibly attend to and select crucial pre-learned features for DRL using approximate inference instead of backpropagation, thereby improving the efficiency and flexibility of DRL. Potential avenues for future work in both domains are discussed in this paper. . Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Review of Algorithms's Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms's Complexities on Different Valued So...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Majeed, Asad Iftikhar, Mahnoor Ali, Raja Hashim Arshad, Usama Shabbir, Muhammad Zeeshan Zeeshan Ijaz, Ali Ali, Nisar Aftab, Ali Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant. © 2023 IEEE.

关键词： Computational complexity

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Distributed operational management of microgrids: a second order dual update approach

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IFAC-PapersOnLine 2023年第2期56卷 1540-1545页

作者： Giulio Ferro Michela Robba Federico Delfino Rabab Haider Anuradha M. Annaswamy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02141 USA Department of of Electrical Electronic Telecommunications Engineering and Naval Architecture University of Genova

This paper develops a Distributed Energy Management System (EMS) to optimally allocate electric and thermal power production in a polygenerative microgrid. The EMS problem is formulated as a multiperiod convex optimization problem and solved using AL-SODU, a new distributed algorithm based on the augmented Lagrangian method with second order dual updates. The proposed AL-SODU algorithm significantly outperforms state of the art algorithms employing first order dual updates, with 15-times speedup in convergence speed. A case study of the EMS based on AL-SODU is conducted on the Smart Polygeneration Microgrid (SPM) located on the Savona Campus of University of Genova (Italy). The EMS determines the optimal schedules for electric and thermal power plants every 15-minutes. This real time scheduling of the microgrid is enabled by the Al-SODU algorithm, which solves the scheduling problem in 1.86s.

关键词： Microgrids distributed control second order methods

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An AOP-Based Security Verification Environment for KECCAK Hash Algorithm

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computers, Materials & Continua 2022年第11期73卷 4051-4066页

作者： Hassen Mestiri Imen Barraj Mohsen Machhout Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Higher Institute of Applied Sciences and Technology of Sousse University of SousseTunisia Electronics and Micro-Electronics Laboratory Faculty of Sciences of MonastirUniversity of MonastirTunisia Electrical Engineering Department Systems Integration&Emerging Energies(SI2E)National Engineers School of SfaxUniversity of SfaxTunisia Higher Institute of Computer Science and Multimedia of Gabes University of GabesTunisia

Robustness of the electronic cryptographic devices against fault injection attacks is a great concern to ensure *** to significant resource constraints,these devices are limited in their *** increasing complexity of cryptographic devices necessitates the development of a fast simulation environment capable of performing security tests against fault injection *** is a good choice for Electronic System Level(ESL)modeling since it enables models to run at a faster *** enable fault injection and detection inside a SystemC cryptographic model,however,the model’s source code must be *** altering the source code,Aspect-Oriented Programming(AOP)may be used to evaluate the robustness of cryptographic *** might replace conventional cryptanalysis methods in the real *** the ESL,we discuss a unique technique for simulating security fault attacks on cryptographic *** current study presents a fault injection/detection environment for assessing the KECCAK SystemC model’s resistance against fault injection *** approach of injecting faults into KECCAK SystemC model is accomplished via the use of weaving faults in AspectC++based on AOP programming *** confirm our technique by applying it to two scenarios using a SystemC KECCAK hash algorithm case study:The first concerns discuss the effect of the AOP on fault detection capabilities,while the second concerns discuss the effect of the AOP on simulation time and executable file *** simulation results demonstrate that this technique is fully capable of evaluating the fault injection resistance of a KECCAK *** demonstrate that AOP has a negligible effect on simulation time and executable file size.

关键词： Aspect-Oriented Programming SystemC AspectC++ cryptographic circuits KECCAK fault detection fault attacks

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Harvesting Curvatures for Communication-Efficient Distributed Optimization

Harvesting Curvatures for Communication-Efficient Distribute...

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Asilomar Conference on Signals, systems & computers

作者： Diogo Cardoso Boyue Li Yuejie Chi João Xavier Department of Electrical and Computer Engineering Carnegie Mellon University USA Institute for Systems and Robotics IST-University of Lisbon Portugal

This paper considers the design of distributed optimization algorithms in the server-client setting, with the goal of achieving better communication efficiency, a crucial desiderata in modern distributed machine learning applications. One popular approach to reduce communication is to impose more computation between communication rounds, by letting each client optimize an approximation of the global objective function-constructed using the local objective function and aggregated history information-and exchange the updates with the parameter server for global consensus. In particular, judiciously incorporating second-order information in the local approximation often leads to better communication efficiency when the problem is ill-conditioned. However, existing methods construct the second-order correction term in an isotropic manner, without taking into account the curvature information of the global objective function accumulated over the course of the algorithm. This paper proposes a novel algorithm that refines this idea by constructing a second-order correction term using a BFGS-style update formula, where the kernel matrix is updated recursively using only history gradients to harvest curvature information for accelerating convergence. Numerical experiments demonstrate improved communication efficiency over competitive baseline algorithms on both synthetic and real datasets.

关键词： Machine learning algorithms Memory management Machine learning Approximation algorithms Linear programming Servers History

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Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games

arXiv

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arXiv 2024年

作者： Li, Tao Hammar, Kim Stadler, Rolf Zhu, Quanyan The Department of Electrical and Computer Engineering New York University United States The Division of Network and Systems Engineering KTH Royal Institute of Technology Sweden

Asymmetric information stochastic games (AISGs) arise in many complex socio-technical systems, such as cyber-physical systems and IT infrastructures. Existing computational methods for AISGs are primarily offline and can not adapt to equilibrium deviations. Further, current methods are limited to particular information structures to avoid belief hierarchies. Considering these limitations, we propose conjectural online learning (COL), an online learning method under generic information structures in AISGs. COL uses a forecaster-actor-critic (FAC) architecture, where subjective forecasts are used to conjecture the opponents’ strategies within a lookahead horizon, and Bayesian learning is used to calibrate the conjectures. To adapt strategies to nonstationary environments based on information feedback, COL uses online rollout with cost function approximation (actor-critic). We prove that the conjectures produced by COL are asymptotically consistent with the information feedback in the sense of a relaxed Bayesian consistency. We also prove that the empirical strategy profile induced by COL converges to the Berk-Nash equilibrium, a solution concept characterizing rationality under subjectivity. Experimental results from an intrusion response use case demonstrate COL’s faster convergence over state-of-the-art reinforcement learning methods against nonstationary attacks. © 2024, CC BY.

关键词： Cost functions

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Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

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computer systems Science & engineering 2023年第9期46卷 3847-3864页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana murthy Ravuri Daniel E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenangMalaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science University of Central AsiaNarynKyrgyzstan Faculty of Computers and Information South Valley UniversityQenaEgypt

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is *** are the major reason behind crop quality degradation and diminished crop ***,accurate pest detection is essential to guarantee safety and crop *** identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological ***,some progress has been made in agriculture by employing machine learning(ML)to classify and detect *** study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)*** presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image *** neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the *** last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification *** simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

关键词： Sustainable agriculture crop monitoring pest management insect classification computer vision

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Feasibility of Pointcloud-based Ultrasound-CT Registration towards Automated, Robot-Assisted Image-Guidance in Spine Surgery*

Feasibility of Pointcloud-based Ultrasound-CT Registration t...

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International Symposium on Medical robotics (ISMR)

作者： Xihan Ma Xiao Zhang Yang Wang Christopher Nycz Arno Sungarian Songbai Ji Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA University of Massachusetts Memorial Medical Center Worcester MA USA Department of Biomedical Engineering Worcester Polytechnic Institute Worcester MA USA Department of Biomedical Engineering and the Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

Image-guidance has been shown to improve spine surgical accuracy and patient outcome. Such image guidance can be achieved by registering low-cost, realtime, and radiation-free intraoperative ultrasound (US) with preoperative computed tomography (CT). Employing robotic US system (RUSS) allows automated acquisition of a wide 3D volume, facilitating efficient and accurate registration with CT. However, the registration between CT and robotic 3D US remains an open problem mainly due to (i) the lack of relevant testbed and dataset, and (ii) the modality discrepancy between CT and US. To address these challenges, we present a custom-built lumbar spine phantom for multimodal imaging with rigidly attached fiducials. The phantom is scanned by CT and in-house RUSS. A novel pointcloud-based registration pipeline is presented to register CT and robotic 3D US data of the spine phantom. Preliminary experiments demonstrate efficient (0.53 ± 0.02 seconds) registration of the lumbar spine with an accuracy of 3.57 mm in terms of fiducial registration error (FRE), which is robust to varying initial alignment of the pointclouds. These results show initial feasibility of adopting the proposed registration pipeline in robotic US guided spine surgery. Future work includes further evaluation of registration performance in ex vivo spine samples with denser US acquisitions to further improve registration accuracy.

关键词： Accuracy Three-dimensional displays Ultrasonic imaging Medical robotics Computed tomography Spine Pipelines

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Automated Security Response through Online Learning with Adaptive Conjectures

arXiv

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arXiv 2024年

作者： Hammar, Kim Li, Tao Stadler, Rolf Zhu, Quanyan Division of Network and Systems Engineering KTH Royal Institute of Technology Sweden Department of Electrical and Computer Engineering New York University United States

We study automated security response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed, non-stationary game. We relax the standard assumption that the game model is correctly specified and consider that each player has a probabilistic conjecture about the model, which may be misspecified in the sense that the true model has probability 0. This formulation allows us to capture uncertainty and misconception about the infrastructure and the intents of the players. To learn effective game strategies online, we design Conjectural Online Learning (COL), a novel method where a player iteratively adapts its conjecture using Bayesian learning and updates its strategy through rollout. We prove that the conjectures converge to best fits, and we provide a bound on the performance improvement that rollout enables with a conjectured model. To characterize the steady state of the game, we propose a variant of the Berk-Nash equilibrium. We present COL through an advanced persistent threat use case. Testbed evaluations show that COL produces effective security strategies that adapt to a changing environment. We also find that COL enables faster convergence than current reinforcement learning techniques. © 2024, CC BY-SA.

关键词： Nash equilibrium

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Whole-Body Model-Predictive Control of Legged Robots with MuJoCo

arXiv

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arXiv 2025年

作者： Zhang, John Z. Howell, Taylor A. Yi, Zeji Pan, Chaoyi Shi, Guanya Qu, Guannan Erez, Tom Tassa, Yuval Manchester, Zachary Robotics Institute Carnegie Mellon University United States Google Deep-Mind United Kingdom Department of Electrical and Computer Engineering Carnegie Mellon University United States

We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model-predictive control (MPC) of quadruped and humanoid robots: the iterative LQR (iLQR) algorithm with MuJoCo dynamics and finite-difference approximated derivatives. Building upon the previous success of model-based behavior synthesis and control of locomotion and manipulation tasks with MuJoCo in simulation, we show that these policies can easily generalize to the real world with few sim-to-real considerations. Our baseline method achieves real-time whole-body MPC on a variety of hardware experiments, including dynamic quadruped locomotion, quadruped walking on two legs, and full-sized humanoid bipedal locomotion. We hope this easy-to-reproduce hardware baseline lowers the barrier to entry for real-world whole-body MPC research and contributes to accelerating research velocity in the community. Our code and experiment videos will be available online at: https://***/mujoco ilqr. Copyright © 2025, The Authors. All rights reserved.

关键词： Predictive control systems

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