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Optimal Multiobjective Operation of Multicarrier Energy Hub Taking Energy Buffering Into Account

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2025年第1期2025卷

作者： Mohammadi-Zaferani, Mohammad-Mehdi Ebrahimi, Reza Ghanbari, Mahmood Islamic Azad Univ Dept Elect Engn Gorgan Branch Gorgan Iran

This paper introduces a pioneering model for short-term planning of an energy hub (EH) that goes beyond traditional approaches by considering a comprehensive multicarrier energy system. The proposed model focuses on minimizing energy buffering costs while ensuring system operation and optimizing economic performance. The novelty of this study lies in its integrated approach, which simultaneously addresses operational efficiency, energy storage requirements, and overall system performance. The EH in this study is modeled as a prosumer within a day-ahead energy market, where both inflows and outflows of energy are optimized. The system's capability to interact with upstream energy networks, including gas, heat, and electricity, is a critical aspect of the model. This interaction is managed through various technologies that enhance the hub's ability to meet local demands efficiently. By employing an advanced improved particle swarm optimization (IPSO) algorithm, this model solves the complex multiobjective optimization problem inherent in EH management. The proposed model's effectiveness is validated through extensive simulation on a test system, where its performance is compared with conventional heuristic optimization algorithms. The results demonstrate the superior efficiency and applicability of the IPSO algorithm, confirming that the proposed model offers a significant advancement in the field of sustainable energy management.

关键词： energy buffering energy hub energy management energy storage system optimization algorithm

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Enhanced Meta-Heuristic Approach for Echo Suppression in Synthetic Aperture Radar Using Non-Periodic Interrupted Sampling

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TRAITEMENT DU SIGNAL 2025年第2期42卷 1097-1110页

作者： Chukka, Anoosha Battula, Tirumala Krishna Anil Neerukonda Inst Technol & Sci Dept ECE Visakhapatnam 531162 India Jawaharlal Nehru Technol Univ Kakinada Dept ECE Kakinada 533003 India

Echo cancellation is a deception jamming technique designed to nullify the target's echo and prevent its detection by enemy radar. Traditional methods rely on time-domain synchronization, but they struggle to fully suppress echoes when there is an amplitude mismatch. This limitation often leaves the target partially visible in synthetic aperture radar (SAR) images, reducing the effectiveness of the jamming technique. Non-periodic interrupted sampling modulation (NP-ISM) is applied as it produces a continuous jamming strip instead of discrete false targets whereas in Periodic sampling multiple false targets emerge even the real target echo is canceled which makes it easy to be countered by enemy radar. The improved henry gas solubility optimization algorithm (IHGSOA) is applied in the echo cancellation model to optimize the random rectangular envelope pulse train in the NP-ISM which enhances signal cancellation. This significantly reduces the Peak to Average Ratio (PAR), enabling more effective target echo suppression. A polynomial non-linear frequency modulation (PNLFM) is employed to reduce the PAR leading to significant improvement in signal-to-noise ratio (SNR), enabling more effective target echo cancellation. The proposed IHGSOA-based echo cancellation model effectively aligns the cancellation and echo signals through time-delay synchronization, minimizing errors even in the presence of a mismatch. The simulation results show that using the above cancelling system the desired target echo amplitude drops to 50% and 40% of its original target echo, hence the system has a very high cancellation effect. Thus, the effectiveness of the IHGSOA is evaluated by comparing its performance with other conventional algorithms.

关键词： echo cancellation synthetic aperture radar (SAR) non-periodic interrupted sampling modulation improved henry gas solubility optimization algorithm peak to average ratio polynomial non-linear frequency modulation (PNLFM) NLFM

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A Sparsity-Aware Fault Diagnosis Framework Focusing on Accurate Isolation

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第2期19卷 1356-1365页

作者： Xiu, Xianchao Miao, Zhonghua Liu, Wanquan Shanghai Univ Sch Mechatron Engn & Automation Shanghai 200444 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China

In this article, we propose an efficient fault diagnosis framework to achieve accurate fault isolation. The core is to introduce the l(2,0)-norm sparsity constrained optimization to reduce the variable redundancy and determine the variable number, which is different from the existing sparse variants. In order to illustrate the idea, this article takes principal component analysis (PCA) as an essential step. First, a sparsity-aware PCA is constructed by taking advantage of the l(2,0)-norm constrained optimization. Afterward, a two-stage monitoring strategy is designed, including fault detection and fault isolation. Once the fault is detected, the sparsity level is then shrunk to achieve accurate fault isolation. Moreover, an alternating direction method of multipliers-based optimization algorithm is developed with detailed implementation. Finally, the detection improvement and accurate isolation performance are validated by two simulated examples, the Tennessee Eastman benchmark process, and a practical cylinder-piston process.

关键词： l(2,0)-norm fault diagnosis (FD) optimization algorithm two-stage principal component analysis (TSPCA)

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Sliding Mode Control of FMII Systems: Handling Rice Fading Issues Under 2-D Frame

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第11期53卷 6909-6920页

作者： Lv, Xinyu Niu, Yugang Park, Ju H. East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Yeungnam Univ Minist Educ Dept Elect Engn Gyongsan 38541 South Korea

The Lth order Rice model can effectively describe imperfect transmission, including amplitude attenuation, time delays, and stochastic disturbance, and is a popular channel fading model in one-dimensional (1-D) systems. However, due to the state evolution feature of two-dimensional (2-D) systems, the previous state signals of 2-D systems are not as directly determinable as those of 1-D systems such that the construction of the conventional and obvious Lth order Rice model become difficulty for 2-D systems. Motivated by these, this work will investigate the sliding mode control (SMC) problem of the 2-D Fornasini-Marchesini (FMII) system under Rice fading channels. First, the previous p-step state signals are defined according to the evolution feature of the FMII model. Afterwards, a new applicable Lth-order Rice fading model for 2-D FMII systems is constructed, which not only has 2-D structural characteristics but also has similar properties to 1-D cases. A 2-D-type sliding function involving the predefined previous L-step state signals and channel attenuation coefficients is designed, which exhibits a similar structural feature to the FMII model. A feasible SMC law is designed via the available fading states to achieve the ultimately uniformly boundedness in the mean square of the closed-loop system and the reachability of the sliding domain around the specified sliding surface. Furthermore, an optimization-solving algorithm is introduced to minimize the convergent bound. Finally, an example is applied to demonstrated the proposed results.

关键词： optimization algorithm previous state signals rice fading model sliding mode control (SMC) two-dimensional (2-D) Fornasini-Marchesini (FMII) system

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An Energy-Aware and Fault-Tolerant WSN Routing Approach Using Serial Exponential Newton Metaheuristic algorithm for Agricultural Lands

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CYBERNETICS AND SYSTEMS 2023年第8期54卷 1494-1517页

作者： Herlin, L. T. Fred, A. Lenin Mar Ephraem Coll Engn & Technol Comp Sci & Engn Marthandam Tamil Nadu India Mar Ephraem Coll Engn & Technol Comp Sci & Engn Marthandam 629171 Tamil Nadu India

Wireless Sensor Network (WSN) is becoming a promising platform, in which Sensor Nodes (SNs) can sense and monitor the physical or environmental conditions and send the data to the Base Station (BS) through multi-hop routing. Here, opting for the right Cluster Head Selection (CHS) is the key to enhancing the clustering routing protocol performance. Here, a new optimization model to prolong the lifetime of the network and progress the energy efficiency in the routing process is anticipated. The Serial Exponential Newton Meta heuristic algorithm (SExpNMA) is developed for CHS and routing. The parameters like energy, Link Lifetime (LLT), and delay, are considered as the fitness function of the proposed SExpNMA to select the optimal solution. Ultimately, the performance analysis clearly reveals that the SExpNMA technique attains the maximum residual energy of 0.515 J, LLT of 0.346, the throughput of 0.617, and minimum delay of 0.236 s, respectively.

关键词： Cluster head selection energy fault tolerance link lifetime optimization algorithm routing WSN

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Run Time Assured Reinforcement Learning for Safe Satellite Docking

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2023年第1期20卷 25-36页

作者： Dunlap, Kyle Mote, Mark Delsing, Kaiden Hobbs, Kerianne L. Parallax Adv Res Beavercreek OH 45431 USA Pytheia Corp Atlanta GA 30308 USA Cedarville Univ Cedarville OH 45314 USA US Air Force Res Lab Dayton OH 45431 USA

Reinforcement learning promises high performance in complex tasks as well as low online storage and computation cost. However, the trial-and-error learning approach of reinforcement learning could explore unsafe behavior in the search for an optimal solution. Run time assurance (RTA) approaches can be applied to monitor behavior and ensure safety constraint satisfaction during reinforcement learning. This paper investigates the effect of RTA on reinforcement learning training performance in terms of training efficiency, safety constraint satisfaction, control efficiency, task efficiency, and training duration. For the purposes of demonstration, a custom reinforcement learning environment is created where the objective is to develop a policy that moves a satellite into docking position with another satellite in a two-dimensional relative-motion reference frame. Six different policies are trained. The first features no RTA, the second features no RTA but a higher penalty for safety violations, and four others use different RTA techniques to enforce a dynamic velocity constraint during training. The trained policies are analyzed with standardized test points. It is shown that the policies trained without RTA do not learn to adhere to the constraint, whereas all policies trained with RTA do learn to adhere to the constraint. Although more complex RTA frameworks can be better for operational use, it is found that a simple RTA framework provides the best overall results for reinforcement learning training.

关键词： Reinforcement Learning Satellites Neural Networks Spacecraft Docking optimization algorithm Closed Loop

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Optimal Robust Two-Body Trajectory Design with Corrective Maneuvers

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JOURNAL OF SPACECRAFT AND ROCKETS 2023年第6期60卷 1966-1982页

作者： Kelly, Scott Geller, David Utah State Univ Mech & Aerosp Engn Dept Logan UT 84322 USA

The focus of this research is the optimization of impulsive transfer trajectories in the presence of stochastic effects. A trajectory design method is introduced that accounts for a normally distributed initial state dispersion and minimizes the sum of nominal impulsive & UDelta;V plus 3s trajectory correction maneuver (TCM) magnitude. Four main problems are presented. First, a deterministic optimal trajectory is developed;in the coplanar cases, the optimal solution is Hohmann transfer. For the second problem, an initial state dispersion and a target position dispersion constraint are introduced. It is shown to be possible to modify the nominal two-impulse trajectory to satisfy the dispersion constraint. In the third problem, a TCM is performed at the optimal point along the deterministic optimal trajectory, resulting in a more efficient method to influence the target position dispersion. Problem 4 is the development of a robust trajectory, where the nominal impulsive maneuvers and 3s TCM dV are simultaneously optimized. The result is a different nominal trajectory and TCM that is less expensive than the total cost of problem 3. The optimal TCM is rapidly computed along each nominal trajectory using the numerically propagated state transition matrix history as a step inside the optimization algorithm.

关键词： Spacecraft Design Orbital Maneuvers optimization algorithm Optimal Control Problem Geostationary Transfer Orbit Sun Synchronous Orbit Stochastic Optimal Control Robust optimization Gradient-Based Design optimization State-Transition Equation

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Facial emotion recognition on video using deep attention based bidirectional LSTM with equilibrium optimizer

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第19期82卷 28681-28711页

作者： Vedantham, Ramachandran Reddy, Edara Sreenivasa Vasireddy Venkatadri Inst Technol CSE Dept Nambur Andhra Pradesh India Acharya Nagarjuna Univ ANU Coll Engn & Technol Nambur Andhra Pradesh India

Facial emotion recognition (FER) from videos is now considered a significant role in HCI (Human-Computer Interaction). The dynamic variations shown by various facial movements need to be realized quickly without degrading the recognition performance. Therefore, the procedure of classifying the facial emotions from videos is now demanded as a challenging and interesting issue. This work proposes a practical methodology for identifying emotions through facial expressions from videos. At first, the Lucas-Kanade (LK) based optical flow scheme is used for motion detection from the input videos. After finding the set of LK frames, the pre-processing scheme is applied. In this process, the Viola-Jones algorithm is utilized for face detection, and then the gray scale conversion is involved. Moreover, the FAST corner detection approach is used to detect the facial landmark points over the gray scale frame. The Neighborhood Difference Features (NDF) are extracted in feature extraction (FE). The optimal set of features is selected from the mined features using the Modified Plant Genetics-Inspired Evolutionary optimization (MPGEO) algorithm in the feature selection (FS). Finally, the chosen features are fed into the Deep Attention-based Bidirectional LSTM with Equilibrium Optimizer (DABLEO) classifier for the emotion classification. The proposed scheme is performed in the Python software using four standard datasets like FAMED, CK+, AFEW, and MMI, and it delivers the classification accuracy of 96.5%, 99.2%, 90%, and 92% individually. As related to other schemes, the proposed scheme is better for all performances of emotion recognition.

关键词： Emotion recognition optimization algorithm Deep network Optical flow Feature extraction And selection

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Decentralized Weapon-Target Assignment Under Asynchronous Communications

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第2期46卷 312-324页

作者： Hendrickson, Katherine Ganesh, Prashant Volle, Kyle Buzaud, Paul Brink, Kevin Hale, Matthew Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA Univ Florida Dept Mech & Aerosp Engn Shalimar FL 32579 USA Air Force Res Lab Nav Control Branch Weap Dynam Eglin AFB FL 32542 USA

The weapon-target assignment problem is a classic task assignment problem in combinatorial optimization, and its goal is to assign some number of workers (the weapons) to some number of tasks (the targets). Classical approaches for this problem typically use a centralized planner leading to a single point of failure and often preventing real-time replanning as conditions change. This paper introduces a new approach for distributed, autonomous assignment planning executed by the weapons where each weapon is responsible for optimizing over distinct subsets of the decision variables. A continuous, convex relaxation of the associated cost function and constraints is introduced, and a distributed primal-dual optimization algorithm is developed that will be shown to have guaranteed bounds on its convergence rate, even with asynchronous computations and communications. This approach has several advantages in practice due to its robustness to asynchrony and resilience to time-varying scenarios, and these advantages are exhibited in experiments with simulated and physical commercial off-the-shelf ground robots as weapon surrogates that are shown to successfully compute their assignments under intermittent communications and unexpected attrition (loss) of weapons.

关键词： Weapon Target Assignment Asynchronous Communications Robots optimization algorithm COTS Mixed Integer Linear Programming Fire and Forget Histograms Computing Evolutionary algorithm

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Unsupervised Feature Selection via Graph Regularized Nonnegative CP Decomposition

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第2期45卷 2582-2594页

作者： Chen, Bilian Guan, Jiewen Li, Zhening Xiamen Univ Dept Automat Xiamen 361005 Peoples R China Xiamen Key Lab Big Data Intelligent Anal & Decis M Xiamen 361005 Peoples R China Univ Portsmouth Sch Math & Phys Portsmouth PO1 3HF England

Unsupervised feature selection has attracted remarkable attention recently. With the development of data acquisition technology, multi-dimensional tensor data has been appeared in enormous real-world applications. However, most existing unsupervised feature selection methods are non-tensor-based which results the vectorization of tensor data as a preprocessing step. This seemingly ordinary operation has led to an unnecessary loss of the multi-dimensional structural information and eventually restricted the quality of the selected features. To overcome the limitation, in this paper, we propose a novel unsupervised feature selection model: Nonnegative tensor CP (CANDECOMP/PARAFAC) decomposition based unsupervised feature selection, CPUFS for short. In specific, we devise new tensor-oriented linear classifier and feature selection matrix for CPUFS. In addition, CPUFS simultaneously conducts graph regularized nonnegative CP decomposition and newly-designed tensor-oriented pseudo label regression and feature selection to fully preserve the multi-dimensional data structure. To solve the CPUFS model, we propose an efficient iterative optimization algorithm with theoretically guaranteed convergence, whose computational complexity scales linearly in the number of features. A variation of the CPUFS model by incorporating nonnegativity into the linear classifier, namely CPUFSnn, is also proposed and studied. Experimental results on ten real-world benchmark datasets demonstrate the effectiveness of both CPUFS and CPUFSnn over the state-of-the-arts.

关键词： Unsupervised feature selection nonnegative CP decomposition optimization algorithm classification

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