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Tightening QC Relaxations of AC Optimal Power Flow Through Improved Linear Convex Envelopes

IEEE Transactions on Power Systems 2025年第2期40卷 1465-1480页

作者： Narimani, Mohammad Rasoul Molzahn, Daniel K. Davis, Katherine R. Crow, Mariesa L. Department of Electrical and Computer Engineering NorthridgeCA91330 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Texas A&M University Electrical and Computer Engineering Department College StationTX77840 United States Missouri University of Science and Technology Electrical and Computer Engineering Department RollaMO65409 United States

AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed various convex relaxations that bound the optimal objective values of AC OPF problems. The QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear function whose projections are the nonlinear expressions appearing in the polar representation of the power flow equations. We construct a polytope-shaped convex envelope around this nonlinear function and derive convex expressions for the nonlinear terms using its projections. Second, we use sine and cosine expression properties, along with changes in their curvature, to tighten this convex envelope. We also propose a coordinate transformation to tighten the envelope by rotating power flow equations based on individual bus-specific angles. We compare these enhancements to a state-of-the-art QC relaxation method using PGLib-OPF test cases, revealing improved optimality gaps in 68% of the cases. © 1969-2012 IEEE.

关键词： Load flow optimization

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Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

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computers, Materials & Continua 2024年第9期80卷 4325-4340页

作者： Zhaojie Sun Linlin Xing Longbo Zhang Hongzhen Cai Maozu Guo Department of Computer Science and Technology Shandong University of TechnologyZibo255000China Department of Agricultural Engineering and Food Science Shandong University of TechnologyZibo255000China Department of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China

Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

关键词： Deep learning biomedical joint extraction triple classification multi-granularity 2D convolution

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An innovative Telugu text summarization framework using the pointer network and optimized attention layer

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Multimedia Tools and Applications 2024年第37期83卷 84539-84564页

作者： M, Varaprasad Rao Chakma, Kunal Jamatia, Anupam Rudrapal, Dwijen Department of Computer Science & amp Engineering National Institute of Technology Agartala Tripura799046 India Department of Computer Science & amp Engineering CVR College of Engineering Telangana Hyderabad501510 India

Summarizing lengthy text involves distilling crucial information into a concise form by covering the key events in the source text. Previous researchers mostly explored the supervised approaches for the task, but due to its strong reliance on the quality of text features, the resulting summaries often lack precision and coherence. The performance of the state-of-the-art summarizers becomes poor when applied to various Indian languages due to several challenges. The current research paper proposes a summarization approach for Telugu text based on a Pointer Network with an Optimised Attention Layer (PN-OAL). In this approach, the attention layer’s weights are adjusted using a hybrid optimization method that combines the Fusion of Coyote Optimization and Squirrel Search method (FCO-SSA). Weight optimization is conducted in order to address the goal function, which involves the maximization of the cosine similarity between the source text and the summary text. A novel loss function is incorporated within the framework of the constructed network architecture to generate a robust summary. The proposed approach is experimented with alternative baseline approaches in order to validate its efficacy for summarizing Telugu documents. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network architecture

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Multi-agent Path Planning Based on Conflict-Based Search (CBS) Variations for Heterogeneous Robots

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Journal of Intelligent and Robotic Systems: Theory and Applications 2025年第1期111卷 1-12页

作者： Bai, Yifan Kotpalliwar, Shruti Kanellakis, Christoforos Nikolakopoulos, George Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå797187 Sweden

This article introduces a novel Multi-agent path planning scheme based on Conflict Based Search (CBS) for heterogeneous holonomic and non-holonomic agents, designated as Heterogeneous CBS (HCBS). The proposed methodology employs a hybrid A∗ algorithm for non-holonomic car-like robots and a conventional A∗ algorithm for holonomic robots. Following this, a body conflict detection strategy is utilized to construct the conflict tree, bridging the initial path planning with the resolution of conflicts among agents. Moreover, we present two variants of HCBS: the Enhanced Conflict-Based Search (EHCBS) and the Depth-First Conflict-Based Search (DFHCBS). We evaluate the efficacy of our proposed algorithms—HCBS, EHCBS, and DFHCBS—against a standard prioritized planning algorithm, focusing on success rates and computational efficiency in environments with varying numbers of agents and obstacles. The empirical results demonstrate that EHCBS exhibits superior computational efficiency in small, dense environments, while DFHCBS performs well in larger-scale environments. This highlights the adaptability of our proposed approaches in various settings, proving the computational advantage of EHCBS and DFHCBS over traditional methods. © The Author(s) 2025.

关键词： Multi agent systems

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A Framework for Industrial Inspection System using Deep Learning

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Annals of Data Science 2024年第2期11卷 445-478页

作者： Hridoy, Monowar Wadud Rahman, Mohammad Mizanur Sakib, Saadman Department of Mechatronics & amp Industrial Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh Department of Mechanical Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh Department of Computer Science & amp Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh

Industrial Inspection systems are an essential part of Industry 4.0. An automated inspection system can significantly improve product quality and reduce human labor while making their life easier. However, a deep learning-based camera inspection system requires a large amount of data to classify the defective products accurately. In this paper, a framework is proposed for an industrial inspection system with the help of deep learning. Additionally, A new dataset of hex-nut products is proposed containing 4000 images, i.e., 2000 defective and 2000 non-defective. Moreover, different CNN architectures, i.e., Custom CNN, Inception ResNet v2, Xception, ResNet 101 v2, ResNet 152 v2, are experimented with the concept of transfer learning on the new hex-nut dataset. Fine-tuning the CNN architectures is performed by freezing the last 14 layers, which provided the optimal architecture, i.e., Xception (last 14 layers trainable, excluding the fully connected layer). The proposed framework can efficiently separate the defective products from the non-defective products with 100% accuracy on the hex nut dataset. Furthermore, the proposed optimal Xception architecture has experimented on a publicly available casting material dataset which produced 99.72% accuracy, outperforming existing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Industry 4.0

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

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An optimization driven deep belief network model for crop yield prediction in IoT based smart agriculture

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Multimedia Tools and Applications 2024年 1-23页

作者： Patel, Jaideep Chandel, Sonal Telang Sonker, Abhilash Department of Computer Science Engineering Sagar Institute of Research and Technology Bhopal India Department of Computer Science Engineering Indian Institute of Information Technology Bhopal India Department of Information Technology Madhav Institute of Technology & Science Gwalior India

Crop yield Prediction based on environmental, soil, water, and crop parameters has been an active area of research in agriculture. Many studies have shown that these parameters can have a significant impact on crop yields, and that accurate predictions of crop yields can be made by considering them together. Despite the progress made in crop yield prediction, several challenges still remain, such as the lack of high-quality and diverse data, the high cost of technology, and the lack of domain knowledge for certain crops and regions. Thus, Crop yield forecasting models can reasonably estimate yields, but better yield forecasting performance is still desirable. Thus, in order to enhance the accuracy of crop yield prediction in IoT, a novel optimization driven deep belief neural network model is developed in this study. IoT sensors can collect data on various environmental factors such as temperature, humidity, soil moisture, as well as crop parameters such as growth stage and leaf area index. These data can then be used to train a proposed deep learning-based model, namely an optimization driven deep belief neural network (DBNN) along with an effective crocodile optimization algorithm, to predict crop yields. To improve the performance of DBNN, adaptive crocodile optimization (ACO) algorithm is used. Using this algorithm, weight parameters of DBNN are chosen optimally. Results of the article depict that the proposed model achieve accuracy of 97.26%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart agriculture

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LEViT- Leaf Disease identification and classification using an enhanced Vision transformers(ViT) model

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Multimedia Tools and Applications 2024年 1-32页

作者： Prashanthi, Bh. Krishna, A. V. Praveen Rao, Ch. Mallikarjuna Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram522502 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India

The variety of crops, differences in climate, and the multiplicity of disease symptoms make early identification and evaluation of leaf diseases a challenging task. Although deep-learning methods have been created for this very reason, they often need adjustments to suit various regions and types of crops. This study presents a vision transformer-based deep learning model incorporating Explainable Artificial Intelligence (XAI) features to effectively detect and classify leaf diseases. The model is evaluated using the New Plant Disease Dataset, encompassing data from 14 plants with 38 leaf disease classifications. The primary objective is to enhance accuracy in leaf disease classification through the utilization of vision transformer learning. The proposed model, LEViT (Leaf Disease Identification and Classification using an Enhanced Vision Transformers Model), builds upon the ViT_B_32 model. It incorporates additional layers such as dense layers, dropout, and L2 regularization to improve performance and mitigate overfitting. Specialized preprocessing techniques tailored for transformers are employed to refine data quality. Incorporating Grad-CAM (Gradient-weighted Class Activation Mapping) enhances interpretability, enabling the identification of crucial regions influencing the model’s decisions within images. The model exhibits remarkable efficacy, boasting a training accuracy of 95.22%, validation accuracy of 96.19%, and test accuracy of 92.33%. These results underscore its robust ability to accurately identify leaf diseases across diverse crop types. Comparative analysis with state-of-the-art models in the field demonstrates LEViT’s superior performance, establishing it as a leading solution for leaf disease detection and classification. LEViT’s advanced architecture, coupled with fine-tuning, positions it as a highly promising tool for advancing crop management and yield improvement, thereby significantly contributing to the field of precision agriculture. © The

关键词： Crops

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Flu-Net: two-stream deep heterogeneous network to detect flu like symptoms from videos using grey wolf optimization algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7733-7745页

作者： Gupta, Himanshu Imran, Javed Sharma, Chandani Department of Computer Science and Engineering Quantum University Roorkee India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

The outbreak of COVID-19 (also known as Coronavirus) has put the entire world at risk. The disease first appears in Wuhan, China, and later spread to other countries, taking a form of a pandemic. In this paper, we try to build an artificial intelligence (AI) powered framework called Flu-Net to identify flu-like symptoms (which is also an important symptom of Covid-19) in people, and limit the spread of infection. Our approach is based on the application of human action recognition in surveillance systems, where videos captured by closed-circuit television (CCTV) cameras are processed through state-of-the-art deep learning techniques to recognize different activities like coughing, sneezing, etc. The proposed framework has three major steps. First, to suppress irrelevant background details in an input video, a frame difference operation is performed to extract foreground motion information. Second, a two-stream heterogeneous network based on 2D and 3D Convolutional Neural Networks (ConvNets) is trained using the RGB frame differences. And third, the features extracted from both the streams are combined using Grey Wolf Optimization (GWO) based feature selection technique. The experiments conducted on BII Sneeze-Cough (BIISC) video dataset show that our framework can 70% accuracy, outperforming the baseline results by more than 8%. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Efficient Squeezeformer Using Fastformer Attention for American Sign Language Fingerspelling Recognition

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Journal of The Institution of Engineers (India): Series B 2025年 1-18页

作者： Karkidholi, Srijana Chebrolu, Srilatha Turaga, V. K. Hanuman Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh Tadepalligudem534101 India

This paper centers on leveraging Convolution-Augmented Transformer Models originally designed for Automatic Speech Recognition (ASR) in the realm of Sign Language—specifically, American Sign Language Fingerspelling Recognition (ASLFR). Established ASR models like Conformer and Squeezeformer have demonstrated efficacy in ASLFR. In this endeavor, we aim to explore alternative ASR models such as Zipformer and Branchformer;and assess their performance and potential advantages over Conformer and Squeezeformer. Our paper comes up with six improvised model setups, out of which an improvised Squeezeformer with an Additive Attention mechanism from Fastformer resulted in lesser training time (920 min vs 1280 min), lesser model parameters (9.16 M vs 9.74 M), and comparative Normalized Levenstein Distance inference score (0.661 vs 0.684) as compared to the baseline model: improvised Squeezeformer using LlaMA Attention considered. The above best model is followed by an improvised Squeezeformer with Fastformer Attention and Efficient Channel Attention incorporated in the Depthwise Separable Convolution, with a slight increase in its performance but with a trade-off with its model complexity. © The Institution of Engineers (India) 2025.

关键词： Speech analysis

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