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An SAV Method for Imaginary Time Gradient Flow Model in Density Functional Theory

Advances in Applied mathematics and Mechanics 2023年第3期15卷 719-736页

作者： Ting Wang Jie Zhou Guanghui Hu Department of Mathematics Faculty of Science and TechnologyUniversity of MacaoMacao SARChina School of Mathematics and Computational Science Xiangtan UniversityXiangtanHunan 411105China Zhuhai UM Science&Technology Research Institute ZhuhaiGuangdong 519030China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications University of MacaoMacao SARChina

In this paper,based on the imaginary time gradient flow model in the density functional theory,a scalar auxiliary variable(SAV)method is developed for the ground state calculation of a given electronic structure *** handle the orthonormality constraint on those wave functions,two kinds of penalty terms are introduced in designing the modified energy functional in SAV,i.e.,one for the norm preserving of each wave function,another for the orthogonality between each pair of different wave functions.A numerical method consisting of a designed scheme and a linear finite element method is used for the ***,the desired unconditional decay of modified energy can be obtained from our method,while computationally,both the original energy and modified energy decay behaviors can be observed successfully from a number of numerical *** importantly,numerical results show that the orthonormality among those wave functions can be automatically preserved,without explicitly preserving orthogonalization *** implies the potential of our method in large-scale simulations in density functional theory.

关键词： Density functional theory gradient flow scalar auxiliary variable unconditional energy stability orthonormalization free

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Collision-Free Linear Time Mutual Visibility for Asynchronous Fat Robots 24

Collision-Free Linear Time Mutual Visibility for Asynchronou...

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25th International Conference on Distributed computing and Networking, ICDCN 2024

作者： Bhagat, Subhash Dey, Paulomi Ray, Rajarshi Department of Mathematics Indian Institute of Technology Jodhpur Jodhpur India St.Thomas' College of Engineering and Technology Kolkata India School of Mathematical & Computational Sciences Indian Association for the Cultivation of Science Kolkata India

ISBN: (纸本)9798400716737

This paper studies the mutual visibility problem for a distributed system of n ≥ 3 asynchronous opaque fat robots under the obstructed visibility model. Robots are represented as unit discs in the Euclidean plane and they block both motions and visions of the other robots through them. The mutual visibility problem requires the robots to reposition themselves in such a way that all the robots in the system become mutually visible to each other. Collision-free movement is one of the desirable features that any algorithm for fat robots needs to satisfy. This paper proposes a distributed collision-free mutual visibility algorithm for asynchronous fat robots with externally visible lights. The proposed algorithm uses O(1) colors for the lights of the robots, assumes one-axis agreement, and runs in O(n) asynchronous rounds. © 2024 ACM.

关键词： Robots

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Streamlining Attendance with Fast Face Recognition Model 8

Streamlining Attendance with Fast Face Recognition Model

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sundar, Koushik Ravikumar, S. Jeyalakshmi, J. Eugene Berna, I. Samuel, Prithi Citibank Na Jersey CityNJ United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Campus Chennai India

ISBN: (纸本)9798350376425

Facial recognition technology has become increasingly prevalent in today's world, with applications ranging from mobile phones to banking and workplace security. This research proposes a facial recognition-based solution to replace the manual attendance tracking process, streamlining the process and reducing human error. The proposed system integrates facial recognition, registration forms, and a Redis database to automate attendance tracking. By leveraging the ML search algorithm (similarity approach) and the Insight face Python module, the system can accurately identify individuals within a database. The system's implementation involves two phases: the initial phase focuses on integrating facial recognition, registration forms, and database access. The second phase involves utilizing software such as Streamlit and Dashboard to enhance user experience and visualization. This innovative solution offers a more efficient and accurate alternative to traditional manual attendance tracking methods, improving overall efficiency and reducing administrative burdens. By automating the process, the system contributes to a more streamlined and secure environment. © 2024 IEEE.

关键词： Database systems

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Benchmarking model chemistry composite calculations for vertical ionization potential of molecular systems

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Chinese Chemical Letters 2025年第2期36卷 508-511页

作者： Bingwei Wang Yihong Ding Xiao Tian Key Laboratory of Carbon Materials of Zhejiang Province Wenzhou Key Lab of Advanced Energy Storage and ConversionZhejiang Province Key Lab of Leather EngineeringCollege of Chemistry and Materials EngineeringWenzhou UniversityWenzhou 325035China School of Mathematics and Science Hebei GEO UniversityShijiazhuang 050031China Laboratory of Theoretical and Computational Chemistry Institute of Theoretical ChemistryJilin UniversityChangchun 130023China

In chemical science,the vertical ionization potential(VIP)is a crucial metric for understanding the electronegativity,hardness and softness of chemical material systems as well as the electronic structure and stability of *** since the last century,the model chemistry composite methods have witnessed tremendous developments in computing the thermodynamic properties as well as the barrier ***,their performance in realm of the vertical electron processes of molecular systems has been rarely *** this study,we for the first time benchmarked the model chemistry composite methods(e.g.,CBS-QB3,G4 and W1BD)in comparison with the commonly used Koopmans's theorem(KT),electron propagator theory(e.g.,OVGF,D2,P3 and P3+)and CCSD(T)methods in calculating the VIP for up to 613 molecular systems with available experimental *** large-scale test calculations strongly showed that the CBS-QB3 model chemistry composite technique can be well recommended to calculate VIP from the perspectives of accuracy,economy and ***,the VIP values of up to 7 molecules were identified to have the absolute errors of larger than 0.3 e V at all calculation levels,which have strong hints that their VIP experimental values should be re-investigated.

关键词： Model chemistry Vertical ionization potential Composite methods Molecular systems CBS-QB3

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An inexact augmented lagrangian algorithm for training leaky ReLU neural network with group sparsity

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 10097-10139页

作者： Wei Liu Xin Liu Xiaojun Chen Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

The leaky ReLU network with a group sparse regularization term has been widely used in the recent years. However, training such network yields a nonsmooth nonconvex optimization problem and there exists a lack of approaches to compute a stationary point deterministically. In this paper, we first resolve the multi-layer composite term in the original optimization problem by introducing auxiliary variables and additional constraints. We show the new model has a nonempty and bounded solution set and its feasible set satisfies the Mangasarian-Fromovitz constraint qualification. Moreover, we show the relationship between the new model and the original problem. Remarkably, we propose an inexact augmented Lagrangian algorithm for solving the new model, and show the convergence of the algorithm to a KKT point. Numerical experiments demonstrate that our algorithm is more efficient for training sparse leaky ReLU neural networks than some well-known algorithms.

关键词： sparse neural network leaky ReLU group sparsity penalty method inexact augmented Lagrangian method

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Methodical Tamil Character Recognition Using Fabricated CNN Model

Methodical Tamil Character Recognition Using Fabricated CNN ...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Bhavani, M. Ravikumar, S. Prithi, S. Arockia Raj, Y. Rajendiran, Babu Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Chennai India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350348217

Recognizing text is one of the most challenging problems in computer vision. To put it simply, handwritten character recognition is the process of recognising text in photographs, papers, and other media and converting it into a form that a computer can read. You can solve this issue by employing the image classification method. We will be able to recognise the user's typed Tamil characters in this paper. We designed a brand new model, MVGG16, to handle character recognition and classification. Its basic structure is analogous to that of VGG16, but we added a few more layers to improve accuracy. So far, 156 Tamil characters have been learnt, with 350 images each class. In experimental settings, this model achieves an accuracy of 91.8%. As an input method, this work makes use of a whiteboard where the user can scribble the Tamil character to be recognised. The character will be sent for verification after the user has written it and the image has been exported. Once the model identifies the character, it will show it to the user. © 2023 IEEE.

关键词： Character recognition

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Automated Hematocyte Classification using Hybrid Deep Learning Model

Automated Hematocyte Classification using Hybrid Deep Learni...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： Bhavani M Prithi Samuel Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Numerous applications of deep learning have already shown their efficacy, and more and more people are beginning to accept that deep learning is better than older machine learning methods. Deep learning algorithms, in particular, have enormous benefits in the medical profession, where a huge quantity of pictures must be analysed and assessed. The categorisation of blood cells, one of the most difficult problems in blood diagnostics, intends to create a deep-learning model. The nucleus of cells provides important data to specialists for disease classification, as the massive diseases can be differentiated by observing the classification of hematocytes. This addresses two primary goals: giving a stained image of a WBC and classifying it as either polynuclear or mononuclear. Second, using a stained picture, identify the eosinophils, neutrophils, lymphocytes, and monocytes to determine the kind of hematocytes. The classification of hematocytes is resource-intensive; hence, this work, intend to present a novel framework to improve the detection and classification of hematocytes.

关键词： Deep learning Pervasive computing Machine learning algorithms Social networking (online) Lymphocytes Cells (biology) Feature extraction Medical diagnostic imaging Eosinophils Diseases

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Intrusion Detection System in Mobile Cloud computing Using Bat Optimization Algorithm-Support Vector Machine 2

Intrusion Detection System in Mobile Cloud Computing Using B...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Alabdeli, Haider Yamsani, Nagendar Anitha, D. Chaithra, K.N. Bindu, G. The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350317558

Mobile Cloud computing (MCC) is provided in various industries to acquire cloud-based services by using mobile technology. MCC is employed to minimize mobile device limitations by enabling them to offload computations to remote cloud. It contains significant information about organizations;hence, to avoid this environment from attacks a security solution is required. An Intrusion Detection System (IDS) is a solution to these security problems. The Bat Optimization Algorithm-based Support Vector Machine (BOA-SVM) is developed for IDS by utilizing machine learning in MCC in this research. Firstly, data is acquired by UNSW-NB15 dataset and then min-max normalization is established for normalizing acquired data. GWO is employed for Feature Selection (FS) that determines relevant features for classification. At last, SVM is established to classify IDS effectively and accurately. BOA-SVM attains a better accuracy of 99.78% compared to existing techniques such as Edge-based Hybrid Intrusion Detection Framework (EHIDF), Information Gain-Chi-Square-Particle Swarm Optimization (IG-CS-PSO), and improved Self-adaptive Evolutionary Extreme Learning Machine (SaE-ELM) respectively. © 2024 IEEE.

关键词： Bat Optimization Algorithm Intrusion Detection System Machine Learning Mobile Cloud computing Support Vector Machine

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Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

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Computers, Materials & Continua 2022年第4期71卷 1459-1471页

作者： Anupam Garg Anshu Parashar Dipto Barman Sahil Jain Divya Singhal Mehedi Masud Mohamed Abouhawwash Computer Science and Engineering Department Thapar Institute of Engineering and TechnologyPatialaIndia School of Computer Science and Statistics Trinity College DublinDublinIreland University Institute of Biotechnology Chandigarh UniversityMohaliIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Autism Spectrum Disorder (ASD) is a developmental disorderwhose symptoms become noticeable in early years of the age though it canbe present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completelybut can be reduced if detected early. An early diagnosis is hampered by thevariation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergenceof deep learning approaches in various fields, medical experts can be assistedin early diagnosis of ASD. It is very difficult for a practitioner to identifyand concentrate on the major feature’s leading to the accurate prediction ofthe ASD and this arises the need for having an automated approach. Also,presence of different symptoms of ASD traits amongst toddlers directs tothe creation of a large feature dataset. In this study, we propose a hybridapproach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and preciseprediction of ASD. The proposed framework gives more accurate predictionalong with the recommendations of predicted results which will be a vital aidclinically for better and early prediction of ASD traits amongst toddlers.

关键词： Deep learning explainable artificial intelligence autism spectrum disorder machine learning

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