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Molecule opacity study on low-lying states of CS

Chinese Physics B 2022年第10期31卷 362-370页

作者： Rui Li Jiqun Sang Xiaohe Lin Jianjun Li Guiying Liang Yong Wu Department of Physics College of ScienceQiqihar UniversityQiqihar 161006China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Faculty of Foundation Space Engineering UniversityBeijing 101416China School of Data Science and Artificial Intelligence Jilin Engineering Normal UniversityChangchun 130052China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100084China

CS molecule, which plays a key role in atmospheric and astrophysical circumstances, has drawn great attention for long time. Owing to its large state density, the detailed information of the electronic structure of CS is still lacking. In this work, the high-level MRCI+Q method is used to compute the potential energy curves, dipole moments and transition dipole moments of singlet and triplet states correlated with the lowest dissociation limit of CS, based on which high accurate vibration-rotation levels and spectroscopic constants of bound states are evaluated. The opacity of CS relevant to atmospheric circumstance is computed at a pressure of 100 atms for different temperatures. With the increase of temperature,band systems from different transitions mingle with each other, and band boundaries become blurred, which are originated from the increased population on vibrational excited states and electronic excited states at high temperature.

关键词： CS transition dipole moment opacity excited state

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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Invertibility of Discrete-Time Linear Systems with Sparse Inputs 63

Invertibility of Discrete-Time Linear Systems with Sparse In...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Poe, Kyle Mallada, Enrique Vidal, Rene University of Pennsylvania Applied Mathematics and Computational Science Group PA19104 United States Johns Hopkins University Mallada Is with the Department of Electrical and Computer Engineering MD21218 United States University of Pennsylvania Radiology Computer and Information Science Statistics and Data Science Departments of Electrical and Systems Engineering PA19104 United States

ISBN: (纸本)9798350316339

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024 IEEE.

关键词： State space methods

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Quantization of Bandlimited Graph Signals

Quantization of Bandlimited Graph Signals

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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Krahmer, Felix Lyu, He Saab, Rayan Veselovska, Anna Wang, Rongrong Technical University of Munich and Munich Center for Machine Learning Department of Mathematics & Munich Data Science Institute Garching/Munich Germany University of California San Diego Department of Mathematics & Halicioglu Data Science Institute San Diego United States Michigan State University Department of Computational Mathematics Science and Engineering Department of Mathematics East Lansing United States

ISBN: (纸本)9798350328851

Graph models and graph-based signals are becoming increasingly important in machine learning, natural sciences, and modern signal processing. In this paper, we address the problem of quantizing bandlimited graph signals. We introduce two classes of noise-shaping algorithms for graph signals that differ in their sampling methodologies. We demonstrate that these algorithms can be efficiently used to construct quantized representatives of bandlimited graph-based signals with bounded amplitude. Moreover, for one of the algorithms, we provide theoretical guarantees on the relative error between the quantized representative and the true signal. © 2023 IEEE.

关键词： Quantization (signal)

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Machine Learning and OpenCV: A Powerful Combination for Driver Drowsiness Detection

Machine Learning and OpenCV: A Powerful Combination for Driv...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Siva Kumar, A. Gopirajan, P.V. Ramasamy, Jayaraj Jackson, Beulah School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur Chennai603203 India School Of Computing Srm Institute Of Science And Technology Department Of Computational Intelligence Kattankulathur Campus Chennai603203 India Botho University Faculty Of Engineering And Technology Department of IT Botswana Vel Tech Rangarajan Dr. Sagunthala R & D Institute Of Science And Technology Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

Driver fatigue is a significant contributor to road accidents worldwide, and there is a need for efficient driver drowsiness detection systems to prevent such accidents. Computer vision techniques like OpenCV have recently been utilized to develop such strategies. This proposed work investigates the use of machine learning models and OpenCV Python package for driver drowsiness detection. The proposed system uses a camera to continuously capture the driver's face and applies image processing techniques to identify drowsiness. The system detects blinks by monitoring the driver's eyes and measuring the frequency and duration of eyelid closures. The real-time experiments conducted with the proposed method showed high accuracy in detecting drowsiness, and the system can be implemented in various settings, including cars and heavy vehicles. Ultimately, the system has some potential to reduce accidents caused by driver fatigue, thereby enhancing road safety. © 2024 IEEE.

关键词： Support vector machines

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Detection of Wormhole Attacks Using the DCNNBiLSTM Model to Secure the MANET 3rd

Detection of Wormhole Attacks Using the DCNNBiLSTM Model to ...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Rajalakshmi, B. Anandhi, R.J. Moorthi, K. Kavin, Balasubramanian Prabhu Dhanaraj, Rajesh Kumar New Horizon College of Engineering Ring Road Bellandur Post Bengaluru560103 India Department of Computational Intelligence College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Department of Data Science and Business Systems College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Pune India

ISBN: (纸本)9789819708918

The obvious response to an anytime, anywhere network in the current era of heterogeneous networks is MANET. It is a collection of wirelessly communicative mobile gadgets that operate on their own. In order to stop the damage that rogue network nodes could do, security in MANET is a crucial duty. A category of denial-of-service (DoS) assault, the wormhole attack bombards the network with numerous phoney packets and messages in an effort to exhaust its resources. Wormhole attacks are a particular type of network layer attack that imitates routing algorithms. Because of deep learning's exceptional performance in many detection and identification tasks, this research presents an approach for intelligent and effective attack detection in MANET. Our goal is to mimic a wormhole attack in a network environment with many wormhole tunnels. The CNN layer is used by the DCNNBiLSTM architecture to extract features from input data, and BiLSTMs are used to predict sequences. Five performance indicators were used to compare CNN, LSTM and DCNNBiLSTM under wormhole attack. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Improving the Efficiency of Multimodal Approach for Chest X-Ray 2nd

Improving the Efficiency of Multimodal Approach for Chest...

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2nd International Conference on Advances in Computing Research, ACR 2024

作者： Upadhya, Jiblal Vargas, Jorge Poudel, Khem Ranganathan, Jaishree Computational and Data Science Middle Tennessee State University MurfreesboroTN37132 United States Department of Engineering Technology Middle Tennessee State University MurfreesboroTN37132 United States Department of Computer Science Middle Tennessee State University MurfreesboroTN37132 United States

ISBN: (纸本)9783031569494

Medical data analysis with limited data has been a challenging problem in machine learning. Privacy regulations limit the sharing and use of patient data, making it challenging to access and aggregate large datasets for research purposes. To address this challenge, we propose a deep learning-based Multimodal network that incorporates data from multiple data sources: images, and text in the learning process. When faced with limited data of one type, incorporating data from multiple sources can enhance the model’s performance and generalization. Our approach integrates the text sub-model and image sub-model with deep network of Dense 512 using the transfer learning process. Specifically, the goal is to explore the impact of hyperparameter adjustments on the performance of the model with different learning rates. Evaluation of our proposed approach on the Indiana University Chest X-ray dataset that consists of textual (radiological reports) and visual (X-ray images) show that our approach significantly outperforms several baseline methods in classification accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Large datasets

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Word2vec neural model-based techniqueto generate protein vectors for combating COVID-19: a machine learning approach

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International Journal of Information Technology (Singapore) 2022年第7期14卷 3291-3299页

作者： Adjuik, Toby A. Ananey-Obiri, Daniel Department of Biosystems and Agricultural Engineering University of Kentucky Lexington KY United States Department of Computational Data Science and Engineering Department North Carolina Agricultural and Technical State University Greensboro NC United States

The world was ambushed in 2019 by the COVID-19 virus which affected the health, economy, and lifestyle of individuals worldwide. One way of combating such a public health concern is by using appropriate, rapid, and unbiased diagnostic tools for quick detection of infected people. However, a current dearth of bioinformatics tools necessitates modeling studies to help diagnose COVID-19 cases. Molecular-based methods such as the real-time reverse transcription polymerase chain reaction (rRT-PCR) for detecting COVID-19 is time consuming and prone to contamination. Modern bioinformatics tools have made it possible to create large databases of protein sequences of various diseases, apply data mining techniques, and accurately diagnose diseases. However, the current sequence alignment tools that use these databases are not able to detect novel COVID-19 viral sequences due to high sequence dissimilarity. The objective of this study, therefore, was to develop models that can accurately classify COVID-19 viral sequences rapidly using protein vectors generated by neural word embedding technique. Five machine learning models;K nearest neighbor regression (KNN), support vector machine (SVM), random forest (RF), Linear discriminant analysis (LDA), and Logistic regression were developed using datasets from the National Center for Biotechnology. Our results suggest, the RF model performed better than all other models on the training dataset with 99% accuracy score and 99.5% accuracy on the testing dataset. The implication of this study is that, rapid detection of the COVID-19 virus in suspected cases could potentially save lives as less time will be needed to ascertain the status of a patient. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Artificial intelligence Continuousbag-of-words COVID-19 Natural language processing Receiver operator characteristic curve Word vectors

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Optimizing Forwarding Strategies in Sparsely Delay-Tolerant Networks with Short Contact Durations 15

Optimizing Forwarding Strategies in Sparsely Delay-Tolerant ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nemera, Yadesa Bedasa Jule, Leta Tesfaye Sivakumar, S. Pavithra, J. Sampathkumar, A. Rastogi, Ravi Dambi Dollo Univeristy Department of Information Technology Ethiopia Dambi Dollo University College of Natural and Computational Science Department of Physics Dambi Dollo Ethiopia Nehru Institute of Engineering and Technology Department of Cse Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Artificial intelligence and data science Coimbatore India Dambi Dollo Univeristy Department of Computer Science Ethiopia Koneru Lakshmaiah Education Foundation Department of Cse AP Guntur India

ISBN: (纸本)9798350370249

Delay-tolerant networks (DTNs) are specific settings that experience either instability or the absence of a complete path between the source and destination nodes. The contact lifetime between pairs of network nodes is constrained by the mobility of nodes and the restricted bandwidth of peer- to-peer wireless transmission. In sparsely delay-tolerant networks, the re-establishment of contact between pairs of nodes is significantly delayed beyond the duration of the initial contact. Hence, the need for careful thought in designing a buffer control forwarding policy arises due to the limited contact period. Although forwarding algorithms have been designed extensively, much research has not been done on forwarding when there is a short contact time. When the message exchange between the pairs of network nodes is not completed during the contract period, they must wait until they are within communication range once more. To tackle the issue, we have suggested an innovative forwarding policy in buffer management that considers the duration of interaction. To assess the effectiveness of the proposed effort, we utilized the Opportunistic Network Environment (ONE) networking simulator. The suggested approach is combined with the MaxProp routing protocol buffer control forwarding policy and evaluated using two distinct metrics: bundle delivery rate and average delivery delay. In addition, we evaluated the performance of the suggested work by considering the number of nodes, buffer size, and simulation time. Across all the trials conducted in this thesis, the simulation results consistently demonstrate that our suggested method beats the existing forwarding strategy. © 2024 IEEE.

关键词： Buffer Management Forwarding Policy Contact Duration DTN Sparse DTN

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FISTA ITERATES CONVERGE LINEARLY FOR DENOISER-DRIVEN REGULARIZATION

arXiv

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arXiv 2024年

作者： Sinha, Arghya Chaudhury, Kunal N. Department of Computational and Data Sciences Indian Institute of Science Bangalore India Department of Electrical Engineering Indian Institute of Science Bangalore India

The effectiveness of denoising-driven regularization for image reconstruction has been widely recognized. Two prominent algorithms in this area are Plug-and-Play (PnP) and Regularization-by-Denoising (RED). We consider two specific algorithms PnP-FISTA and RED-APG, where regularization is performed by replacing the proximal operator in the FISTA algorithm with a powerful denoiser. The iterate convergence of FISTA is known to be challenging with no universal guarantees. Yet, we show that for linear inverse problems and a class of linear denoisers, global linear convergence of the iterates of PnP-FISTA and RED-APG can be established through simple spectral *** Codes 94A08, 41A25, 65F10 © 2024, CC BY.

关键词： Inverse problems

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