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Excitation protocols for nonlinear phononics in bismuth and antimony

Physical Review Materials 2024年第1期8卷 015202-015202页

作者： Anubhab Haldar Zhengjie Huang Xuedan Ma Pierre Darancet Sahar Sharifzadeh Department of Electrical and Computer Engineering Boston University Boston Massachusetts 02215 USA Center for Nanoscale Materials Argonne National Laboratory Argonne Illinois 60439 USA Northwestern Argonne Institute of Science and Engineering Evanston Illinois 60208 USA Consortium for Advanced Science and Engineering The University of Chicago Chicago Illinois 60637 USA Division of Materials Science and Engineering Boston University Boston Massachusetts 02215 USA

We study the optical generation and control of coherent phonons in elemental bismuth (Bi) and antimony (Sb) using a classical equation of motion informed by first-principles calculations of the potential energy surface and the frequency-dependent macroscopic dielectric function along the zone-centered optical phonon coordinates. Using this approach, we demonstrate that phonons with the largest optomechanical couplings also have the strongest degree of anharmonicity among the zone-centered modes, a result of the broken-symmetry structural ground state of Bi and Sb. We show how this anharmonicity, explaining the light-induced phonon softening observed in experiments, prevents the application of standard phonon-amplification and annihilation protocols. We introduce a simple linearization protocol that extends the use of such protocols to the case of anharmonic phonons in broken-symmetry materials, and demonstrate its efficiency at high displacement amplitudes. Our formalism and results provide a path for improving optical control in nonlinear phononics.

关键词： First-principles calculations Optical phonons Optoelectronics

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Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals 47

Deep Learning Networks for Human Knee Abnormality Detection ...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350365597

Early knee problem management relies on precise identification and classification of abnormalities. Surface electromyography (sEMG) and goniometer signals offer non-invasive screening for muscle activity and joint angle patterns, yet their complexity poses challenges in extracting critical diagnostic information. This paper proposes a novel deep-learning method using sEMG and goniometer data for knee abnormality diagnosis. The proposed ResNeXt model, employing CNNs and multi-kernel modules, is evaluated on the UCIEMG dataset. Experimental results demonstrate ResNeXt's superior accuracy, precision, recall, and F1-score compared to baseline models (CNN and LSTM). Res NeXt achieves the best performance with combined EMG and goniometer data, reaching 96.37% accuracy and 93.77% F1-score, with fewer trainable parameters, indicating computational efficiency. The findings indicate ResNeXt's effectiveness in identifying knee abnormalities using biosensor data, particularly sEM G and goniometer signals, aiding early disease detection and treatment. © 2024 IEEE.

关键词： Learning systems

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Convolutional Neural Network with CBAM Module for Fitness Activity Recognition Using Wearable IMU Sensors 9

Convolutional Neural Network with CBAM Module for Fitness Ac...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350318241

Wearable motion sensors are gaining traction for fitness activity tracking due to their utility in assisted living, wellness tracking, and fitness training applications. Such sensors typically incorporate accelerometers and gyroscopes to monitor and log physical activities. This work proposes a deep-learning approach for identifying fitness activities using wearable inertial measurement units (IMUs) signals. Specifically, a convolutional neural network (CNN) integrated with a convolutional block attention module (CBAM) provides the core framework. The CBAM module applies channel and spatial attention mechanisms to enhance CNN's capacity to discriminate salient features. Multichannel IMU time-series data, comprising accelerometer and gyroscope readings as subjects perform various exercises, supply input to the model. By learning via an attention-enhanced architecture, the model can extract robust and informative representations to categorize activities accurately. Experiments on a public IMU dataset with five exercise types across over twenty participants evaluated the approach. Results showed over 99.58% subject-independent activity recognition accuracy when augmenting the CNN with CBAM, constituting significant performance gains. The research findings indicate that an efficient deep-learning solution with attention modeling can recognize fitness activities reliably from on-body sensors. The proposed methodology holds substantial promise for applications in sports analytics and personalized health tracking. © 2024 IEEE.

关键词： Gyroscopes

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Leveraging Residual Deep Neural Networks and Multi-Device Sensors for Heterogeneous Activity Recognition 47

Leveraging Residual Deep Neural Networks and Multi-Device Se...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Department of Applied Statistics Faculty of Applied Science Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350365597

This study introduces a novel approach to identifying human activities using wearable sensors, particularly smart-phones and smartwatches. By leveraging deep learning neural networks and data from the HHAR dataset, which includes accelerometer and gyroscope data from individuals engaged in various activities, our method, centered around the HAR-Res NeXt model, accurately detects six activities. Utilizing residual connections and multi-kernel blocks, our approach effectively captures temporal and spatial relationships in sensor data. Experimental results demonstrate superior performance to standard machine learning algorithms and other deep learning approaches for human activity recognition. HAR-ResNeXt achieves high accuracy rates, particularly in classifying smartphone sensor data, underscoring its adaptability across diverse scenarios. Comparative analysis reveals the effectiveness of smartphone sensors and emphasizes the importance of multi-modal sensor fusion for accurate activity detection. © 2024 IEEE.

关键词： Smartphones

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Modified aquila optimizer for forecasting oil production

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Geo-Spatial Information science 2022年第4期25卷 519-535页

作者： Mohammed A.A.Al-qaness Ahmed A.Ewees Hong Fan Ayman Mutahar AlRassas Mohamed Abd Elaziz State Key Laboratory for Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Systems University of Bisha Department of e-BishaKingdom of Saudi Arabia Department of Computer Damietta UniversityDamiettaEgypt School of Petroleum Engineering China University of Petroleum(East China)QingdaoChina Department of Mathematics Faculty of ScienceZagazig UniversityZagazigEgypt Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman 346United Arab Emirates Faculty of Computer Science&Engineering Galala UniversitySuze 435611Egypt

Oil production estimation plays a critical role in economic plans for local governments and ***,many studies applied different Artificial Intelligence(AI)based meth-ods to estimate oil production in different *** Adaptive Neuro-Fuzzy Inference System(ANFIS)is a well-known model that has been successfully employed in various applica-tions,including time-series ***,the ANFIS model faces critical shortcomings in its parameters during the configuration *** this point,this paper works to solve the drawbacks of the ANFIS by optimizing ANFIS parameters using a modified Aquila Optimizer(AO)with the Opposition-Based Learning(OBL)*** main idea of the developed model,AOOBL-ANFIS,is to enhance the search process of the AO and use the AOOBL to boost the performance of the *** proposed model is evaluated using real-world oil produc-tion datasets collected from different oilfields using several performance metrics,including Root Mean Square Error(RMSE),Mean Absolute Error(MAE),coefficient of determination(R2),Standard Deviation(Std),and computational ***,the AOOBL-ANFIS model is compared to several modified ANFIS models include Particle Swarm Optimization(PSO)-ANFIS,Grey Wolf Optimizer(GWO)-ANFIS,Sine Cosine Algorithm(SCA)-ANFIS,Slime Mold Algorithm(SMA)-ANFIS,and Genetic Algorithm(GA)-ANFIS,***,it is compared to well-known time series forecasting methods,namely,Autoregressive Integrated Moving Average(ARIMA),Long Short-Term Memory(LSTM),Seasonal Autoregressive Integrated Moving Average(SARIMA),and Neural Network(NN).The outcomes verified the high performance of the AOOBL-ANFIS,which outperformed the classic ANFIS model and the compared models.

关键词： Oil production ANFIS opposition-based learning(OBL) Aquila Optimizer(AO) time series forecasting Tahe oilfield Sunah oilfield

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Enhancing the Security of the MAVLink with Symmetric Authenticated Encryption for Drones

Enhancing the Security of the MAVLink with Symmetric Authent...

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International Conference on Internet of Things: Systems, Management and Security

作者： Burak Tufekci Atakan Arslan Cihan Tunc Kirill Morozov Department of Computer Science and Engineering The University of North Texas Denton TX USA Information Security Center of Research for Advanced Technologies Gebze Turkiye

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

The Micro Air Vehicle Link (MAVLink) protocol plays a crucial role in enabling communication between drones and Ground Control Stations (GCS). With the increasing adoption of drones, the security and integrity of MAVLink protocol have also become paramount. This paper delves into the fundamental aspects of MAVLink, highlighting its security challenges and proposing potential solutions to enhance its resilience against cyber threats. The study presents findings on the vulnerabilities of the MAVLink protocol and introduces a novel approach to securing communications on the MAVLink protocol for drones. The paper evaluates different Authenticated Encryption with Associated Data (AEAD) schemes and their impact on the performance of MAVLink, emphasizing the importance of encryption and authentication for ensuring data security. The results demonstrate the efficiency of the Chacha20Poly1305 encryption mechanism in execution time, the AES-OCB3 scheme as the least CPU-intensive, and AES-CCM in terms of memory usage. By adding the AEAD scheme into the MAVLink payload, we not only provide message confidentiality, integrity, and authenticity but also provide MAVLink header integrity to the drone environment.

关键词： Protocols Data security Memory management Authentication Encryption Internet Security Drones Payloads Resilience

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Technical and economic feasibility assessment for hybrid energy system electricity and hydrogen generation: A case study

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Global Energy Interconnection 2025年第1期8卷 62-81页

作者： Paul C.Okonkwo Samuel Chukwujindu Nwokolo El Manaa Barhoumi Ibrahim B.Mansir Usman Habu Taura Barun Kumar Das Ahmed Bahgat Radwan Wilfred Emori Ephraim Bonah Agyekum Khalid Al Kaaf Mechanical&Mechatronics Engineering Department Dhofar UniversitySalalah 211the Sultanate of Oman Department of Physics Faculty of Physical SciencesUniversity of CalabarCalabar PMB 1115Nigeria Electrical&Computer Engineering Department Dhofar UniversitySalalah 211the Sultanate of Oman Department of Mechanical Engineering Prince Sattam bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Centre for Energy Research and Training Ahmadu Bello UniversityPMB 1045ZariaNigeria Oil&Gas Research Center Sultan Qaboos UniversityMuscat 123the Sultanate of Oman Department of Mechanical Engineering Rajshahi University of Engineering&TechnologyRajshahi 6204Bangladesh Center for Advanced Materials Qatar UniversityDoha 2713Qatar School of Materials Science and Engineering Sichuan University of Science and EngineeringZigong 643000PR China Department of Nuclear and Renewable Energy Ural Federal University Named after the First President of Russia BorisEkaterinburg 620002Russia Western Caspian University Baku AZ1001Azerbaijan Department of Civil and Environmental Engineering Dhofar UniversitySalalah 211the Sultanate of Oman

Hydrogen is emerging as a promising alternative to fossil fuels in the transportation *** study evaluated the feasibility of estab-lishing hydrogen refueling stations in five cities in Oman,Duqm,Haima,Sur,Al Buraymi,and Salalah,using Hybrid Optimization of Multiple Electric Renewables(HOMER)*** hybrid energy systems,photovoltaic-wind turbine-battery,photovoltaic-battery,and wind turbine-battery were analyzed for each *** indicated that Duqm offers the lowest net present cost(NPC),levelized cost of energy,and levelized cost of hydrogen,making it the most cost-effective ***,Sensitivity analysis showed that as the life of electrolyzer increases during operation,the initial capital expenditure is distributed over a longer operational period,leading to a reduction in the *** so,renewable energy systems produced no emissions which supports Oman’s mission *** comprehensive analysis confirms the feasibility of establishing a hydrogen refueling station in Duqm,Oman,and highlights advanced optimization techniques’superior capability in designing cost-effective,sustainable energy systems.

关键词： Cities Economic indicator Hydrogen production Optimization Solar

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Evolutionary Approach to Security Games with Signaling 31

Evolutionary Approach to Security Games with Signaling

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31st International Joint Conference on Artificial Intelligence, IJCAI 2022

作者： Żychowski, Adam Mańdziuk, Jacek Bondi, Elizabeth Venugopal, Aravind Tambe, Milind Ravindran, Balaraman Faculty of Mathematics and Information Science Warsaw University of Technology Poland Center for Research on Computation and Society Harvard University United States Robert Bosch Centre for Data Science and AI IIT Madras India Department of Computer Science and Engineering IIT Madras India

ISBN: (纸本)9781956792003

Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information. Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory. We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary Computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of benchmark games, based on dense or locally-dense graphs that reflect real-world SGS settings. In the majority of 342 test game instances, EASGS outperforms state-of-the-art methods, including a reinforcement learning method, in terms of time scalability, nearly constant memory utilization, and quality of the returned defender's strategies (expected payoffs). © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Chromosomes

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Elucidation of the governing factors of crystallographic texture development in metal additive manufacturing processing;focusing on the silicide product fabrication as an example

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Keikinzoku/Journal of Japan Institute of Light Metals 2022年第6期72卷 344-349页

作者： Hagihara, Koji Ishimoto, Takuya Nakano, Takayoshi Department of Physical Science and Engineering Nagoya Institute of Technology Gokiso Aichi Nagoya-shi466-8555 Japan Division of Materials & Manufacturing Science Graduate School of Engineering Osaka University Osaka Suita-shi Japan Anisotropic Design & Additive Manufacturing Center Graduate School of Engineering Osaka University Osaka Suita-shi Japan Center for Aluminum and advanced Materials Research and International Collaboration Institute of Light Metals Toyama Toyama-shi Japan

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Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning

arXiv

引用

arXiv 2024年

作者： Ding, Zezhong Xiang, Yongan Wang, Shangyou Xie, Xike Zhou, S. Kevin School of Data Science School of Computer Science and Technology School of Biomedical Engineering Data Darkness Lab MIRACLE Center Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou China

In the realm of distributed systems tasked with managing and processing large-scale graph-structured data, optimizing graph partitioning stands as a pivotal challenge. The primary goal is to minimize communication overhead and runtime cost. However, alongside the computational complexity associated with optimal graph partitioning, a critical factor to consider is memory overhead. Real-world graphs often reach colossal sizes, making it impractical and economically unviable to load the entire graph into memory for partitioning. This is also a fundamental premise in distributed graph processing, where accommodating a graph with non-distributed systems is unattainable. Currently, existing streaming partitioning algorithms exhibit a skew-oblivious nature, yielding satisfactory partitioning results exclusively for specific graph types. In this paper, we propose a novel streaming partitioning algorithm, the Skewness-aware Vertex-cut Partitioner (S5P), designed to leverage the skewness characteristics of real graphs for achieving high-quality partitioning. S5P offers high partitioning quality by segregating the graph’s edge set into two subsets, head and tail sets. Following processing by a skewness-aware clustering algorithm, these two subsets subsequently undergo a Stackelberg graph game. Our extensive evaluations conducted on substantial real-world and synthetic graphs demonstrate that, in all instances, the partitioning quality of S5P surpasses that of existing streaming partitioning algorithms, operating within the same load balance constraints. For example, S5P can bring up to a 51% improvement in partitioning quality compared to the top partitioner among the baselines. Lastly, we showcase that the implementation of S5P results in up to an 81% reduction in communication cost and a 130% increase in runtime efficiency for distributed graph processing tasks on *** Codes 97P30 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

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