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Quantum Spin Probe of Single Charge Dynamics

Physical Review Letters 2024年第13期133卷 130802页

作者： Jonathan C. Marcks Mykyta Onizhuk Yu-Xin Wang (王语馨) Yizhi Zhu Yu Jin Benjamin S. Soloway Masaya Fukami Nazar Delegan F. Joseph Heremans Aashish A. Clerk Giulia Galli David D. Awschalom Pritzker School of Molecular Engineering Q-NEXT Materials Science Division Department of Chemistry Joint Center for Quantum Information and Computer Science Department of Physics

Electronic defects in semiconductors form the basis for emerging quantum technologies, but many defect centers are difficult to access at the single-particle level. A method for probing optically inactive spin defects would reveal semiconductor physics at the atomic scale and advance the study of new quantum systems. We exploit the intrinsic correlation between the charge and spin states of defect centers to measure the charge populations and dynamics of single substitutional nitrogen spin defects in diamond. By probing their steady-state spin population, read out at the single-defect level with a nearby nitrogen vacancy center, we directly measure the defect ionization—corroborated by first-principles calculations—an effect we do not have access to with traditional coherence-based quantum sensing.

关键词： Charge dynamics Quantum sensing Nitrogen vacancy centers in diamond

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Task Assignment of Heterogeneous Robots Based on Large Model Prompt Learning 1

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2nd International Conference on Cognitive Computation and Systems, ICCCS 2023

作者： Deng, Mingfang Wang, Ying Lu, Lingyun Zhao, Huailin Yuan, Xiaohu School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China School of Physics and Electronic Information Yantai University Shandong Yantai China Nanjing Research Institute of Electronic Engineering Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

In order to ensure that the heterogeneous robot clusters performing the task can complete the target search at the specified location of the target object that the user needs to search according to their own field of view capabilities, we propose a task assignment algorithm for heterogeneous indoor robot clusters based on the robot’s own field of view constraints. In particular, the heterogeneous robot clusters need to be parsed by linguistic commands to obtain the assignment results. Therefore, we solve the task assignment of heterogeneous robot clusters by performing cue learning on a large model to achieve the maximum utilization of heterogeneous robots while satisfying the field-of-view constraint;the simulation verifies the effectiveness of the task assignment of this method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Learning systems

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Repairing Reed-Solomon Codes over Prime Fields via Exponential Sums

Repairing Reed-Solomon Codes over Prime Fields via Exponenti...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Con, Roni Shutty, Noah Tamo, Itzhak Wootters, Mary Tel Aviv University Blavatnik School of Computer Science Israel Stanford University Stanford Institute for Theoretical Physics United States Tel Aviv University Department of Electrical Engineering-Systems Israel Stanford University Department of Computer Science and Electrical Engineering United States

ISBN: (纸本)9781665475549

This paper presents several repair schemes for lowrate Reed Solomon (RS) codes over prime fields that can repair any node by downloading a constant number of bits from each surviving node. The resulting total bandwidth is higher than the bandwidth incurred during the trivial repair;however, this is still interesting in the context of leakage-resilient secret sharing. In that language, our results give attacks that show that k-out-of-n Shamir's Secret Sharing over prime fields for small k is not leakage resilient, even if the parties only leak a constant number of bits. To the best of our knowledge, these are the first such *** another application, we provide decoding schemes for RS codes over prime fields, where the entire RS codeword is recovered by transmitting a constant number of bits from each *** results follow from a novel connection between exponential sums and repair of RS codes. In particular, we show that nontrivial bounds on certain exponential sums imply the existence of efficient nonlinear repair schemes for RS codes over prime fields. © 2023 IEEE.

关键词： Reed-Solomon codes

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Drones Classification based on Millimeter Wave Radar Cross Section via Deep Learning

Drones Classification based on Millimeter Wave Radar Cross S...

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IEEE Conference on Vehicular Technology (VTC)

作者： Lina Wang Li Meng Oluyomi Simpson Pan Cao School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

The wide use of drones is playing a vital role in the era of low-altitude economy, but meanwhile its misuse could also presents a threat to the privacy, property damage, health and public safety. Therefore, there is a timely need to enhance the capability to detect and recognize the flying drones, which however is challenging owing to the small size, slow speed and low altitude of small drones. In the 5G and beyond, the widely deployed base stations are becoming more and more advanced, especially with the large spectrum like the millimetre wave (mmWave) frequency band. This provides a great potential to turn the network of base stations into a network of mmWave radars for the drones’ detection by leveraging the integrated sensing and communication (ISAC) techniques. This work aims to classify the drones based on their mmWave radar cross section (RCS) real data that will be converted to two-dimensional (2D) RCS images. Thus, 2D Convolutional Neural Network (CNN) is applied to the image sets to achieve the drone classification. The satisfactory testing results verify the proposed drone classification method.

关键词： Base stations Vehicular and wireless technologies Radar cross-sections Two-dimensional displays Radar imaging Integrated sensing and communication Convolutional neural networks Millimeter wave communication Drones Testing

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Defending Against Digital Threats: Machine Learning Techniques for Cyber Persecution Detection 5

Defending Against Digital Threats: Machine Learning Techniqu...

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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Krishnan, Kesavan Kumar, Vijay Jalil, Norazira Binti A Kiran, Ajmeera School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Graphic Era Hill University Department of Physics Dehardun India Graphic Era Deemed to be University Dehradun India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331528553

Cyber persecution has become a widespread problem on the social media. It has resulted in omissions such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work employs natural language processing and Machine Learning techniques to construct a cyber harassment detection model utilizing data from cyberbullying, hate speech tweets from Twitter, and personal assaults from Wikipedia. The optimal approach is investigated by two feature extraction methods and six classifiers (SVM, Random Forest, XGBoost, MLP, and Logistic Regression). This study summarizes the general procedure for detecting cyberbullying and, more crucially, the technique. During experimentation, SVM, Random Forest, and Logistic Regression performed well with an average of 90%. Our model has been created to detect offensive comments on social media websites. © 2024 IEEE.

关键词： Adversarial machine learning

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Conceptual design of an autonomous unmanned aerial vehicle

Conceptual design of an autonomous unmanned aerial vehicle

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Afzal, Aftab Al Dhafari, Luqman Al Manai, Ahmed Akbari, Akbar Sheikh Miah, Md Salim Hossain, Mohammad Sayeed Military Technological College Dept. Aeronautical Engineering Muscat Oman Leeds Beckett University School of Built Environment Engineering and Computing Leeds United Kingdom University of Hertfordshire School of Physics Engineering and Computer Science Hertfordshire United Kingdom

ISBN: (纸本)9798350322972

A conceptual design of a medium size unmanned aerial vehicle (UAV) is reported in the present study. This study is part of a bigger research investigating into the ability of an advanced UAV to autonomously traverse over an aircraft and accurately detect locations of defects on the aircraft skin to assist in further investigation of subsurface defects utilizing thermal and ultrasound techniques. The aim of the research study is to minimize the inspection time of a commercial aircraft to the order of an hour. This paper focusses on the aerodynamics and structural design of a UAV capable of carrying the weights of optical, thermal and ultrasound cameras as required. The design requirements include the ability of the UAV to traverse the aircraft outer skin autonomously sending both its GPS location and captured images to a secure data drive within the vicinity of the aircraft hangar and the ability to wirelessly charge its battery and thereby improve its hovering duration, i.e., its endurance. To achieve the preliminary design, the aerodynamic coefficients are estimated using computational fluid dynamics analysis, as well as smart design of fan shrouds to minimize the vortex shedding and hence improve its overall efficiency. © 2023 IEEE.

关键词： Conceptual design

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Neural Machine Translation by Fusing Key Information of Text

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computers, Materials & Continua 2023年第2期74卷 2803-2815页

作者： Shijie Hu Xiaoyu Li Jiayu Bai Hang Lei Weizhong Qian Sunqiang Hu Cong Zhang Akpatsa Samuel Kofi Qian Qiu Yong Zhou Shan Yang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Science and Technology on Altitude Simulation Laboratory Sichuan Gas Turbine Establishment Aero Engine Corporation of ChinaMianyang621000China School of Power and Energy Northwestern Polytechnical UniversityXi’an710072China School of Computer Science Southwest Petroleum UniversityChengdu610500China Department of Chemistry Physics and Atmospheric SciencesJackson State UniversityJacksonMS 39217USA

When the Transformer proposed by Google in 2017,it was first used for machine translation tasks and achieved the state of the art at that *** the current neural machine translation model can generate high quality translation results,there are still mistranslations and omissions in the translation of key information of long *** the other hand,the most important part in traditional translation tasks is the translation of key *** the translation results,as long as the key information is translated accurately and completely,even if other parts of the results are translated incorrect,the final translation results’quality can still be *** order to solve the problem of mistranslation and missed translation effectively,and improve the accuracy and completeness of long sentence translation in machine translation,this paper proposes a key information fused neural machine translation model based on *** model proposed in this paper extracts the keywords of the source language text separately as the input of the *** the same encoding as the source language text,it is fused with the output of the source language text encoded by the encoder,then the key information is processed and input into the *** incorporating keyword information from the source language sentence,the model’s performance in the task of translating long sentences is very *** order to verify the effectiveness of the method of fusion of key information proposed in this paper,a series of experiments were carried out on the verification *** experimental results show that the Bilingual Evaluation Understudy(BLEU)score of the model proposed in this paper on theWorkshop on Machine Translation(WMT)2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test *** experimental results show the advantages of the model proposed in this paper.

关键词： Key information transformer fusion neural machine translation

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Predictions of H-mode access and edge pedestal instability in the EHL-2 spherical torus

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Plasma science and Technology 2025年第2期27卷 54-63页

作者： Yumin WANG Kai LI Zhuo HUANG Yiliang LIU Shuyu DAI Jie ZHANG Yanqing HUANG Xiang GU Yihang ZHAO Shuai XU Erhui WANG Dong GUO Yuejiang SHI Huasheng XIE Yunfeng LIANG Minsheng LIU the EHL-2 Team Hebei Key Laboratory of Compact Fusion Langfang 065001People’s Republic of China ENN Science and Technology Development Co. Ltd.Langfang 065001People’s Republic of China Centre for Theoretical and Computational Physics College of PhysicsQingdao UniversityQingdao 266071People’s Republic of China College of Computer Science South-Central Minzu UniversityWuhan 430074People’s Republic of China Key Laboratory of Materials Modification by Laser Ion and Electron Beams(Ministry of Education)School of PhysicsDalian University of TechnologyDalian 116024People’s Republic of China Department of Plasma Physics and Fusion Engineering University of Science and Technology of ChinaHefei 230026People’s Republic of China College of Physics and Electronic Engineering Hengyang Normal UniversityHenyang 421002People’s Republic of China Forschungszentrum Jülich GmbH Institute of Fusion Energy and Nuclear Waste Management-PlasmaphysikJülich 52425Germany

The EHL-2 spherical torus is designed to demonstrate proton-boron(p-11B)fusion within a compact spherical *** planned heating system includes a negative ion-based neutral beam injection(N-NBI),two positive ion-based NBI systems(P-NBI),electron cyclotron resonance heating(ECRH),ion cyclotron resonance heating(ICRH),and high harmonic fast wave(HHFW),with a total power output of 31 *** to scaling law estimates,the device is capable of achieving H-mode *** plasma density,,n_(e,min)at the minimum L-H power threshold,P_(lh),is estimated to be 4.4×10^(19)m^(-3).The pedestal parameters were calculated using the REPED *** B as the primary impurity ion,the predicted pedestal width and height are lower compared to the typical case with carbon *** pedestal collisionality for EHL-2 is estimated to range between 0.06 and 0.17,indicating the potential for significant energy loss due to edge localized modes(ELMs).The heat flux on the divertor plate has been calculated using the JOREK *** peak heat fluxes during ELM bursts are approximately 31.0 MW/m^(2)at the lower inboard target and 39.5 MW/m^(2)at the lower outboard target.A preliminary design of the resonant magnetic perturbation(RMP)coils has been completed to both control type-I ELMs and correct error *** system comprises 16 coils arranged into 24 *** ELM control mode,a 14/2 component is generated at 1.7 G/kAt,with a current of 4.9 kA required to achieveσChirikow=1 at the resonant surface,where the normalized poloidal magnetic flux is *** error field(EF)modulation mode,2/1 and 3/1 components are generated at 3.5 G/kAt and 2.8 G/kAt,respectively.

关键词： H-mode spherical torus(ST) EHL-2 pedestal resonant magnetic perturbations

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A Machine Learning Framework for Bearing Fault Detection in Three-Phase Induction Motors 4

A Machine Learning Framework for Bearing Fault Detection in ...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Rohouma, Wesam Zaitouny, Ayham Wahid, Md Ferdous Ali, Hassan Refaat, Shady S. University of Doha for Science and Technology College of Engineering Technology P.O. Box 24449 Doha Qatar University UAEU College of Science Department of Mathematical Sciences United Arab Emirates University at Qatar Mechanical Engineering Texas A&M Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom

ISBN: (纸本)9798350306262

Three-phase induction motors are widely employed in industry due to their rugged performance and easy maintenance. Bearing faults in three phase induction motors are responsible for 40%-50% of unplanned shutdowns in industrial settings. Therefore, early detection of bearing faults is essential to implement preventive measures and enhance planning of maintenance strategies. This paper thus proposes a machine learning (ML) framework that consistently monitors acceleration and temperature of bearing to detect bearing faults. The results show that the ML framework using k-nearest neighbor (k-NN) and support vector machine (SVM) approaches is better than the variation-based thresholding approach, where the former method is able to detect faulty conditions with more than 99% accuracy. © 2024 IEEE.

关键词： Condition monitoring

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Clustering Using Deep Belief Networks for Recommended Items 15

Clustering Using Deep Belief Networks for Recommended Items

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

作者： Sampangi Rama Reddy, B.R. Jha, Rakhi Sharma, Neeraj School of Sciences Department of Physics & Electronics Karnataka Bangalore India ARKA JAIN University Department of Computer Science & IT Jharkhand Jamshedpur India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350370249

Clustering is a vital statistics evaluation technique that organizations similar information factors together. Clustering is widely utilized in diverse packages, which include market segmentation, consumer profiling, and recommender structures. In recommender structures, clustering is used to group comparable items together and advise them to users based on their pursuits. One popular approach for performing clustering in recommender structures is the use of Deep perception Networks (DBNs). DBNs are deep learning architectures that have been utilized in a spread of applications, such as herbal language processing, computer vision, and recommendation systems. DBNs are generative models that make use of a couple of hidden layers of neurons to extract functions from the enter information. This hierarchical shape permits the version to learn complicated representations of the enter facts, making it a powerful device for clustering. In recommender structures, DBNs may be used to cluster objects based totally on their attributes or consumer interactions with them. This clustering can then be used to advise comparable gadgets to users. one of the blessings of the use of DBNs for clustering in recommender systems is they can cope with excessive-dimensional records, making them suitable for representing objects with more than one attributes. Similarly, DBNs are capable of learn hidden relationships and similarities among gadgets, that may result in more correct and personalized guidelines. © 2024 IEEE.

关键词： Recommender systems

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