检索结果-内蒙古大学图书馆

A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion–relaxation correlation MRI

引用

computers in Biology and Medicine 2024年 175卷 108508-108508页

作者： Zong, Fangrong Wang, Lixian Liu, Huabing Xue, Bing Bai, Ruiliang Liu, Yong School of Artificial Intelligence Beijing University of Post and Telecommunication Beijing100876 China Institute of Biophysics Chinese Academy of Sciences Beijing100101 China Beijing Limecho Technology Co. Ltd. Beijing102200 China School of Engineering and Computer Science Victoria University of Wellington Victoria6140 New Zealand Interdisciplinary Institute of Neuroscience and Technology Zhejiang University School of Medicine Hangzhou310020 China MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou310030 China

Multi-dimensional diffusion–relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings. © 2024 Elsevier Ltd

关键词： Compressed sensing

来源：评论

学校读者我要写书评

暂无评论

Adaptive Electric Vehicle Charge Controllers Using Deep Reinforcement learning for Real-Time Optimization

Adaptive Electric Vehicle Charge Controllers Using Deep Rein...

引用

Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： N. Kavitha Mohammad Bdair P. Chandrasekar Veerakumar S M. Hema Kumar Vinay Kumar Department of EEE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India School of Architecture Computing & Engineering University of East London London Uk Department of EEE Bannari Amman Institute of Technology Erode Tamil Nadu India Department of ECE Sona College of Technology Salem Tamil Nadu India Department of Artificial Intelligence and Machine Learning B N M Institute of Technology Bengaluru India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The increasing adoption of electric vehicles necessitates intelligent charging systems to address challenges such as grid overload, renewable energy integration, and user prioritization. This paper presents a new framework in optimizing the efficiency of electric car charging: Hierarchical Multi-Agent Deep Reinforcement learning. In this architecture, a global coordinator relies on Proximal Policy Optimization to coordinate across stations, and the local agents follow Deep Q-Networks to manage their resources at the station level. Test results: A comparison of the test results with regular methods shows an average waiting time reduced by 30%, a 15% increase in the usage of renewable energy, and a reduction of 22% in the system latency. For grid loads, consistent results from the operation of this architecture ensure that minimal unserved energy demand leads to the balanced serving of loads. This method, therefore, aligns well with sustainable energy goals through simultaneous improvement in effectiveness of charging infrastructure and on the flexible scale of solution.

关键词： Renewable energy sources Vehicle-to-grid Deep reinforcement learning Electric vehicle charging Real-time systems Automobiles Resource management Optimization Standards Testing

来源：评论

学校读者我要写书评

暂无评论

Less is More: Facial Landmarks can Recognize a Spontaneous Smile 022

arXiv

引用

arXiv 2022年

作者： Tushar, Tahrim Faroque Yang, Yan Hossain, Zakir Naim, Sheikh Motahar Mohammed, Nabeel Rahman, Shafin Department of Electrical and Computer Engineering North South University Bangladesh Biological Data Science Institute The Australian National University Canberra Australia CSIRO Agriculture & Food Canberra Australia CSIRO Machine Learning & Artificial Intelligence Future Science Platform Canberra Australia Amazon Web Services United States

Smile veracity classification is a task of interpreting social interactions. Broadly, it distinguishes between spontaneous and posed smiles. Previous approaches used hand-engineered features from facial landmarks or considered raw smile videos in an end-to-end manner to perform smile classification tasks. Feature-based methods require intervention from human experts on feature engineering and heavy pre-processing steps. On the contrary, raw smile video inputs fed into end-to-end models bring more automation to the process with the cost of considering many redundant facial features (beyond landmark locations) that are mainly irrelevant to smile veracity classification. It remains unclear to establish discriminative features from landmarks in an end-to-end manner. We present a MeshSmileNet framework, a transformer architecture, to address the above limitations. To eliminate redundant facial features, our landmarks input is extracted from Attention Mesh, a pre-trained landmark detector. Again, to discover discriminative features, we consider the relativity and trajectory of the landmarks. For the relativity, we aggregate facial landmark that conceptually formats a curve at each frame to establish local spatial features. For the trajectory, we estimate the movements of landmark composed features across time by self-attention mechanism, which captures pairwise dependency on the trajectory of the same landmark. This idea allows us to achieve state-of-the-art performances on UVA-NEMO, BBC, MMI Facial Expression, and SPOS datasets. © 2022, CC BY-NC-ND.

关键词： Relativity

来源：评论

学校读者我要写书评

暂无评论

Development of Efficient Wireless Sensor Network for IoT Applications

Development of Efficient Wireless Sensor Network for IoT App...

引用

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

作者： S. Shankar G. Deepika G. Devi S. Ramesh Suraj Srivastava S Sandeep Kumar Department of Electronics and Communication Systems Sri Krishna Arts and Science College Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering St. Peter's Engineering College Hyderabad Telangana India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Depattment of Computational Intelligence Institute of Artificial Intelligence and Machine Learning Saveetha University Chennai Tamil Nadu India Department of Computer science and Engineering I.K. Gujral Punjab Technical University SahibzadaAjit Singh Nagar Punjab India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

This research study provides an overview of the effort that went into developing reliable “Wireless sensor networks” for IoT applications, including both theoretical study and practical testing. The report also includes the findings of an analysis of variance and regression analysis, which pinpoint the significance of such aspects as network architecture, traffic load, and ambient conditions on the performance of Wireless Sensor Network (WSN). Finally, study provides a discussion on the future research directions, as well as the design and implementation of a Wireless Sensor Network (WSN) for Internet of Things (IoT) applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cost-Effective Communication in UDN in Indoor and Outdoor Environment via machine learning

Cost-Effective Communication in UDN in Indoor and Outdoor En...

引用

artificial intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： K Nattar Karman V. Velmurugan Kommisetti Murthy Raju T. Sajana V. Vijayalakshmi JoshuvaArockia Dhanraj Departmeru of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala R&D Institute of science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women West Godavari Andhra Pradesh India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective Data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management.

关键词： Costs Mobility models machine learning Knowledge discovery Scheduling Energy efficiency Concurrent engineering

来源：评论

学校读者我要写书评

暂无评论

CAN VIRTUAL STAINING FOR HIGH-THROUGHPUT SCREENING GENERALIZE?

arXiv

引用

arXiv 2024年

作者： Tonks, Samuel Nguyen, Cuong Hood, Steve Musso, Ryan Hopely, Ceridwen Titus, Steve Doan, Minh Styles, Iain Krull, Alexander School of Computer Science University of Birmingham Birmingham United Kingdom Artificial Intelligence & Machine Learning GSK South San FranciscoCA94080 United States GSK Drug Metabolism & Pharmacokinetics GSK Medicines Research Centre Gunnels Wood Road Stevenage HertfordshireSG1 2NY United Kingdom GSK Genome Biology 1250 S Collegeville Rd CollegevillePA19426 United States GSK Bioimaging 1250 S Collegeville Rd CollegevillePA19426 United States School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom

The large volume and variety of imaging data from high-throughput screening (HTS) in the pharmaceutical industry present an excellent resource for training virtual staining models. However, the potential of models trained under one set of experimental conditions to generalize to other conditions remains underexplored. This study systematically investigates whether data from three cell types (lung, ovarian, and breast) and two phenotypes (toxic and non-toxic conditions) commonly found in HTS can effectively train virtual staining models to generalize across three typical HTS distribution shifts: unseen phenotypes, unseen cell types, and the combination of both. Utilizing a dataset of 772,416 paired bright-field, cytoplasm, nuclei, and DNA-damage stain images, we evaluate the generalization capabilities of models across pixel-based, instance-wise, and biological-feature-based levels. Our findings indicate that training virtual nuclei and cytoplasm models on non-toxic condition samples not only generalizes to toxic condition samples but leads to improved performance across all evaluation levels compared to training on toxic condition samples. Generalization to unseen cell types shows variability depending on the cell type;models trained on ovarian or lung cell samples often perform well under other conditions, while those trained on breast cell samples consistently show poor generalization. Generalization to unseen cell types and phenotypes shows good generalization across all levels of evaluation compared to addressing unseen cell types alone. This study represents the first large-scale, data-centric analysis of the generalization capability of virtual staining models trained on diverse HTS datasets, providing valuable strategies for experimental training data generation. © 2024, CC BY-SA.

关键词： Biological organs

来源：评论

学校读者我要写书评

暂无评论

Explaining GPT-4's Schema of Depression Using machine Behavior Analysis

arXiv

引用

arXiv 2024年

作者： Ganesan, Adithya V. Varadarajan, Vasudha Lal, Yash Kumar Eijsbroek, Veerle C. Kjell, Katarina Kjell, Oscar N.E. Dhanasekaran, Tanuja Stade, Elizabeth C. Eichstaedt, Johannes C. Boyd, Ryan L. Schwartz, H. Andrew Flek, Lucie Department of Computer Science Stony Brook University United States Department of Psychology Lund University Sweden United States Department of Psychology Stanford University United States Institute for Human-Centered AI Stanford University United States Department of Psychology School of Behavioral and Brain Sciences The University of Texas Dallas United States Bonn-Aachen International Center for Information Technology University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Use of large language models such as ChatGPT (GPT-4) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders, like depression. However, we have a limited understanding of GPT-4's schema of mental disorders, that is, how it internally associates and interprets symptoms. In this work, we leveraged contemporary measurement theory to decode how GPT-4 interrelates depressive symptoms to inform both clinical utility and theoretical understanding. We found GPT-4's assessment of depression: (a) had high overall convergent validity (r = 0.71 with self-report on 955 samples, and r = 0.81 with experts judgments on 209 samples);(b) had moderately high internal consistency (symptom inter-correlates r = .23 to .78) that largely aligned with literature and self-report except that GPT-4;(c) underemphasized suicidality's - and overemphasized psychomotor's - relationship with other symptoms, and (d) had symptom inference patterns that suggest nuanced hypotheses (e.g. sleep and fatigue are influenced by most other symptoms while feelings of worthlessness/guilt is mostly influenced by depressed mood). © 2024, CC BY-SA.

关键词： Measurement theory

来源：评论

学校读者我要写书评

暂无评论

Optimizing QoS in Internet of Multimedia Things using Cross-Layer Design and artificial Bee Colony

Optimizing QoS in Internet of Multimedia Things using Cross-...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Ahmed A. Khalifa Shreyas J Gururaj H L Udayaprasad P K Srinidhi N N Department of Networking and Security College of Information Technology University of Fujairah Fujairah UAE Switching and Traffic Department National Telecommunication Institute Cairo Egypt Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Department of Computer Science & Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Futuristic demands of multimedia applications involve comprehensive sharing of dynamic data attributes within the Multimedia Items (IoMT) Internet sense. Exploring the existing research void in this area reveals need of building an integrated routing strategy for IoMT that can cope with multiple network characteristics, physical artifacts, and with cost-effective networks and distribution modules satisfy connectivity requirements. This paper suggest a layer cross architect-layerure optimization for IoMT applications with a emphasis on connectivity efficiency factors such as rising latency, energy limitations, and optimizing low-cost energy usage. The proposed work models and integrates a swarm intelligence paradigm which involve intelligent computing of artificial Bee Colony (ABC) maintaining effective link creation and selects the best path from source to target IoT nodes. The proposed research achieve the strongest routing usefulness for multimedia data exchange over IoT. To ensure this, the output of the proposed device is validated with an comprehensive simulation test. Results tested using MATLAB simulation method indicate an improvement of 8% in energy usage, 10% increase in throughput, 8% decrease in end to end delay and 10% increase in packet delivery ratio.

关键词： Cross layer design Energy consumption Routing Throughput artificial bee colony algorithm Delays Multimedia communication

来源：评论

学校读者我要写书评

暂无评论

Innovations in Voice Navigation Using Object Detection to Empower the Visually Impaired

Innovations in Voice Navigation Using Object Detection to Em...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Ranjeet Suryawanshi Annepu Arudra Dankan Gowda V Mathu Uthaman Swathi Pai M VK Satya Prasad Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth's college of Engineering Kolhapur Maharashtra India Department of Computer Science & Engineering Rajiv Gandhi Institute of Technology R T Nagar Cholanagar Bangalore Karnataka India Department of Electronics & Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Computer Science and Engineering Mahaguru Institute of Technology Kattachira Kayamkulam Alappuzha India Department of Artificial intelligence and machine learning NMAM Institute of Technology Nitte Karnataka India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India

The project focuses on addressing the challenges faced by visually impaired individuals in navigating environments, especially when encountering obstacles or misplacing essential tools like walking sticks. A novel solution is proposed: a voice-controlled smart stick, designed to enhance the autonomy of visually impaired people in their daily activities without reliance on others. This research introduces a sophisticated walking stick equipped with ultrasonic sensors, operated via voice commands for ease of use and accessibility The voice-controlled smart stick is distinguished by its user-friendly interface, rapid responsiveness, minimal power consumption, lightweight design, and ease of folding and handling. This project not only offers a practical solution for the visually impaired but also signifies a significant advancement in assistive technology, enhancing independence and safety in daily life.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Revolutioning IIot: A Smart Appliance of an Internet of Things Over Industrial Manufacturing Unit to Make Error-Free Circumstances

Revolutioning IIot: A Smart Appliance of an Internet of Thin...

引用

Cybernation and Computation (CYBERCOM), International Conference on

作者： Kiran Chand Ravi T. Vaishnavi Anni Princy B Vijay Anand Kandaswamy M Dinesh S Lakshmisridevi Department of ECE MLR Institute of Technology Hyderabad India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

A future where cyberspace and the real world are inseparable is the goal of the Internet of Things (IoT). The potential for the development of robust industrial services and applications, colloquially known as the Industrial Internet of Things (IIoT), is enormous. From the acquisition of raw materials to the provision of customer service and support, the Internet of Things (IoT) was utilized across numerous sectors by industries. The Internet of Things (IoT) offers a great opportunity to develop significant manufacturing-related services and applications, and it is widely regarded as a crucial technology of the industrial revolution. In addition, it facilitates communication between intelligent devices, allowing them to exchange information and data-crucial for complex systems to make decisions in the here-and-now. In this paper, a new method called Error-Free Smart Industrial Control System (EFSICS) is introduced to improve the efficiency of industrial operations through the use of IIoT. To test how well this method works, it is cross-validated with the traditional model called Classical Industrial Control System (CICS). Regarding the manufacturing aspects in particular, the suggested method is well-suited to the effects of the Internet of Things on sustainable development. Researchers are now at the forefront of cyber integrated manufacturing research, which will further illuminate the present state of the internet of things (IoT) in manufacturing.

关键词： Industries Industrial control Production Real-time systems Raw materials Manufacturing Maintenance Sustainable development Streams Industrial Internet of Things

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：