检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Saranya Ganesh, S. Beucler, Tom Tam, Frederick Iat-Hin Gomez, Milton S. Runge, Jakob Gerhardus, Andreas Institute of Earth Surface Dynamics University of Lausanne Vaud Lausanne Switzerland Thuringia Jena Germany Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Berlin Germany

Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we introduce a Multidata (M) causal feature selection approach that simultaneously processes an ensemble of time series datasets and produces a single set of causal drivers. This approach uses the causal discovery algorithms PC1 or PCMCI that are implemented in the Tigramite Python package. These algorithms utilize conditional independence tests to infer parts of the causal graph. Our causal feature selection approach filters out causally-spurious links before passing the remaining causal features as inputs to ML models (Multiple linear regression, Random Forest) that predict the targets. We apply our framework to the statistical intensity prediction of Western Pacific Tropical Cyclones (TC), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging). Using more stringent significance thresholds in the conditional independence tests helps eliminate spurious causal relationships, thus helping the ML model generalize better to unseen TC cases. M-PC1 with a reduced number of features outperforms M-PCMCI, non-causal ML, and other feature selection methods (lagged correlation, random), even slightly outperforming feature selection based on eXplainable Artificial Intelligence. The optimal causal drivers obtained from our causal feature selection help improve our understanding of underlying relationships and suggest new potential drivers of TC intensification. © 2023, CC BY.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Moreau Envelopes-Based Personalized Asynchronous Federated Learning: Improving Practicality in Network Edge Intelligence

Moreau Envelopes-Based Personalized Asynchronous Federated L...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Anwar Asad Mostafa M. Fouda Zubair Md Fadlullah Mohamed I. Ibrahem Nidal Nasser Department of Computer Science Lakehead University Thunder Bay Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Science University of Western Ontario London ON Canada School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt College of Engineering Alfaisal University Saudi Arabia

Federated learning is a promising approach for training models on distributed data, driven by increasing demand in various industries. However, federated learning framework faces several key challenges, including communication bottlenecks and client data heterogeneity. Personalized asynchronous federated learning addresses these challenges by customizing the model for individual users based on their local data while trading model updates asynchronously. In this paper, we propose the Personal-ized Moreau Envelopes-based Asynchronous Federated Learning (APFedMe). Our approach combines the strengths of Moreau En-velopes to handle optimization problems and asynchronous weight updates to improve communication efficiency while mitigating heterogeneity data challenges through a personalized learning environment. We evaluate our approach on several datasets and compare it with the baseline PFedMe method. Our experiments demonstrate that the proposed APFedMe outperforms other meth-ods in terms of convergence speed and communication efficiency. Overall, our work contributes to developing more effective and efficient federated learning methods that can be applied in various real-world scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

User Acceptance of New HCI Technologies

User Acceptance of New HCI Technologies

引用

International IEEE Conference and Workshop: Óbuda on electrical and Power engineering (CANDO-EPE)

作者： Jozsef Katona Attila Kovari Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eger Hungary Department of Natural Sciences Institute of Engineering University of Dunaujvaros Dunaujvaros Budapest Hungary

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing Mobility IoT Device Networks with Dynamic RIS in mMIMO-Cooperative NOMA 6G Systems

Optimizing Mobility IoT Device Networks with Dynamic RIS in ...

引用

Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Khalil Hassan Mohamed Hassan Khalid Hamid Elmuntaser Hassan Rania A. Mokhtar Mamoon M. Saeed Dept. of Comm. Systems Uni. of Science & Technology Omdurman Sudan Dept. of Electrical Eng Omdurman Islamic Uni. Omdurman Sudan Dept. of Computer Science Al-Neelain University Khartoum Sudan Department of Computer Engineering Taif University Taif KSA Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Advanced technologies like reconfigurable intelligent surfaces (RISs), massive multiple-input multiple-output (mMIMO), and non-orthogonal multiple access (NOMA) are crucial for the upcoming 6G communications landscape, particularly for IoT applications. This study uniquely explores the effects of RIS dynamics on the performance of mMIMO downlink (DL) cooperative NOMA systems in dense IoT environments. It introduces a novel methodology for modeling dynamic traffic distribution and integrating RIS, focusing on key performance metrics such as network capacity, spectral efficiency (SE), latency, and energy efficiency (EE). Various scenarios are evaluated, considering different distributions of IoT devices and signal-to-noise ratios (SNRs). Using simulation software, the research analyzes performance under unstable channel conditions with varying device distances, speeds, and power locations, employing 256-QAM modulation and successive interference cancellation (SIC). The results demonstrate that dynamic RIS significantly enhances capacity, spectrum efficiency, latency, and EE, paving the way for optimized IoT network architecture in future wireless systems.

关键词： 6G mobile communication Wireless communication NOMA Interference cancellation System performance Reconfigurable intelligent surfaces Network architecture Dynamic scheduling Internet of Things Resource management

来源：评论

学校读者我要写书评

暂无评论

An Efficient Technique For Detection Of Faulty Sensors In Planar Antenna Array

An Efficient Technique For Detection Of Faulty Sensors In Pl...

引用

International Conference on electrical, Communication, and computer engineering (ICECCE)

作者： Afaq Ahmed Bhatti Fawad Zaman Syes Muzahir Abbas Gridsada Phanomchoeng Shafqat Ullah Khan Athar Waseem Department of Electrical & Computer Engineering COMSATS University Islamabad Pakistan School of Engineering Faculty of Science and Engineering Macquarie University Sydney NSW Australia Department of Mechanical Engineering Chaulalongkorn University Bangkok Thailand Department of Electronics University of Buner Buner Pakistan Department of Electrical Engineering International Islamic University Islamabad Pakistan

Planar antenna arrays are considered as the one of the important parts in the applications like broadcasting, sonar and radar communication. In order to produce the directed beam of desired angles, the planar antenna array is used with large number of antenna elements. In the presence of faulty element(s) the resultant radiation pattern is distorted with high magnitude side lobes and reduced gain. The process of detection is discussed widely in linear arrays but for the planar arrays this study is limited. This study developed the process of the detection of the faulty antenna(s) in the planar array using a detection algorithm by comparing the weights of array with faulty antennas with the weights of arrays with healthy antennas. The analysis was varied for different orientations of 4 × 4 and 6 × 6 arrays with single and multiple element failures. The detection algorithm was found to be efficient in detecting the faulty elements with great accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sparrow Search Optimization with Deep Belief Network based Wind Power Prediction Model

Sparrow Search Optimization with Deep Belief Network based W...

引用

2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Aruna, M. Narayana, Patel Badari Kumar, S. Narasimha Walid, Abul Ala Patra, Jyoti Prasad Kumar, B. Suresh Nmit Department of Eee Karnataka Bangalore India Mahatma Gandhi Institute of Technology Department of Mechanical Engineering TS Hyderabad India Chaitanya Bharati Institute of Technology Mechanical Engineering Department Gandipet Hyderabad India Department of Computer Science and Engineering Khulna9203 Bangladesh Odisha University of Technology and Research Outr Mahalaxmi Vihar Ghatikia Techno Campus Faculty Electrical Odisha Bhubaneswar India Chaitanya Bharathi Institute of Technology Department of Eee Telangana Hyderabad India

ISBN: (纸本)9781665474511

Wind power is a clear feature of intermittent, nonstationary, and difficult fluctuations, making it challenging for achieving consistent wind power generation. Assuming the restricted nature of typical energy resources and the developing difficulties of environmental problems, several countries are starting with developing novel energy resources which are considered for environmental and renewable safety. Amongst the several novel energy resources, wind energy was abundant, doesn't cause pollution, has minimum cost, and does not deplete. Accurate wind power predictive is enhance the reliability and safety of power grid function. Therefore, this study presents a sparrow search optimization with deep belief network for wind power prediction (SSODBN-WPP) technique. The SSODBN-WPP technique follows a two stage process namely prediction and parameter tuning. At the initial stage, the SSODBN-WPP technique employs DBN method for wind power prediction. Next, the SSO algorithm is used to adjust the core hyperparameters of the DBN algorithm. The efficacy of the SSODBN-WPP method is tested on a comprehensive set of simulations that take place on wind power dataset. A comparison study of the SSODBN-WPP technique reported its betterment over other predictive approaches. © 2023 IEEE.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

Understanding Bulk-Bitwise Processing In-Memory Through Database Analytics

arXiv

引用

arXiv 2022年

作者： Perach, Ben Ronen, Ronny Kimelfeld, Benny Kvatinsky, Shahar The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa Israel Taub Faculty of Computer Science Technion – Israel Institute of Technology Haifa Israel

Bulk-bitwise processing-in-memory (PIM), where large bitwise operations are performed in parallel by the memory array itself, is an emerging form of computation with the potential to mitigate the memory wall problem. This paper examines the capabilities of bulk-bitwise PIM by constructing PIMDB, a fully-digital system based on memristive stateful logic, utilizing and focusing on in-memory bulk-bitwise operations, designed to accelerate a real-life workload: analytical processing of relational databases. We introduce a host processor programming model to support bulk-bitwise PIM in virtual memory, develop techniques to efficiently perform in-memory filtering and aggregation operations, and adapt the application data set into the memory. To understand bulk-bitwise PIM, we compare it to an equivalent in-memory database on the same host system. We show that bulk-bitwise PIM substantially lowers the number of required memory read operations, thus accelerating TPC-H filter operations by 1.6×–18× and full queries by 56×–608×, while reducing the energy consumption by 1.7×–18.6× and 0.81×–12× for these benchmarks, respectively. Our extensive evaluation uses the gem5 full-system simulation environment. The simulations also evaluate cell endurance, showing that the required endurance is within the range of existing endurance of RRAM devices. © 2022, CC BY.

关键词： Computation theory

来源：评论

学校读者我要写书评

暂无评论

3-sCHSL: Three-Stage Cyclic Hybrid SFS and L-SHADE Algorithm for Single Objective Optimization

3-sCHSL: Three-Stage Cyclic Hybrid SFS and L-SHADE Algorithm...

引用

Congress on Evolutionary Computation

作者： Heba Abdel-Nabi Mostafa Z. Ali Arafat Awajan Rami Alazrai Mohammad I. Daoud Ponnuthurai N. Suganthan Robert Reynolds Department of Computer Science Princess Sumaya University for Technology Amman Jordan Faculty of Computer & Information Technology Jordan University of Science & Technology Irbid Jordan Computer Science Department Mutah University Karak Jordan Department of Computer Engineering German Jordanian University Amman Jordan School of Electrical & Electronic Engineering Nanyang Technological University Singapore KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar College of Engineering Wayne State University Detroit USA

This paper proposes a novel hybridization of two metaheuristic algorithms to solve the real-parameter single objective numerical optimization problems. The proposed Three-stage Cyclic Hybrid SFS and L-SHADE (3-sCHSL) algorithm integrates the high-level interactions of Stochastic Fractal Search (SFS) and L-SHADE algorithms in a single framework. Furthermore, a guided control population initialization strategy is injected in 3-sCHSL to address the stagnation and diversity loss issues as the evolution process evolves. The performance of the proposed algorithm is tested under different complexity levels of different transformations of the CEC 2021 benchmark suite with dimension 20. The experimental results demonstrated the efficiency and competitiveness of the proposed algorithm against recent state-of-the-art algorithms. In addition, 3-sCHSL achieved a superior performance when evaluating two engineering design problems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FMCW Radar-Based Enhancing Moving Target Positioning Accuracy in Spectrogram Mapping

FMCW Radar-Based Enhancing Moving Target Positioning Accurac...

引用

International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Nattakarn Shutimarrungson Seksan Eiadkaew Akkarat Boonpoonga Krit Athikulwongse Suramate Chalermwisutkul Department of Business Computer Mahasarakham Business School Mahasarakham University Maha Sarakham Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Communication and Smart System Engineering The Sirindhorn International Thai- German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In this paper, we propose a methodology aimed to enhancing the accuracy of moving target positioning using FMCW radar within spectrogram mapping. Utilizing a 10 GHz operating frequency, the FMCW radar effectively detects moving targets. However, the interpretation of intermediate frequency (IF) signals posed challenges due to multiple reflections and resulting ambiguous distances between objects. For solving this problem, we propose two steps of solution: employing an enveloping method and normalization of the IF signal. This process can extract maximum amplitude peaks specific to the target. Experimental validation involved detecting a drone moving away from the radar. Comparative analysis pre and post signal processing in spectrogram terms demonstrate the efficacy of our technique. Furthermore, verification of the detection range of radar was conducted using the GSM-015 GNSS speed meter. Results confirm the accuracy of the positioning capabilities of radar, emphasizing the effectiveness of the proposed approach.

关键词： Visualization Accuracy Refining Radar detection Radar Radar antennas Reflection

来源：评论

学校读者我要写书评

暂无评论

Bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model 5

Bayesian Optimization with Stacked Sparse Autoencoder based ...

引用

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Shaikh, Irfan Abdul Karim Krishna, P. Vamsi Biswal, Swagat Gourav Kumar, A. Sasi Baranidharan, S. Singh, Kamlesh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Telangana Hyderabad India Odisha University of Tecnology and Research Department of Electrical Engineering Orissa Bhubaneswar India Institute of Engineering and Technology Srinivas University Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Karnataka Dakshina Kannada India Department of Mba and Research Center Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9781665474672

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique. © 2023 IEEE.

关键词： Electronic money

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：