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检索条件"主题词=software algorithms"

共 12566 条记录，以下是881-890 订阅

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Recovering Channel Amplitude Reciprocity with Data-Aided Gain Imbalance Estimation in an SFLC-OFDM System

Recovering Channel Amplitude Reciprocity with Data-Aided Gai...

引用

International Conference on Information Networking

作者： Songmin Lee Jingon Joung Jihoon Choi Juyeop Kim Department of Electrical Engineering Sookmyung Women’s University Seoul South Korea School of Electrical and Electronics Engineering Chung-Ang University Seoul South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

This paper proposes a practical scheme for estimating and compensating channel amplitude asymmetry in a SpaceFrequency Line Code (SFLC) - Orthogonal Frequency Division Multiplexing (OFDM) system. SFLC assumes channel reciprocity using Time Division Duplexing (TDD) for estimating downlink (DL) channel state information via uplink (UL). However, in practice, channel amplitudes between UL and DL are asymmetric, causing unexpected phase rotation in decoded symbols. To address the issue, we propose a data-aided gain calibration technique to maintain channel amplitude symmetry and improve the performance of SFLC decoding. We design an estimator of the phase rotation based on a mathematical model. We then propose a calibration scheme that compensates for channel amplitude asymmetry by adjusting the receive gain of an antenna. We also provide experimental results with implementation on a software modem. A testbed using USRPs is established, and experiments with real-time signal processing are conducted to verify the functionality. The results show that the phase rotation is properly estimated and effectively nullified via the proposed scheme.

关键词： Codes OFDM software algorithms Channel estimation Symbols Modems software Stability analysis Calibration Uplink

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Steportation: An Effective Walking-based Locomotion Method for VR

Steportation: An Effective Walking-based Locomotion Method f...

引用

software Engineering and Computer Science (CSECS), International Conference on

作者： Luqiunan Zhong Yigang Wang Shi Li School of Media and Design Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

The virtual space can often be infinite, but the user's space is always constrained. In order to mitigate the limitations of a constrained physical space to explore an infinite virtual environment, many artificial locomotion techniques have been proposed. One of the most commonly used locomotion techniques is teleportation, which allows users to explore the entire virtual environment without moving. However, this method is unnatural and can lead to spatial disorientation, lack of motion perception and sense of presence. Subsequent studies have also proposed improvements to enhance immersion of the teleportation, but the perception of motion is still distinct from that of real walking. In this paper, we introduce our method called “Steportation”, which combines natural direct walking and walking-based teleportation. The key is that users are able to trigger teleportation like natural walking and implicitly switch between teleportation and natural walking using the ‘Teleport Circle’ mechanism, eliminating the need for hand controllers. We compare our method with conventional controller-pointing teleportation, gaze teleportation and jumping teleportation, and evaluate these methods in terms of efficiency, accuracy, ease of use, and immersion through a series of experiments. The experimental results show that our method improves sense of presence and immersion while maintaining efficiency and usability.

关键词： Legged locomotion Hands software algorithms Virtual environments Switches Teleportation Space exploration Usability Wearable devices software engineering

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Design of an STM32-Based Intelligent Warehouse Sorting Device

Design of an STM32-Based Intelligent Warehouse Sorting Devic...

引用

Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Chang Liu Xiaogang Zhang Ke Bian Shiyu Song School of Traffic and Electrical Engineering Dalian University of Science and Technology Dalian China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This paper focuses on the intelligent methods of warehouse sorting in logistics. By using the STM32F4 processor, it studies the warehouse sorting technology from the perspective of visual recognition. The purpose of this research is to improve warehouse efficiency, reduce waste, and achieve work automation. Firstly, the overall control scheme of the system and the control ideas of each module are determined, and the corresponding hardware circuits are developed. Next, based on information such as the hardware circuit and algorithms, the software design is completed. The results show that under certain conditions, this device can complete the task of intelligent warehouse sorting through color recognition.

关键词： Visualization software design Circuits Warehousing software algorithms Color Control systems Hardware Sorting Testing

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A Research on CUBE Filtering Algorithm Based on GPU Parallel Acceleration

A Research on CUBE Filtering Algorithm Based on GPU Parallel...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Xiaokai Li Li Wang Guangyun Li Institute of Geospatial Information Information Engineering University Zhengzhou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

This paper focuses on execution efficiency for Combined Uncertainty and Bathymetry Estimator (CUBE) algorithm. By analyzing the key steps of the algorithm, this paper shows how to improve algorithm efficiency by using Octree index and Graphics Processing Unit (GPU) parallel computing. The former is used to establish rules grids and neighborhood searches, while the latter is used to calculate the filtering calculation task in parallel. The combination of the two improves the overall execution efficiency of the algorithm. This paper designs experiment to test efficiency of the algorithm when processing the Multi-Beam Echo Sounder (MBES) data, and compare with the CUBE filter module of CARIS HIPS software. The experimental results show that the algorithm used in this paper can increase the filtering speed by about 13.8 times when dealing with ten million levels of data, and the efficiency of this algorithm in processing millions of MBES data is significantly better than the efficiency of CARIS HIPS software; The algorithm has better denoising effect than CARIS HIPS software in some cases.

关键词： Uncertainty Filtering software algorithms Octrees Noise reduction Graphics processing units Filtering algorithms software Indexes Hip

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A Robust BMS Framework for DC Fast Charging in Smart Grid-Integrated EVs

A Robust BMS Framework for DC Fast Charging in Smart Grid-In...

引用

Next Generation Communication & Information Processing (INCIP), International Conference on

作者： S.P. Sreeja D. Sreenivasulu Alok Jain Neeraj Chahuan Fatimah Dhari Jabbar Babu Naik Gugulothu Master of Computer Application New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering BMS Institute of Technology and Management Bengaluru Karnataka India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Smart grids are now connected to electric vehicles (EVs) so that micro-grids could radically in the future use EV batteries with the acronym V2G and G2V. It presents an electrical architecture of a smart Battery Management System (BMS) for DC fast charging as a Forward and Backward part which is designed to fit both V2G and G2V siding and the topological diagram. Such architecture uses a combined complex hardware associated to smart software algorithms to charge and discharge the battery using optimized all four operations of the energy transfer that are set up in order to achieve nominal state of charge and optimize the time charge for safety purpose. The real time operating software performs control over the system through the use of an on-board processor using dedicatedly designed algorithms, including novel predictive-based machine learning algorithms for smart energy performance optimization and preventive maintenance. Cyber-security protocols and renewable energy are also embedded in the design of the proposed BMS for a side of a renewable energy grid. Our simulation results prove that the proposed BMS improves vehicle range by storing more energy and increasing the battery life. A novel Battery Management System for DC fast charging EVs is presented in this paper.

关键词： Vehicle-to-grid Renewable energy sources Machine learning algorithms software algorithms Battery management systems Computer architecture software Batteries Fast charging Computer security

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PROCA: Programmable Probabilistic Processing Unit Architecture with Accept/Reject Prediction & Multicore Pipelining for Causal Inference

PROCA: Programmable Probabilistic Processing Unit Architectu...

引用

IEEE Symposium on High-Performance Computer Architecture

作者： Yihan Fu Anjunyi Fan Wenshuo Yue Hongxiao Zhao Daijing Shi Qiuping Wu Jiayi Li Xiangyu Zhang Yaoyu Tao Yuchao Yang Bonan Yan Institute for Artificial Intelligence Peking University Beijing China Beijing Advanced Innovation Center for Integrated Circuits School of Integrated Circuits Peking University Beijing China Independent Researcher School of Electronic and Computer Engineering Peking University Shenzhen China Chinese Institute for Brain Research Beijing China

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Causal inference is an important field in data science and cognitive artificial intelligence. It requires the construction of complex probabilistic models to describe the causal relationships between random variables. Probabilistic models rely on probabilistic programming as a flexible framework. However, the computing speed of probabilistic programming is often hindered by the extensive use of Markov chain Monte Carlo (MCMC) algorithms, even though they are powerful in Bayesian inference. To accelerate MCMC, this work presents PROCA, a programmable MCMC-based probabilistic processing unit architecture. PROCA exploits processing-in-memory function units to generate new samples of Markov chains. PROCA is programmable to execute the computation for arbitrary forms of posterior distribution formulas that software probabilistic programming frameworks support. We develop a novel accept/reject prediction methodology to accelerate the sequential MCMC computation, thereby introducing efficient multi-core pipelining methods. We implement and validate the PROCA architecture with commercial process development kits. The implementation is evaluated based on 9 representative benchmarks, covering PyMC official tutorial probabilistic problems, single-variable probabilistic problems, and real-world causal inference problems. Our comprehensive experiments demonstrate that PROCA achieves a speedup of 172~4871 $\times$ compared to Intel Xeon Gold CPU, $42 \sim 1058 \times$ compared to NVIDIA A100 GPU, and $1.765 \times$ over state-of-the-art MCMC accelerators, respectively. PROCA achieves comparable statistical robustness to the software probabilistic programming frameworks. Compared with state-of-the-art MCMC domain-specific accelerators, our design boosts the energy efficiency by $9.47 \times$.

关键词： Computational modeling software algorithms Graphics processing units Computer architecture Tutorials Programming Probabilistic logic software Computational efficiency Pipeline processing

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Design Matrix Factorization and Knowledge-Guided Clustering for Trust-Aware Cross-Domain Recommendation Systems

Design Matrix Factorization and Knowledge-Guided Clustering ...

引用

software Engineering and Computer Science (CSECS), International Conference on

作者： Jiaqi Chang Fusheng Yu School of Mathematical Sciences Beijing Normal University Beijing People's Republic of China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

The core challenge in designing an algorithm for trust-aware cross-domain recommendation systems lies in effectively integrating ratings from all domains (target-domain and source-domains) and trust relationships to improve recommendation performance in the target-domain. These systems involve two types of information fusion: intra-domain information fusion, which integrates intra-domain trust relationships and ratings within each domain (target-domain or source-domain), and cross-domain information fusion, which fuses information from all domains through inter-domain trust relationships as a bridge. The existing algorithms are limited in that they only address either intra-domain information fusion or cross-domain information fusion, failing to comprehensively utilize both intra-domain and inter-domain trust relationships for integrating information from all domains. To address this limitation, we propose a novel approach that combines inter-domain information fusion and cross-domain information fusion. Based on this approach, we design a matrix factorization and knowledge-guided clustering based trust-aware cross-domain collaborative filtering algorithm (MFKC _ TCCFA). Experimental results on a dataset show that MFKC_TCCFA outperforms existing state-of-the-art algorithms.

关键词： Computer science Social networking (online) Fuses Collaborative filtering Soft sensors software algorithms Semantics Clustering algorithms Recommender systems software engineering

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Adaptive Model Predictive Control for Electrothermal Conversion Device: A Virtual Power Plant Operation Mode

Adaptive Model Predictive Control for Electrothermal Convers...

引用

IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Zhijian Yang Zhongkai Yi Da Chen Xi Yao Mingguo Ren Rongxi Guo School of Electrical Engineering & Automation Harbin Institute of Technology Harbin China China (Mobile/Shanghai) Information Communication Technology Co. Ltd Shanghai China China Mobile communications Group Heilongjiang Co. Ltd Harbin China

ISBN: (数字)9798331517540

ISBN: (纸本)9798331517557

Some mathematical models are commonly used to express the storage and conversion between electric energy and heat energy in the electric-heat conversion system participating in the virtual power plant scheduling and the simulation calculation of electric and thermal systems. However, in the actual electric-heat conversion, the electric-heat conversion and storage is a very complex and time-varying process of various parameters. Therefore, it is not reasonable to simply use mathematical models to participate in the simulation calculation of power system and thermal system and the scheduling of virtual power plants. To solve this problem, this paper proposes an automatic parameter correction method for electric-heat conversion system based on AMPC adaptive model predictive control algorithm. In order to participate in the scheduling of virtual power plant more accurately, firstly, the fine modeling is carried out in Ansys, and the operation results show that the simulation results based on mathematical model operation are not completely reliable. Based on this, an adaptive model predictive control (AMPC) algorithm is proposed. The parameters in the mathematical model are automatically corrected in real time to equivalently express the complexity and time-varying of the conversion between electric and thermal energy. The simulation results and the actual results are corrected by correcting the parameters. Finally, a simple electric heating device model is used for example analysis to verify the reliability of the algorithm. The main contribution of this paper is that the system parameters can be corrected in real time by Ansys software and AMPC algorithm, so that the electrothermal simulation is closer to the real situation.

关键词： Adaptation models Analytical models Simulation software algorithms Virtual power plants Predictive models Prediction algorithms Mathematical models Predictive control Resistance heating

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Group Method of Data Handling (GMDH) in Forecasting Electric Power Consumption

Group Method of Data Handling (GMDH) in Forecasting Electric...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Artem S. Babin Mikhail I. Baryshnikov Yuriy E. Gapanyuk Information Processing and Management Systems Department Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The group method of data handling is considered one of the most effective methods for solving the problem of supervised learning, while there are a few effective software libraries aimed at automating this method. The aim of the paper is to increase the efficiency of supervised learning problem solving by implementing a software library aimed at automating the group data handling method. The article analyses in detail the general approach, which is the basis of the group data handling method, as well as specific algorithms for implementing the method: COMBI, MULTI, MIA, RIA. The creation of an effective software library aimed at implementing the GMDH algorithms is considered. Experiments have been carried out for electric power consumption time series forecasting problem, showing the advantages of the developed library.

关键词： software libraries Data handling software algorithms Time series analysis Supervised learning C++ languages Prediction algorithms Power systems Forecasting Python

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Adapting Aggregation Rule for Robust Federated Learning under Dynamic Attacks

Adapting Aggregation Rule for Robust Federated Learning unde...

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SEAMS International Workshop on software Engineering for Adaptive and Self-Managing Systems, ICSE

作者： Chenyu Hu Mingyue Zhang Nianyu Li Jialong Li Zheng Yang Muneeb Ul Hassan Kenji Tei Southwest University Chongqing China ZGC National Laboratory Beijing China Waseda University Tokyo Japan Deakin University Melbourne Australia Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798331501815

ISBN: (纸本)9798331501822

FL systems inevitably suffer from poisoning attacks in distributed environments, which manipulate the global model by compromising clients. A class of practical methods, namely Byzantine Robust Aggregation Rules (BRARs), has been proposed to counter these poisoning attacks. However, when poisoning attacks exhibit dynamic and diverse characteristics, the capabilities of existing BRARs are highly limited. To address this issue, we propose SARA, aimed at dynamically adapting the aggregation rule in response to dynamic attacks. Specifically, we have designed a framework capable of dynamically adjusting aggregation rules. This framework can monitor the performance metrics of the FL system, analyze the effectiveness of the current aggregation rules in defending against poisoning attacks, and adjust relevant modules within the aggregation rules based on a carefully designed Upper Confidence Bound (UCB)based algorithm, thereby enhancing the defense capability of the aggregation rules. The evaluations on two different datasets under two attack scenarios demonstrate SARA's effectiveness in maintaining FL robustness with negligible computation overhead.

关键词： Adaptation models Adaptive systems Federated learning Autonomous systems Heuristic algorithms software algorithms Robustness Monitoring software engineering

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