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检索条件"任意字段=2021 International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2021"

共 8431 条记录，以下是51-60 订阅

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One-for-All: An Unified learning-based Framework for Efficient Cross-Corner Timing Signoff 24

One-for-All: An Unified Learning-based Framework for Efficie...

引用

43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Zhu, Linyu Cai, Yichen Guo, Xinfei Shanghai Jiao Tong University Shanghai China Shanghai China

ISBN: (纸本)9798400710773

In advanced technology nodes, the proliferation of process corners poses significant challenges in timing signoff, particularly in estimating wire-induced interconnect delay across process corners. This paper proposes a learning-based framework to perform cross-corner timing prediction efficiently and accurately. It seamlessly integrates learning-based reference corner selection and topology-aware interconnect timing prediction into the broader timing signoff steps and engineering Change Order (ECO) processes. Unlike previous methods, it only requires information about one single known corner while accurately predicting all unknown corners. Evaluated on two mainstream industry processes, the proposed framework surpasses alternative machine learning models and existing strategies, with an impressive average accuracy enhancement of 62.6% and 95.3% respectively, and maintains a low mean absolute error (MAE) under 0.37ps and 0.01ps. Additionally, a faster version of the framework is developed to predict interconnect delay directly from a single extracted SPEF, yielding over 2× speedup. Moreover, the single-corner approach featured by the framework significantly accelerates ECO processes by over 10× compared to standard timing signoff flows. The proposed framework is also set to be open-sourced at a later date. © 2024 Copyright is held by the owner/author(s).

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Research and Application of Intermediate Experiment Digitization Basic Platform

Research and Application of Intermediate Experiment Digitiza...

引用

Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE international conference on

作者： Jian Cao Feng Zhao Ziming Liu Deshui Tong Yuanfang Wang Shu Wang Oil production technology research institute PetroChina Liaohe Oilfield Liaoning China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

This paper focuses on the development and application of an intelligent basic platform for intermediate experiments, serving the oil production process. Through a series of methods such as hardware intelligent upgrading and transformation, establishing connections between programmable logic controllers and hardware devices based on Modbus protocol, designing configuration software to read and control display data in real time, the intelligent basic platform for intermediate experiments has been successfully developed. The research and development achievements include the design and completion of three sets of configuration systems, intelligent control of hardware working status, and real-time data reading and graphical display, providing efficient and intelligent platform support for experimental research in the industry.

关键词： Industries Protocols machine learning algorithms Oils Hardware Software Real-time systems Data mining intelligent control Testing

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An intelligent Recognition Method of Wireless Communication Signal Modulation Based on DANet Attention Mechanism 16

An Intelligent Recognition Method of Wireless Communication ...

引用

16th international conference on Signal Processing systems, ICSPS 2024

作者： Li, Xiao Zhang, Wantong Li, Jia Chu, Zhaohui Li, Yuan Ran, Yuzhou Cao, Kaicheng Li, Jian Information Science Academy of China Electronics Technology Group Corporation Beijing100086 China School of Information and Communication Engineering Yibin Institute University of Electronic Science and Technology of China Chengdu611731 China Department of IoT Technology Research China Mobile Research Institute Beijing100053 China National Defense Science and Technology Innovation Institute Academy of Military Sciences Beijing100071 China

ISBN: (纸本)9781510689251

To address the challenge of modulation type recognition for communication signals in complex electromagnetic environments, this paper proposes a modulation recognition method based on a dual attention mechanism (DANet) combined with deep learning. DANet is integrated into the convolutional neural networks (CNN), and to improve the low recognition accuracy for PSK modulation types, the CNN architecture is modified by adding four convolutional kernels of different sizes after the input layer to fully extract features. Experimental results show that the proposed method accurately recognizes 21 modulation types under signal-to-noise ratios (SNR) of 6, 8, and 10 dB, achieving an accuracy of 94.83%. The model's parameter count is reduced to 1.39M, outperforming similar methods. Additionally, the model is successfully deployed on an embedded platform, demonstrating significant engineering application value. © 2025 SPIE.

关键词： Signal modulation

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machine learning-Based Power Loss Analysis in Photovoltaic systems 1

Machine Learning-Based Power Loss Analysis in Photovoltaic S...

引用

1st IEEE international conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Tomar, Anuradha Kumar, Hritik Netaji Subhas University of Technology Department of Instrumentation & Control Engineering India

ISBN: (纸本)9798331542108

This research estimates the power loss in photovoltaic (PV) systems through a Random Forest model by using 15-min interval data for two years (2021-2023) of operation from a university campus located in Delhi, India. Irradiance, temperature and the dust concentration were used in the analysis to calculate and estimate actual and theoretical potentials and power losses based on factual, modelled and theoretical determinations under Standard test conditions (STC). Seasonal changes are of particular interest and therefore, the power loss is to be analyzed based on three power quality reports obtained over three different seasons in 2023. The results of the model show the Mean absolute error (MAE) to be 29.145 W along with Root mean square error (RMSE) to be 57.72 W. Interactions with power loss observed seasonally proved to have variations in dust and power loss, temperature, and so on;all of which confirmed the effectiveness of Random Forest model in providing valuable predictions of season-sensitive data. These results would be significant to enhance the efficiency of the PV system and how energy technologies for dusty areas should be designed. © 2025 IEEE.

关键词： Mean square error

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Anomaly-Based Detection of Log4j Attack in SDN

Anomaly-Based Detection of Log4j Attack in SDN

引用

international conference on Smart systems and Wireless Communication, SSWC 2024

作者： Deshmukh, Pruthviraj Nitin Harish, R. Sangeetha, V. Praveen, K. TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Information Systems BITS Pilani India

ISBN: (纸本)9789819613472

The widely used Java library Log4j logging framework has lately come under attack from a major vulnerability known as CVE-2021–44228, also known as Log4Shell. Through the use of malicious log messages, the vulnerability enables attackers to compromise vulnerable systems by executing remote code. A key component of SDN systems is the RYU controller, an open-source software-defined networking (SDN) controller that provides a wide range of functionalities. It can communicate and interact with SDN-capable switches and other network elements. It is used in SDN installations for managing, controlling, and supporting network automation and orchestration. This paper proposes a new anomaly-based Log4j detection approach in SDN networks. We have used multiple machine-learning classifiers to analyze and detect anomalies in network traffic generated by Log4j attacks to get the best achievable accuracy for real-time model development. The conducted experiment results that the proposed approach can give high accuracy up to 98% in detecting Log4j attack in SDN environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： E-learning

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Applying machine learning to Improve Weather Forecasting in a Mediterranean Area: Preliminary Experiments 4th

Applying Machine Learning to Improve Weather Forecasting in...

引用

4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Yousaf, Umair De Rango, Alessio Furnari, Luca D’Ambrosio, Donato Senatore, Alfonso Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (纸本)9783031812439

We investigate the effectiveness of machine learning (ML) as a complementary tool in enhancing the accuracy of precipitation forecasting in the Calabria region (southern Italy), a Mediterranean area frequently affected by extreme rainfall events, with significant material damage and potential loss of human lives. The operational weather forecasting system developed at the University of Calabria, based on the Weather Research and Forecasting (WRF) model, is considered to this end. We test two ML models, namely an artificial neural network and a random forest, to improve the accuracy of WRF forecasting. We provide several WRF output variables to the ML models. After a proper calibration (learning and test phases) over a two-year-long extended dataset, we assess the results to the original WRF outcomes (validation phase). Preliminary experiments resulted in a significant improvement by the WRF-ML coupled systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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machine learning Based Pose Identification System with Force-Sensitive Resistors for Four-Step Balance Testing 10

Machine Learning Based Pose Identification System with Force...

引用

10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Sanongyat, Thanapat Kamyod, Chayapol Temdee, Punnarumol Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center School of Applied Digital Technology Chiang Rai Thailand

ISBN: (纸本)9798331543273

This study proposes a system to identify poses based on the 4-stage balance test for fall risk assessment using custom-developed, pressure-sensitive footwear equipped with force-sensitive resistors (FSRs) and machine learning (ML) models. The system captures and analyzes pressure distribution data during balance tests to predict types of poses. Data was collected from 30 healthy participants aged 18-25 years performing standard balance tests. Preprocessing steps included data cleaning, normalization, and feature extraction. Three ML models - Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GB) - were evaluated. The LSTM model achieved the highest performance in terms of accuracy, precision, recall, and F1-scores, demonstrating its capability in handling sequential data. The system shows promise as a cost-effective tool for pose identification to aid fall risk assessment, though further research is needed to expand the dataset diversity and refine the algorithms. This study provides a foundation for developing scalable tools to assess and mitigate fall risks, particularly among vulnerable populations. © 2025 IEEE.

关键词： Risk assessment

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Task Reasoning of Service Robots with Fused Multi-modal Information and Ontology Knowledge 3rd

Task Reasoning of Service Robots with Fused Multi-modal Info...

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3rd international conference on machine learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Liu, Boyang Liu, Guoliang Tian, Guohui Jiang, Jian Wang, Shanmei Shandong University Jinan China Shandong Massdatas Development Co. Ltd. Jinan China Inspur Intelligent Technology Co. Ltd. Jinan China

ISBN: (纸本)9789819624676

Family environment is one of main area for service robot applications. Complex multi-modal scene and users’ high requirements for service levels are challenges that service robots face. In order to improve the service level of service robots in multi-modal family scene, this paper proposes a service robot task reasoning mechanism that integrates multi-modal information and ontology knowledge. The system manages user and environment information by ontology knowledge base, obtains fused multi-modal information including vision, voice, and scene knowledge in real time, and reasons service task based on fine-tuned LLM (Large Language Model). The system is designed based on the edge-cloud collaborative architecture and has high service levels in experimental tests of multi-modal family elderly care scene. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robot applications

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FedScale: A Federated Unlearning Method Mimicking Human Forgetting Processes 18th

FedScale: A Federated Unlearning Method Mimicking Human Forg...

引用

18th international conference on Wireless Artificial intelligent Computing systems and Applications, WASA 2024

作者： Huang, Wenshu Wu, Huiwen Fang, Liming Zhou, Lu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen China Research Center for Data Hub and Security Zhejiang Laboratory Hangzhou311000 China

ISBN: (纸本)9783031714634

The heightened demands for privacy protection, combined with legal mandates granting users the right to be forgotten, emphasize the growing importance of endowing models with the capability to forget specific sample information. Unlearning is akin to the reverse process of model training, which involves the elimination of the influence of training data on a machine learning model. In the context of federated learning, conducting unlearning is notably more demanding, as data is dispersed across multiple client endpoints. Due to privacy and security concerns, many conventional machine unlearning methods are no longer applicable. This paper draws inspiration from the forgetting processes in the human brain, highlighting that memory attrition is not solely a passive process. Therefore, we dichotomize the federated unlearning process into two components: active forgetting and passive forgetting. We make respective modifications to the updates of these two components to achieve efficient unlearning. We propose an approach called FedScale, which only sacrifices a fraction of storage space, enabling a rapid federated unlearning process without the need for retraining the federated model when it becomes necessary to forget a user’s data. Experimental evaluations are conducted on four datasets, demonstrating the effectiveness of FedScale unlearning. Furthermore, we show its resilience against backdoor attacks and membership inference attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Investigating the Transferability of Evasion Attacks in Network Intrusion Detection systems Considering Domain-Specific Constraints 25th

Investigating the Transferability of Evasion Attacks in N...

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25th international conference on Information Security Applications, WISA 2024

作者： Mbow, Mariama Roman, Rodrigo Seal, Ayan Wang, Kevin I-Kai Mohanty, Sraban Kumar Sakurai, Kouichi Department of Information Science and Technology Kyushu University Fukuoka Japan Lab University of Malaga Málaga Spain PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand

ISBN: (纸本)9789819616237

Recently, adversarial attacks against machine learning-based network intrusion detection systems (NIDS) have gained significant attention in cybersecurity. This study investigates the transferability of these adversarial attacks in the context of NIDS. In addition, most existing studies in adversarial learning against NIDS adopted attack strategies designed originally for image classification without considering network traffic characteristics. However, this is impractical and will fail in the real world as network traffic features are constrained, and ignoring the functional behavior of the network traffic features leads to invalid network traffic flow or produces adversarial samples that do not retain their original functionality (malicious or benign). To address these issues, we propose a constrained momentum iterative fast gradient sign method (C-MIFGSM) to generate adversarial network flows that can successfully evade an ML-based IDS through transfer-based attacks while preserving the functional behavior of the network traffic. Our approach was validated using several target NIDS models built with the NSLKDD benchmark dataset. Experimental results demonstrate that even without knowledge of the target model and under feature constraints, it is possible to generate adversarial network traffic flows that achieve a high evasion attack success rate against NIDS built with deep learning and classical ML models. For example, the attack degraded the detection rate of the DoS traffic drops from 92.35% to 20.27% for the MLP model with an evasion increase rate of 78.04%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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