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共 2254 条记录，以下是681-690 订阅

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Prediction of CODLAG Propulsion System Parameters Using Kolmogorov-Arnold Network

Prediction of CODLAG Propulsion System Parameters Using Kolm...

引用

international Symposium on intelligent Systems and Informatics (SISY)

作者： Ivan Lorencin Vedran Mrzljak Igor Poljak Darko Etinger Faculty of Informatics Juraj Dobrila University of Pula Pula Croatia Faculty of Engineering University of Rijeka Rijeka Croatia Maritime Department University of Zadar Zadar Croatia

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

Predicting the parameters of a Combined Diesel-Electric and Gas (CODLAG) propulsion system is crucial for optimizing the design, performance, and reliability of these complex engineering systems. This paper presents the implementation of the Kolmogorov-Arnold Network (KAN) for predicting CODLAG system parameters. The KAN, based on Kolmogorov's superposition theorem, can accurately approximate nonlinear relationships within the system. We provide a comprehensive overview of CODLAG systems, their operational principles, and the challenges in parameter prediction. Experimental results demonstrate that increasing hidden layers, optimizing learning rates, and adjusting batch sizes and epochs significantly improve prediction accuracy. The findings highlight the robustness of KAN in modeling complex systems and pave the way for future research to enhance the predictive capabilities of engineering systems.

关键词： Training Accuracy Uncertainty Propulsion Predictive models Reliability theory Reliability engineering Robustness data models Convergence

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Should supervised discretisation always be trusted unreservedly? On combining characteristics of supervised and unsupervised discretisation algorithms in two-step processing 27

Should supervised discretisation always be trusted unreserve...

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27th international conference on Knowledge Based and intelligent Information and engineering Sytems, KES 2023

作者： Stanczyk, Urszula Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper presents a description of the research methodology dedicated to a two-step discretisation process applied to the input numeric data, with combining the characteristics of selected supervised and unsupervised algorithms, which leads to extended processing of some attributes in train and test sets. The methodology was illustrated with the investigations carried out in the domain of stylometric analysis of texts, for two datasets prepared for the task of binary authorship attribution. The several variants of transformed input data obtained were subjected to exploration using two selected machine learning methods capable of inducing knowledge from both continuous and categorical forms, namely the PART and J48 classifiers. The results from the experiments indicate that, as can be expected, supervised transformations of data work well enough, however, they do not always return the best outcome. The two-step processing of some attributes shows sufficient promise to warrant a closer study, as opposed to always unconditionally relying only on supervised algorithms as outperforming all other approaches. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0)

关键词： Authorship attribution data representation Pattern recognition Stylometry Supervised discretisation Unsupervised discretisation

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A Highway Lane Changing Trajectory Planning Method Considering Vehicle Motion Characteristics and Anthropomorphization

A Highway Lane Changing Trajectory Planning Method Consideri...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Gang Li Deying Feng Dawei Wang Dongxin Xu Zhengfeng Chen Jian Wu School of Mechanical and Automotive Engineering Liaocheng University Liaocheng China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Highway lane-changing trajectory planning is integral in intelligent vehicles. In trajectory planning, considering vehicle motion characteristics can give full play to vehicle motion performance and enhance the feasibility of planned traj ectory, and the acceptability and comfort can be improved by fully considering the driver characteristics. It's necessary to consider both the vehicle and driver characteristics during the trajectory planning to ensure feasibility and improve comfort. Therefore, a highway lane-changing traj ectory planning way is proposed, considering driver characteristics and vehicle motion. Firstly, the inverse reinforcement learning (IRL) method is adopted to learn the expert lane-changing traj ectories in the HighD dataset, and the IRL trajectory planner is obtained. Second, a risk potential field is introduced to incorporate environmental information to make appropriate lane change timing decisions and improve environmental adaptability. Finally, a model predictive control (MPC) trajectory optimizer is designed according to the point-mass model to improve the feasibility of IRL trajectories. The simulation results suggest that this method's lane change timing decision and trajectory optimization are safe and effective under different working conditions. The lane change trajectory that considers the vehicle motion characteristics and anthropomorphic characteristics is successfully obtained. In summary, the proposed method can improve the feasibility and comfort of trajectory planning and increase the safety of automated vehicles during lane changing.

关键词： Road transportation Trajectory planning Simulation Reinforcement learning Predictive models Mathematical models Trajectory Timing Safety Trajectory optimization

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On the Arm Swing Model During Human Walking for Wrist-Worn Rotational Electret Energy Harvester

On the Arm Swing Model During Human Walking for Wrist-Worn R...

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Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)

作者： Tomoya Miyoshi Xutao Mei Yuji Suzuki Dept. of Mechanical Engineering The University of Tokyo Tokyo Japan

In this report, a machine-learning model for a wrist-worn rotational electret energy harvester (REEH) is presented. By using random forest regression (RFR) on a motion capture dataset, an arm swing model is developed to predict the output power during human walking. Compared to conventional linear regression, the present model shows better agreement with the experimental data of seven subjects wearing a REEH with a wireless output power measurement system.

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Multi-stage Citrus Detection based on Improved Yolov4 22

Multi-stage Citrus Detection based on Improved Yolov4

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5th international conference on Control and Computer Vision, ICCCV 2022

作者： Yi, Bingliang Kong, Bin Xu, Chengjie Institute of Intelligent Machines Chinese Academy of Sciences China College of Mechanical Engineering Chongqing Three Gorges University China

ISBN: (纸本)9781450397315

At present, the research of Citrus recognition is basically aimed at the detection of Citrus in mature stage. This paper proposes a citrus detection algorithm based on improved yolov4, which can detect citrus in each growth stage. Based on yolov4, Introducing CBAM attention mechanism to improve the feature extraction ability of backbone networks;Increase the 22nd layer output of feature extraction network to improve the small target detection rate;A short connection feature fusion method is designed to increase the utilization of shallow feature information;Add a detection head with a scale of 152∗152 for small-scale targets. It is proved by experiments on the self-built citrus data set, the improved CBAM-F-YOLOv4 can effectively detect citrus in each stage, and the mean Average Precision (mAP) is 6.2 percentage points higher than the original algorithm, reaching 87.3%. The detection results show that the improved algorithm greatly improves the detection ability of occlusiona overlap and small-scale citrus. © 2022 Owner/Author.

关键词： Feature extraction

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Heuristic and Meta Dendral Systems: A Review 22

Heuristic and Meta Dendral Systems: A Review

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22nd international Arab conference on Information Technology (ACIT)

作者： AlFarsi, Ghaliya Tawafak, Ragad M. Malik, Sohail Iqbal Alsideiri, Abir Mathew, Roy Malaysia AlBuraimi Univ Coll Univ Tenaga Nas Coll Grad Studies Buraimi Oman AlBuraimi Univ Coll Dept Informat Technol Buraimi Oman

ISBN: (纸本)9781665419956

Among numerous uses of Artificial Intelligence, Expert System is the one that exploits human information to take care of issues that normally would require human knowledge. Expert systems are intended to convey the understanding and information found in the experts in a specific field and take choices dependent on the gathered learning of the learning base alongside an arrangement of standards and principles of an inference engine and in the meantime, justify those decisions with the help of explanation facility. The derivation motor is constantly refreshed as new ends are drawn from each new sureness in the learning base which triggers additional rules, heuristics and standards in the inference engine. This paper clarifies the essential engineering of Expert System;it's the first since forever achievement DEndRAL which turned into a stepping stone in the Artificial Intelligence field The expert system represents knowledge acquired from human masters as information or standards inside the PC. These rules and data can be called upon to solve complex problems. This paper gives a review of the Expert system to be specific Dendral that is utilized in Chemical sciences from its invention. The reveals in this System can be a greatly important expansion of Artificial Intelligence.

关键词： Heuristic Dendral expert system artificial intelligence system learning Performance

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Flash Flood Simulation Between Slănic and Vărbilău Rivers in Vărbilău Village, Prahova County, Romania, Using Hydraulic Modeling and GIS Techniques 8th

Flash Flood Simulation Between Slănic and Vărbilău Rivers...

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8th international conference on Modelling and Development of intelligent Systems, MDIS 2022

作者： Popescu, Cristian Bărbulescu, Alina Technical University of Civil Engineering Bucharest 124 Lacul Tei Boulevard Bucharest Romania Transilvania University of Braşov 5 Turnului Street Brașov Romania

ISBN: (纸本)9783031270338

Flash flooding occurs in different hilly regions with circular shaped catchments across Europe, including Romania, when heavy rains or rapid melting of the snow are encountered. Vărbilău is a village in the Subcarpathian hills, susceptible to flash floods. In some situations, roads and buildings are affected. This study aims to analyze the flash flood from the 22nd of July, 2018, compare it with the same phenomena formed at higher and lower flow rates, and determine the impact on settlements and roads. The research is done based on field observations, hydrological data from the Romanian National Institute of Hydrology, Geographic Information System (GIS) database, as well as by processing information in GIS environment (ArcMap 10.2.2) and hydraulic modeling in HEC-RAS 6.2. data is imported in GIS and prepared for the flooding simulation in HEC-RAS. After the model is obtained based on flow values, the affected buildings and roads are quantified in GIS. The area is prone to flooding. Upstream from the confluence the Slănic valley is much more affected than the valley of Vărbilău because the buildings are in the proximity of the river and the valley is narrow, while downstream from the confluence only two bridges and one county road are affected. The utility of protective levees is presented in the paper, while road accessibility for the emergency intervention authorities registers higher values in the northern part. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Rivers

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2021 IEEE 22nd international conference on High Performance Switching and Routing, HPSR 2021

2021 IEEE 22nd International Conference on High Performance ...

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22nd IEEE international conference on High Performance Switching and Routing, HPSR 2021

ISBN: (纸本)9781665440059

The proceedings contain 76 papers. The topics discussed include: qualitative communications for augmented reality and virtual reality;a joint computer vision and reconfigurable intelligent meta-surface approach for interference reduction in beyond 5G networks;on the use of machine learning and network tomography for network slices monitoring;energy efficient and high performance modified mesh based 2-D NoC architecture;run-to-completion versus pipelined: the case of 100 Gbps packet parsing;a proof of optimality on EDF scheduling in sink-tree packetized networks;increasing vehicles perception using cooperative relaying and priority-based beaconing;flexible semantic-based data networking for IoT domains;the role of intent-based networking in ICT supply chains;and intelligent routing approach based on machine learning and SDN for heterogeneous IoTs.

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Attention-CNN-Aided Specific Emitter Identification Method with Limited Radio Frequency dataset 22

Attention-CNN-Aided Specific Emitter Identification Method w...

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22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Wang, Shufei Zhang, Yibin Sun, Jinlong Ohtsuki, Tomoaki Gui, Guan College of Telecommunications and Information Engineering Njupt Nanjing China Keio University Department of Information and Computer Science Yokohama Japan

ISBN: (纸本)9781665470674

Specific emitter identification (SEI) plays a more and more important role to recognize unknown radio frequency (RF) devices in the field of physical layer security. Existing deep learning (DL)-based SEI methods have been proposed for solving problems that enough RF dataset can be obtained at the model training stage. In many real application scenarios, however, only the limited dataset can be collected which causes the conventional DL methods unstable. To solve this problem, in this paper, we propose a robust SEI method using a self-attention mechanism and convolutional neural network (attention-CNN). To be specific, we collect a real RF dataset from the automatic dependent surveillance-broadcast (ADS-B) signal. Here, the ADS-B dataset is limited in some special cases and data augmentation is used to improve the features of the dataset. Simulation results show that the proposed method achieves better identification performance than the benchmark. © 2022 IEEE.

关键词： Deep learning

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Enhancing One-Class Anomaly Detection in Unlabeled datasets Through Unsupervised data Refinement

Enhancing One-Class Anomaly Detection in Unlabeled Datasets ...

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international Symposium on intelligent Systems and Informatics (SISY)

作者： Jiyan Salim Mahmud Imre Lendak Data Science and Engineering Department Eötvös Loránd University (ELTE) Budapest Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

Effective anomaly detection in unlabeled datasets is crucial for numerous applications across different domains. This paper presents a novel methodology for one-class anomaly detection that leverages unsupervised learning techniques to filter and purify normal data which leads to enhancing the training process in real world scenarios where no labels are available. The initial phase employs an Isolation Forest (IF) algorithm to segregate preliminary normal data points from potential outliers. To refine the selection of truly normal data, we introduce a centroid-based filtering method where distances from each point to the data centroid are calculated and analyzed. We then select a subset of these points, ensuring their purity as representative normal data points. This purified dataset forms the basis for training a one-class Support Vector Machine (SVM), Isolation Forest (IF) and Auto-Encoder (AE), focusing solely on these selected normal points. Our methodology was tested across two distinct public real-world datasets, evaluating performance metrics such as F1-Score, Precision and Recall validating the effectiveness of our approach. Results indicate that the refined training data set contributes to improved models' performance up to 20%. This research contributes to the field of anomaly detection by providing a systematic approach to handling and training on unlabeled data, thereby proposing a scalable solution for real-world anomaly detection challenges.

关键词： Training Support vector machines Measurement Systematics Filtering Computational modeling Training data Forestry Anomaly detection Unsupervised learning

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