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A Novel Distributed K-Means Clustering Algorithm for big Text data 23

A Novel Distributed K-Means Clustering Algorithm for Big Tex...

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7th international conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Li, Min Li, Meijing Cheng, Yonglong Ryu, Keun Ho College of Information Engineering Shanghai Maritime University Shanghai201306 China Big Data and Artificial Intelligence Laboratory Industrial and Commercial Bank of China Software Development Center Shanghai201306 China Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9781450399883

With the advancement of diverse industries, text clustering has garnered significant attention in fields such as data mining, document retrieval, and pattern classification. the K-Means algorithm, a simple, efficient, and widely adopted clustering approach, has been extensively studied for its applicability in text data analysis. However, the exponential growth of information presents a formidable challenge when clustering large-scale data. the traditional K-Means algorithm proves ineffective in handling high-dimensional, massive text data, and the initial centroid selection significantly impacts the clustering outcomes. To address these limitations, this research introduces a clustering algorithm based on the virtual centroid and nearest centroid strategy. Furthermore, we incorporate the MapReduce parallelization framework to tackle the clustering problem associated with vast text data. Experimental results demonstrate the algorithm's commendable scalability and effective handling of large datasets. © 2023 ACM.

关键词： MapReduce

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YOLO-MBS: A lightweight ship target detection method 5

YOLO-MBS: A lightweight ship target detection method

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5th international conference on Computer Vision and data Mining, ICCVDM 2024

作者： Lu, Quanguo Chen, Weixuan Xiao, Jian Wang, Hongzhou Luo, Pingping Li, Haohua Zhu, Zhifang Zhan, Xiaohuang Liu, Xuwei Nanchang Institute of Technology Jiangxi Province Key Laboratory of Precision Drive & Equipment Nanchang330099 China Jiangxi Province Institute of Mechanical Science Jiangxi Nanchang330002 China

ISBN: (纸本)9781510682863

In the task of ship target detection, due to the complex image background and more irrelevant interference, the difficulty of ship target detection is increased. A lightweight and efficient ship object detection algorithm YOLO-MBS based on improved Yolov5 is proposed. Firstly, the backbone network adopts MobileNetv3, which uses lightweight deep separable convolution and residual block structures and a new activation function h-swish to effectively reduce the number of parameters of the network model and improve the calculation speed. then, the E-IOU loss function is introduced to improve the regression accuracy and convergence speed of the prediction box. Secondly, in order to allow the machine to select the region of interest and extract effective features more fully, the SimAM non-parametric attention mechanism is added. Finally, the BiFPN feature pyramid network structure is used to make the features of different scales better integrated and utilized. the experimental results regarding the Seaships dataset indicate that the volume of the improved algorithm model is compressed to 21.8% of the original YOLOv5 model, the training time is shortened by 46.62 %, the parameter quantity is compressed to 18.5 % of the original model, the calculation amount is reduced to 16.45 % of the original model, and the average accuracy is improved to 98.8 %. the experimental results demonstrate that the improved algorithm significantly reduces model parameters, thereby shortening both training and inference times, while maintaining high-precision detection performance. © 2024 SPIE.

关键词： Image segmentation

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Preventive Maintenance Method of Brake Pads According to CNN-LSTM Model 5

Preventive Maintenance Method of Brake Pads According to CNN...

引用

5th international conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Wang, Shuo Yu, Zhenliang School of Mechanical and Power Engineering Yingkou Institute of Technology Yingkou115014 China

ISBN: (纸本)9781643684840

In this paper, a preventive maintenance method of brake pads according to CNN-LSTM is proposed to solve the problem of brake pads reaching their service life and failing to call for maintenance in time. In this method, CNN network is used to complete the multidimensional extraction of spatial features of brake pads, and LSTM network is used to complete the capture of time series features, and the spatial-temporal relation between the initial signal of brake disc and the abrasion value is constructed. When the abrasion value exceeds the specified range, intelligent warning is carried out to realize the preventive maintenance of brake pads. According to the data, it can be seen that the CNN-LSTM hybrid algorithms can accurately monitoring the abrasion value of brake disc, the MAE and RMSE value are decreased, and the Determining parameter R2 is increased. © 2024 the Authors.

关键词： Preventive maintenance

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SatNet: A Lightweight Satellite Image Classification Model Using Deep Convolutional Neural Network 5

SatNet: A Lightweight Satellite Image Classification Model U...

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5th IEEE international conference on Telecommunications and Photonics, ICTP 2023

作者： Ulla, Sana Shipra, Elmeeh Hasan Tahmeed, Mohammad Ahnaf Saha, Pallab Palash, Md Istakiak Adnan Hossam-E-Haider, Md Bangladesh University of Professionals Department of Ict Dhaka Bangladesh Military Institute of Science and Technology Department of Eece Dhaka Bangladesh

ISBN: (纸本)9798350393477

Deep Learning approach is essential for classifying remotely sensed images, identifying objects in satellite images, and using those results for numerous purposes. In light of the large number of datasets, image classification requires high accuracy and training performance, making the approach crucial to developing reliable and accurate classification models. As the field of satellite imaging has advanced, researchers are more inclined to rely on techniques for the automatic and efficient classification of satellite images. However, many well-known networks need a lot of parameters and computing power, which makes them challenging to use for classification tasks. A Lightweight Deep Convolutional Neural Network (Deep CNN) called SatNet is proposed for satellite image categorization which uses less computing power and is an efficient model for multiclass classification problems. Five transfer learning models - Xception, InceptionV3, VGG19, ResNet50, and DenseNet201 were taken into consideration in order to make comparisons. the Satellite Image Classification dataset-Rsi-cb256is used by the model during testing. It effectively outperforms other measures and surpasses ResNet50 and VGG19 outputs, with an accuracy rate of 99.15%. A quicker and easier method of classifying satellite image data is possible with the proposed Deep CNN model. © 2023 IEEE.

关键词： Convolutional neural networks

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15th international conference on Computer Science and its Applications, CSA 2023 and 17th KIPS international conference on Ubiquitous Information Technologies and Applications, CUTE 2023

15th International Conference on Computer Science and its Ap...

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15th international conference on Computer Science and its Applications, CSA 2023 and 17th KIPS international conference on Ubiquitous Information Technologies and Applications, CUTE 2023

ISBN: (纸本)9789819724468

the proceedings contain 73 papers. the special focus in this conference is on Computer Science and its Applications and Ubiquitous Information Technologies and Applications. the topics include: Emulator-Based Configuration on QoS Measurement: A Case Study of DRL Actions on Mininet and RYU Testbed;GNN Embedded SFC Provisioning Scheme for Efficient Resources in SDN/NFV;fixed Value Output Scheduling Strategy of Photovoltaic-Storage System;A Study on Improvement of EV Charging Occupancy Prediction Efficiency;An Integrated Instant NeRF and Simulation Based 3D Reconstruction for Immersive City Twin;development of Central Air Conditioning Circulation System for Condominium Unit and Evaluation of the Indoor Environment in Summer;malware Detection System Based on Static-Dynamic Preprocessing Techniques Combined in an Ensemble Model;value Interpretations of Qingdao Historic Urban Areas Based on big data Analysis from an Official Discourse Perspective;multi-factor Relay-Node Selection Algorithm for IoV in 3D Mountain Scenarios;Ocular Disease Classification Using CNN with Deep Convolutional Generative Adversarial Network;quality Defect Diagnosis Method for Prefabricated Steel Structure Residences Based on Artificial General Intelligence;Confidence-Aided Disparity Map Refinement with LiDAR-Stereo Fusion;APS: Adaptive Priority-Aware Sketch with Low Overhead for Heterogeneous Traffic;HACC: Hierarchical Automatic Selection of Congestion Control Algorithms;analyzing the Effects of Human Detection in Top-Down Pose Estimation for Crowd Situation Recognitions;dual Vision-Based Reinforcement Learning: Solving Robot Manipulation Task with Both Static-View and Active-View Cameras;Precise 5G Traffic Forecasting by Using Search-Economics Algorithm to Fine-Tune the GRU Weights;Non-Line-of-Sight Error Reduction Algorithm for UWB Positioning;numerical Feature Preprocessing Method for Daily Solar Radiation Pattern Classification.

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Highly Sensitive Dual Channel PCF Based SPR Biosensor for Multi-Analyte Sensing 5

Highly Sensitive Dual Channel PCF Based SPR Biosensor for Mu...

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5th IEEE international conference on Telecommunications and Photonics, ICTP 2023

作者： Hasan, Md. Shahedul Ahmed, Yousuf Faisal, Mohammad Bangladesh University of Engineering & Technology Department of Eee Dhaka1205 Bangladesh

ISBN: (纸本)9798350393477

In this research, a highly sensitive multichannel Photonic Crystal Fiber (PCF) based Surface Plasmon Resonance (SPR) sensor is presented for the detection of the refractive index (RI) of any unknown analyte. the proposed biosensor has two analyte slots, and it can detect two different analytes simultaneously. In Channel 1 (Ch-l), the employed plasmonic material is Gold (Au), whereas in Channel 2 (Ch-2), a combination of Gold and Tantalum Pentoxide (Ta2O5) serves as the plasmonic material. the circular PCF incorporates a total of seventeen air holes which can be easily fabricated by applying existing fabrication techniques. Numerical analysis based on Finite element method (FEM) is used to simulate and assess the sensor's performance. the proposed sensor shows remarkable sensitivity in a broad sensing range of RI 1.33-1.41. the sensor attains maximum wavelength sensitivity of 11,000 nm/RIU for Ch-1 and 41,000 nm/RIU for Ch-2. Moreover, the sensor shows an impressive maximum amplitude sensitivity of 1459.34 RIU-1 and 629.10 RIU-1 for channel-l and channel-2 respectively. To the best of our knowledge the proposed sensor achieves highest amplitude sensitivity compared to all other multi-channel sensors reported before. the minimum sensor resolution is 2.44 × 10-6 refractive index unit (RIU). It is expected that our designed highly sensitive sensor will be effectively applied to identify a diverse range of substances, including chemicals, organic or inorganic compounds and other different analytes. © 2023 IEEE.

关键词： Refractive index

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Spatiotemporal Features Recognition and Prediction of Urban Hot Spots Based on Trajectory data 5

Spatiotemporal Features Recognition and Prediction of Urban ...

引用

5th international conference on Electronic engineering and Informatics, ICEEI 2023

作者： Cao, Yu Sun, Kai School of Artificial Intelligence Application Shanghai Urban Construction Vocational College Shanghai201415 China Science and Technology Department Shanghai Urban Construction Vocational College Shanghai201415 China

ISBN: (纸本)9781643685021

With the continuous progress of urbanization, the construction of cities is gradually accelerated, and the urban population density is constantly increasing. Hot spots represent regions with more trips, greater population flow and higher travel demand. With the increasing complexity of traffic data, traditional methods for predicting hot spots can no longer meet the current situation. this also provides opportunities for the development of deep learning techniques. Given the continuous expansion in scale and intricacy of trajectory data, its relevance has become increasingly significant in domains such as urban planning, traffic management, and business decision-making. Aiming at the hot spot identification problem of trajectory data, this paper, inspired by the traffic prediction model, applied the STGCN model to the urban hot spot prediction problem and improved it. the model consists of four parts: cluster processing, spatial feature extraction, time feature extraction and hot spot region prediction. the experimental outcomes demonstrate that the model exhibits superior predictive performance across diverse datasets compared to conventional methods, showcasing robust generalization capabilities and practical applicability. © 2024 the Authors.

关键词： Recurrent neural networks

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Power grid engineering data knowledge retrieval and graph construction technology 5

Power grid engineering data knowledge retrieval and graph co...

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5th IEEE international Electrical and Energy conference, CIEEC 2022

作者： Zhou, Jia Li, Longfang Bai, Yang State Grid Nantong Power Supply Company Nantong China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

the power grid infrastructure project has the characteristics of large scale, long period, and multiple subjects. therefore, it is determined that a large amount of data with rich sources and complex formats will be generated in the whole process of power grid engineering from design, construction to acceptance. However, due to the lack of effective data extraction and sorting technical means, these data results have a single search method and related queries are difficult, and they cannot be displayed intuitively. this article takes the noise-containing multi-source heterogeneous infrastructure engineering data as the research object, and relies on natural language processing technology to carry out research on the information extraction model and method and the knowledge map construction technology. this method automatically analyzes the natural semantics contained in the text data of the power grid infrastructure project, excavates the valuable information contained therein, and then constructs a data knowledge graph to realize hierarchical storage, visual display and related information recommendation. © 2022 IEEE.

关键词： Knowledge graph

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A Wireless Animal Robot Stimulation System based on Neuronal Electrical Signal Characteristics 3

A Wireless Animal Robot Stimulation System based on Neuronal...

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3rd IEEE international conference on Information technology, big data and Artificial Intelligence, ICIBA 2023

作者： Yan, Rui Qiu, Zengrui Chen, Zhaozhen Yang, Junqing Wang, Hui Cui, Tianle Huai, Ruituo Shandong University of Science and Technology College of Electrical and Automation Engineering Qingdao China

ISBN: (纸本)9781665490788

As a stimulus signal, coded electrical signals can control the motion behavior of animals, which has been widely used in the field of animal robots. In current research, most of the stimulus signals used by researchers are traditional waveform, such as square waves. To enrich the stimulus waveform, a wireless animal robot stimulation system based on neuronal electrical signal characteristics is presented in this paper. the stimulator uses the CC1101 wireless module to control animal behavior through brain stimulation. the LabVIEW-based graphical user interface(GUI) can manipulate brain stimulation remotely while the stimulator powered by battery. Additionally, the spikes of animals have been simulated by this system through Direct Digital Synthesizer(DDS) algorithm. the GUI enable users to customize the combination of these analog spike signals. the recombined signals are sent to the stimulator through CC1101 as stimulus signals. In vivo experiments conducted on five pigeons verified the efficacy of the stimulation mechanism. the analog spike signal with an amplitude of 3-5V successfully caused the pigeon's turning behavior. the feasibility of the analog spike signals as stimulus signals was successfully verified. Increased the diversity of stimulus waveform in the field of animal robots. © 2023 IEEE.

关键词： Graphical user interfaces

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Hyperdimensional Consumer Pattern Analysis with Quantum Neural Architectures using Non-Hermitian Operators 5

Hyperdimensional Consumer Pattern Analysis with Quantum Neur...

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5th international conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Goyal, Shivam Singh, Sunil K. Kumar, Sudhakar Sarin, Saket Gupta, Brij B. Arya, Varsha Chui, Kwok Tai Chandigarh College of Engineering and Technology Chandigarh Chandigarh India Asia University Taichung Taiwan Hong Kong Metropolitan University Hong Kong Hong Kong

ISBN: (纸本)9798400709418

In an era inundated with high-dimensional consumer data, the need for advanced hyper-dimensional pattern analysis poses a significant computational challenge. this research pioneers the use of quantum computing to revolutionize consumer technology. Modern data streams, including images, audio, sensor data, and more, require an agile solution to overcome dimensionality challenges that traditional machine learning and classical computing struggle with. Our work combines Quantum Neural Architectures (QNAs) with Non-Hermitian Operators (NHOs), harnessing NHOs' unique non-unitary properties for quantum speedup in feature extraction, dimensionality reduction, and pattern recognition. this approach allows for simultaneous processing of large datasets through quantum parallelism, demonstrating substantial gains in efficiency and accuracy compared to classical and Hermitian quantum methods. We also explore quantum cryptography, offering insights into quantum-safe encryption and cryptographic primitives. this multidisciplinary effort represents a paradigm shift in quantum technology's application across consumer domains. this paper presents a quantum-inspired framework for hyperdimensional consumer pattern analysis, supported by mathematical rigor and empirical validation. the fusion of NHOs and QNAs heralds a new era in consumer technology, marked by exceptional computational power, robust security, and transformative potential. © 2023 ACM.

关键词： Large datasets

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