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Proceeding of PRIS 2021 - 2021 3rd international conference on pattern recognition and Intelligent Systems

Proceeding of PRIS 2021 - 2021 3rd International Conference ...

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3rd international conference on pattern recognition and Intelligent Systems, PRIS 2021

ISBN: (纸本)9781450390392

The proceedings contain 19 papers. The topics discussed include: metric learning for context-aware recommender systems;food safety pre-warning system based on robust principal component analysis and improved Apriori algorithm;toward tracing the source of web attacks targeted at web applications;method of physical inventory checking on cigarette stereoscopic warehouse based on uav;improved correlation filter visual tracker by using scale estimation network;face anti-spoofing by using feature fusion;age estimation from facial images using transfer learning and k-fold cross-validation;synthetic aperture radar image target recognition based on hybrid attention mechanism;a deep learning model capable of producing heatmap probabilities for characters in natural scenes;and research on distortion correction of particleboard surface defect image.

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A Neural Network Model-Based Inversion Method for Stratified Seafloor Geoacoustic Parameters in Shallow Water 3

A Neural Network Model-Based Inversion Method for Stratified...

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3rd international conference on Geology, Mapping and Remote Sensing, ICGMRS 2022

作者： Zhang, Xiaorou Cui, Zhiqiang Wang, Qile Zhu, Hanhao Liu, Wei Chai, Zhigang Zhejiang Ocean University School of Marine Science and Technology Zhoushan China Zhejiang Ocean University School of Naval Architecture and Maritime Zhoushan China The 23rd Research Institute of China Electronic Technology Group Corporation Shanghai China

ISBN: (数字)9781665485951

ISBN: (纸本)9781665485951

At this stage, in the inversion research of shallow sea geoacoustic parameters, the traditional inversion method focuses on calculating the parameter inversion under the default seafloor layering, and does not clarify the seafloor layering. Therefore, when dealing with the experimental data under the unknown seabed stratification, the geoacoustic parameters that conform to the specific seabed stratification cannot be accurately given. Aiming at such problems, this paper proposes a layered shallow sea geoacoustic parameters inversion method based on a layered physical model of the seafloor and a neural network algorithm model. First, a subsea physical model with a layered structure is selected through simulation, and the fast field sound field method (FFM) is used to calculate the sound field, then the theoretical prediction value of the shallow sea acoustic pressure field under different layered conditions is obtained. Second, the pattern recognition neural network algorithm is established to analyze the characteristics of the predicted sound pressure field under different seabed layers. Finally, according to the layered results, the sound pressure field is substituted into the seabed parameters inversion model of the corresponding layers, and the seabed parameters inversion is performed to obtain the inversion results. Experiments results show that the number of model iterations in the neural network algorithm is less and the efficiency is higher. The problem of seabed stratification is effectively solved through the characteristic analysis of the sound pressure field, which greatly improves the convenience of the inversion work. © 2022 IEEE.

关键词： Acoustic fields

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Long-Term Traffic Characterization in a Large-Scale Cellular Network Based on Limited Time Series 3

Long-Term Traffic Characterization in a Large-Scale Cellular...

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3rd international conference on Electronics and Communication;Network and Computer Technology (ECNCT)

作者： Li, Sisi Chen, Yishuai Li, Naipeng Su, Jian Guo, Yuchun Zhao, Yongxiang Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

Understanding long-term (e.g., one year) traffic characteristics of cellular base stations (BSs) is of great value to network operators. However, there are rarely modeling results about them. In this paper, we characterize the long-term (i.e., one year) traffic patterns of thousands of BSs in a large-scale cellular network of China. We first find that the traffic distribution among BSs is highly skewed and BSs' traffic varies dramatically in a year. In order to cluster meaningful BS traffic patterns, we use a new clustering method, in which a BS's monthly traffic is represented by its rank in the BS's traffic time series. In this method, we find that the thousands of BSs have six typical traffic patterns, and the patterns are interpretable: they are clearly related to two important events in China: 1) Spring Festival when a lot of people return hometown to reunion with family, 2) Double 11 Shopping Festival when a lot of people shop online. They are also related to the BSs' geographic location and address information. Our measurement and analysis results provide useful information for cellular network providers to understand and plan their networks, and our clustering method can be applied in similar traffic pattern mining problems.

关键词： Cellular network Cellular traffic pattern recognition Data mining Measurement analysis

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FPGA Implementation of Textural Feature Extractor and Distance Based Classifier for Multi Modal Biometric pattern recognition System Using Multi Resolution Approach

FPGA Implementation of Textural Feature Extractor and Distan...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： S. Hariprasath M. Santhi T.N. Prabakar V. Koushick Aravind. P T. Gunasekaran Department of ECE Saranathan College of Engineering Trichy Department of ECE Sri Sairam College of Engineering Anekal Bangalore Department of ECE Veltech Institute of Science and Technology Chennai Department of Electrical and computer Engineering Mattu University Ethiopia Electrical and Electronics Engineering Section University of Technology and Applied Sciences Muscat Sultanate of Oman

The design of biometric pattern recognition with better accuracy, high robustness and good permanence is a significant design issue in pattern recognition system. A pattern recognition system in which the biological and physiological traits of the user are employed to authenticate the users is known as biometric pattern recognition system. Unimodal biometric systems do not have the required characteristics like solidity, circumvention and permanence. When noisy data acquired from the sensor is given as input, the uni modal biometrics system's accuracy may reduce. Besides the noisy input, less variation of parameters of input data belonging to same class may be a cause for decrease in accuracy of the system. Several such limitations may be removed by applying input data from multi biometric sources. In this work, the design of a textural feature extractor and the classifier for a bimodal biometric system is described. The bimodal biometric system is constructed by fusion of iris code vector and palmprint code vector at feature level. In iris pattern, using the gray level dependent matrix values obtained from wavelet packet decomposed sub-images, the features are extracted Using gabor kernel, features are extracted from the palmprint pattern. The feature vector is formed by the concatenation of both the features after normalization is employed. By binarisation, the binary feature vector is created Using Hamming Distance and its variant, the classifier produces the classification result. The RTL description of the Hamming Distance Classifier (HDC) is written using VHDL 2008 language, Verilog HDL, and the implementation is performed on Spartan 6 FPGA. With a speed grade of 4, the propagation delay of the critical path is 330.12 ns. The obtained delay is sufficient for implementing the classifier as an off-chip design for real-time implementation.

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HybridTrajNet: Enhancing Pedestrian Trajectory Prediction with Multi-Scale Attention and Large Selective Kernel

HybridTrajNet: Enhancing Pedestrian Trajectory Prediction wi...

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Information and Communication Engineering (JCICE), international Joint conference on

作者： Xiaoke Wang Wenzao Li Ran Tang Xiaoming Zhang School of Communication Engineering Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798350388725

ISBN: (纸本)9798350388732

Pedestrian trajectory prediction plays a pivotal role in domains such as autonomous driving, intelligent surveillance, and urban planning. Traditional forecasting methods often fall short when faced with complex environments and the dynamic nature of social interactions among pedestrians, adversely affecting prediction accuracy. To address these challenges, this study introduces HybridTrajNet, an enhanced trajectory prediction model that integrates a Multi-scale Attention (EMA) and Large Selective Kernel (LSK). The EMA module accentuates critical features for forecasting by allocating attention weights across various scales; concurrently. The LSK module enhances recognition of pedestrian behavioral patterns through the similarity of local features. Empirical results on the SDD demonstrate that HybridTrajNet achieves a 2.04% improvement in ADE and a 2.19% improvement in FDE compared to baseline models.

关键词： Adaptation models Pedestrians Surveillance Urban planning Predictive models Trajectory pattern recognition Kernel Forecasting Intelligent systems

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Image Texture Analysis with Local Binary patterns: A Review 3

Image Texture Analysis with Local Binary Patterns: A Review

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3rd IEEE international Virtual conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021

作者： Srilatha, N. Reddy, V. Lokeswara JNT University Anantapauramu CSE Department Anantapuramu India KSRM College of Engineering CSE Department Kadapa India

ISBN: (纸本)9781665426916

Local binary pattern (LBP) and its changed forms have been widely used in the many applications of different fields such as image processing, computer vision, model recognition and texture analysis etc. In this paper, we did extensive research-based study on LBP and its forms. The pros and cons of standalone LBP methods which used in many applications were evaluated in this study. Finally, we interpret based on their defects and focused the gapes of recent forms of LBP model signifier. © 2021 IEEE.

关键词： Local binary pattern

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Algorithm for Fault Diagnosis System of Pumping Units Based on Fuzzy Logic and Neural Networks

Algorithm for Fault Diagnosis System of Pumping Units Based ...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Guannan Zhang No.4 Operation Area of No.6 Oil Production Plant of Daqing Oilfield Co. Ltd Daqing Heilongjiang China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In order to reduce the exploitation cost, improve the economic benefit and ensure the sustainable exploitation of crude oil, it is particularly urgent to innovate the oil extraction technology. This paper introduces two advanced intelligent technologies, fuzzy logic and neural network. Fuzzy logic can deal with uncertain and fuzzy information, which provides an effective means to deal with the complex faults of pumping units. Neural network, through its powerful self-learning and pattern recognition ability, provides a high-accuracy solution for fault classification and recognition of pumping units. Through experimental verification, this paper finds that the fault diagnosis method of pumping unit based on fuzzy logic and neural network is significantly better than the traditional method in accuracy and efficiency. This method can not only identify fault types and causes more accurately, but also realize timely early warning and prediction of faults, thus greatly improving the safety and production efficiency of oil fields. The fault diagnosis technology based on fuzzy logic and neural network provides strong support for the intelligent development of oil fields, and also lays a solid foundation for the intelligent, efficient and safe exploitation of oil fields in the future.

关键词： Fault diagnosis Fuzzy logic Accuracy Oils Neural networks Production Solids pattern recognition Safety Optimization

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Automatic Poincare section 3

Automatic Poincare section

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3rd international conference on Applied Machine Learning, ICAML 2021

作者： Cheng, Jie Wang, Ruike Li, Wenshi School of Electronics and Information En Soochow University Suzhou215006 China Faculty of Science Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781665421256

An attractor-lock-in Poincare section is formed through three space points of the minimum, mean and maximum of the attractor under test, with parameter needless manual selection. The key algorithm steps of automatic Poincare section include (a) digging out three geometry feature points as minimum, mean and maximum;(b) through three basic statistical points making a plane needed;(c) cut-off points counting by close contact measure and close range fusion;(d) using the spectral entropy complexity and construction creep rate in chaos spring test as gold standards of control experiments. Test data are provided by classical Chua’s equation and unified chaotic equation and random number instruction in Matlab tool. The results show that the automatic Poincare section and its cut-off point number can finish three classifying recognition on period, chaos or random states of the test three dimensional data. This case study will promote economically the information geometry, and further be applied to a wide range of pattern recognition fields. © 2021 IEEE

关键词： pattern recognition

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A Tunnel Structural Health Monitoring and Damage Identification Algorithm Based on Deep Reinforcement Learning

A Tunnel Structural Health Monitoring and Damage Identificat...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Yike Wei Bin Yang China Merchants Chongqing Testing Center for Highway Engineering CO. LTD Chongqing China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

With the continuous promotion of infrastructure construction such as water conservancy, transportation, and municipal engineering in China, the number of tunnels put into operation is increasing, making the healthy service and longterm operation of tunnel structures an urgent and important task. During tunnel operation, structural diseases such as cracks, voids, voids, and water seepage occur frequently. These diseases not only weaken the strength of the tunnel structure and shorten its service life, but also pose a serious threat to the safety of people's lives and property. This paper proposes a tunnel structure health monitoring and damage identification algorithm based on deep reinforcement learning (DRL) to address this issue. This algorithm fully utilizes the advantages of deep learning (DL) in feature extraction and pattern recognition, as well as the ability of reinforcement learning (RL) in decision optimization and adaptive learning. By constructing a model for the interaction between intelligent agents and the environment, it achieves real-time monitoring of the health status of tunnel structures and damage recognition. The experimental results show that the algorithm can accurately identify the damage situation of tunnel structures, effectively monitor the health status of tunnels, and provide strong technical support for the safe operation of tunnels.

关键词： Water conservation Feature extraction Deep reinforcement learning Real-time systems pattern recognition Intelligent agents Monitoring Optimization Testing Diseases

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A Novel ML classifier Using Modern Hopfield Neural Networks

A Novel ML classifier Using Modern Hopfield Neural Networks

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Computing and Machine Intelligence (ICMI), international conference on

作者： Mohamed Abdelkarim Hanan Ahmed Kamal Doaa Shawky Siemens Digital Industries Software Faculty of Engineering Cairo University Zewail City of Science and Technology

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

State-of-the-art classification neural networks, used for images and various data types, are complex and require significant energy and computational resources relying on super-vised gradient back-propagation. In contrast, the Hopfield Neural Network (HNN) is simpler, being a single-layer, fully connected network that mimics the human brain's associative memory network, making it easy to implement and computationally efficient. Its compatibility with oscillatory neural networks (ONNs) makes it ideal for lightweight machine learning applications in the Internet of Things (IoT) era. Normally, HNN has been primarily used for associative memory aiding in image processing, pattern recognition, and more, but this paper introduces it as a classifier. The proposed HNN classifier is adaptable to various datasets, including images and tabular data, and requires zero training time, making it suitable for resource-limited environments. It represents a significant leap in classification, with the highest accuracy reported for HNN classifiers to the best of our knowl-edge, achieving 96% accuracy on the MNIST dataset, a 36% percentage improvement over previous models.

关键词： Training Associative memory Accuracy MIMICs Hopfield neural networks Machine learning pattern recognition

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