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Uncovering the ANPR Performance Gap: A Commercial Systems Analysis 5th

Uncovering the ANPR Performance Gap: A Commercial Systems An...

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5th Southern African Conference for Artificial Intelligence Research

作者： Zandamela, Frank Seletani, Rofhiwa Malatjie, Patrick Kunene, Dumisani Nana, Muhammad Ahmed Sekopa, Teboho CSIR Def & Secur ZA-0001 Pretoria South Africa

ISBN: (纸本)9783031782541;9783031782558

Automatic number plate recognition (ANPR) systems play a vital role in global security and crime prevention. However, despite their importance, there is a notable lack of research assessing commercial off-the-shelf ANPR systems. Given that ANPR systems are not universally applicable due to regional variations in number plates, performance varies per region. A recent two-year study of a commercial ANPR system revealed a concerning average accuracy of 70%. This falls significantly short of the global average of 90%, compromising the reliability of the ANPR results. This study aims to address this knowledge gap by investigating factors that influence the performance of ANPR systems and comparing the effectiveness of commercial off-the-shelf solutions on South African vehicle plates. Four cloud-based ANPR solutions and an ANPR camera were assessed with a custom dataset. The custom dataset contains vehicle plates from different provinces and a government department. The study revealed that the camera viewing angle significantly affected the accuracy of the deployed ANPR system. Cloud-based ANPR APIs outperformed the ANPR camera system by 6.67%. In addition, most ANPR systems have difficulty detecting and correctly recognising number plate characters when there are multiple vehicles in the image. These findings underscore the need for region-specific optimisation and more comprehensive evaluation frameworks to enhance the reliability and accuracy of commercial ANPR systems in diverse real-world scenarios.

关键词： Automatic number plate recognition Automatic number plate recognition Commercial systems Performance evaluation Optical Character Recognition

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Numerical Solution to a Problem of Optimizing Placement and Flow Rates of Wells 2nd

Numerical Solution to a Problem of Optimizing Placement and ...

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2nd International Conference on information Technologies and Their Applications

作者： Bagirov, Arzu Gunkina, Tatiana Handzel, Alexander Inst Control Syst Baku Azerbaijan North Caucasus Fed Univ Stavropol Russia

ISBN: (纸本)9783031734199;9783031734205

The paper proposes an approach to the numerical solution of the problem of optimizing wells' placement and the values of their flow rates. The selection of rational placement of wells is the first and extremely important task that oil and gas companies face when designing the development of hydrocarbon fields. Typically, standard patterns are used for well placement, in which wells are located at the nodal points of various regular grids. The method proposed in this paper for determining the optimal placement of wells and their flow rates is based on considering an oil reservoir as a dynamic system with distributed parameters and on application of the methods for optimal control of such systems. The optimization goal is to achieve a uniform distribution of reservoir pressure over the area of the oil deposit after withdrawing the planned volume of oil from it. Fulfillment of this criterion ensures, in particular, a uniform rise of bottom water and prevents early water flooding of production wells. The state of the reservoir is described by the equation of non-stationary two-dimensional filtration of weakly compressible oil. Thus, the problem under consideration is a problem of optimal control of distributed systems, the solution of which is carried out by reducing the original problem to a discrete problem for which the gradient of the objective functional is determined, which allows to use standard first-order optimization methods. The paper presents the results of numerical experiments and their analysis.

关键词： Oil Field Filtration Placement of Wells Flow Rate Distributed System Optimal Control

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Multi-granularity Semantic Representation and Rule-Based Labeling for Relation Classification 4th

Multi-granularity Semantic Representation and Rule-Based Lab...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Zhou, Zhenyu Zhang, Qinghua Du, Hongfei Xie, Qin Feng, Shuo Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

ISBN: (纸本)9789819603534;9789819603541

Relation classification aims to determine the relationship between a given entity pair. Many existing methods based on Pre-trained Language Models (PLMs) exhibit weaknesses in the generalization of text embedding. Meanwhile, to cope with imbalanced datasets, researchers have proposed to apply contrastive learning to improve the classification ability of the models, but most of them lack effective data enhancement methods, thus the advantage of contrastive learning in dealing with imbalanced data is underexploited. Focusing on these two problems, the MSRL (Multi-granularity Semantic Representation and Rule-Based Labeling) model is proposed. Firstly, by incorporating semantic representations from different levels of granularity within PLMs, the MSRL achieves entity embedding with better transfer learning and improved generalisation performance. Secondly, MSRL designed a data enhancement method based on the prompt template: reconstructing the labels according to logic rules aims to make them more similar to the input instances in terms of text structure and ensure that the embedding of both is closer in the embedding space, thus fully leveraging contrastive learning. Finally, experiments on public datasets show that the proposed MSRL outperforms existing mainstream baseline models.

关键词： Relation Classification Multi-granularity Contrastive Learning Text Restructuring

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Reinforcement Learning for Optimizing Routing in the Production Supply of Matrix Production Systems 5th

Reinforcement Learning for Optimizing Routing in the Product...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Ried, Florian Niederdraenk, Simon Fottner, Johannes Tech Univ Munich Chair Mat Handling Mat Flow Logist Boltzmannstr 15 D-85748 Garching Germany

ISBN: (纸本)9783031807596;9783031807602

Matrix production systems offer the flexibility to meet an increasingly individualized and volatile customer demand. However, production supply processes within these systems have rarely been investigated in detail despite playing an integral role in their performance. Contributing to closing this research gap, this work utilizes reinforcement learning for routing in the production supply of matrix production systems. In particular, it focuses on dispatching orders to the vehicles and scheduling the orders within a route. Various constraints are considered to simulate a realistic setting, including order time windows, vehicle battery limitations, and a vehicle capacity allowing to transport multiple items at once. A reinforcement learning framework is conceptualized and implemented, assigning orders to vehicles based on various route construction heuristics. Its observation space contains abstract information about current orders of the matrix production supply environment and specific data on the vehicles for the reinforcement learning agent to select both a vehicle and a heuristic. The action and observation spaces are complemented by a multi-criteria reward function, prompting the agent to learn not to violate any constraints of the environment while simultaneously choosing actions that lead to the most cost-effective routes after route optimization. The reinforcement learning route constructor approach is trained and deployed on a discrete-event simulation of a matrix production system, which is connected to the reinforcement learning framework via a socket interface. The approach has proven to be successful by outperforming two non-reinforcement learning heuristics for route construction.

关键词： Reinforcement learning Dynamic pickup-and-delivery problem Matrix production systems Routing

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A Study of Performance Portability in Plasma Physics Simulations 11th

A Study of Performance Portability in Plasma Physics Simulat...

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11th Latin American Conference on High Performance Computing

作者： Ruzicka, Josef Asch, Christian Meneses, Esteban Rampp, Markus Laure, Erwin Natl High Technol Ctr Natl Adv Comp Lab San Jose CA USA Costa Rica Inst Technol Sch Comp Cartago Costa Rica Max Planck Gesell Max Planck Comp & Data Facil Garching Germany

ISBN: (纸本)9783031800832;9783031800849

The high-performance computing (HPC) community has recently seen a substantial diversification of hardware platforms and their associated programming models. From traditional multicore processors to highly specialized accelerators, vendors and tool developers back up the relentless progress of those architectures. In the context of scientific programming, it is fundamental to consider performance portability frameworks, i.e., software tools that allow programmers to write code once and run it on different computer architectures without sacrificing performance. We report here on the benefits and challenges of performance portability using a field-line tracing simulation and a particle-incell code, two relevant applications in computational plasma physics with applications to magnetically-confined nuclear-fusion energy research. For these applications we report performance results obtained on four HPC platforms with server-class CPUs from Intel (Xeon) and AMD (EPYC), and high-end GPUs from Nvidia and AMD, including the latest Nvidia H100 GPU and the novel AMD Instinct MI300A APU. Our results show that both Kokkos and OpenMP are powerful tools to achieve performance portability and decent "out-of-the-box" performance, even for the very latest hardware platforms. For our applications, Kokkos provided performance portability to the broadest range of hardware architectures from different vendors.

关键词： Parallel Programming Performance Portability Plasma Physics

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Research on Fusion Modeling for Active Magnetic Bearings Based on Mechanism and Data Driven 4th

Research on Fusion Modeling for Active Magnetic Bearings Bas...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Sun, Xinhao Gao, Xiaoting Feng, Yong Cui, Enchang Liaoning Univ Shenyang Peoples R China Jilin Univ Changchun Peoples R China

ISBN: (纸本)9789819603534;9789819603541

Active magnetic bearings (AMBs) are a new type of high-performance bearings with advantages such as frictionlessness, high rotational speed, and high controllability. However, the rotor mass of AMBs cannot be absolutely uniformly distributed, leading to medium-term unbalanced forces at the same frequency as the rotational speed during high-speed operation, which in turn causes unbalanced vibration. Additionally, the rotor of AMBs transitions from being rigid to flexible at high speeds. Especially at the critical rotational speed where the resonance point is reached, the physical form of the AMB rotor differs significantly from that of a rigid one, and its characteristic data is difficult to collect due to the resonance. Based on the aforementioned problems, this paper implements sample generation for bearing data based on the second-order transfer function mechanism model of AMBs and Generative Adversarial Networks (GANs). Subsequently, an in-depth analysis of the problems with the generated results is conducted, leading to a generation method that combines the mechanism model of the second-order critical rotational speed with the Wasserstein Generative Adversarial Networks (WGAN). Finally, a fusion modeling method for AMBs that integrates mechanisms and data is achieved.

关键词： Active magnetic bearings Fusion modeling Data driven Generative adversarial networks

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Bearing Fault Diagnosis with Small Samples Based on Multi-scale Convolutional Kernels Attention Network 5th

Bearing Fault Diagnosis with Small Samples Based on Multi-sc...

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5th China Intelligent Robotics Academic Conference

作者： Xu, Yong Huang, Ruyi Xian, Shuai Zhong, Yong South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 511442 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Peoples R China Hong Kong Univ Sci & Technol Dept Ind Engn & Decis Analyt Hong Kong 999077 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Bearings are widely used in equipment such as robots. Therefore, timely diagnosis of their faults is the necessary basis for maintaining production safety. With the promotion of intelligence technology, deep learning has become an important tool for the intelligent diagnosis of bearing faults and has received much research attention. However, existing research models usually require many failure samples to achieve preset performance. In actual production, because of the rugged operating environment of bearings and the short operating time under abnormal conditions, failure data is missing. To address this problem, an intelligent fault diagnosis model called the multi-scale convolutional kernels attention network (MSCKAN) is proposed in this context. The model is grounded in a two-dimensional convolutional neural network (2D-CNN), which obtains input through the reconstruction of time-domain samples. Then, it combines multiscale convolution kernels and the efficient channel attention (ECA) mechanism to increase its feature extraction and recognition performance for small samples, thereby reducing the need for fault data. The performance of the MSCKAN was tested through the constructed small-sample experimental datasets and compared with that of other models. The testing results strongly prove that the MSCKAN has superior performance in small-sample diagnosis for bearing faults.

关键词： Bearing Fault Diagnosis MSCKAN Small Samples

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A Two-Stage Approach for Multimodal Emotion Cause Pair Extraction that is Based on Large Language Models 5th

A Two-Stage Approach for Multimodal Emotion Cause Pair Extra...

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5th China Intelligent Robotics Academic Conference

作者： Li, Jinyu Chen, Fei Xu, Longmei Dalian Maritime Univ Dalian 116026 Liaoning Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Emotions play a crucial role in human communication;however, past research in emotion analysis has focused primarily on the text modality, neglecting the importance of audio and visual modalities in emotion recognition. To address this issue, we propose an innovative multimodal emotion-cause pair net (MECPN) framework that captures emotions and their causes in conversations more comprehensively by integrating information from text, audio, and visual modalities, thereby achieving more accurate emotion analysis. The MECPN framework consists of two key modules: the multimodal emotion recognition (MER) module and the multimodal cause extraction(MCE) module. TheMERmodule usesRoBERTa, OpenSMILE, and VisExtNet to extract text, audio, and visual features, effectively integrating multimodal information through the MultiAttn multimodal fusion network. TheMCEmodule uses the MiniGPT-v2 model to integrate visual and textual information, generate natural language responses, and extract emotions that cause utterances through similarity matching. The experimental results on the ECF2.0 dataset show that the F1 score of the MECPN framework in the emotional cause extraction task is 0.2692, and the weighted average F1 score is 0.2574. Ablation experiments confirmed the importance of the VisExtNet, DialogueRNN, and MultiAttn modules in the model, demonstrating the effectiveness of these components in processing multimodal information and capturing the dialog context.

关键词： Emotion cause pair extraction Multimodal fusion Multimodal emotion recognition Large language models Promotion of learning

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An Analysis on Balance Model of Exploration and Exploitation Under Decoupled-Learning Pattern for Large-Scale Particle Swarm Optimizers 5th

An Analysis on Balance Model of Exploration and Exploitation...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Ni, Wenke Guo, Weian Li, Dongyang Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Tongji Univ Sino German Coll Appl Sci Shanghai Peoples R China

ISBN: (纸本)9789819770007;9789819770014

To effectively tackle large-scale optimization problems, numerous efforts have been made by various swarm intelligence methodologies to strike a balance between two critical functions: exploration and exploitation. However, in the majority of existing particle swarm optimizers, these two functions often remain indistinguishable throughout the optimization process. To mitigate the mutual interference between exploration and exploitation, designers of optimizers have attempted to design these functions separately. Nonetheless, there has been limited research into analyzing the roles and the relationship between exploration and exploitation. Building upon our previous work, we have redesigned several state-of-the-art large-scale particle swarm optimizers using a decoupled learning pattern. Through comprehensive experimental analysis on the synergistic effect of exploitation and exploration, we delve into the optimization performance and introduce a mathematical model to illustrate their interrelationship. Our investigation and analysis reveal that most existing algorithms exhibit sensitivity in the parameter settings for exploration and exploitation, an aspect that has not been adequately addressed before. Based on the proposed mathematical analysis of the parameters, we provide clear balance model for algorithm designers and users on how to balance these two factors when solving large-scale optimization problems.

关键词： Large scale optimization problems Exploration and exploitation Decoupled learning pattern Balance model

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Adaptive Hierarchical Clustering Based Student Group Exercise Recommendation via Multi-objective Evolutionary Method 5th

Adaptive Hierarchical Clustering Based Student Group Exercis...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Wang, Ziang Sun, Yifei Cao, Yifei Yang, Jie Shi, Wenya Zhang, Ao Ju, Jiale Yin, Jihui Yan, Qiaosen Yang, Xinqi Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Exercise recommendation is an important research direction in intelligent education. The most advanced exercise recommendation methods focus on the association between each student and each knowledge concept and exercise, and model the exercise recommendation as a Multi-Objective Optimization Problem (MOP). However, this method is based on each individual student to recommend exercises, which will cause the recommendation process to consume too much time when the number of students is large. One approach to solve this problem is to recommend exercises based on student clusters after clustering students with similar characteristics. According to the specific characteristics of the student group, this paper proposes an Adaptive Hierarchical Clustering algorithm with Ward's method. The algorithm can adaptively adjust the within-cluster variance threshold for different student sets to make appropriate clustering for them. After clustering, the exercise recommendation process, which is modeled as a MOP, will be carried out in clusters. The experimental results show that when using different Multi-Objective Evolutionary Algorithms (MOEAs) to solve the exercise recommendation process, the recommendation performance after clustering is improved compared with that before clustering, and the total recommendation time for the student group is greatly reduced.

关键词： Intelligent Education Exercise Recommendation Multi-Objective Optimization Hierarchical Clustering

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