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Efficient Anti-Glare Ceramic Decals Defect Detection by Incorporating Homomorphic Filtering

computer Systems science & engineering 2021年第3期36卷 551-564页

作者： Xin Chen Ying Zhang Lang Lin Junxiang Wang Jiangqun Ni Southeast Digital Economic Development Institute Quzhou324000China School of Mechanical and Electronic Engineering Jingdezhen Ceramic InstituteJingdezhen333400China School of Data and Computer Science Sun Yat-Sen UniversityCanton510000China

Nowadays the computer vision technique has widely found applicationsin industrial manufacturing process to improve their efficiency. However, it ishardly applied in the field of daily ceramic detection due to the following twokey reasons: (1) Low detection accuracy as a result of ceramic glare, and (2) Lackof efficient detection algorithms. To tackle these problems, a homomorphic filtering based anti-glare ceramic decals defect detection technique is proposed in thispaper. Considering that smooth ceramic surface usually causes glare effects andleads to low detection results, in our approach, the ceramic samples are takenin low light environment and their luminance and details restored by a homomorphic filtering based image enhancement technique. With relatively high quality preprocessed images, an effective ceramic decal defect detection algorithm isthen designed to rapidly locate those out-of-bounds defects and further estimatetheir size. The experimental results show that the proposed scheme could achieveits desired performance.

关键词： Ceramic glaring homomorphic filtering ceramic decal border extraction out-of-bounds detection

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Adaptation of Compositional data Analysis in Deep Learning to Predict Pasture Biomass Proportions 29

Adaptation of Compositional Data Analysis in Deep Learning t...

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29th Irish Conference on Artificial Intelligence and Cognitive science, AICS 2021

作者： Narayanan, Badri Saadeldin, Mohamed Albert, Paul McGuinness, Kevin O'Connor, Noel E. Namee, Brian Mac School of Computer Science University College Dublin Ireland School of Electronic Engineering Dublin City University Ireland Insight SFI Centre for Data Analytics VistaMilk

Dry biomass weight measurements from a quadrat in a paddock for grass, clover and weeds when expressed as percentages of total dry herbage mass are compositional in nature. Unlike real valued regression problems, prediction of compositional data is handled differently in statistics because of its closure property where the components of the composition are positive data adding up to a constant sum and is therefore constrained in the simplex space, in our case 100%. Our motivation in this paper was to study whether the adaptation of compositional data analysis (CoDa) techniques in deep learning improves the prediction results over the best performing deep learning model we used in our earlier paper [Narayanan et al., 2021]. Although the log ratio transformation of targets is an appropriate adaptation of CoDa and is interesting for Biomass prediction, our study indicates that the CoDa adaptation does not improve the prediction errors over our earlier method. © 2021 CEUR-WS. All rights reserved.

关键词： Biomass

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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Hyperparameter Tuning and Model Evaluation in Causal Effect Estimation

arXiv

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arXiv 2023年

作者： Machlanski, Damian Samothrakis, Spyridon Clarke, Paul Department of Computer Science and Electronic Engineering University of Essex United Kingdom Institute for Analytics and Data Science University of Essex United Kingdom Institute for Social and Economic Research University of Essex United Kingdom

The performance of most causal effect estimators relies on accurate predictions of high-dimensional non-linear functions of the observed data. The remarkable flexibility of modern Machine Learning (ML) methods is perfectly suited to this task. However, data-driven hyperparameter tuning of ML methods requires effective model evaluation to avoid large errors in causal estimates, a task made more challenging because causal inference involves unavailable counterfactuals. Multiple performance-validation metrics have recently been proposed such that practitioners now not only have to make complex decisions about which causal estimators, ML learners and hyperparameters to choose, but also about which evaluation metric to use. This paper, motivated by unclear recommendations, investigates the interplay between the four different aspects of model evaluation for causal effect estimation. We develop a comprehensive experimental setup that involves many commonly used causal estimators, ML methods and evaluation approaches and apply it to four well-known causal inference benchmark datasets. Our results suggest that optimal hyperparameter tuning of ML learners is enough to reach state-of-the-art performance in effect estimation, regardless of estimators and learners. We conclude that most causal estimators are roughly equivalent in performance if tuned thoroughly enough. We also find hyperparameter tuning and model evaluation are much more important than causal estimators and ML methods. Finally, from the significant gap we find in estimation performance of popular evaluation metrics compared with optimal model selection choices, we call for more research into causal model evaluation to unlock the optimum performance not currently being delivered even by state-of-the-art procedures. © 2023, CC BY.

关键词： Petroleum reservoir evaluation

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Three-Dimensional Station Distribution Design for TDOA Positioning System of Sea Launch Site 14th

Three-Dimensional Station Distribution Design for TDOA Posit...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Chen, Maolin Li, Xianglu Liu, Changjiang Ji, Zhengyu Sun, Yimao Institute of Electronic Engineering China Academy of Engineering Physics Sichuan Province Mianyang City China Troop of PLA Sichuan Province Chengdu City78090 China School of Computer Science Sichuan University Sichuan Province Chengdu City China

ISBN: (纸本)9789819969272

In recent years, USA, Russia and China have carried out many sea launch and recovery experiments, sea launch site exhibits better safety and economical efficiency compared with land launch site, which can provide launch service for spacecraft with different orbits. GNSS/INS integrated navigation system is adopted by rockets to acquire positioning results, nevertheless, owing to the inherit vulnerability of GNSS and possible INS failure, other navigation system is required to offer independent multi-source positioning data. TDOA positioning system is able to calculate rocket’s position utilizing time measurement data from telemetering signals, on one hand, the reliability can be promoted, and on the other hand, real-time central monitoring can be achieved, which aroused an increasing number of interests. However, present TDOA system usually apply land stations to obtain time measurements and localization results, because the characteristics of flat station distribution along the vertical direction, the vertical positioning precision cannot be satisfactory, especially for the need of rocket recovery on the sea launch site. To solve this problem, this work first discusses the precision influence of different surveying ships distribution layouts, "T" shape distribution is recommended for surveying ships according to evaluation results, then introduces air-based surveying station into TDOA positioning system to improve vertical localization accuracy, probes the performance variation utilizing different UAVs, including tethered UAV, multi-rotor UAV, medium-scale vertical takeoff and landing fixed wing UAV and large-scale fixed wing UAV, confirms the optimal UAV height in the rocket recovery key area under 10 km height, then researches on surveying station optimal distribution layout strategy according to predefined trajectory. This work can enhance the positioning precision and reliability, and provide three-dimensional surveying station distribution guidance in sea launc

关键词： Recovery

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Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice

arXiv

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arXiv 2023年

Hyperparameters play a critical role in machine learning. Hyperparameter tuning can make the difference between state-of-the-art and poor prediction performance for any algorithm, but it is particularly challenging for structure learning due to its unsupervised nature. As a result, hyperparameter tuning is often neglected in favour of using the default values provided by a particular implementation of an algorithm. While there have been numerous studies on performance evaluation of causal discovery algorithms, how hyperparameters affect individual algorithms, as well as the choice of the best algorithm for a specific problem, has not been studied in depth before. This work addresses this gap by investigating the influence of hyperparameters on causal structure learning tasks. Specifically, we perform an empirical evaluation of hyperparameter selection for some seminal learning algorithms on datasets of varying levels of complexity. We find that, while the choice of algorithm remains crucial to obtaining state-of-the-art performance, hyperparameter selection in ensemble settings strongly influences the choice of algorithm, in that a poor choice of hyperparameters can lead to analysts using algorithms which do not give state-of-the-art performance for their data. © 2023, CC BY.

关键词： Petroleum reservoir evaluation

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Dynamic Transfer Learning Switching Approach Using Resource Benchmark in Edge Intelligence

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IEEE Internet of Things Journal 2025年第13期12卷 25148-25170页

作者： Nkenyereye, Lionel Rajkumar, Chellakannu Lee, Boon Giin Chung, Wan-Young Pukyong National University Artificial Intelligence Convergence Education and Research Group Busan48513 Korea Republic of Pukyong National University Department of Electronic Engineering Busan48513 Korea Republic of University of Nottingham Ningbo China School of Computer Science Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute Ningbo315104 China Pukyong National University Department of AI Convergence Department of Electronic Engineering Busan48513 Korea Republic of

Machine learning (ML) techniques are applied for profiling computing and processing resources data collected while running deep neural network models on edge devices. Adaptive deep neural network (DNN) model switching requires proper benchmarking for categorizing AI models based on their applications and computational resources enabled by their processing accelerators. Based on benchmark metrics, DNN models can be classified into tiny, low, small, medium, and large resources, then identify DNN models that perform well within resource constraints. Ensure efficient resource allocation, latency management, and trade-off between accuracy and resource. In this work, we propose a benchmark for edge transfer artificial intelligence learning service (TALS) that uses ML techniques. They aim at classifying DNN models by their target edge applications while running edge inferences. We used both unsupervised learning (UL) and supervised learning (SL) techniques to identify the most effective features for the TALS models and to benchmark the performance of edge devices. To achieve this, two approaches were investigated: first, determining features based on edge inference’s computing resources profiling using principal component analysis;and second, classifying the DNN models at the target application level using a regression approach based on historical resource utilization data. In addition, we propose a dynamic model transfer learning that switches between a set of pre-trained and optimized and quantized DNN models based on the cost function. ML techniques learn resource-aware prediction from new resource allocation data and ensure that the multicriteria switch cost selects the inference task models that meet the edge resource constraint requirements. The experimental results highlight a strong relationship between the supervised learning model and the clustering execution method. The dynamic switching approach on real edge devices demonstrates dynamic switching between models

关键词： Adversarial machine learning

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Learned Bloom Filter for Multi-key Membership Testing 28th

Learned Bloom Filter for Multi-key Membership Testing

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Li, Yunchuan Wang, Ziwei Yang, Ruixin Zhao, Yan Zhou, Rui Zheng, Kai University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China Aalborg University Aalborg Denmark Cloud Database Innovation Lab of Cloud BU Huawei Technologies Co. Ltd. Chengdu China

ISBN: (纸本)9783031306365

Multi-key membership testing refers to checking whether a queried element exists in a given set of multi-key elements, which is a fundamental operation for computing systems and networking applications such as web search, mail systems, distributed databases, firewalls, and network routing. Most existing studies for membership testing are built on Bloom filter, a space-efficient and high-security probabilistic data structure. However, traditional Bloom filter always performs poorly in multi-key scenarios. Recently, a new variant of Bloom filter that has combined machine learning methods and Bloom filter, also known as Learned Bloom Filter (LBF), has drawn increasing attention for its significant improvements in reducing space occupation and False Positive Rate (FPR). More importantly, due to the introduction of the learned model, LBF can well address some problems of Bloom filter in multi-key scenarios. Because of this, we propose a Multi-key LBF (MLBF) data structure, which contains a value-interaction-based multi-key classifier and a multi-key Bloom filter. To reduce FPR, we further propose an Interval-based MLBF, which divides keys into specific intervals according to the data distribution. Extensive experiments based on two real datasets confirm the superiority of the proposed data structures in terms of FPR and query efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data structures

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So Far Yet So Near: Time Series data Augmentation with Exploring non-Semantic Boundaries based on Reinforcement Learning

So Far Yet So Near: Time Series Data Augmentation with Explo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haoran Li Zhibo Zhang Jiarong Kang Xun Jiang Xiaoli Gong Jin Zhang Zhe Sun Andrzej Cichocki College of Computer Science Nankai University Tianjin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine and Faculty of Health Data Science Juntendo University Japan Polish Academy of Science Systems Research Institute Warsaw Poland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstream task performance. We argue that data augmentation should preserve time series semantic consistency and expand the non-semantic information space. In this paper, we reformulate data augmentation as a semantic path planning problem between original data and augmented data, modeled as a Markov Decision Process (MDP). We propose a reinforcement learning-based algorithm (RL) named FreqSYN, where the action space is defined by a set of learnable Gaussian kernels that perturbs the frequency domain of the original data to generate augmented samples. The confidence coefficients of augmented data in semantically relevant classification tasks are used as a reward to iteratively refine the FreqSYN. Our method is validated across four datasets, achieving state-of-the-art performance, with a 2% improvement in F1 score over the SimPSI method. The code and models are available at https://***/NKU-EmbeddedSystem/FreqSYN.

关键词： Semantics Time series analysis Signal processing algorithms Reinforcement learning data augmentation Prediction algorithms Path planning data models Kernel Speech processing

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Design of Multimedia database Online Integration System Based on Artificial Intelligence 2

Design of Multimedia Database Online Integration System Base...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Li, Jiaqi Xiong, Gaojie Wu, Xing Harbin Institute of Technology Computer Sciense and Technology Guangdong Shenzhen China University of Electronic Science and Technology School of Information and Software Engineering Sichuan Chengdu China Zhejiang Vocational Academy of Art Zhejiang Hangzhou China

ISBN: (纸本)9798350317558

Multimedia database online integration system plays an important role in today's information age. However, the performance of the integrated system is limited by the problems of low accuracy, insufficient recall rate and long running time of traditional methods in processing multimedia data. This paper designs an online integration system based on artificial intelligence (AI) and verifies its effectiveness and feasibility through experiments so as to overcome these problems. In the study, multimedia datasets suitable for the experimental needs are selected and the AI-based online integration system is compared with traditional methods. Through the analysis of accuracy, recall, F1 value and mean square error, it is found that the online integration system based on AI has obvious advantages in multimedia data integration. The results of this paper demonstrate the feasibility and effectiveness of an online integration system based on AI. It is able to enhance the accuracy and efficiency of multimedia data integration while reducing runtime and resource consumption. © 2024 IEEE.

关键词： artificial intelligence integration efficiency multimedia database online integration

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