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Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing

SN computer science 2024年第4期5卷 363页

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Amar, Oumkeltoum Chekroun, Mohamed Hasidi, Oussama Lachihab, Oussama Computer Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakech Morocco SEIA Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco R & amp D and Engineering Center Reminex Managem Group Marrakech Morocco Computer and Systems Engineering Laboratory (L2IS) Cadi Ayyad University Marrakech Morocco

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional Long Short-Term Memory (ConvLSTM). Focused on the Zinc flotation circuit at CMG Managem Group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvLSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth behavior through video data, we gain insights into mineral concentration fluctuations, a critical aspect of traditional analysis. By offering a comprehensive solution that combines cutting-edge technology, meticulous data analysis, and practical augmentation methods, we aim to elevate not only mineral processing efficiency but also a broader spectrum of resource utilization and sustainable practices. This study offers a promising solution to elevate mineral processing efficiency and accuracy in the face of data limitations. It underscores the potential for transformative impacts on mineral processing operations, paving the way for more effective resource utilization and sustainable practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Data augmentation Industrial data analysis Machine learning Mineral processing

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Private Computation of Boolean Functions Using Single Qubits 15th

Private Computation of Boolean Functions Using Single Qubit...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Rahmani, Zeinab Pinto, Armando N. Barbosa, Luis S. Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Instituto de Telecomunicações Aveiro Portugal International Iberian Nanotechnology Laboratory Braga Portugal Department of Computer Science University of Minho Braga Portugal Institute of Systems and Computer Engineering Technology and Science Braga Portugal

ISBN: (纸本)9783031856990

Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed for evaluating binary Boolean functions using single qubits. Complexity analyses demonstrate a reduction of 66.7% in required quantum resources, achieved by utilizing single qubits instead of multi-particle entangled states. However, the quantum communication cost has increased by 40% due to the amplified exchange of qubits among participants. Furthermore, we bolster security by performing additional quantum operations along the y-axis of the Bloch sphere, effectively hiding the output from potential adversaries. We design the corresponding quantum circuit and implement the proposed protocol on the IBM Qiskit platform, yielding reliable outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Qubits

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An innovative evaluation of microstructure and tribological behavior of AISI 1060 and CuZn37Pb2 under laboratory and complex working conditions

Tribologia: Finnish Journal of Tribology

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Tribologia: Finnish Journal of Tribology 2024年第3期41卷 16-36页

作者： Bougoffa, M. Seyf Eddine Benchaa, Sayhia Abdelhak, Hadibi Ouissem, Benmesbah Si-Hem, Benkhaled Yasmine, Labiod Laboratory of Advanced Technologies in Mechanical Production Department of Mechanical Engineering University of Badji Mokhtar Laboratory LMI Annaba Algeria Faculté des Mathématiques et des Sciences de la Matière Université de Ouargla Ouargla30000 Algeria Laboratory Networks and Systems Laboratory-LRS Department of computer science University of Badji Mokhtar P.o.Box 12 Annaba23000 Algeria

Wear is a significant industrial issue caused by the interaction of multiple complex factors rather than solely by material properties. CuZn37Pb2 and AISI 1060 steel are particularly susceptible to wear due to extensive industrial applications. This study developed a wear test tool, machined on a horizontal lathe, for testing under dry and lubricated conditions. A tribological comparison was conducted between the lathe test and a tribometer, examining factors like surface roughness, load, sliding speed, wear track diameter, track width, contact temperature, wear loss, and wear rate relative to the friction coefficient. Experiments were performed with torques ranging from 25 to 100 N, speeds of 0.30, 0.40, and 0.50 m/s, and wear track diameters of 4, 6, 8, and 10 mm. Worn surfaces and wear tracks were analyzed using optical microscopy and SEM-EDS. The influence of temperature (50 °C to 200 °C) on friction properties was studied, showing that sample morphology and test type greatly affect tribological response. Despite a wear rate calculation error below 8.12%, results indicated differences between laboratory and real-world tribological responses. This study enhances wear understanding by examining numerous previously unstudied characteristics and shows that, although wear cannot be entirely eliminated, it can be significantly minimized. laboratory testing provides prototypes for industrial challenges, effectively linking academic research with industry needs. © 2024 The Authors.

关键词： Tribology

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Depth First:Optimal Path Discovery Between Designated Nodes in Random Ring-Based Graphs

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China Communications 2024年第9期21卷 225-241页

作者： Li Qi Xu Jiasheng Zhang Haonan Kang Huquan Fu Luoyi Long Fei Wang Xinbing Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Media Convergence Production Technology and Systems Xinhua News AgencyBeijing 100077China

This paper focuses on optimally determining the existence of connected paths between some given nodes in random ring-based *** as a fundamental underlying structure in network modeling,ring topology appears as commonplace in many realistic *** this,we consider graphs composed of rings,with some possible connected paths between *** prior knowledge of the exact node permutations on rings,the existence of each edge can be unraveled through edge testing at a unit cost in one *** problem examined is that of determining whether the given nodes are connected by a path or separated by a cut,with the minimum expected costs *** the problem into different cases based on different topologies of the ring-based networks,we propose the corresponding policies that aim to quickly seek the paths between nodes.A common feature shared by all those policies is that we stick to going in the same direction during edge searching,with edge testing in each step only involving the test between the source and the node that has been tested *** simple searching rule,interestingly,can be interpreted as a delightful property stemming from the neat structure of ring-based networks,which makes the searching process not rely on any sophisticated *** prove the optimality of the proposed policies by calculating the expected cost incurred and making a comparison with the other class of *** effectiveness of the proposed policies is also verified through extensive simulations,from which we even disclose three extra intriguing findings:i)in a onering network,the cost will grow drastically with the number of designated nodes when the number is small and will grow slightly when that number is large;ii)in ring-based network,Depth First is optimal in detecting the connectivity between designated nodes;iii)the problem of multi-ring networks shares large similarity with that of two-ring networks,and a larger number of ties between ri

关键词： connectivity analysis cost minimization path discover ring-based graph

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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MOEA/D With Spatial–Temporal Topological Tensor Prediction for Evolutionary Dynamic Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 764-778页

作者： Wang, Xianpeng Zhao, Yumeng Tang, Lixin Yao, Xin Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Shenyang110819 China Northeastern University Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Lingnan University Department of Computing and Decision Sciences Hong Kong University of Birmingham School of Computer Science Birmingham United Kingdom

When solving dynamic multiobjective optimization problems, most evolutionary algorithms (EAs) attempt to predict the initial population in a new environment by mining the relationships between solutions during historical environment changes. However, the complex relationships between solutions and the limited amount of available data often make it difficult to extract useful information efficiently, which may deteriorate the prediction accuracy. To address this problem, this article proposes a spatial–temporal topological tensor-based prediction method to generate the initial population in a new environment under the decomposition framework of MOEA/D. The method relies on the idea that the population distribution in each environment has topological similarity along the time dimension in the objective space, which makes it efficient to represent the population distribution in terms of a tensor and predict new solutions along each decomposition axis in a new environment by an improved tensor-based multishort time series prediction method. Experimental results on various benchmark problems and a real-world problem show that the proposed method is competitive or even superior to state-of-the-art dynamic multiobjective EAs based on prediction strategies. 1997-2012 IEEE.

关键词： Tensors

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Traffic Sign Classification Using Deep and Quantum Neural Networks

Traffic Sign Classification Using Deep and Quantum Neural Ne...

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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic signs

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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Energy Efficient Hardware Acceleration of Neural Networks wi...

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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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SQL Injection Detection in Cloud Computing with Machine Learning Algorithms 23

SQL Injection Detection in Cloud Computing with Machine Lear...

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6th International Conference on Networking, Intelligent systems and Security, NISS 2023

作者： Hadji, Abdelhakim Raouyane, Brahim Rachdi, Mohamed Department of Mathematics and Computer Science Faculty of Sciences Ain Chock Computer Sciences and Systems Laboratory Hassan Ii University of Casablanca Casablanca Morocco Isga Casablanca Morocco

ISBN: (纸本)9798400700194

Due to the coronavirus crisis, a lot of companies all over the world started a fast digitalization of their business and became more comfortable with the digital world. In this way, a lot of people in the digital domain, including net developers and system engineers, are exposed to different types of risks that they may not completely know or understand. SQL injection is one such vulnerability that can be exploited by attackers to inject malicious data into a database via an application programming interface (API) call request that uses SQL syntax. Cloud computing (IaaS) and network as a service (Naas) provide companies with a wide range of advantages. However, the same advantages can also pose certain vulnerabilities to the organization. In this paper we will focus on SQL injection attacks and make a comparison between different Machine learning algorithms that can be used to detect SQL injection attacks, such as "Naive Bayes"and "K-NN", SVN, and decision tree, we will also use a deep learning algorithm CNN, this comparison will show the potential that machine learning algorithms can offer in terms of cloud security. © 2023 ACM.

关键词： Cloud computing

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CMD: Real-Time Compliant Mask Detection using Transfer Learning 23

CMD: Real-Time Compliant Mask Detection using Transfer Learn...

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2023 International Conference on Robotics, Control and Vision Engineering, RCVE 2023

作者： Serrano, Pamela Anne C. Mendoza, Jhorcen P. Tarun, Ivan George L. Lopez, Vidal Wyatt M. Abu, Patricia Angela R. Ateneo Laboratory for Intelligent Visual Environments Department of Information Systems and Computer Science Ateneo de Manila University Quezon City Philippines

ISBN: (纸本)9798400707742

Wearing masks has served as one of the key practices to contain the spread of COVID-19. This study aims to offer an enhanced approach to the automated monitoring of mask-wearing compliance by developing models that identify correctly masked, incorrectly masked, occluded unmasked, and non-occluded unmasked faces through transfer learning and deploying them in real time. A curated dataset of 1200 images with equal representation of all four classes was first prepared by selecting and relabeling images from publicly available datasets such as MAFA, WIDER FACE, and MaskedFace-Net. Transfer learning was then performed on the pre-Trained models MobileNetV3 Small, ResNet50V2, VGG16, Xception, and YOLOv5 Small Classification. Upon model evaluation, YOLOv5 Small Classification emerged as the most balanced model with the second-best inference speed (23.0 ms) and a relatively high accuracy (87.78%). For the real-Time deployment, ResNet50V2 had the best overall performance, having mostly accurate detections and obtaining the second-best FPS value (4.53). Future work may involve deployment in embedded systems and exploring multi-face classification. © 2023 ACM.

关键词： computer vision

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