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Hunger games search optimization with deep learning model for sustainable supply chain management

Discover Internet of Things 2023年第1期3卷 10页

作者： Xu, Zheng Jain, Deepak Kumar Neelakandan, S. Abawajy, Jemal School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian China Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science and Engineering R.M.K Engineering College Taluk India Deakin University Geelong Australia

The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use. © The Author(s) 2023.

关键词： Food supply

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KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

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Iran Journal of computer Science 2023年第4期6卷 307-319页

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Tehran Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © The Author(s) 2023.

关键词： Iris segmentation Localization MobileNetV2 model Unconstrained environment

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Low-disturbance Nonlinear control of TBM Cutterhead Speed in Coal Mine Tunneling 50

Low-disturbance Nonlinear Control of TBM Cutterhead Speed in...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Liu, Nannan Wang, Tong Wang, Jianmin Fan, Yingda School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Department of Computer Science University of Pittsburgh Pittsburgh United States

ISBN: (纸本)9781665464543

To minimize the disturbance of the Tunnel Boring Machine (TBM) cutterhead on the surrounding rock during the coal mine roadway excavation process and ensure that the cutterhead rotation speed achieves fast tracking performance with minimal overshoot, we propose a direct adaptive robust control method for the cutterhead rotation speed hydraulic system based on inversion design. This method considers the strong disturbances such as loads and motion affecting the cutterhead hydraulic drive system, as well as the uncertainties in the cutterhead model. We establish the nonlinear model of the cutterhead hydraulic drive system and employ virtual control to reduce the model order. Using Lyapunov functions, we ensure the stability of the entire system and derive the control law for the cutterhead rotation speed controller, along with parameter-adaptive laws acting as parameter estimators. We validate the effectiveness of the proposed control strategy through joint simulation using AMESim and Simulink. The results show that the designed cutterhead rotation speed controller achieves high tracking accuracy and good adaptability. © 2024 IEEE.

关键词： Hydraulic drives

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Max Flow Vulnerability of Undirected Planar Networks 32

Max Flow Vulnerability of Undirected Planar Networks

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32nd Italian Symposium on Advanced Database Systems, SEBD 2024

作者： Balzotti, Lorenzo Franciosa, Paolo G. Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Statistical Science Sapienza University of Rome p.le Aldo Moro 5 Rome00185 Italy

In the real world, planar networks commonly model utility distribution networks such as gas pipelines, water distribution, city's electric grid, and transportation networks like railways and highways. Evaluating the vulnerability of these networks is crucial on several point of view: in a military context in order to prevent terrorist attack, or for maintenance to prevent wear and tear or natural disasters from inflicting extremely severe damage. This work aims to evaluate the vulnerability of undirected planar network studying the decrease of maximum st-flow when an edge or a vertex is removed, that is, we propose algorithms for computing the max flow vitality of edges and vertices. Our algorithms are applied to real world scenarios, and we prove that high vitality values are well approximated in time close to the time currently required to compute st-max flow O(n log log n). All our algorithms work in O(n) space. © 2024 Copyright for this paper by its authors.

关键词： Natural gas pipelines

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A Real-Time Support with Haptic Feedback for Safer Driving Using Monocular Camera 23rd

A Real-Time Support with Haptic Feedback for Safer Driving...

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23rd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2024

作者： De Magistris, Giorgio Guercio, Lorenzo Starna, Francesco Russo, Samuele Kryvinska, Natalia Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome via Ariosto 25 Rome00185 Italy Department of Psychology Sapienza University of Rome via Dei Marsi 78 Roma00185 Italy Faculty of Management Comenius University in Bratislava Bratislava Slovakia

ISBN: (纸本)9783031806063

Each year, car accidents impact billions of people, resulting in numerous casualties. Consequently, road safety remains a top priority for nations worldwide. This project aims to enhance driver safety through a feedback system that relies solely on a monocular camera mounted atop the vehicle. The proposed system is a real-time application designed to warn drivers of imminent road hazards, which are classified by their level of risk. Our method employs various computer vision techniques and incorporates a simple 2D-3D correspondence to estimate the longitudinal and lateral distances of objects ahead of the vehicle, under certain simplifying assumptions. The system conducts a comprehensive danger analysis by evaluating potential hazards within the vehicle’s path. Depending on the danger level, warnings are delivered to the driver with varying degrees of invasiveness, using haptic feedback. The proposed method was tested on the KITTI dataset, yielding positive results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hazards

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Cluster Synchronization of Kuramoto Oscillators via Pacemakers and Mean-phase Feedback control 63

Cluster Synchronization of Kuramoto Oscillators via Pacemake...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Kokubo, Ryota Matsubara, Mitsuaki Kato, Rui Ishii, Hideaki Tokyo Institute of Technology Department of Computer Science Yokohama226-8502 Japan Tokyo Institute of Technology Department of Systems and Control Engineering Yokohama226-8502 Japan Villeurbanne69622 France The University of Tokyo Department of Information Physics and Computing Tokyo113-8656 Japan

ISBN: (纸本)9798350316339

Brain networks typically exhibit characteristic synchronization patterns where several synchronized clusters coexist. On the other hand, neurological disorders are considered to be related to pathological synchronization such as excessive synchronization of large populations of neurons. Motivated by these phenomena, this paper presents two approaches to control the cluster synchronization of Kuramoto oscillators. One is based on the use of pacemakers to the clusters, and the other is based on feeding back the mean phases to the clusters. We first show conditions on the pacemaker weights and the feedback gains for the network to achieve cluster synchronization. Then, we propose a method to find optimal feedback gains through convex optimization. A numerical example demonstrates the effectiveness of the proposed methods. © 2024 IEEE.

关键词： Pacemakers

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Empowering citizens by a blockchain-Based Robinson list

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International Journal of computers and Applications 2022年第10期44卷 920-928页

作者： Cirillo, Albenzio Dalena, Vito Mauro, Antonio Mogavero, Francesco Pennino, Diego Pizzonia, Maurizio Vitaletti, Andrea Zecchini, Marco Fondazione Ugo Bordoni Roma Italy Roma Tre University–Eng. Dept. Roma Italy Sapienza University of Rome Department of Computer Control and Management Engineering Roma Italy

Robinson lists are adopted in many countries to protect phone subscribers against commercial spam calls. In its essence, they collect the denial of the subscribers to be contacted by marketing operators. Nowadays, all Robinson lists run as centralised services. Namely, citizens have to rely on trusted third parties for the management of their own choices. The Blockchain allows us to envision the development of a decentralised Robinson list in which citizens are finally empowered to autonomously manage their choices. In this paper, we present a proof-of-concept (PoC) of a decentralised Robinson list developed with the Algorand technology. To the best of our knowledge, this is the first attempt to implement a Robinson list on a blockchain technology. The PoC has been designed to support the requirements provided by Fondazione Ugo Bordoni, the manager of the Italian Robinson list. The evaluation of the PoC shows that our decentralised solution provides satisfactory performance, in terms of scalability, latency and costs, when compared to the current centralised service. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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Improving finite-difference time-domain memory bandwidth by using block floating-point arithmetic 31

Improving finite-difference time-domain memory bandwidth by ...

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31st Telecommunications Forum, TELFOR 2023

作者： Pijetlovic, Stefan Kastelan, Ivan Subotic, Milos Pjevalica, Nebojsa RT-RK Institute for Computer Based Systems Novi Sad Serbia University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798350303131

Finite-difference time-domain is a numerical method used for modelling of computational electrodynamics. The method is resource intensive, especially regarding memory usage since multiple memory accesses are required per single computation. In other words, memory, or more precisely its speed, is a limiting factor for the overall performance. Existing solutions focus either on structures with great parallelism such as graphical processing units or custom designs. The former have issues with underutilized resources, while the latter require significant time and effort to achieve similar or slightly better performances. The approach taken during this research was to target the memory itself and find a way to improve the efficiency of memory usage across multiple platforms. The main contribution of the paper is using block floating-point in a novel way regarding the finite-difference time-domain method. The presented solution is a verification of earlier research, proving that the performance of this algorithm can indeed be improved using the suggested methods. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Detecting Credit Card Fraud using Machine Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2021年第24期15卷 108-122页

作者： Almuteer, Arjwan H. Aloufi, Asma A. Alrashidi, Wurud O. Alshobaili, Jowharah F. Ibrahim, Dina M. Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Computers and Control Engineering Department Faculty of Engineering Tanta University Tanta Egypt

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. In the past, fraud practitioners were identified using rule-based master frameworks, which ignored a variety of variables, including the outlandishly imbalanced nature of positive and negative cases. Using named information, we provide an approach to fraud detection that uses Convolutional Neural Networks (CNNs) and is based on CNNs. An element lattice speaks to a plethora of interchange information and uses a convolutional neural organization to recognize a large number of idle examples for each of those examples. A considerable business bank's boss presentation is compared with several best-in-class techniques in trials on truly monstrous exchanges. Our objective is to combine CNN with LSTM and Auto-encoder to increase credit card fraud detection while improving the previous models' performance. By using these four models;CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy = 0.99, the CNN model has accuracy = 0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&L-STM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models. © 2021. All Rights Reserved.

关键词： Budget control

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Degradable Tracking System based on Hardware Multi-Model Estimators

Degradable Tracking System based on Hardware Multi-Model Est...

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2022 Resilience Week, RWS 2022

作者： Kiriakidis, Kiriakos Croteau, Brien Severson, Tracie Rodriguez-Seda, Erick Robucci, Ryan Islam, Riadul Rahman, Saad United States Naval Academy Department of Weapons Robotics and Control Engineering Department of Cyber Science United States University of Maryland Department of Computer Science and Electrical Engineering United States

ISBN: (数字)9781665488198

ISBN: (纸本)9781665488198

Sensing systems onboard unmanned vehicles operate in an environment of constrained computational resources. A cyber-Attack may primarily aim to degrade these computing devices and, ultimately, incapacitate the sensing system itself. To prepare a prototype tracking system for degradation, this paper proposes distributed hardware implementation of a Multiple Model estimator on two FPGA units and, after an attack, adaptation of the estimator by leveraging Dynamic Partial Reconfiguration of the single surviving FPGA. The method ensures that the most likely models of the estimator are loaded on to the fabric of the surviving FPGA with minimal interruption. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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