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Research on Site Selection optimization of UAV Commuting Stations Based on Self-learning Ant-lion Algorithm 22

Research on Site Selection Optimization of UAV Commuting Sta...

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3rd Asia-Pacific conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Hong, Chao Yang, Haoqiang Li, X.I.N. Lian, Zhigang College of Electronical and Information Engineering Shanghai DianJi University Shanghai China

ISBN: (纸本)9781450395786

With the development of science and technology, the performance of unmanned aerial vehicles is getting stronger and stronger, and the manned unmanned aerial vehicle commuting system is about to come out. However, how to maximize the benefits of site selection on the premise of avoiding the commuting drone site interfering with the operation of the surrounding area yet to be studied. Aiming at this problem, this paper studies a commuter UAV site location system based on the influence of regional multi-attributes. In this site selection system, the best comprehensive evaluation of the relevant attributes of the surrounding area is used as the criterion, and several of them are selected as the optimal decision-making scheme. In this paper, a self-learning ant lion algorithm based on multi-directional elite strategy is proposed, and the above model is optimized and simulated. The experimental results show that the self-learning ant-lion algorithm can quickly obtain the optimal location scheme of the system and achieve the maximum final fitness value of the system, which verifies the feasibility and reliability of the algorithm. This research on site selection optimization for UAV commuting is the primary premise of the shared UAV commuting system. At the same time, this research provides theoretical support and practical reference for site selection of fully automatic control air traffic systems. © 2022 ACM.

关键词： Unmanned aerial vehicles (UAV)

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A Survey on intelligent Reconfigurable/Reflecting Surface in 6G Communication

A Survey on Intelligent Reconfigurable/Reflecting Surface in...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Diya Singh Ayush Kumar Rohan Patil Saurav Gupta School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

intelligent Reflecting/Re-configurable surfaces (IRSs) aligns and regulates the wireless network environment and efficiently controls the wavefront (Phase, Amplitude, Frequency, Polarization). Given the recent emphasis on establishing a secure, cost-efficient 6G network, IRS can potentially be a key factor in establishing 6G wireless network from urban to remote areas. It has emerged to be a highly promising technology. The survey paper classifies the recent research investigations on IRS into various different yet important categories. For IRS aided vehicular communications, the paper presents a detailed analysis on the development, impact and effects on V2X, V2V and UAV communications. The paper also includes a detailed analysis on Machine learning models for IRS based wireless communication towards 6G, which highlights the complex problems in wireless communication such as beamforming. The paper covers major categories related to deployment, future works and challenges etc.

关键词： 6G mobile communication Surveys Array signal processing Surface waves Wireless networks Line-of-sight propagation Machine learning Autonomous aerial vehicles Vehicle dynamics Vehicle-to-everything

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A Technique Based on Ensemble Machine learning for The Analysis of Electronic Nose Signals

A Technique Based on Ensemble Machine Learning for The Analy...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International conference on

作者： Amit Taneja Santhosh Kumar Rajamani Deepika Shekhawat Yerrolla Chanti Kapil Joshi Ashish Garg AIT-CSE Chandigarh University Department of E.N.T MAEER MIT Pune’s MIMER Medical College and Dr BSTR Hospital TalegaonDabhade Pune Maharashtra India Department of Computer Science & Engineering Manipal University Jaipur Rajasthan School of Computer Science and Artificial Intelligent SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Numerous applications have been documented for electronic nose (e-nose) devices, which are valued for their speed, affordability, and lack of invasiveness. The e-nose scheme is a useful instrument for analyzing volatile chemicals as a distinct 'fingerprint,' especially in the meat industry. The pattern identification algorithm's capacity to decode e-nose signals is crucial to the e-nose system's effectiveness in a wide variety of uses. However, positive results using ensemble approaches have been reported in a number of data types. In this study, an ensemble learning strategy is proposed for e-nose signal analysis, particularly for evaluating beef quality. Not only are learning algorithms and sensor array optimization using ensemble approaches. Ensemble FSA is created by combining three filter-based feature selection algorithms (FSAs)—reliefF, chi-square, and Gini index—for sensor array optimization. When using a single FSA on a collection of homogenous e-nose data for beef quality monitoring, it is common for the results to be inconsistent or unexpected. In addition, multi-class regression and categorization problems are handled by using ensemble learning techniques. We use bagging and boosting algorithms, shown here by Random Forest (RF) and Adaboost. Both a support vector machine (SVM) and a decision tree are used as independent learners, and their results are compared to those of the ensemble. Experiments show that our ensemble method is effective and generalizable for e-nose signal processing. Optimized sensor combination using filter-based FSA consistently outperforms previous methods in both classification and regression. Additionally, despite using a reduced sensor complement, Adaboost as a boosting algorithm yields the highest quality prediction.

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Inventory optimization for Deteriorating Products Using Exponential Demand Forecasting and Machine learning with Carbon Emission Constraints

Inventory Optimization for Deteriorating Products Using Expo...

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Disruptive Technologies (ICDT), International conference on

作者： Pratibha Rajawat Mandeep Mittal Biswajit Sarkar Department of Mathematics Bennett University Greater Noida India Department of Industrial Engineering Yonsei University Seoul South Korea

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In today's business environment, companies implement highly advanced machine learning algorithms to calculate product demand and evaluate inventory levels. In the present research, we constructed a Degradation-based inventory model that incorporates Carbon emission, partial waiting lists, and exponentially forecasted demand. Since degradation and the rate of imperfections are both unpredictable and often difficult to accurately measure, we are treating them as fuzzy variables to represent a more realistic real-world scenario. This study proposed a method to determine the optimal order quantity and replenishment schedule that minimizes the average total cost while accounting for carbon emission expenditures. The sign distance approach is used for the defuzzification process, and demand is assumed to increase exponentially. The proposed method has been validated with numerical examples that demonstrate how demand for deteriorating products can be calculated monthly, enabling firms to optimize inventory management based on expected demand. Furthermore, a comparison between monthly predicted demand and fixed demand has been conducted, highlighting the advantages of the exponential demand model.

关键词： Schedules Machine learning algorithms Demand forecasting Carbon dioxide Machine learning Predictive models Inventory management Numerical models Disruptive technologies optimization

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Data-driven multi period operation optimization of active distribution networks

Data-driven multi period operation optimization of active di...

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Data Science and Computer Application (ICDSCA), IEEE International conference on

作者： Tong Xiaoning Wang Yueqiang Qiu Zhangquan Huang Yang State Grid Shanghai Electric Power Company Changxing Power Supply Company Shanghai China

Traditional physical models are difficult to model and analyze the uncertainty factors of the power grid, and have weak adaptability to changes in ADN structure and new elements. This paper proposes a data-driven active distribution network operation optimization strategy, which uses data-driven replacement process simulation to directly obtain control strategies from real-time environmental operation information. The implicit internal logic between the optimal operation of the operation scenario and the state of the control equipment can be explored through extreme learning machine, so the mapping relationship between the operation control strategy and distribution network operation scenario can be established. The data-driven reactive power optimization method proposed in this article does not rely on actual system power flow calculations, achieving optimized scheduling of active distribution networks under data-driven conditions, and providing a foundation for control strategies of active distribution networks.

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The Application of Dynamic Simulation and PID Control in the Kinematic Analysis of Suspended Crane

The Application of Dynamic Simulation and PID Control in the...

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Energy and Power Engineering, Control Engineering (EPECE), International conference on

作者： Na Meng Sen Yang Wenzheng Sun Longfa Chen College of intelligent Equipment Shandong University of Science and Technology Tai’an Shandong China

ISBN: (数字)9798350366099

ISBN: (纸本)9798350366105

With changes in speed, cranes inevitably experience swinging, making precise lifting difficult. Taking a suspended crane as an example, this study analyzes the mathematical characteristics of the crane. Through MATLAB/SIMULINK simulation analysis, it focuses on examining the operational performance of the crane under different working conditions. Conventional PID control parameter optimization is applied to the crane’s load swing angle. Comparative analysis is conducted on experiments with and without an anti-swing control system. The results indicate that the PID controller can rapidly and effectively reduce swinging angles. This research provides valuable references for the dynamic optimization, system parameter selection, and swing control of suspended cranes.

关键词： Vibrations Cranes PI control Force Dynamics Mathematical models PD control

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Image-Based Malware Classification Powered by Deep learning based DenseNet-121 for Enhancing Cybersecurity

Image-Based Malware Classification Powered by Deep Learning ...

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Advancement in Computation & Computer Technologies (InCACCT), International conference on

作者： Ramakrishna Tumati Peda Gopi Arepalli Software Application Development Engineer Intel USA Department of CSE Vignan’s Nirula Institute of Technology and Science for Women Guntur

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Cybersecurity is threatened by the increased number of malware and the growing emergence of sophisticated malware types. Traditional anti-malware software, commonly binarily dependent, cannot effectively cope with new and disguised threats. In this paper, authors investigate the possibility of rank-based image classification of malware using DenseNet-121 within the context of the Malimg dataset. The malware binaries introduced here are instead transformed into grayscale images: this way, the task becomes an image classification, where the model is capable of distinguishing fine features and structures which are distinctive of families of malware. DenseNet-121 uses a densely connection which enables feature propagation, reduces vanishing gradient and improves the representation learning since the features are reused at different levels. Numerous experiments on the Malimg dataset show that DenseNet-121 performs with high accuracy and is less sensitive to dataset bias than the other models in the field. The work further explores the effects of several preprocessing methods, data augmentation, and hypers parameter optimization on the model results. The results further emphasize the effectiveness of image-based analysis as a supplementary approach to malware detection, which is extendable and malleable against the emerging malicious software threats.

关键词： Deep learning Representation learning Accuracy Computer architecture Feature extraction Malware Computer security Tuning optimization Image classification

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Advancements and Challenges in Quantum Machine learning for Medical Image Classification: A Comprehensive Review

Advancements and Challenges in Quantum Machine Learning for ...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Md. Farhan Shahriyar Gazi Tanbhir Department of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Quantum technologies are rapidly advancing as image classification tasks grow more complex due to large image volumes and extensive parameter updates required by traditional machine learning models. Quantum Machine learning (QML) offers a promising solution for medical image classification. The parallelization of quantum computing can significantly improve speed and accuracy in disease detection and diagnosis. This paper provides an overview of recent studies on medical image classification through a structured taxonomy, highlighting key contributions, limitations and gaps in current research. It emphasizes moving from simulations to real quantum computers, addressing challenges like noisy qubits and suggests future research to enhance medical image classification using quantum technology.

关键词： Support vector machines Quantum computing Accuracy Reviews Computational modeling Qubit Machine learning Convolutional neural networks Medical diagnostic imaging Image classification

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Exploration and Classification of Materials using Tactile Sensors and Machine learning

Exploration and Classification of Materials using Tactile Se...

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Power Electronics, Computer Applications (ICPECA), IEEE International conference on

作者： Yiting Lin The Governor’s Academy Massachusetts United States of America

Advancing sensing systems is one of the core issues of Electrical Engineering. The sense of touch gathers information through the direct contact of skin and an object, making tasks such as manipulating objects and ascertaining their properties, such as temperature, roughness, and stiffness, possible. Tactile sensors replicate one or more dimensions of the sense and have potential applications in robotics and quality control. In this research, I propose a new method that uses a simple tactile sensor and machine learning algorithms to analyze materials. Two datasets, one with common materials such as felt, cotton, and wood, and another with wrinkled and smooth paper to simulate defects, are created. Two Machine learning models, Support Vector Machine and Convolutional Neural Networks, evaluated the datasets. After optimization, both models reached accuracies around 90% for both datasets, demonstrating that combining Machine learning and simple tactile sensors for material classification and defect detection is a viable research direction. This research throws new light on applying Machine learning to traditional engineering disciplines such as Embedded Systems.

关键词： Support vector machines Temperature sensors Temperature Tactile sensors Machine learning Skin Sensors

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NeuroSpectrum Deep learning Architecture for Automated Spectrum Analysis Resource optimization

NeuroSpectrum Deep Learning Architecture for Automated Spect...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International conference on

作者： K. Tamilselvan T. Senthil B. Buvaneswari Santhiya S Mukesh Soni Department of Information Technology AVS Engineering College Salem India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies chittoor India Department of Information Technology Panimalar Engineering College Chennai India Department of Computer science and Engineering Excel Engineering College Namakkal India Department of Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Reducing the availability of spectrum, as well as the nature of allocating channels in the present wireless telecommunication networks, is one of the critical issues facing modern wireless technology. Such spectrum sensing methods may not be highly flexible or efficient in environments where the radio spectrum undergoes frequent changes. This work proposes an improved deep learning spectrum sensing model based on CNN with reinforcement learning. The proposed system design calls for a pioneering dual mechanism, incorporating both spectrum analysis and a resource allocation learning model. The framework achieved remarkable performance metrics: 96 The spectrum sensing method achieved a 3% occurrence rate, a 94.8% precision rate for resource allocation, and a 92.7% efficiency in dynamic spectrum use. In the present investigation, we have employed it for different topologies and got 45% improvement in spectrum efficiency and 87% less interference than the conventional techniques. The work challenges deep learning-based spectrum sensing as well as management and indicates that they are prepared for the following generation of the wireless communication network.

关键词： Deep learning Wireless sensor networks Wireless networks Transfer learning Training data Sensors Resource management optimization Spectral analysis Radio spectrum management

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