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Augmenting the Crayfish Optimization with Gaussian Distribution Parameter for Improved Optimization Efficiency 1

Augmenting the Crayfish Optimization with Gaussian Distribut...

引用

1st ieee international conference on Cognitive Robotics and intelligent systems, ICC - ROBINS 2024

作者： Daulat, Himani Varma, Tarun Chauhan, Krishna Malviya National Institute of Technology Department of Electronics and Communication Engineering Rajasthan Jaipur India Vit Bhopal University Department of Electronics and Communication Engineering Madhya Pradesh Bhopal India

ISBN: (纸本)9798350372748

In the realm of optimization algorithms, the Crayfish Optimization Algorithm (COA) has emerged as a promising metaheuristic approach influenced by the swarming behavior of crayfish. Many adjustments have been made to successfully improve the search efficiency of meta-heuristic algorithms. Since the beginning, these adjustments have mainly been made to elevate the caliber of the solutions by using various clustering techniques and learning theories, frequently ignoring the population and fitness *** designing an optimization algorithm, diversity is more important. Although COA has strong optimization capabilities, it frequently experiences premature convergence in complex optimization environments. Additionally, two exploitation stages make its exploration capabilities less robust. To overcome these limitations, this paper suggests a control approach that assesses COA's population and fitness diversity by incorporating the Gaussian Distribution (GD) parameter. The new algorithm is called GD-COA, and its performance is verified by optimizing 27 uni-model and multi-model benchmark functions. The comparison results for the relatively few iterations show that GD-COA is more effective and efficient than traditional COA. © 2024 ieee.

关键词： Optimization

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Integrated Drill Boom Hole-Seeking control via Reinforcement Learning

Integrated Drill Boom Hole-Seeking Control via Reinforcement...

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2023 ieee international conference on Unmanned systems, ICUS 2023

作者： Yan, Haoqi Xu, Haoyuan Gao, Hongbo Ma, Fei Li, Shengbo Eben Duan, Jingliang School of Mechanical Engineering University of Science and Technology Beijing Beijing China School of Information Science and Technology University of Science and Technology of China Hefei China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (纸本)9798350316308

intelligent drill boom hole-seeking is a promising technology for enhancing drilling efficiency, mitigating potential safety hazards, and relieving human operators. Most existing intelligent drill boom control methods rely on a hierarchical control framework based on inverse kinematics. However, these methods are generally time-consuming due to the computational complexity of inverse kinematics and the inefficiency of the sequential execution of multiple joints. To tackle these challenges, this study proposes an integrated drill boom control method based on Reinforcement Learning (RL). We develop an integrated drill boom control framework that utilizes a parameterized policy to directly generate control inputs for all joints at each time step, taking advantage of joint posture and target hole information. By formulating the hole-seeking task as a Markov decision process, contemporary mainstream RL algorithms can be directly employed to learn a hole-seeking policy, thus eliminating the need for inverse kinematics solutions and promoting cooperative multi-joint control. To enhance the drilling accuracy throughout the entire drilling process, we devise a state representation that combines Denavit-Hartenberg joint information and preview hole-seeking discrepancy data. Simulation results show that the proposed method significantly outperforms traditional methods in terms of hole-seeking accuracy and time efficiency. © 2023 ieee.

关键词： Reinforcement learning

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Design and Implementation of Smart Irrigation System Using Internet of Things 4

Design and Implementation of Smart Irrigation System Using I...

引用

4th international conference on Emerging systems and intelligent Computing, ESIC 2024

作者： Ramesh Naidu, P. Satish, E.G. Yadav, Bibek Ansari, Rizwan Raut, Pradeep Kumar Bolla, Dileep Reddy Nitte Meenakshi Institute of Technology Department of Cse Yelahanka Bangalore India

ISBN: (纸本)9798350349856

A large amount of water is needed for agriculture. Due to insufficient rainfall, the demand for water cannot be *** conventional irrigation systems, irrigated areas are either under-irrigated or over-irrigated, which adversely affects collect growth and results in water waste. A system that can be automated is needed. By keeping an eye on the soil moisture and other environmental factors, to provide plants with the water they need, the proposed irrigation system is intended. Sensor data is stored in the sky by the system, which is based on the Internet of Things Sensor data is stored in the sky by the system, which is based on the Internet of Things. The farmer can remotely monitor and manage all of the water pumps via the Internet using an Android application. It consists of a wireless sensor node with an ESP32 CAM that uses a Wi-Fi module to send sensor data to the cloud and a relay to control the pump. An automated irrigation system that prevents water waste in precision agriculture is presented in the article. © 2024 ieee.

关键词： Internet of things

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Applied Artificial Intelligence as Event Horizon Of cyber Security

Applied Artificial Intelligence as Event Horizon Of Cyber Se...

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2022 international conference on Business Analytics for technology and Security, ICBATS 2022

作者： Ali, Atif Septyanto, Arif Wicaksono Chaudhary, Iqra Hamadi, Hussam Al Alzoubi, Haitham M. Khan, Zulqarnain Fareed Pmas Arid Agriculture University School of Computer Science Rawalpindi Pakistan Kalimantan Institute of Technology Department of Information Systems Majoring in Mathematics and Information Technology Balikpapan Indonesia Department of Computer Science Islamabad Pakistan Khalifa University Center for Cyber-Physical Systems United Arab Emirates School of Business Skyline University College Sharjah Sharjah United Arab Emirates University of Karachi Department of Criminology Karachi Pakistan

ISBN: (纸本)9781665409209

The world is digitizing at an unprecedented rate. Information systems and technology are changing or evolving very fast. With advances in technology, the speed of processes, and the amount of data generated daily, it is almost impossible for systems to be protected and monitored by human beings without the help of applications or intelligent systems that facilitate the work. Due to the large number of electronic devices connected to the internet, cybersecurity experts face many challenges inadequately protecting systems. At present, all the support that can be obtained is needed to prevent and mitigate cyber-attacks and data breaches. In this paper, we discussed AI security trends and data breaches caused by malicious attacks. © 2022 ieee.

关键词： cybersecurity

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Multi-stage Transfer Learning for Corn Leaf Disease Classification

Multi-stage Transfer Learning for Corn Leaf Disease Classifi...

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2023 ieee international conference on Automatic control and intelligent systems, I2CACIS 2023

作者： Yong, Cao Macalisang, Jonel Hernandez, Alexander A. Technological University of the Philippines College of Science Manila Philippines Technological University of the Philippines College of Industrial Technology Manila Philippines National University College of Computing and Information Technologies Manila Philippines

ISBN: (纸本)9798350321302

Corn is one of the prime commodities in many parts of the world. However, corn yield is affected by natural environment factors such as weather, soil condition, humidity, and diseases. Using machine learning, this study proposes a multi-stage transfer learning for corn leaf disease classification and presents initial experiment results. Results show that InceptionV3 achieves 99% accuracy while Xception attains 96% accuracy, and InceptionResNetV2 performs at 94% accuracy. Also, the multi-stage transfer learning model is compared with other models considering quality measures such as accuracy and training time. This study indicates that the multi-stage transfer learning models developed is comparable with existing deep learning models. Future extension of this work is proposed to improve the performance of the corn leaf disease classification models. © 2023 ieee.

关键词： Deep learning

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Application of Computer Numerical control and Monitoring Techniques in Mechanical Processing 6

Application of Computer Numerical Control and Monitoring Tec...

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6th international conference on intelligent Computing and control systems, ICICCS 2022

作者： Huang, Xueyun Shenzhen Polytechnic Shenzhen518055 China

ISBN: (纸本)9781665410359

In the field of machine manufacturing, the effective use of CNC machining technology and monitoring technology is of great significance, because it can realize the scientificity and perfection of machine manufacturing. The article briefly discusses CNC technology and outlines CNC machining technology. The characteristics of CNC technology clarify the importance of CNC technology in machining technology, and then increase the research on the use of CNC technology in machining technology. From the aspects of parts processing, trajectory planning, offline programming, etc., it explains the intelligent robot CNC technology in mechanical manufacturing. Specific applications in the. © 2022 ieee.

关键词： intelligent robots

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Privacy-Preserving Model Predictive control Using Secure Multi-Party Computation 31

Privacy-Preserving Model Predictive Control Using Secure Mul...

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31st international conference on Electrical Engineering, ICEE 2023

作者： Adelipour, Saeed Haeri, Mohammad Sharif University of Technology Electrical Engineering Tehran Iran

ISBN: (纸本)9798350312560

In this paper, a secure multi-party computation strategy based on secret sharing is used to derive a privacy-preserving model predictive control for a class of cyber-physical systems. In the proposed framework, the underlying optimization problem of model predictive control is solved by a variation of projected gradient method. All required computations are carried out by outsourced computation units at the cloud level, while data privacy is maintained using a secret sharing scheme. The original values of system private parameters are not revealed to any external eavesdroppers and the cloud computing units. Simulation results demonstrate the efficiency of the proposed method in terms of performance and privacy. © 2023 ieee.

关键词： Model predictive control

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Lightweight Many-to-Many Anonymous Authenticated Key Agreement Scheme for intelligent Vehicular Ad-hoc Networks 24

Lightweight Many-to-Many Anonymous Authenticated Key Agreeme...

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24th ieee international conference on High Performance Computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Wen-Chen Zhang, Xiao-Jun Wang, Hao Mu, Li-Ming Tang, Hao-Yu School of Computer Science Research Center for Cyber Security Southwest Petroleum University Chengdu610500 China Nsfocus Information Chengdu Technology Co. LTD Chengdu610213 China

ISBN: (纸本)9798350319934

In this paper, we propose a lightweight many-to-many anonymous authenticated key agreement scheme for intelligent VANETs, providing a high level of security by evading communication with malicious vehicle users. By combining fuzzy extractors with cryptographic hash functions, each user with a unique biometric and password could successfully log into an intelligent vehicle embedded with the trusted platform device (TPD). By designing a specific polynomial function and according to the Chinese remainder theorem, the scheme enables all vehicle users to make different anonymous authenticated key agreements with different cloud servers. We provide security analysis of the scheme, including resistance to impersonation attack, message authentication and integrity, conditional identity anonymity, and unlinkability. We also conduct performance evaluations to demonstrate the feasibility of the scheme in the deployment of intelligent VANETs. © 2022 ieee.

关键词： Authentication

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Robust Operation and control of Smart Distribution Grid Under cyber Threats 2

Robust Operation and Control of Smart Distribution Grid Unde...

引用

2nd international conference on Sustainable Mobility Applications, Renewables and technology, SMART 2022

作者： Sankar, V.C. Jishnu Sriharan, R. Nair, Manjula G. Bel, Carlos Alvarez Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amritapuri India Institute for Energy Engineering Universidad Politécnica de Valencia Spaincal Valencia Spain

ISBN: (数字)9781665471466

ISBN: (纸本)9781665471466

This paper proposes an intelligent anomaly detector integrated with droop controllers in Distributed Generators (DGs) for enhancing the system resilience condition during the event of cyber-attacks/threats in the smart distribution grid. Two different droop controllers are considered, among these the P-F controller is utilized by DGs of medium voltage grids and the P-V droop controller is used by DGs in low voltage microgrids. The ANN-based intelligent controller can detect the anomaly/cyber threat information from the DSO/Main control center or from the individual microgrid control center and can switch the DGs to autonomous operating mode using the P-V/P-F droop controllers and thereby supporting the grid at the time of cyber vulnerability and grid instability condition. The proposed idea is simulated and verified the performance using MATLAB/Simulink. © 2022 ieee.

关键词： Neural networks

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FTGraph: A Flexible Tree-Based Graph Store on Persistent Memory for Large-Scale Dynamic Graphs

FTGraph: A Flexible Tree-Based Graph Store on Persistent Mem...

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2024 ieee international conference on Cluster Computing, CLUSTER 2024

作者： Sun, Gan Zhou, Jiang Li, Bo Gu, Xiaoyan Wang, Weiping He, Shuibing Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Cyberspace Security Defense Beijing China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350358711

Traditional in-memory graph systems often suffer from scalability due to the limited capacity and volatility of DRAM. Emerging non-volatile memory (NVM) provides an opportunity to achieve highly scalable and high-performance graph stores for its large capacity and persistence characteristics. However, directly deploying current in-memory graph storage systems on NVM would cause significant inefficiencies in NVM access, as their graph organization designed for DRAM may incur higher write amplification, crash inconsistency and costly concurrency control overhead in NVM for write-intensive work-loads. In this paper, we propose FTGraph, a Flexible Tree-based Graph storage system, for both efficient dynamical graph updates and analysis. To achieve this goal, we introduce a novel degree-aware suffix bit tree to effectively manage vertices and edges of the graph, enabling adaptability to real-world power-law degree distributions while significantly reducing NVM writes. Based on it, we adopt two optimization methods, logical vertex ID translation and sequential storage, for vertices with very high degrees within the tree to enhance graph analysis operations. We further integrate 8B NVM atomic writes with optimistic version-based concurrency control through a dual bitmap design to ensure low-overhead, log-free crash consistency and reduce read-writer contention. Experimental results show that FTGraph achieves up to 21.2× higher update performance and up to 85.4 × higher analysis performance, compared with state-of-the-art dynamic graph systems implemented on NVMs. © 2024 ieee.

关键词： Concurrency control

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