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Optimization of Concurrent Testing Processes in Space Launches Based on Genetic algorithms 24

Optimization of Concurrent Testing Processes in Space Launch...

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3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Shi, Zhenshun Pan, Zhengqiang Geng, Mingyang College of Systems Engineering National University of Defense Technology Hunan Changsha China Satellite Launch Center of Xichang Sichuan Xichang China

ISBN: (纸本)9798400707308

The increasing frequency of space launch missions underscores their vital role in national security and technological advancement. These missions involve a complex set of tasks, each reliant on limited resources and subject to various constraints. Traditionally, mission planning has depended on the expertise of personnel, leading to potential biases and inefficiencies. Thus, there is a critical need for automated methods that facilitate objective and rapid task planning, minimizing human error. Modeling the task planning for these missions poses significant challenges due to the vast search space and multiple resource constraints. As the complexity of tasks and their inter-dependencies increase, finding optimal solutions becomes more difficult. To address this, this paper introduces a dynamic resource intelligent scheduling model that incorporates executable task lists, resource constraints, and the progress of ongoing tasks. Utilizing advanced optimization techniques such as genetic algorithms, simulated annealing, and particle swarm optimization, our model effectively identifies solutions consistent with manual calculations. This innovative approach enhances task scheduling efficiency and ensures optimal outcomes, marking a significant advancement in space launch mission planning. © 2024 Copyright held by the owner/author(s).

关键词： Genetic algorithms

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A Scalable and Secure Facial Recognition Attendance System Leveraging CNN algorithms for Real-Time Accuracy and Efficiency

A Scalable and Secure Facial Recognition Attendance System L...

引用

2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Kumar, Aakash Sharma, Jiya Renjith, P.N. Vellore Institute of Technology School of Computer Science and Engineering India

ISBN: (纸本)9798331529833

The facial recognition attendance system represents a modern approach to attendance tracking, designed to improve efficiency, accuracy, and security in various organizational settings. Advanced biometric technologies are leveraged to capture and encode facial features, creating unique digital identifiers for each individual. The methodology employs cutting-edge facial recognition algorithms powered by convolutional neural networks (CNNs) with a 99.5% recognition accuracy rate. These algorithms extract and encode facial vectors with sub-millisecond latency, which are then compared in real-time against a secure, encrypted database of preregistered individuals. Upon a successful match, the system automatically records the attendance, ensuring an accurate and efficient process. With an intuitive web interface built on ReactJS and a backend supported by *** and MongoDB, the system provides seamless access to attendance records and administrative functionalities. The entire attendance recording process is optimized for speed, typically completing within two seconds per individual. Additionally, advanced anti-spoofing measures and integration support with existing HR management systems redefine traditional attendance management practices with a scalable, robust, and secure solution. (Abstract) © 2025 IEEE.

关键词： Efficiency

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An extensive critique on advances in digital control techniques for smart grid and micro grid operation

An extensive critique on advances in digital control techniq...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sarathkumar, D. Bashkaran, K. Manivel, Murugesan Jayakumar, T. Sivakumar, R. Anandhakumar, C. Kongu Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638 060 India Kongunadu College of Engineering and Technology Department of Biomedical Engineering Tamil Nadu Thottiam621215 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India V.S.B Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 639111 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

The integration of digital control techniques within smart grids and microgrids represents a pivotal advancement in the optimization, reliability, and efficiency of modern electrical networks. This paper explores the latest developments in digital control strategies, focusing on their application to the operation of smart grids and microgrids. Key areas of discussion include the implementation of advanced algorithms for real-time monitoring and control, the role of artificial intelligence and machine learning in predictive maintenance and decision-making, and the impact of distributed control systems on grid stability and resilience. Furthermore, the paper examines the challenges and solutions associated with integrating renewable energy sources and energy storage systems into the grid, emphasizing the importance of adaptive and scalable control frameworks. Through a comprehensive review of recent research and case studies, this paper provides insights into the future trends and potential breakthroughs in digital control technologies, highlighting their critical role in the evolution of smart grid and microgrid infrastructures. The findings suggest that continued innovation in this field will significantly enhance the operational capabilities of electrical grids, paving the way for more sustainable and resilient energy systems. © 2025 IEEE.

关键词： Smart power grids

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Optimizing Urban Waste Collection Routes: Comparative Study of Shortest Path algorithms

Optimizing Urban Waste Collection Routes: Comparative Study ...

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2025 international conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Begum, Chimakurthy Mounika Reddy, Kamalapuram Vigneswara Neeraja Bhaskaran, Sreebha Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

Collection of waste is one of the important goals of Waste Management Unit (WMU), where collecting waste decreases the amount of time, expenses, and the impact of waste collectors on the environment. The work is to create a set of routes for waste collection that will take minimal time to get through several collection centers given the capabilities of the collection vehicles, and road systems and traffic conditions. For waste collection points, ten major stations in Bengaluru were selected along with the disposal point. For this purpose, several algorithms such as Dijkstra's Algorithm, Bellman-Ford Algorithm, Floyd-Warshall Algorithm, Johnson's Algorithm, Nearest Neighbour Algorithm, and Ant Colony Optimization (ACO) were designed and evaluated. algorithms were measured based on parameters such as the time taken, the fuel consumed, the CO2 produced, the effectiveness of the trip that was made. The theoretical and applied objectives of the project correlate with the goals of the integration of improved optimization possibilities with operational issues with the purpose of defining such effective methods in relation to the containment of fuel consumption, the decrease in emissions and the waste management optimization in the urban environment. © 2025 IEEE.

关键词： Wastewater disposal

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Intelligent Heart Disease Prediction using Ant Colony Optimization based Machine Learning algorithms 2

Intelligent Heart Disease Prediction using Ant Colony Optimi...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Shafi, Shaik Ch, Abhilash Manoj, A. Snehil, B. Tpk, Nandan Anusha, M. Electronics and Communication Engineering B V Raju Institute of Technology Medak Narsapur India ECE BVRIT Medak Narsapur India

ISBN: (纸本)9798331505745

Cardiovascular diseases continue to be the primary cause of death worldwide, responsible for around 31% of all deaths each year, as reported by the World Health Organization (WHO). Although significant progress has been made in their diagnosis and treatment, heart disease remains highly prevalent. It has risen significantly due to unhealthy dietary habits and reduced physical activity. Predicting heart diseases using patient-related data, such as blood pressure, cholesterol levels, and other health metrics, has emerged as one of the most complex tasks for human analysts in modern times. As a result, there is a growing global demand for timely and accurate predictive models in identifying heart disease in healthcare industry, predominantly in the field of cardiology. In this paper, an intelligent heart disease prediction using ant colony optimization using machine learning algorithms is proposed. It includes two phases namely: 1) Feature Selection and 2) Classification. Ant Colony Optimization (ACO) is used to reduce the redundancy in feature selection from the large data set and helps to upgrade the performance of the classifier in the next stage. Then, we exploit the different classification algorithms such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and XGBoost (XGB). These algorithms are chosen for their ability to deliver high accuracy, computational efficiency, and optimized memory usage, making them well-suited for this application. Furthermore, performance of the machine learning algorithms has been evaluated using accuracy, precision, F 1 score, recall and support parameters. The results illustrate that the amalgamation of the ACO algorithm and SVM classifier has the utmost performance for use in heart disease estimation. © 2025 IEEE.

关键词： Random forests

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Beyond Traditional ML: LLM-Powered Rule-Based Hydraulic Pump Fault Diagnosis 1

Beyond Traditional ML: LLM-Powered Rule-Based Hydraulic Pump...

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Arumuganainar, Adityan Kushal Abhishek, P.D. COEP Technological University Pune India B.M.S. College of Engineering Bangalore India Vellore institute of technology Vellore India

ISBN: (纸本)9798350355611

The emergence of LLMs (large language models) has proved to be an exceptional game changer for various industries as they have operated effectively and excelled across multiple domains. In this regard, the most effective approach is studied through rule-based LLMs, specifically the LLMs used for diagnosing hydraulic pump defects, paying special attention to triplex hydraulic pumps. As is well known, machine learning algorithms are of practical use in real-time situations only if large labeled datasets and features are available. leveraging LLMs to derive and refine diagnostic rules based on descriptive data patterns, a novel, interpretable approach is proposed that significantly enhances fault classification accuracy, achieving a final accuracy of 68.25%. The approach allows the most common defects to be well recognized: blocked inlets and bearing wear as well as seal leakage and their combinations. This highlights the potential of using LLMs techniques in today's intricate engineering systems for evaluation. © 2025 IEEE.

关键词： Hydraulic equipment

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Design of Autonomous Robotic Vehicle Integrated With Obstacle Avoidance Facility and Wireless Connectivity

Design of Autonomous Robotic Vehicle Integrated With Obstacl...

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2025 international conference on Computer, Electrical and Communication engineering, ICCECE 2025

作者： Thakur, Arunava Kabiraj Mandal, Supantha Das, Prodipta Choudhury, Uttam Techno Main Salt Lake Department of Electrical Engineering Kolkata India Techno India University Department of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798331513894

This research article presents the architecture and implementation of an autonomous vehicle which is capable of following a particular lane and also avoiding obstacles. By incorporating different types of sensors, including infrared sensors for line detection and an ultrasonic sensor for distance measurement, the vehicle navigates its movement effectively. This mechanism consists of an Arduino based microcontroller which processes sensor inputs to execute movement commands, such as forward, turn left, and turn right. A servo motor is employed to periodically scan the surroundings for obstacles, allowing the vehicle to adapt its path in real-time. The system integrates motor drivers to control the vehicle's motion, ensuring precise maneuverability. Experimental results demonstrate the vehicle's ability to follow a designated path while efficiently avoiding obstacles, showcasing its potential applications in robotics and automation fields. This project emphasizes the importance of sensor integration and control algorithms in developing autonomous vehicle systems. © 2025 IEEE.

关键词： Ultrasonic sensors

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IoT-Based Secure Healthcare Framework Using Blockchain Technology with Nature-Inspired Optimization algorithms 3rd

IoT-Based Secure Healthcare Framework Using Blockchain Techn...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Tripathy, Ramamani Satyanarayana Murty, P.T. Maram, Balajee Garg, Ankit Daniya, T. Santhosh Kumar, B. Computer Science and Engineering Chitkara University Himachal Pradesh India Shri Vishnu Engineering College for Women Bhimavaram India AIT-Department of Computer Science and Engineering Chandigarh University Mohali GharuanPunjab140055 India GMR Institute of Technology Rajam532127 India Telangana Ibrahimpatnam501506 India

ISBN: (纸本)9789819730896

This study introduces a novel method for resolving security and privacy issues in IoT-based healthcare systems by combining blockchain technology with optimization algorithms that are inspired by natural processes. Concerns concerning the danger of private patient data have been raised by the healthcare industry's explosive growth of IoT applications. We provide a comprehensive framework that makes use of the security capabilities built into blockchain to guarantee data integrity, authentication, and access control in order to reduce these risks. In order to improve system performance and resource allocation, we also develop optimization algorithms that are inspired by nature. The architecture of the framework includes smart contracts, communication protocols, and secure data flow to automate business processes within the healthcare ecosystem. Our method improves security while maximizing resource utilization by fusing blockchain's tamper-proof record with the adaptability of nature-inspired algorithms. The effectiveness of the suggested framework is assessed using simulations or real-world testing and benchmarked against current techniques. In the end, this research helps build a robust and effective healthcare framework that protects patient information, respects privacy, and improves the overall effectiveness of IoT-enabled healthcare systems. Additionally, the system's effectiveness and scalability can be improved by including optimization techniques that draw inspiration from nature. In order to create a safe and effective healthcare environment, this research paper offers a comprehensive framework that blends IoT, blockchain, and nature-inspired optimization algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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An FMS reconfigurable Model With S4PR Petri Nets by Controllable Transitions

An FMS Reconfigurable Model With S4PR Petri Nets by Controll...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Mowafak Hassan Abdul-Hussin Dept. of Communication Engineering University of Technology Baghdad Iraq

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

The reconfigurable model of the flexible manufacturing systems (FMSs) with Petri Net by controllable transitions (CTs) is presented in this work. The Petri Net (PN) can provide effective procedures to solve deadlock handling in the FMS. Accurately eliminating deadlocked states in FMS and dynamic processes demonstrates the effectiveness of CTS. A novelty algorithm is presented to obtain the maximum permissive behaviors with fewer supervisors, which are obtained fully live. S4PRs, a system of sequential systems with shared resources, are used to implement the PNs that are effective and practical for describing the FMS interaction processes. The motivation analyzes and simulates FMS models using the PN simulator to solve the deadlock. The influence of a reachability graph (RG) is essential for detecting the states of deadlocked (DL). This paper focuses on the influence of CTs in PN that are controlling all resource utilization. In particular, the dynamic model of different kinds of FMS can have optimal solutions given by CTs.

关键词： Analytical models Frequency modulation Communication systems Heuristic algorithms Petri nets System recovery Resource management Flexible manufacturing systems

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Optimizing MPPT Control of DC-DC Boost Converters Using Metaheuristic Search algorithms and Nonlinear PID (N-PID) Controllers

Optimizing MPPT Control of DC-DC Boost Converters Using Meta...

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international conference on Artificial Intelligence in Renewable Energetic systems, IC-AIRES 2024

作者： Khellaf, L. Djellal, A. Mayache, H. Sheta, A. Department of E.E.A National Higher School of Technology and Engineering Annaba23005 Algeria Computer Science Department Southern Connecticut State University New HavenCT06515 United States

ISBN: (纸本)9783031803000

Photovoltaic systems have proven to be one of the most widely used renewable energies and the best replacement for conventional energy. Yet, their non-linear nature remains a challenge when it comes to extracting maximum power from photovoltaic modules. Therefore, in this work, a nonlinear PID controller has been used to meet the requirements of the photovoltaic system. In addition, to improve system performance and response, metaheuristic search algorithms were introduced into the tuning process of both the NPID controller and conventional PID controller parameters in order to compare them. The use of Artificial Intelligence to fine-tune the controller parameters will enable the optimum values of proportional, integral, derivative and nonlinear gains to be determined as system condition change. Finally, a comparison between the algorithms applied is conducted in terms of efficiency, rise time, settling time and overshoot as well as the overall system stability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Three term control systems

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