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Design and Application of Folding Forming Deployable Mechanism 4

Design and Application of Folding Forming Deployable Mechani...

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4th international conference on artificial Intelligence, Robotics and Control, AIRC 2023

作者： Guan, Yuntao Liu, Yongxi Zhuang, Zheming Qin, Erke Wang, Zichen Zhang, Ze Tang, Zhao Dai, Jian S. Tianjin University School of Mechanical Engineering Tianjin China Southern University of Science and Technology Department of Mechanical and Energy Engineering Shenzhen China

ISBN: (纸本)9798350348248

this paper proposes a folding forming deployable mechanism, an equivalent scheme for the 3-RSR parallel mechanism, which uses a one-piece board as material by using the art of origami as a reference. this scheme effectively improves the defects of the conventional mechanism, which are easy to wear at the hinge and complicated in the fabrication process. the fabrication process and assembly scheme of both single and multiple deployable mechanisms are proposed respectively, then equivalent models are built and simulations are completed. the simulation results show that the overall motion of the proposed deployable mechanism is stable and has a high folding rate and a large working space. the possible applications and usage of the deployable mechanism are discussed and designed. the deployable mechanism proposed in this paper has high stability, simple structure, quick assembly, and wide application, and has great development space and application value. © 2023 IEEE.

关键词： Machine design

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Deep Learning for Driver Vigilance and Road Safety 4

Deep Learning for Driver Vigilance and Road Safety

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4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kaushik, Priyanka Yadav, Khushbu Sincija, C. Gopika, B.S. Kumar, Narendra Kaushik, Manas Manglik Chandigarh India Madhav University Dept. of Cse Sirohi India Dhanalakshmi Srinivasan University Dept. of Computer Science and Engineering Coimbatore India Dhanalakshmi Srinivasan University Dept. of Electrical and Electronics Engineering Coimbatore India Usha Martin University Dept. of Computing& It Ranchi India Manipal University Dept. of Mechanical and Industrial Engineering Jaipur India

ISBN: (纸本)9798350364729

AI Sentinel is a groundbreaking project aimed at revolutionizing road safety through the use of cutting-edge deep learning technology to enhance driver awareness. Given the significant role of driver drowsiness in road accidents, there is an urgent need for efficient real-time detection systems for preventive measures. this paper introduces a novel Python-based system for detecting driver drowsiness, leveraging the OpenCV library for Face Eye Detection implementation. By focusing on the color characteristics of the face and utilizing a camera to capture facial expressions, the system identifies signs of fatigue. OpenCV, known for its prowess in computer vision tasks, is the primary technology employed in this system, enabling accurate detection of drowsiness indicators, particularly in the eyes. the system's real time functionality ensures prompt responses to the driver's condition, effectively mitigating the risk of accidents. through meticulous analysis of facial characteristics, the system can detect subtle changes indicative of drowsiness, issuing timely warnings to the driver to stop or take a break, thus reducing the likelihood of accidents. the proactive design of the system makes it a valuable tool for enhancing traffic safety. Experimental results validate the effectiveness of the proposed approach in accurately detecting driver drowsiness and delivering real-time notifications, underscoring its potential to significantly reduce traffic incidents caused by fatigued drivers. the practical relevance of the system in addressing road safety challenges is emphasized by its ability to identify drowsiness and issue timely warnings, positioning it as a proactive solution to minimize road accidents caused by inattentive driving. Overall, this study contributes to advancing traffic safety by introducing a reliable method for real-time detection of drowsy driving, leveraging colorful facial traits analysis and OpenCV technology integrated with Python programming. © 20

关键词： Deep learning

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Enhanced Honey Badger Algorithm for Resource Allocation and Task Scheduling in Cloud Environment 4

Enhanced Honey Badger Algorithm for Resource Allocation and ...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Rajagopal, R. Arunarani, A.R. Arivarasi, A. Ingle, Anup Ravichandran, T. Prakash, R. Vijaya Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India School of Computing College of Engineering and Technology Srm Instutite of Science and Technology Department of Compuational Intelligence Tamilnadu Chennai India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunications Maharashtra Pune India Karpagam College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350300888

Cloud computing is a technology that offers dynamic resources to the users with enhanced scalability and flexibility. the major concerns in cloud environment that has direct impact on the throughput of cloud system and contentment of cloud users is the problem of task scheduling and resource allocation. the time taken to execute the tasks and cost incurred for the computation are the significant objectives that affect the performance of the cloud system. this work proposes a multi-objective task scheduling and resource allocation technique using meta-heuristic optimization algorithm. Enhanced Honey Badger algorithm (EHBA) is employed to schedule the tasks and allocate computing resources effectively while minimizing the time and cost objectives. the performance of the proposed technique is assessed in a simulation environment, CloudSim, which mimics the settings of real cloud computing system. Various measures such as Time-to-Execute, Cost-to-Compute, Task-to-Resource utilization and Time-to-Respond are used to assess the performance of the suggested EHBA method for efficient task scheduling and resource allocation. the experimental results produced by the proposed method is also compared against the state-of-the-art studies that employ meta-heuristic optimization algorithms. the outcomes revealed that EHBA outperformed other methods by executing the tasks in a minimum time with reduced cost and maximum utilization of the computing resources. © 2023 IEEE.

关键词： Resource allocation

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DMDA: A Computational Resource Allocation Approach for IoT Devices in Fog Computing 4

DMDA: A Computational Resource Allocation Approach for IoT D...

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4th international conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Pramono, Luthfan Hadi Shen, Shan-Hsiang National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331506490

Fog Computing enables the efficient offloading of computational tasks from IoT Devices to Fog nodes, enhancing processing efficiency and reducing latency. this paper introduces the Dynamic Matching with Deferred Acceptance (DMDA) algorithm, designed to optimize the allocation of GPU and CPU resources within Fog Computing environments, specifically for smart city applications. the DMDA algorithm allocates resources to IoT Devices based on parameters such as data type, computational load, priority, and responsiveness. Devices processing image or video data are given priority for GPU allocation, while other devices are assigned to CPUs based on their cumulative performance scores. Devices with lower priority are reassigned to CPU resources or excluded when resource availability is exhausted, significantly enhancing performance under high-demand scenarios. the algorithm employs a Mixed-Integer Linear programming (MILP) model to maximize the aggregate score of allocated devices while adhering to resource constraints. Experiments conducted with 50 to 1,000 devices demonstrate that both the total Device Score rate and Allocated Device rate reach 100%, indicating optimal allocation. Empirical findings show that the DMDA algorithm improves resource utilization, reduces processing times, and ensures effective workload distribution across large-scale IoT deployments. © 2024 IEEE.

关键词： Fog computing

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A Rule-Based Deep Learning Method for Predicting Price of Used Cars 4th

A Rule-Based Deep Learning Method for Predicting Price of Us...

引用

4th international conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Ayo, Femi Emmanuel Awotunde, Joseph Bamidele Misra, Sanjay Ajagbe, Sunday Adeola Mishra, Nishchol Department of Computer Science McPherson University Seriki-Sotayo Abeokuta Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science and Communication Ostfold University College Halden Norway Department of Computer Engineering Ladoke Akintola University of Technology Ogbomoso Nigeria Rajiv Gandhi Technical University Bhopal India

ISBN: (纸本)9789819900466

the growth of internet used automobile shopping has been tremendous in recent years. It is important for sellers to correctly evaluate the price of the used cars. From the literature, classification is an approach that assists in data interpretation by analyzing data and providing a second opinion on data among other tasks most of the classifiers used for car price prediction suffered from lower accuracy ratios and higher error ratios. In this work, a hybrid feature selection model for predicting the price of used cars was developed. the developed model includes a CfsSubsetEval to determine the best feature subset, rule-based GA to search for the best solution, and a neural network for the final price prediction based on the selected features. the developed model was implemented in a Java programming language. the evaluation showed that the developed hybrid feature selection model achieved better performances with the correlation coefficient, mean absolute error and root mean square error of 0.9818, 1.1809, and 1.5643 respectively, compared to other related state-of-the-art algorithms. the results validated the efficiency of the developed model for used car price prediction. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Java programming language

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Traffic Flow Prediction Based on Transformer and Multi-Spatial-Temporal Encoder-Decoder 4

Traffic Flow Prediction Based on Transformer and Multi-Spati...

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4th international conference on Computer engineering and Application, ICCEA 2023

作者： Zhang, Yidi Gu, Xiang School of Information Science and Technology Nantong University Nantong China

ISBN: (纸本)9798350347548

Traffic flow prediction is one of the key research directions of intelligent transportation systems, and accurate traffic flow prediction can effectively reduce traffic congestion and improve road utilization. With the rapid development of artificial intelligence, many machine learning and deep learning models have been successfully applied to traffic flow prediction. Due to the many factors influencing traffic flow, the extremely complex nonlinear relationships between these factors, and the complex temporal and spatial correlations of traffic flow, traffic flow prediction is considered a challenging problem. In this paper, a new Multi-Spatial-Temporal Encoder-Decoder Model (MST-EDM) based on Transformer is proposed to capture the temporal correlation of traffic flow more accurately. Weekly time series encoder block, daily time series encoder block and adjacent time series encoder block are used to extract weekly time series correlation, daily time series correlation and adjacent time series correlation. the temporal correlation of different time slices is fused by feature. Experiments were conducted using the PEMS04 highway datasets from the Caltrans Performance Measurement System. the results show that our model significantly outperforms state-of-the-art baselines. © 2023 IEEE.

关键词： Time series

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Customs Fraud Detection Method Based on artificial Intelligence and Data Analytics 4

Customs Fraud Detection Method Based on Artificial Intellige...

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4th international conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Gancheva, Veska Popov, George Nakov, Ognyan Raynova, Kamelia Popova, Antoaneta Technical University of Sofia Dept. of Programming and Computer Technologies Sofia Bulgaria Technical University of Sofia Dept. of Cybersecurity Sofia Bulgaria University of Sofia Dept. of Computer Systems Technical Sofia Bulgaria Technical University of Sofia Dept. of Information Technologies in Industry Sofia Bulgaria

ISBN: (纸本)9798350391183

Because of the increased market opening and massive data collection brought about by globalization, the need of maintaining control over customs procedures has increased. However, the integration and processing of customs data are complicated by its extreme imbalance. Finding autonomous, computationally sophisticated solutions for customs handling is therefore crucial. the study described in this paper aims to propose an artificial intelligence-based method for customs fraud detection through data analytics. A machine learning-based approach for detecting customs fraud is proposed. the research methodology includes data preprocessing, data analytics, combining results, and deriving precise solutions from results obtained. the aim is to extract meaningful information from a dataset. For the purpose of classifying customs fraud, seven algorithms for machine-learning-kNN, SGD, Constant, Logistic Regression, Random Forest, Neural Network, and Naive Bayes-Are chosen and empirically assessed. © 2024 IEEE.

关键词： Adversarial machine learning

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Temporal-Spectral Multi-Scale Sub-Band Common Spatial Pattern Algorithm for Motor Imagery Brain-computer Interfaces 4

Temporal-Spectral Multi-Scale Sub-Band Common Spatial Patter...

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4th international conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Sun, Dazhuang Yang, Xingguang Chen, Cheng Guo, Yuzhu School of Automation Science and Electrical Engineering Beihang University Beijing China Casic Simulation Technology Co. Ltd Beijing China

ISBN: (纸本)9798350380323

Motor Imagery Brain-Computer interfaces (MI-BCI), as one of the most common paradigms, has widespread applications. However, the leading common spatial pattern encoding algorithms can be prone to over-fitting and sensitive to noise. this study aims at extracting accurate temporal and spectral information which is related to the motor imagery. To address these challenges, we propose a novel multi-scale sub-band mixed temporal-spectral common spatial pattern (msSBMCSP) algorithm. this method improves the adaptability of MI-BCI to inter-subject or inter-session variabilities. the framework involves several key steps: preprocessing raw EEG signals followed by segmentation into epochs using a sliding window technique. Each epoch is further divided into multiple sub-bands at various scales. Depending on the frequency scale, two distinct CSP spatial filtering methods are employed. this results in sub-block CSP patterns for each frequency band, generating a multi-scale CSP feature set with time and frequency overlaps. these features are then compiled into a comprehensive global feature dictionary. A sparse regression technique is utilized to score these features based on their discriminative power, allowing for the selection of the most informative features and sub-blocks to construct a sparse subset. Ultimately, a Support Vector Machine (SVM) is trained on this refined feature set to accurately decode motor imagery tasks. the new algorithm was verified on two public datasets and achieved state-of-the-art performance. the dedicated handcrafted features are computationally cheap and easy to be deployed in practical BCI applications. © 2024 IEEE.

关键词： Brain computer interface

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A Forefront System for Sugarcane Leaf Disease Classification Using Deep Learning 4

A Forefront System for Sugarcane Leaf Disease Classification...

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4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Sakib, Abu Noman Md Partha, Sajal Basak Shojib, Mohammad Anik Islam Hasan, Md Mahamudul Shuvo, Badiuzzaman Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9798331530938

the agriculture sector has already seen a favorable advancement because of the involvement of machine learning (ML) methods. Sugarcane industry faces a gruesome drawback because of several types of leaf diseases. Nowadays, ML methods have come handy also to detect the diseases at the very early stage. Our work represents a cutting-edge deep learning method with a goal to classify five popular sugarcane leaf diseases: Bacterial Blight, Mosaic, RedRot, Rust, Yellow, and can also identify which one is Healthy. this study also conducts a comparative analysis of various state-of-the-art models, evaluating their training time, parameters, accuracy and model size using the same dataset. Based on this evaluation, the optimal model was selected for final evaluation. Several pre-trained models were modified to enhance speed and reduce storage requirements. the system can identify the disease category for every given sugarcane leaf image using the EfficientNetB0 model, which demonstrated the best performance, and also provides the probability of correct identification. Several preprocessing steps are involved such as removing noise, addressing missing data, and standardizing images to ensure maximum accuracy. the models were trained, validated, and tested on a dataset of 4,800 sugarcane leaf images from the Sugarcane Leaf Disease Dataset. Among all the models, EfficienNetB0, InceptionV3 and ResNet50 achieved high accuracy (98.75%, 98.54% and 98.54%, respectively). However, EfficienNetB0 was ultimately chosen due to its faster training, smaller size, and fewer parameters, making it ideal for this study. © 2025 IEEE.

关键词： Contrastive Learning

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the HA Based Robot Navigation using MBS Method 4

The HA Based Robot Navigation using MBS Method

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4th IEEE international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Murugan, G. Al-Farouni, Mohammed Al-Hilali, Aqeel Vats, Satvik Bader, Nabaa M. Al-Ansari, Ahmed Taaban, Nabaa Kareem Gitam University 207 Nagadenehalli Doddaballapur taluk Karnataka Bengaluru561203 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Al-Farahidi University Baghdad Iraq Graphic Era Hill University Department of Cse India Graphic Era Deemed to Be University Uttarakhand Dehradun248007 India National Universitsy of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Al-kunooze University College Dental Department Basra Iraq Kut University College Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

Multiple methods of human-aware navigation are developing in tandem with the emergence of social robots. this article presents a comprehensive standard for the quantitative evaluation of robot navigation methods. System designers may use the Social Robot Planner Benchmark (SRPB), an automated quantitative tool that generates consistent performance indicators for algorithms, to choose the optimum strategy for certain applications. As an alternative to conventional measures of work performance, our standard provides innovative social metrics such as the degree to which humans feel comfortable in the robot's environment and the degree to which robot actions seem natural, with a focus on how reliable human tracking is. Efficient navigation of service robots in scenarios involving human assistance requires a delicate balance between optimizing the robot's path and ensuring user comfort and safety. Our approach employs Bayesian inference for predicting the user's navigation goal and time-dependent path planning to achieve this balance effectively. By leveraging an interaction model and computing the belief about the user's intended destination, our method provides accurate predictions regularly updated based on new observations. through a comprehensive evaluation, we demonstrate that our approach maintains a comfortable separation between the robot and the user while complying with social distance norms and minimizing the difference in arrival time. Statistical analysis reveals the superiority of our approach in terms of human-robot distance, significantly enhancing user comfort and safety during navigation scenarios. © 2024 IEEE.

关键词： Robot programming

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