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optimization design of precast casing pipe unit

Optimization design of precast casing pipe unit

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3rd International conference on Mechatronics and intelligent Materials (MIM 2013)

作者： Dong, Jun Gao, Hongping Qiao, Xinchao Dong, Jianguang Kawasaka Taiyuan Heavy Ind Co Ltd High Tech Zone GAOXINGUOJI 1206 Taiyuan 030006 Peoples R China

ISBN: (纸本)9783037857106

The level of the mould design directly influence the quality of track slab, influence the smoothness and stability of the a high-speed railway further .Precast casing pipe unit is one of the key parts in the mould, which definitely influence the quality of track slab. Combined with the track slab manufacturing in Nanjing-Anqing special line, we have discussed five schemes on precast casing pipe unit of the mould, and the best scheme been formed after comparison and optimization. It is proved by the process of the track slab production that the best scheme can ensure precision of track slab, be operationally convenient, bring obvious benefit and is worth spreading.

关键词： CRTS I ballastless track slab mould precast casing pipe unit design scheme optimization plug type scheme fixed post

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Exploring Deep learning Architectures for Enhanced Traffic Sign Recognition: A Comparative Analysis 3

Exploring Deep Learning Architectures for Enhanced Traffic S...

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3rd IEEE World conference on Applied Intelligence and Computing, AIC 2024

作者： Kumar Gupta, Amit Mathur, Priya Singh, Riya Sharma, Sana Manipal University Jaipur Computer Science and Engineering Rajasthan Jaipur India Poornima Institute of Engineering and Technology Department of Mathematics Rajasthan Jaipur India

ISBN: (纸本)9798350384598

This research paper investigates the efficacy of various deep learning architectures in Traffic Sign Recognition (TSR) tasks, aiming to contribute to the advancement of intelligent transportation systems. A comprehensive comparative analysis is conducted, evaluating the performance of four prominent architectures: AlexNet, DenseNet, VGG, and a generic CNN, using the GTSRB dataset. Results demonstrate that DenseNet achieves the highest accuracy of 95.12%, outperforming other architectures, with remarkable precision, recall, and F1-score values. The generic CNN also exhibits impressive performance, with an accuracy of 97.31% and comparable metrics. Conversely, AlexNet and VGG achieve accuracies of 88.84% and 85.03%, respectively, with varying performance metrics. These findings underscore the effectiveness of DenseNet and the generic CNN in accurately classifying traffic signs, highlighting their potential for real-world implementation. The study emphasizes the importance of selecting appropriate deep learning architectures tailored to specific tasks, and further analysis may explore strategies to optimize model performance and generalization capability. © 2024 IEEE.

关键词： Traffic signs

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Dyslexia Adaptive learning Model: Student Engagement Prediction Using Machine learning Approach 3rd

Dyslexia Adaptive Learning Model: Student Engagement Predict...

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3rd International conference on Soft Computing and Data Mining (SCDM)

作者： Hamid, Siti Suhaila Abdul Admodisastro, Novia Manshor, Noridayu Kamaruddin, Azrina Abd Ghani, Abdul Azim Univ Putra Malaysia Seri Kembangan Malaysia

ISBN: (纸本)9783319725505;9783319725499

Education barriers are synonym with people with dyslexia life experience. People with dyslexia encounter barriers such as in academic related areas, mistreated with negative reaction on their behaviour and limitation to acquire a suitable support to overcome the barriers. Therefore, this work focus on giving the support to help students with dyslexia deal with their difficulty through adaptively sense their behaviour for engagement perspective. For that reason, we apply machine learning approach that utilises Bag of Features (BOF) image classification to predict student engagement towards the learning content. The engagement prediction was relatively using frontal face of the 30 students. We used Speeded-Up Robust Feature (SURF) key point descriptor and clustered using k-Means method for the codebook in this BOF model. Then, we classify the model using 3 types of classifier which are Support Vector Machine (SVM), Naive Bayes and K-Nearest Neighbour (k-NN) to find the best classification result. Through these methods, we managed to get high accuracy with 97-97.8%.

关键词： Adaptive learning Engagement Dyslexia Machine learning

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Joint space learning for video-based face recognition 3

Joint space learning for video-based face recognition

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3rd IAPR Asian conference on Pattern Recognition, ACPR 2015

作者： Cao, Dong He, Ran Sun, Zhenan Tan, Tieniu Center for Research on Intelligent Perception and Computing CASIA China National Laboratory of Pattern Recognition CASIA China Center for Excellence in Brain Science and Intelligence Technology CAS China

ISBN: (纸本)9781479961009

Popularity of surveillance and mobile cameras provides great opportunities to video-based face recognition (VFR) in less-controlled conditions. This paper proposes a joint space learning method to simultaneously identify the most representative samples and discriminative features from facial videos for reliable face recognition. Specifically, we use a mixture modal by learning multiple feature spaces to capture the data variations where the representative samples in each subspace are learned. Actually, this procedure is a chick to egg problem and an alternate algorithm is developed to monotonically optimize the joint task. In addition, randomized techniques are applied to kernel approximations for capturing the nonlinear structure in data, so that both accuracy and efficiency of our method can be improved. The proposed method performs better than the state-of-the-art video based face recognition methods on Honda, Mobo and YouTube Celebrities databases. © 2015 IEEE.

关键词： Face recognition

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intelligent System for Identification and Classification of Pneumonia using Machine learning 3

Intelligent System for Identification and Classification of ...

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3rd IEEE Asian conference on Innovation in Technology, ASIANCON 2023

作者： Grover, Chakshu Vats, Satvik Sharma, Vikrant Rawat, Priyanshu Bajaj, Madhvan Singh, Sunny Graphic Era Hill University Computer Science and Engineering Dehradun India Data Scientist Nextgen Techedge Pvt. Ltd. India

ISBN: (纸本)9798350302288

The current study introduces an advanced system that utilizes machine learning methodologies to accurately detect and categorize pneumonia cases from chest X-ray images. The utilized system employs the VGG16 model as the foundational architecture for the classifier. It also utilises transfer learning techniques to adjust the model to the merged dataset of chest X-ray images that have been labelled. The model has been enhanced with the utilisation of Grad-CAM, a methodology that offers interpretability for the model's predictions through visual explanations. The training methodology employed involves mini-batch stochastic gradient descent, with a batch size of 8, for a total of 200 epochs. The evaluation of the system's performance is conducted by means of the utilisation of the test dataset. The findings indicate that the system that was created has the ability to effectively and precisely detect and categorise pneumonia based on chest X-ray images. This suggests that the system has the potential to be a valuable tool in the medical field for prompt diagnosis and treatment. © 2023 IEEE.

关键词： Image classification

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Optimal Energy Efficiency Techniques and Security Enhancement in Wireless Sensor Network Using Machine learning 3

Optimal Energy Efficiency Techniques and Security Enhancemen...

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3rd International conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Manikandan, S. Suganthi, S. Gayathiri, R. Anna University Chennai Kongunadu Polytechnic College Tamilnadu Trichy India K Ramakrishnan College of Technology Electronics and Communication Tamilnadu Trichy India Sri Sairam Engineering College Electronics and Instrumentation Tamilnadu Chennai India

ISBN: (纸本)9781665462754

Speech recognition, natural language processing, and image processing are just a few of the many applications that machine learning (ML) has been employed in. The use of ML in wireless sensor networks (WSNs) has recently captured the attention of many researchers, broadening the field of study. Load balance and resource utilization are the problem with WSNs. Numerous routing algorithms can resolve these problems by using less energy and extending the network lifetime in WSNs through optimum routing techniques. Additionally, there are frequently issues like rigidity, taking into account various factors, and relying on models. Machine learning has demonstrated its effectiveness in creating effective procedures to deal with difficult issues in a variety of network features. In a wireless sensor network, routing is the process of determining the best path for data transmission between various sensor nodes in accordance with the situation. A lot of machine learning has been applied to creating energy-efficient routing methods. The key contribution of this study is to give a concise overview of clustering in wireless sensor networks based on three major categories, including classical, optimization, and machine learning techniques. © 2022 IEEE.

关键词： Machine learning

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Net Load Prediction Using Machine learning Methods: A Case Study of Residential Households 3

Net Load Prediction Using Machine Learning Methods: A Case S...

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3rd International conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Herodotou, Panagiotis Tziolis, Georgios Makrides, George Georghiou, George E. PV Technology Laboratory Department of Electrical and Computer Engineering Cyprus for Intelligent Nicosia Cyprus

ISBN: (纸本)9798350375923

As residential solar photovoltaic (PV) systems are increasingly integrated into distribution networks globally, there is a growing necessity to shift from traditional load forecasting to net load forecasting (i.e., predicting the combination of PV and load collectively). Understanding and predicting the daily net energy of a typical household is crucial for designing and sizing appropriate renewable energy systems and storage solutions. This paper compares the performance of different machine learning methods in predicting the net load patterns of a typical household. The database contained PV production and energy load records from 68 actual households in Cyprus, over a period of one year. The results showed that the eXtreme Gradient Boosting (XGBoost) model outperformed all other models yielding a normalized root mean square error of 5.52%. Despite the inherent unpredictability of net load patterns, the findings indicate that the proposed XGBoost model can achieve high prediction accuracies for household electricity net load profiles. © 2024 IEEE.

关键词： Prediction models

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An Adaptive Hybrid Genetic Algorithm for VLSI Standard Cell Placement Problem 3

An Adaptive Hybrid Genetic Algorithm for VLSI Standard Cell ...

引用

3rd International conference on Information Science and Control Engineering (ICISCE)

作者： Chen, Xiongfeng Lin, Geng Chen, Jianli Zhu, Wenxing Minjiang Univ Dept Comp Sci Fuzhou 350108 Peoples R China Minjiang Univ Dept Math Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou 350108 Peoples R China Fuzhou Univ Ctr Discrete Math & Theoret Comp Sci Fuzhou 350108 Peoples R China

ISBN: (纸本)9781509025350

This paper presents an adaptive hybrid genetic algorithm (AHGA) for VLSI standard cell placement problem which belongs to NP-hard combinatorial optimization problem. Based on the distinguishing feature of solution space of the problems with various scale and array or non-array placement style, we correspondingly use some adaptive strategies to greatly reduce the runtime. We make innovations in the adaptive strategies for constructing single crossover meme and accepting placement candidate. The experimental tests are performed on Peko suite3 and ISPD04 benchmark circuits, the results and comparisons show that these strategies are efficient.

关键词： adaptive hybrid genetic algorithm global exploration local exploitation standard cell placemen very large scale integration (VLSI) physical design

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Depression Recognition Method Based on Regional Homogeneity Features from Emotional Response fMRI Using Deep Convolutional Neural Network 21

Depression Recognition Method Based on Regional Homogeneity ...

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3rd International conference on intelligent Medicine and Image Processing (IMIP)

作者： Li, Mi Liu, Minshuai Kang, Jiaming Zhang, Wei Lu, Shengfu Beijing Univ Technol Fac Informat Technol Dept Artificial Intelligence & Automat Beijing 100124 Peoples R China Beijing Int Collaborat Base Brain Informat & Wisd Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Intelligent Percept & Autonomous Con Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China

ISBN: (纸本)9781450390057

The research of deep convolutions neural network (DNN) in the depression recognition has become a popular topic. In this paper, we propose a method for depression recognition based on the regional homogeneity (ReHo) in emotional task state functional magnetic resonance imaging (task-fMRI) using DNN. First, the task-fMRI is extracted by processing the fMRI of emotional stimulation tasks. And the task-fMRI with ReHo (ReHo-task-fMRI) is calculated based on task-fMRI. And then, convolutional networks of DNN (such as VGG16, etc.) pre-trained on ImageNet are used to automatically complete extracting the classification features from ReHo-taskfMRI. Finally, the Kernel Extreme learning Machine (KELM) was used to classify the depression. The results of test set showing that for depression recognition, the sensitivity and specificity of ReHo-task-fMRI were 87.46% and 85.35%, however that of task-fMRI were only 67.69% and 55.44%. This study suggest that compared with emotional task-fMRI, ReHo-task-fMRI can better represent the characteristics of brain dysfunction for patients with depression.

关键词： Major depressive disorder (MDD) Deep convolutions neural network (DNN) Functional magnetic resonance imaging (fMRI) Regional homogeneity (ReHo) Kernel Extreme learning Machine (KELM)

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Artificial Intelligence in Cellular-V2X Networks: A Survey on Dynamic Spectrum Allocation, Traffic Prediction, and Anomaly Detection 3

Artificial Intelligence in Cellular-V2X Networks: A Survey o...

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3rd International conference for Advancement in Technology, ICONAT 2024

作者： Vishwanath, B. Reddy, P Chandrasekhar JNTUH Univ. Department of ECE College of Engg. Hyderabad India

ISBN: (纸本)9798350354171

As cellular-vehicle-to-everything (C-V2X) networks gain importance within emerging intelligent transportation systems, providing effective, secure network management is crucial. This survey paper focuses on the opportunities for applying Artificial Intelligence (AI) to improve the performance of C-V2X networks. AI-assisted techniques for C-V2X network management are investigated, including Deep Q-learning techniques for dynamic spectrum allocation, Machine learning for improved traffic prediction, and anomaly detection algorithms for improving C-V2X network security. The survey reviews existing research in these domains, providing an overview of current advancements, analyzing the strengths and limitations of AI-driven solutions, and highlighting potential areas for further enhancement using AI approaches. The paper also explores methods for evaluating AI-based solutions compared to traditional non-AI methods. Additionally, it identifies key challenges and outlines future research opportunities in the application of AI to C-V2X network management. © 2024 IEEE.

关键词： Deep learning

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