检索结果-内蒙古大学图书馆

7th International Conference on Computer and Communication systems, ICCCS 2022

作者： Zan, Yanzhu Li, Da Fu, Xingzhen Beijing University of Technology Beijing Engineering Research Center for Iot Software and Systems Beijing China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

Accurate quantitative simulation of forest fires is of great significance to the risk control of forest fires. Cellular automata model is often used to simulate forest fires. However, the traditional cellular automata model's cell state setting is too simple, and the conversion rules are not considered comprehensively enough, resulting in unsatisfactory simulation results. In this paper, it is proposed to learn the relationship between the burning state of cells and their own geographical conditions from the historical data of forest fires, and to convert the burning probability into the time required for complete burning of cells, so as to simulate different spreading speeds under different geographical conditions. Experiments show that this is a potentially effective method for forest fire simulation. © 2022 IEEE.

关键词： Cellular automata

来源：评论

学校读者我要写书评

暂无评论

Enhancing Soft Skills in Autistic Children: The Next Generation of Mind Champ's Technological Approach 6

Enhancing Soft Skills in Autistic Children: The Next Generat...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Peiris, Kalna Nelaka, Ruchira Manathunga, Kalpani Department of Computer Systems Engineering Sri Lanka Institute of Information and Technology Sri Lanka Department of Information Technology Sri Lanka Institute of Information and Technology Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information and Technology Sri Lanka

ISBN: (纸本)9798331517878

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively. © 2024 IEEE.

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

An Abstract Domain of Linear Templates with Disjunctive Right-Hand-Side Intervals 9th

An Abstract Domain of Linear Templates with Disjunctive Ri...

引用

9th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2023

作者： Xu, Han Chen, Liqian Fan, Guangsheng Yin, Banghu Wang, Ji College of Computer National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Software Engineering for Complex Systems Changsha410073 China HPCL National University of Defense Technology Changsha410073 China College of Systems Engineering National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819986637

Abstract interpretation provides a general framework for analyzing the value ranges of program variables while ensuring soundness. Abstract domains are at the core of the abstract interpretation framework, and the numerical abstract domains aiming at analyzing numerical properties have received extensive attention. The template constraint matrix domain (also called the template polyhedra domain) is widely used due to its configurable constraint matrix (describing limited but user-concerned linear relationships among variables) and its high efficiency. However, it cannot express non-convex properties that appear naturally due to the inherent disjunctive behaviors in a program. In this paper, we introduce a new abstract domain, namely the abstract domain of linear templates with disjunctive right-hand-side intervals, in the form of ∑iaixi∈⋁j=0p[cj,dj] (where ai ’s and p are configurable and fixed before conducting analysis). Experimental results of our prototype are encouraging: In practice, the new abstract domain can find interesting non-convex invariants that are out of the expressiveness of the classic template constraint matrix abstract domain. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Abstract domain Abstract interpretation Invariant Template constraint matrix

来源：评论

学校读者我要写书评

暂无评论

Research on real-Time facial landmark detection model that can deal with occlusion

Research on real-Time facial landmark detection model that c...

引用

2021 International Conference on Computer Vision and Pattern Analysis, ICCPA 2021

作者： Lv, Shulin Wu, Shang Liu, Jie Gong, Chunye Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410073 China

ISBN: (数字)9781510651937

ISBN: (纸本)9781510651920

With the wide use of face tasks on mobile terminals, facial landmark detection faces new challenges of real-Time and occlusion. Therefore, this paper designs a real-Time facial landmark detection model that can deal with occlusion. The model of this paper includes the backbone network and the auxiliary network. The function of the backbone network is to locate facial landmarks. The backbone network uses the lightweight network module ShufflenetV2 to ensure the realtime reasoning of the model, and uses dynamic convolution and Wing loss function to improve the positioning accuracy of the model. The function of the auxiliary network is to predict landmarks' visibility. The auxiliary network helps the backbone network focusing attention on visible landmarks, so as to deal with occlusion. The experimental results show that the size of the model is 1.3MB, the reasoning speed of the model on Intel i5-8250u CPU is 64.63 pictures per second, and the prediction accuracy on the WFLW dataset of the model is 85.72%. The model performs better than PFLD in size, speed, and accuracy. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Semiconductor Equipment Health Monitoring With Multi-View Data

Semiconductor Equipment Health Monitoring With Multi-View Da...

引用

2023 Winter Simulation Conference, WSC 2023

作者： Ahn, Jeongsun Kim, Hong-Yeon Cho, Sang-Hyun Kim, Hyun-Jung Kim, Hongyeon Choi, Hyeonjeong Ham, Dain Korea Advanced Institute of Science and Technology Department of Industrial and Systems Engineering 291 Daehak Street Daejeon34141 Korea Republic of Wonik Ips Software Team 75 Jinwisandan Street Pyeongtaek17709 Korea Republic of

ISBN: (纸本)9798350369663

Monitoring the state of semiconductor equipment is crucial for ensuring optimal performance and preventing downtime. In previous studies, researchers have attempted to derive a health index that represents the overall condition of the equipment as a single index. However, these studies have often relied solely on time-series data from each sensor, neglecting other important viewpoints engineers consider when monitoring the equipment. To address this limitation, we propose a multi-view data set specifically designed for semiconductor equipment, which incorporates process, trend, and spatial data. In addition, we present a framework for deriving a hierarchical health index based on a multi-view data set. The hierarchical structure is derived using a hierarchical spectral clustering method, and an autoencoder-based health index is used. We have verified the effectiveness of our approach with real data sets, demonstrating its potential as a valuable tool for monitoring the condition of semiconductor equipment. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

引用

2024 International Conference on Advancement in Renewable Energy and Intelligent systems, AREIS 2024

作者： Raji, V. Maheswari, S. Dharinya, S. Sree Chinniah, P. Kavin, K.S. Raja, Kiruba Thangam S.K.P. Engineering College Dept. of Information Technology Tiruvannamalai India Tamilnadu Erode India School of Computer Science Engineering and Information Systems Vit University Dept. of Software and Systems Engineering Vellore India ST. Joseph College of Engineering Dept. of Ece Tamilnadu Sriperumbudur India Government College of Engineering Dept. of Eee Tamil Nadu Tirunelveli India Vit University Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350387230

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey 4

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

引用

4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Ilayaraja, V. Rajamohana, S.P. Jagadeesan, S. Aishwarya, V. Rakesh, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India School of Engineering & Technology Pondicherry University Department of Computer Science Puducherry Karaikal India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Tamil Nadu Vellore632 014 India

ISBN: (纸本)9798331528171

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability. © 2024 IEEE.

关键词： Tropical cyclone

来源：评论

学校读者我要写书评

暂无评论

Brick by Brick - Findings of an of-the-Shelf Automation System Application for Building Block Component Group Disassembly

Brick by Brick - Findings of an of-the-Shelf Automation Syst...

引用

Mechanical engineering and Robotics Research (ICMERR), International Conference on

作者： Dominique Briechle Tobias Geger Lena Schartow Andreas Rausch Institute for Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld Germany

ISBN: (数字)9798331509262

ISBN: (纸本)9798331509279

Automation is one of the key drivers in today's global economy. It ensures the conduction of a manifold of standardized processes which helps tackle the decreasing amount of skilled workers in certain areas as well as reducing cost. Although automation is broadly used nowadays, the overall applicability of automated processes is often limited to use cases with predefined process steps of minor flexibility. However, new and upcoming sustainable industries that are focusing on product life-cycle prolonging (e.g. repair, refurbish, and remanufacture (3Rs)) options to enable Circular Economy-friendly treatment, require a high amount of adaptability in automated systems to tackle the high variability of product compositions in disassembly scenarios. The goal of the conducted case study is therefore to investigate the possibilities of a nowadays available of-the-shelf automation system regarding its functions and mechanisms towards enabling automated adaptability by using disassembly scenarios of component groups consisting of different building blocks as an example scenario and the concluding future research areas. The paper, therefore, investigates the different methods offered by the default system and applies them to various levels of disassembly scenarios. The conducted study helps to clarify the capabilities of the system's functionalities in the overall goal to enhance adaptability in disassembly processes.

关键词： Manifolds Industries Computer vision Automation Costs Focusing Collaborative robots Maintenance engineering Mechanical engineering

来源：评论

学校读者我要写书评

暂无评论

Difference-Adaptive Knowledge Distillation for Resource-Constrained Embedded Devices 24

Difference-Adaptive Knowledge Distillation for Resource-Cons...

引用

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： Xiong, Yi Zhai, Wenjie Zhu, Zongwei School of Software Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China

ISBN: (纸本)9798350319934

Deep Neural Network (DNN) models have brought significant performance gains to machine learning tasks. Nevertheless, the huge storage and computational costs of high-performance models severely limit their deployment on resource-limited embedded devices. Knowledge distillation (KD) is the mainstream approach for models compression, but the existing KD framework has some limitations. First, the student model underperforms when there is a considerable capacity gap between the student and teacher models, which reduces the model compression rate in knowledge distillation. Second, compared with training from scratch, the billions of additional forward propagation of the complex teacher model is time-consuming, resulting longer train time in the distillation process. This paper proposes AKD, an efficient difference-adaptive knowledge distillation framework. AKD consists of two modules. The distillation module uses a modified auxiliary teaching architecture as the backbone, and the adaptive module introduces a difference-adaptive method, which can adjust the teacher model adaptively according to the continuously improving representation ability of the student model. Experimental results on different kinds of datasets and models indicate that AKD not only improves the efficiency of distillation, but also enhances the performance of the compact student model and raise the compression rate. © 2022 IEEE.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

GenreNet: A Deep Based Approach for Music Genre Classification

引用

SN Computer Science 2024年第8期5卷 1135页

作者： Bala Ganesh, N. Bhuvaneswari, M.S. Bhagavathi Sankar, K. Ganesh, P. Department of Software and Systems Engineering Vellore Institute of Technology Tamilnadu Vellore India Department of Smart Computing Vellore Institute of Technology Tamilnadu Vellore India Ciena India Haryana Gurgaon India Tata Consultancy Services Tamilnadu Chennai India

Neural network-based Music Genre Classification is a key component in helping users narrow down the selection of songs and listen to music in a certain genre. Audio segmentation has been done as a preprocessing step on the available audio files using the benchmark dataset “GTZAN”. To improve accuracy, audio segmentation divides a 30-s audio recording into ten 3-s audio files. Segments are utilized to create spectrograms, which are then used to extract features. A spectrogram is a visual depiction of a signal’s frequency band. Applying techniques like magphase and Short Time Fourier Transform to the audio data enhances the quality of the spectrograms. Adaptive Moment estimation, Nesterov-accelerated Adaptive Moment estimation (NADAM), Adaptive Moment estimation with Maximum, and Root Mean Squared Propagation are among the optimization techniques used for feature weight optimization to increase the classification rate. NADAM is found to be effective based on performance measures. The proposed GenreNet model is compared with the state-of-art techniques and performance measures such as Accuracy, Precision, Recall, Root Mean Square Error, F1 Score, Loss and Sensitivity have yielded positive results. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alexnet18 Audio classification CNN MGC Music genre classification Spectrogram SVM

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：