检索结果-内蒙古大学图书馆

作者： Zhao, Wenqing Zhu, Jianlin Huang, Jin Li, Ping Sheng, Bin School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China Department of Computer Science South-central Minzu University Wuhan China Department of Computing and The School of Design The Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Background: Cartoon images play a vital role in film production, scientific and educational animation, video games, and other fields, and are one of the key visual expressions of artistic creation. However, since hand-crafted cartoon images often require a great deal of time and effort on the part of professional artists, it is necessary to be able to automatically transform real-world images into different styles of cartoon images. Although cartoon images vary from artist to artist, cartoon images generally have the unique characteristics of being highly simplified and abstract, with clear edges, smooth color shading, and relatively simple textures. However, existing image cartoonization methods tend to create a number of problems when performing style transfer, which mainly include: (1) the resulting generated images do not have obvious cartoon-style textures;and (2) the generated images are prone to structural confusion, color artifacts, and loss of the original image content. Therefore, it is also a great challenge in the field of image cartoonization to be able to make a good balance between style transfer and content keeping. Methods: In this paper, we propose a GAN-based multi-attention mechanism for image cartoonization to address the above issues. The method combines the residual block used to extract deep network features in the generator with the attention mechanism, and further strengthens the perceptual ability of the generative model to cartoon images through the adaptive feature correction of the attention module to improve the cartoon features of the generated images. At the same time, we also introduce the attention mechanism in the convolution block of the discriminator, which is used to further reduce the image visual quality problem caused by the style transfer process. By introducing the attention mechanism into the generator and discriminator models of the generative adversarial network, our method enables the generated images to have obvious c

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing EEG Analysis for Rapid Brain Activities Detection in Patients

Enhancing EEG Analysis for Rapid Brain Activities Detection ...

引用

Women in computing (InCoWoCo), International Conference for

作者： Abhivyakti Yadav R Kaif Ahmed Annamalai R S.Sudha Mercy CSE Amrita School of Computing Chennai Department of Computer Science and Design R.M.K. Engineering College Chennai

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Electroencephalogram (EEG) analysis plays a critical role in diagnosing various neurological conditions by detecting abnormal brain activities. However, the complex nature of EEG signals poses challenges for traditional analysis methods, which often struggle to handle diverse and high-dimensional data effectively. This paper develops a new technique of EEG signal classification using machine learning and deep learning models. Specifically, attention has been given to the CatBoost, ResNet34D, EfficientNetB0, and EfficientNetB2 models. The proposed approach utilized a vast EEG dataset with 11,000 samples of spectrograms, and it conducted an in-depth comparison of these models. Data preprocessing included features developed with statistical measures and the formation of uniform EEG spectrograms. All these models were evaluated in terms of their KL divergence score, defined as a measure of the divergence between the predicted and target distributions. From all the models considered, CatBoost proved to be one with an excellent performance, having had a KL score value at 0.78 compared to that of deep learning models. This work demonstrates the potential of gradient boosting for EEG-activity classification tasks and can be of benefit as a source of insight for interested applications in real-time brain activity monitoring and diagnostics of neurological issues.

关键词： Deep learning Accuracy Computational modeling Pattern classification Predictive models Brain modeling Electroencephalography Real-time systems Spectrogram Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Humanoid Robot Collaborative Lifting Integrating Executable Judgment

Humanoid Robot Collaborative Lifting Integrating Executable ...

引用

2022 IEEE International Symposium on Product Compliance engineering - Asia, ISPCE-ASIA 2022

作者： Chang, Hua Yi, Pengfei Liu, Rui Dong, Jing Hou, Yaqing Zhou, Dongsheng The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

Humanoid robot collaborative lifting can be used in a variety of scenarios that require repetitive lifting tasks. Most existing studies of humanoid robot collaboration often assume that objects can always be lifted, which may result in damage to both the robot and the object if objects are too heavy to lift. To avoid such situations as much as possible, a collaborative lifting approach integrating executable judgment is proposed. First, a target search and localization method is constructed using monocular vision and marker points to identify the task object. Then, an executable judgment strategy is designed to determine whether the object is overweight or not according to robot force analysis. Finally, a multi-robot joint control model is proposed based on collaborative communication to perform collaborative tasks with different loads based on the judgment results. Experiments on two humanoid robots for different types and weights of targets show the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Anthropomorphic robots

来源：评论

学校读者我要写书评

暂无评论

Performance Verification of Smith Predictor Control Using IMC Scheme via Chemical Reaction Networks and DNA Strand Displacement Reaction 9

Performance Verification of Smith Predictor Control Using IM...

引用

9th IEEE Smart World Congress, SWC 2023

作者： Yao, Jingwang Lv, Hui Xiao, Yijun Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China

ISBN: (纸本)9798350319804

Biochemical process control is often achieved through feedback control strategies for theoretical analysis and optimization of biochemical reaction processes. Here, the design improves the robustness of the biochemical process by taking advantage of the Smith prediction structure and internal mode control within the chemical reaction network theory framework to intervene against the time delays and perturbations present in the biochemical process. First, an IMC representation of Smith prediction is constructed using the presence of parameter rectification of the control method, and then an IMC-MSP control scheme is proposed. A first-order time-lag controlled object is established in the construction of the IMC-MSP control scheme. Besides, CRNs and kinetic representations are established based on the control structure to facilitate the evaluation of the control system performance. In this paper, CRNs representations of parabolic functions as signal inputs are defined for the first time and optimized for stepping signals to facilitate the design and study of biomolecular circuits. Notably, for the CRNs-based IMC-MSP control scheme, on the one hand, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from multiple perspectives of time delays, external perturbations, and filter tuning parameters;on the other hand, using DNA strand displacement is reflected to verify the feasibility of the whole scheme. © 2023 IEEE.

关键词： Chemical reaction networks (CRNs) DNA strand displacement (DSD) Internal model control (IMC control) Smith predictor

来源：评论

学校读者我要写书评

暂无评论

Joint Sensing, Computation, and DAI Resource Management for Integrated Non-Terrestrial and Terrestrial Networks

引用

IEEE Network 2024年

作者： Gong, Yongkang Liu, Xiaonan Yao, Haipeng Xiong, Zehui Cheng, Xiuzhen Niyato, Dusit Shandong University School of Computer Science and Technology China University of Aberdeen School of Natural and Computing Science United Kingdom Beijing University of Posts and Telecommunications School of Information and Communication Engineering China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Nanyang Technological University College of Computing and Data Science China

Integrated Non-Terrestrial and Terrestrial Networks (NTN-TN) have been emerging as a promising architecture to facilitate state sensing, computation migration, and privacy protection for terrestrial users, where both low, medium and geostationary orbit satellites collaboratively support various tasks and allocate resources effectively. Nevertheless, an integration of sensing, computation, and artificial intelligence (AI) in dynamic NTN-TNs has certain challenges in achieving high-efficient task offloading and guaranteeing robust privacy protection. To address these issues, we introduce an intelligent NTN-TN framework, focusing on sensing, computation migration, and privacy protection. Furthermore, we develop a versatile and scalable experimental platform for NTN-TNs to validate related network functions. Simulation results show that the proposed framework reduces network latency compared with advanced benchmarks. Finally, we present potential research directions and innovative solutions in future NTN-TNs. © 2024 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Numerical modeling and optimization of process parameters in continuous extrusion process through response surface methodology, artificial neural network & genetic algorithm

引用

International Journal of Modeling, Simulation, and Scientific computing 2021年第6期12卷 230-248页

作者： Tariku Desta Devendra Kumar Sinha Perumalla Janaki Ramulu Ram Sewak Singh Mechanical Design and Manufacturing Engineering Department School of Mechanical Chemical and Materials Engineering Adama Science and Technology UniversityAdamaEthiopia Department of Electronics Engineering School of Electrical Electronics and Computing Adama Science and Technology University Adama Ethiopia

The product of high complex profile,high strength,high productivity and excellent material properties with infinite length can be produced by Continuous Extrusion(CE)*** numerical simulation of Aluminum(AA 1100)feedstock material at different wheel velocities,product diameter,feedstock temperature,die temperature and friction condition has been carried out using 3D simulation tool design Environment for Forming(DEFORM-3D)in this *** development of mathematical model is carried out to investigate the influence of wheel velocity,extrusion ratio,feedstock temperature,die temperature and friction conditions on total load required for the deformation and extrusion of feedstock material through Response Surface Methodology(RSM).The statistical significance of mathematical model is verified through analysis of variance(ANOVA).The most optimum value of extrusion load has been found to be 136.4 kN through iterative process of Genetic Algorithm(GA)using Artificial Neural Network(ANN).The optimized value of input process variables for minimum value of extrusion load obtained has been found to be 13 Revolutions per Minute(RPM)as wheel velocity,5 mm as product diameter,0.95 as friction condition,650◦C as feedstock temperature and 550◦C as die *** paper with proposed methodology will be helpful for industries working in the area of CE in terms of minimizing energy consumption during production process of bus bars,tubes,wires,cables,sheets,plates,strips,etc.

关键词： Continuous extrusion DEFORM-3D AA1100 response surface methodology artificial neural network genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction of High Heating Value Using ANN Technique Aftermath Natural Disasters 21

Prediction of High Heating Value Using ANN Technique Afterma...

引用

2024 IEEE/ACS 21st International Conference on Computer Systems and Applications, AICCSA 2024

作者： Thakur, Disha Kumar, Sanjay Sharma, Bhisham Dhaou, Imed Ben UIT Himachal Pradesh University Department of Civil Engineering Shimla India UIT Himachal Pradesh University Department of Electrical Engineering Shimla India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Centre for Research Impact & Outcome Punjab 140401 India Hekma School of Engineering Computing and Design Dar Al-Hekma University Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798331518240

Natural disasters, particularly earthquakes and floods, can generate substantial waste, presenting challenges for waste management and energy recovery. This study explores the application of machine learning, specifically Artificial Neural Networks (ANN), to optimize Waste-to-Energy (WTE) conversion processes for disaster-generated Municipal Solid Waste (MSW). Using data from Sundernagar City, India, we constructed and evaluated multiple ANN models (collectively named ANNSNAGAR) to estimate High Heating Value, HHV, based on ultimate/elemental analysis of MSW. The models were rigorously assessed using statistical error metrics. Our best-performing network, with a 9-50-1 architecture (ANNMS), achieved a mean absolute percentage error (MAPE) of 1.47%, demonstrating high predictive accuracy. © 2024 IEEE.

关键词： Artificial Neural Network Energy Energy Content Machine learning Municipal Solid Waste

来源：评论

学校读者我要写书评

暂无评论

Assessing Susceptibility Factors of Confirmation Bias in News Feed Reading 25

Assessing Susceptibility Factors of Confirmation Bias in New...

引用

2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Boonprakong, Nattapat Pareek, Saumya Tag, Benjamin Goncalves, Jorge Dingler, Tilman School of Computing and Information Systems University of Melbourne Parkville VIC Australia Industrial Design Engineering Delft University of Technology Delft Netherlands School of Computing and Information Systems University of Melbourne Melbourne VIC Australia School of Computer Science and Engineering University of New South Wales Sydney NSW Australia School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (纸本)9798400713941

Individuals tend to apply preferences and beliefs as heuristics to effectively sift through the sheer amount of information available online. Such tendencies, however, often result in cognitive biases, which can skew judgment and open doors for manipulation. In this work, we investigate how individual and contextual factors lead to instances of confirmation bias when seeking, evaluating, and recalling polarising information. We conducted a lab study, in which we exposed participants to opinions on controversial issues through a Twitter-like news feed. We found that low-effortful thinking, strong political beliefs, and content conveying a strong issue amplify the occurrences of confirmation bias, leading to skewed information processing and recall. We discuss how the adverse effects of confirmation bias can be mitigated by taking bias-susceptibility into account. Specifically, social media platforms could aim to reduce strong expressions and integrate media literacy-building mechanisms, as low-effortful thinking styles and strong political beliefs render individuals especially susceptible to cognitive biases. © 2025 Copyright held by the owner/author(s).

关键词： cognitive bias confirmation bias individual difference recall social media

来源：评论

学校读者我要写书评

暂无评论

Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data 23

Effective Abnormal Activity Detection on Multivariate Time S...

引用

29th Annual International Conference on Mobile computing and Networking, MobiCom 2023

作者： Niu, Mengjia Zhao, Yuchen Haddadi, Hamed Dyson School of Design Engineering Imperial College London London60015150 United Kingdom Department of Computer Science University of York York60016418 United Kingdom Department of Computing Imperial College London London60015150 United Kingdom

ISBN: (纸本)9781450399906

Multivariate time series (MTS) data collected from multiple sensors provide the potential for accurate abnormal activity detection in smart healthcare scenarios. However, anomalies exhibit diverse patterns and become unnoticeable in MTS data. Consequently, achieving accurate anomaly detection is challenging since we have to capture both temporal dependencies of time series and inter-relationships among variables. To address this problem, we propose a Residual-based Anomaly Detection approach, Rs-AD, for effective representation learning and abnormal activity detection. We evaluate our scheme on a real-world gait dataset and the experimental results demonstrate an F1 score of 0.839. © 2023 Owner/Author(s).

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

How Do HCI Researchers Study Cognitive Biases? A Scoping Review 25

How Do HCI Researchers Study Cognitive Biases? A Scoping Rev...

引用

2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Boonprakong, Nattapat Tag, Benjamin Goncalves, Jorge Dingler, Tilman School of Computing and Information Systems University of Melbourne Parkville VIC Australia School of Computer Science and Engineering University of New South Wales Sydney NSW Australia School of Computing and Information Systems University of Melbourne Melbourne Australia Industrial Design Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400713941

computing systems are increasingly designed to adapt to users' cognitive states and mental models. Yet, cognitive biases affect how humans form such models and, therefore, they can impact their interactions with computers. To better understand this interplay, we conducted a scoping review to chart how Human-Computer Interaction (HCI) researchers study cognitive biases. Our findings show that computing systems not only have the potential to induce and amplify cognitive biases but also can be designed to steer users' behaviour and decision-making by capitalising on biases. We describe how HCI researchers develop algorithms and sensing methods to detect and quantify the effects of cognitive biases and discuss how we can use their understanding to inform system design. In this paper, we outline a research agenda for more theory-grounded research and highlight ethical issues when researching and designing computing systems with cognitive biases in mind as they affect real-world behaviour. © 2025 Copyright held by the owner/author(s).

关键词： bias-aware systems cognitive bias decision-making

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：