检索结果-内蒙古大学图书馆

circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

Frontiers of computer Science 2023年第5期17卷 217-225页

作者： Yajing GUO Xiujuan LEI Lian LIU Yi PAN School of Computer Science Shaanxi Normal UniversityXi’an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature *** view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding *** have constructed a data set which includes eight ***,circ2CBA encodes circRNA sequences using the one-hot ***,a two-layer convolutional neural network(CNN)is used to initially extract the *** CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the *** AUC value of circ2CBA reaches *** of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

关键词： circRNAs RBPs CNN BiLSTM self-attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Identification of Tire Characteristics Using Physics-Informed Neural Network for Road Vehicles 32

Identification of Tire Characteristics Using Physics-Informe...

引用

32nd Mediterranean Conference on control and Automation, MED 2024

作者： Hegedus, Tamas Nemeth, Balazs Gaspar, Peter HUN-REN Institute for Computer Science and Control BudapestH-1111 Hungary

ISBN: (纸本)9798350395440

In this paper, a technique is presented for estimating tire characteristics using a Physics-Informed Neural Network (PINN). By integrating physics-based information during the training process the accuracy of the network is enhanced. Utilizing a probabilistic approach, not only individual parameters are identified but also a confidence interval is determined. Additionally, the paper outlines an iterative algorithm for the case when one of the input signals is not measurable or cannot be estimated accurately. Finally, the approach is validated through simulated and real-world test scenarios. © 2024 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

An Improved Lateral Vehicle control Design Using Ultra-Local Model-Based Slip Estimation 32

An Improved Lateral Vehicle Control Design Using Ultra-Local...

引用

32nd Mediterranean Conference on control and Automation, MED 2024

作者： Fenyes, Daniel Nemeth, Balazs Gaspar, Peter HUN-REN Institute for Computer Science and Control BudapestH-1111 Hungary

ISBN: (纸本)9798350395440

In the paper, a complex estimation and control algorithm is presented for autonomous vehicles. The algorithm has an observer layer that aims to estimate the slip of the vehicle on both the front and rear axles. The observer design is based on the combination of the Linear Parameter Varying (LPV) approach and the ultra-local model technique. The goal of the ultra-local model is to update the cornering stiffness in order for the lateral dynamics of the vehicle model to be updated. A Model Predictive control (MPC) is developed based on the updated model and the estimated slips, that can maintain the lateral stability of the vehicle even under extreme circumstances, e.g., moving on a surface with a low adhesion coefficient. This stabilization is achieved by limiting the slips of the vehicle. The proposed algorithm is tested through a complex test scenario using a high-fidelity simulation software, CarMaker. © 2024 IEEE.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

引用

Neural Computing and Applications 2025年第5期37卷 3435-3454页

作者： Hussen, Noha Salem, Mofreh Eldesouky, Ali I. Sakr, Noha Elghamrawy, Sally Computers and Control Systems Engineering Department Mansoura University Mansoura Egypt Computer Engineering Department Misr Higher Institute for Engineering and Technology Mansoura Egypt

The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the development of robust and efficient drone detection systems has become paramount. Traditional detection methods such as radar and acoustic sensors have limitations in detecting small drones and can be costly to implement. This can lead to lower detection rates and increased security breaches and safety concerns. However, prevailing methodologies often falter in achieving real-time detection with requisite precision and efficacy, particularly within complex operational environments. Motivated by the imperative to surmount these challenges, our research endeavors to introduce a pioneering real-time drone detection framework. Our study encapsulates several seminal advancements. Firstly, we proffer a groundbreaking framework that synergizes advanced integration technique based on texture feature extraction and pattern recognition techniques for real-time drone detection to increase accuracy to detect drones in different conditions such as bad weather and low resolution. Secondly, we introduce an intuitive graphical user interface, enhancing the usability and accessibility of the system in real-time scenario. Lastly, through exhaustive evaluation and comparative analysis, we substantiate the superior performance of our framework in terms of accuracy, precision, and real-time detection capabilities compared to conventional DDS methodologies. Its stability and effectiveness render it a compelling solution for security-focused entities, notably those within air force and military systems. Our experimental results reveal a commendable accuracy rate of 97%, affirming the reliability and precision 98% and recall parameter 98% of our framework in accurately identifying and detecting drones, thus surpassing recent models in the field. © The Author(s), under exclusive

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Indoor Uncertain Semantic Trajectory Similarity Join

引用

Journal of computer Science & Technology 2024年第6期39卷 1441-1465页

作者： Hong-Bo Yin Dong-Hua Yang Kai-Qi Zhang Hong Gao Jian-Zhong Li Faculty of Computing Harbin Institute of TechnologyHarbin 150001China Center of Analysis Measurement and ComputingHarbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

With the widespread deployment of indoor positioning systems, an unprecedented scale of indoor trajectories is being produced. By considering the inherent uncertainties and the text information contained in such an indoor trajectory, a new definition named Indoor Uncertain Semantic Trajectory is defined in this paper. In this paper, we focus on a new primitive, yet quite essential query named Indoor Uncertain Semantic Trajectory Similarity Join (IUST-Join for short), which is to match all similar pairs of indoor uncertain semantic trajectories from two sets. IUST-Join targets a number of essential indoor applications. With these applications in mind, we provide a purposeful definition of an indoor uncertain semantic trajectory similarity metric named IUS. To process IUST-Join more efficiently, both an inverted index on indoor uncertain semantic trajectories named 3IST and the first acceleration strategy are proposed to form a filtering-and-verification framework, where most invalid pairs of indoor uncertain semantic trajectories are pruned at quite low computation cost. And based on this filtering-and-verification framework, we present a highly-efficient algorithm named Indoor Uncertain Semantic Trajectory Similarity Join Processing (USP for short). In addition, lots of novel and effective acceleration strategies are proposed and embedded in the USP algorithm. Thanks to these techniques, both the time complexity and the time overhead of the USP algorithm are further reduced. The results of extensive experiments demonstrate the superior performance of the proposed work.

关键词： filtering-and-verification framework indoor uncertain semantic trajectory inverted index trajectory similarity join

来源：评论

学校读者我要写书评

暂无评论

Advancements in Satellite Remote Sensing for Predicting Large-Scale Wildfire Risks: An Image Processing Algorithmic Framework

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2024年第7期18卷 19-33页

作者： Li, Boxin Ren, Hong’E Tian, Jing College of Computer and Control Engineering Northeast Forestry University Harbin China College of Surveying and Mapping Engineering Heilongjiang Institute of Technology Harbin China

With the escalation of global warming and human activities, large-scale wildfires have become increasingly frequent, posing significant threats to both ecological environments and human societal safety. Satellite remote sensing technology plays a pivotal role in wildfire monitoring and risk assessment, providing extensive geographical coverage and continuous monitoring capabilities. Traditional methods for predicating wildfire risk, however, face limitations in processing large-scale remote sensing data, especially in cloud detection and temporal information analysis. In response to this challenge, a novel set of image processing algorithms has been developed to enhance the efficiency and accuracy of wildfire risk prediction. Initially, a cloud detection and removal algorithm based on deep learning is introduced. This algorithm effectively identifies and eliminates cloud interference in remote sensing images, thereby significantly improving the quality and usability of image data. Subsequently, a temporal information capturing technique is proposed, capable of processing vast amounts of remote sensing data and extracting time series features. This technique provides robust data support for wildfire risk prediction. The application of these technologies not only improves the efficiency of the data processing workflow but also enhances the timeliness and accuracy of the prediction model, holding significant practical importance for guiding actual wildfire prevention and response measures. © 2024 by the authors of this article.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo Vision and Gesture control 6

Authentication and Verification in Human-Robot Cooperative R...

引用

6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Kovacs, Gabor Sziranyi, Tamas HUN-REN Institute for Computer Science and Control Machine Perception Research Laboratory Budapest Hungary

ISBN: (纸本)9798331506520

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators' movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation. © 2025 IEEE.

关键词： Human robot interaction

来源：评论

学校读者我要写书评

暂无评论

Self-Timed Counter Synthesis

Self-Timed Counter Synthesis

引用

2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024

作者： Diachenko, Yuri Stepchenkov, Yuri Khilko, Dmitri Institute Of Informatics Problems Federal Research Center 'Computer Science And Control' Moscow Russia

ISBN: (纸本)9798350395013

Self-timed (ST) circuits have higher reliability in comparison with synchronous counterparts and can serve as a promising circuitry basis for microelectronic control unit implementations for robotic complexes and production systems. However, designing ST circuits is more labor-intensive and non-trivial because of the need to adhere to strict signal generation discipline and build an additional indication subcircuit. The synthesis of combinational ST circuits is quite simply formalized by means of dualizing the logical function system obtained by a logic synthesizer and adding an indication subcircuit. However, when synthesizing ST units with memory, including ST counters, this approach leads to excessive hardware redundancy and, as a consequence, to the resulting solution's low level of performance and soft error tolerance. The article analyzes the circuitry basis for the optimal multi-bit ST counter implementation and proposes a method for synthesizing ST counters based on parameterized templates of the ST counter body and its environment, ensuring self-timed implementation, its full functional compliance with the original description, and close to optimal consumer characteristics. © 2024 IEEE.

关键词： Microelectronics

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal sequence data mining: Traffic flow prediction algorithm based on Recurrent Neural Networks

Advances in Transportation Studies

引用

Advances in Transportation Studies 2024年 64卷 25-40页

作者： Liu, Y.P. Liu, B.N. Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan030008 China Department of Traffic Equipment and Control Engineering School of Traffic Electrical and Control Engineering North China University of Technology Beijing100043 China

Traffic congestion is one of the most common problems in the transportation system. In urban planning and construction, traffic congestion increases the difficulty of control and scheduling, hindering the pace of urban modernization development. The development of intelligent transportation systems has to some extent solved these problems, among which traffic flow prediction is particularly crucial. A period processing layer containing concatenation, dimension amplification, and diffusion convolution was studied and designed. On the grounds of this, a graph neural network prediction model combining spatiotemporal period characteristics was designed. And it further introduced a traffic flow prediction method on the grounds of dynamic topology maps. This model combined adaptive topology generation and parameter learning mechanisms, synchronously processing the complex correlation of traffic data and the heterogeneity between nodes through gated recurrent neural networks. The outcomes indicated that the research model is more excellent than other graph learning methods in three evaluation indicators: Mean square error, mean absolute percentage error, and root mean square error. Their values were 15.63%, 9.87%, and 25.08%, respectively. The evaluation on all four datasets showed excellent performance of the research model, confirming the effectiveness of its adaptive topology and parameter learning strategy. This study had important practical significance for improving the traffic management system, alleviating traffic pressure, and enhancing the travel experience of urban residents. © 2024, Aracne Editrice. All rights reserved.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Robust multi-frequency band joint dictionary learning with low-rank representation

引用

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8075-8088页

作者： Ding, Huafeng Shang, Junyan Zhou, Guohua School of Intelligent Control Changzhou Vocational Institute of Industry Technology Changzhou China School of Computer Science and Artificial Intelligence Changzhou University Changzhou China

Emotional state recognition is an important part of emotional research. Compared to non-physiological signals, the electroencephalogram (EEG) signals can truly and objectively reflect a person’s emotional state. To explore the multi-frequency band emotional information and address the noise problem of EEG signals, this paper proposes a robust multi-frequency band joint dictionary learning with low-rank representation (RMBDLL). Based on the dictionary learning, the technologies of sparse and low-rank representation are jointly integrated to reveal the intrinsic connections and discriminative information of EEG multi-frequency band. RMBDLL consists of robust dictionary learning and intra-class/inter-class local constraint learning. In robust dictionary learning part, RMBDLL separates complex noise in EEG signals and establishes clean sub-dictionaries on each frequency band to improve the robustness of the model. In this case, different frequency data obtains the same encoding coefficients according to the consistency of emotional state recognition. In intra-class/interclass local constraint learning part, RMBDLL introduces a regularization term composed of intra-class and inter-class local constraints, which are constructed from the local structural information of dictionary atoms, resulting in intra-class similarity and inter-class difference of EEG multi-frequency bands. The effectiveness of RMBDLL is verified on the SEED dataset with different noises. The experimental results show that the RMBDLL algorithm can maintain the discriminative local structure in the training samples and achieve good recognition performance on noisy EEG emotion datasets. © 2024 – IOS Press.

关键词： Electroencephalography

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：