检索结果-内蒙古大学图书馆

Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Adaptive multi-channel Bayesian graph attention network for IoT transaction security

引用

Digital Communications and Networks 2024年第3期10卷 631-644页

作者： Zhaowei Liu Dong Yang Shenqiang Wang Hang Su School of Computer and Control Engineering Yantai UniversityYantai264005China Department of Electronics Information and BioengineeringPolitecnico di MilanoMilan20133Italy

With the rapid advancement of 5G technology,the Internet of Things(IoT)has entered a new phase of appli-cations and is rapidly becoming a significant force in promoting economic *** to the vast amounts of data created by numerous 5G IoT devices,the Ethereum platform has become a tool for the storage and sharing of IoT device data,thanks to its open and tamper-resistant ***,Ethereum account security is necessary for the Internet of Things to grow quickly and improve people's *** modeling Ethereum trans-action records as a transaction network,the account types are well identified by the Ethereum account classifi-cation system established based on Graph Neural Networks(GNNs).This work first investigates the Ethereum transaction ***,experimental metrics reveal that the Ethereum transaction network is neither optimal nor even satisfactory in terms of accurately representing transactions per *** flaw may significantly impede the classification capability of GNNs,which is mostly governed by their *** work proposes an Adaptive Multi-channel Bayesian Graph Attention Network(AMBGAT)for Ethereum account clas-sification to address this *** uses attention to enhance node features,estimate graph topology that conforms to the ground truth,and efficiently extract node features pertinent to downstream *** extensive experiment with actual Ethereum transaction data demonstrates that AMBGAT obtains competitive performance in the classification of Ethereum accounts while accurately estimating the graph topology.

关键词： Internet of things Graph representation learning Node classification Security mechanism

来源：评论

学校读者我要写书评

暂无评论

Constrained temperature and relative humidity predictive control:Agricultural greenhouse case of study

引用

Information Processing in Agriculture 2024年第3期11卷 409-420页

作者： Hafsa Hamidane Samira EL Faiz Iliass Rkik Mohamed El Khayat Mohammed Guerbaoui Abdelali Ed-Dahhak Abdeslam Lachhab Computer Engineering and Intelligent Electrical Systems Laboratory ModelingMaterials and Control of Systems TeamHigher School of TechnologyMoulay Ismail University of MeknesMorocco

The importance of Model Predictive control(MPC)has significant applications in the agricultural industry,more specifically for greenhouse’s control ***,the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the *** methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed *** this paper,we introduce an application of Constrained Model Predictive control(CMPC)for a greenhouse temperature and relative *** this purpose,two Multi Input Single Output(MISO)systems,using Numerical Subspace State Space System Identification(N4SID)algorithm,are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation *** this sense,linear state space models were adopted in order to evaluate the robustness of the control *** the system is identified,the MPC technique is applied for the temperature and the humidity *** results show that the regulation of the temperature and the relative humidity under constraints was guaranteed,both parameters respect the ranges 15℃≤T_(int)≤30℃and 50%≤H_(int)≤70%*** the other hand,the control signals uf and uh applied to the fan and the heater,respect the hard constraints notion,the control signals for the fan and the heater did not exceed 0≤uf≤4.3 Volts and 0≤uh≤5 Volts,respectively,which proves the effectiveness of the MPC and the tracking ***,we show that with the proposed technique,using a new optimization toolbox,the computational complexity has been significantly *** greenhouse in question is devoted to Schefflera Arboricola cultivation.

关键词： Constrained model predictive control Greenhouse MISO systems Optimization Relative humidity Temperature

来源：评论

学校读者我要写书评

暂无评论

Digital Selves Based Intelligent Construction Framework

引用

International Journal of Crowd Science 2024年第4期8卷 184-194页

作者： Liu, Xiaomeng Sun, Hongbo Lin, Jiarui School of Computer and Control Engineering Yantai University Yantai264005 China Department of Civil Engineering Tsinghua University Beijing100084 China

Construction is the pillar industry of Chinese national economy, but the profit rate has continued to decline in recent years. The conventional job-centric construction information system cannot meet the requirements for safety, quality, and efficiency. In order to solve the above problems, a digital selves based intelligent construction framework has been proposed which is centred on workers, equipment, and sites. It changes the conventional job-centered construction information system and realizes efficient coordination of information among various departments and stages. Applying the digital selves based intelligent construction framework to the case has been proved to be a good solution to the problem of information sharing. Meanwhile, the authenticity and reliability of the information have been ensured. The system has improved management efficiency and information systematization for all participants. It has enabled companies and workers to have a more comprehensive understanding of themselves and to make timely and effective strategies against hazards. In addition, the system has effectively raised the level of digitalization, automation, and intelligence of construction, making governance more accurate and more effective. © The author(s) 2024.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Pre-Training Physics-Informed Neural Network with Mixed Sampling and Its Application in High-Dimensional Systems

引用

Journal of Systems Science & Complexity 2024年第2期37卷 494-510页

作者： LIU Haiyi ZHANG Yabin WANG Lei School of Mathematics and Physics North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

Recently,the physics-informed neural network shows remarkable ability in the context of solving the low-dimensional nonlinear partial differential ***,for some cases of high-dimensional systems,such technique may be time-consuming and *** this paper,the authors put forward a pre-training physics-informed neural network with mixed sampling(pPINN)to address these *** based on the initial and boundary conditions,the authors design the pre-training stage to filter out the set of the misfitting points,which is regarded as part of the training points in the next *** authors further take the parameters of the neural network in Stage 1 as the initialization in Stage *** advantage of the proposed approach is that it takes less time to transfer the valuable information from the first stage to the second one to improve the calculation accuracy,especially for the high-dimensional *** verify the performance of the pPINN algorithm,the authors first focus on the growing-and-decaying mode of line rogue wave in the Davey-Stewartson I *** case is the accelerated motion of lump in the inhomogeneous Kadomtsev-Petviashvili equation,which admits a more complex evolution than the uniform *** exact solution provides a perfect sample for data experiments,and can also be used as a reference frame to identify the performance of the *** experiments confirm that the pPINN algorithm can improve the prediction accuracy and training efficiency well,and reduce the training time to a large extent for simulating nonlinear waves of high-dimensional equations.

关键词： High-dimensional systems mixed sampling nonlinear wave pre-training physics-informed neural network

来源：评论

学校读者我要写书评

暂无评论

Quantum block coherence with respect to projective measurements

引用

Chinese Physics B 2024年第8期33卷 213-220页

作者：王璞李忠艳孟会贤 School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Mathematics and Physics North China Electric Power UniversityBeijing 102206China

Quantum coherence serves as a defining characteristic of quantum mechanics,finding extensive applications in quantum computing and quantum communication *** study explores quantum block coherence in the context of projective measurements,focusing on the quantification of such ***,we define the correlation function between the two general projective measurements P and Q,and analyze the connection between sets of block incoherent states related to two compatible projective measurements P and ***,we discuss the measure of quantum block coherence with respect to projective *** on a given measure of quantum block coherence,we characterize the existence of maximal block coherent states through projective *** research integrates the compatibility of projective measurements with the framework of quantum block coherence,contributing to the advancement of block coherence measurement theory.

关键词： quantum coherence compatibility projective measurement quantum block coherence

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Short Text Classification Model Integrating Part of Speech Features 4

A Deep Learning Short Text Classification Model Integrating ...

引用

4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Liu, Shiyu Liu, Qicheng Yantai University School of Computer and Control Engineering Yantai China

ISBN: (纸本)9798350394375

In short text classification, extracting text features is crucial. Static word vector training, the traditional method, has limitations such as insufficient semantics and sparse features, while dynamic training word vectors lack part-of-speech features. To overcome these issues, this paper proposes a new text classification model, PB-DPCNN, which combines part-of-speech tagging technology with dynamic word vector training using BERT. Part-of-speech vectors are constructed with part-of-speech attributes through part of speech related weights, and double-layer convolution captures long-distance patterns to improve word embedding richness. The output layer is combined with residual pooling, and the connection layer obtains the final text feature representation. Experimental results on the THUCNews dataset demonstrate that the model enhances each classification index and has good text semantic recognition ability. © 2024 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Improved YOLOv5 with Well-Targeted Solution for Traffic Sign Detection 5

Improved YOLOv5 with Well-Targeted Solution for Traffic Sign...

引用

5th International Conference on Electronic engineering and Informatics, ICEEI 2023

作者： Wei, Guangyi Xu, Jindong Liang, Zongbao Chong, Qianpeng Wang, Yijie School of Computer and Control Engineering Yantai University Yantai China

ISBN: (纸本)9781643685021

Traffic sign detection (TSD) is a significant task in the field of computer vision, which has important applications in traffic safety and driverless driving. However, this fundamental but challenging task still has always influenced by numerous negative factors, such as light intensity, severe weather and distance. In addition, this task is characterized by the small scale and irregular distribution of detected objects in complex traffic scenes. To mitigate these aforementioned challenges, this paper presents a method for TSD task improved on YOLOv5 with specific improvements. First, to alleviate the adverse impact posed by noise and enhance the feature representation, we design an adaptive network to preprocess the input image. Subsequently, we introduce recursive gated convolution and a second-order attention module in the neck to improve the sensibility of the proposed structure for small objects, i.e., traffic signs in the large-scale and complex scene. Finally, control Distance Intersection over Union (CDIoU) loss is utilized to accelerate the convergence progress while ensuring the model detection effect. Experimental results illustrate qualitatively and quantitatively that our method outperforms other state-of-the-art detection methods in TSD. © 2024 The Authors.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Cross-domain recommendation based on meta-networks and attention transfer 4

Cross-domain recommendation based on meta-networks and atten...

引用

4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Xia, Xue Liu, Qicheng Yantai University School of Computer and Control Engineering Yantai China

ISBN: (纸本)9798350394375

Although cross-domain recommendation systems play a crucial role in solving the data sparseness and cold start challenges in recommendation systems, current algorithms primarily rely on the user-item rating matrix for user feature modeling, leading to sparse data. Furthermore, existing cross-domain recommendation algorithms employ mapping transfer methods that result in negative migration issues. This negative migration significantly impacts the capability of cross-domain recommendation systems. Therefore, this paper presents a novel cross-domain recommendation algorithm based on meta-network and attention transfer. In the algorithm, we first utilize pre-trained user embedding information to generate user features within their respective domains. We introduce a reconstructed self-encoder to generate global user embeddings, enhancing the user representation for users in the source and target domain. Next, we leverage the user/item interaction information of the user in the source field to generate migratable user feature information through a meta-network. Finally, through the attention network, we use the user feature embeddings of the target field and the user's migratable information in the source field as the input of the attention network to generate feature embeddings specific to the target field for rating prediction. We conducted experiments on two domain pairs consisting of three Amazon datasets. The results indicate that the algorithm presented in this paper outperforms other benchmark models under MSE and MAE evaluation metrics. © 2024 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection for Bolt Tightening in Automotive Engine Assembly Based on Machine Learning 5

Anomaly Detection for Bolt Tightening in Automotive Engine A...

引用

5th International Conference on computer engineering and Intelligent control, ICCEIC 2024

作者： Zhang, Dong Cui, Zhendong School of Computer and Control Engineering Yantai University Yantai China

ISBN: (纸本)9798331507992

With the advancement of industrial automation, the quality requirements for automotive engine assembly bolt tightening have become increasingly stringent, as they are directly related to engine performance and vehicle safety. Traditional monitoring methods for bolt tightening rely on preset torque criteria, which often overlook anomalies such as material defects and tool wear. To address the issue of limited negative samples in industrial data, this study proposes an innovative positive sample boundary modelling strategy based on artificial intelligence, combined with a random forest algorithm for anomaly detection. By transforming time-series data into high-dimensional feature data, an efficient classifier is constructed through the integration of multiple decision trees. Experimental results from real engine assembly line data demonstrate that the proposed method can quickly and accurately identify abnormal conditions without increasing computational burden, significantly improving assembly quality and reducing failure rates. © 2024 IEEE.

关键词： Bolt tightening

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：