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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

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Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

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computers, Materials & Continua 2024年第4期79卷 607-622页

作者： Kai Wei Song Yu Qingxian Pan School of Computer and Control Engineering Yantai UniversityYantai264005China School of Computer and Information Science Southwest UniversityChongqing400715China

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource *** traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and *** address these issues,this study proposes a novel two-part invalid detection task allocation *** the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous *** to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public *** the second step of the framework,task allocation modeling is performed using a twopart graph matching *** phase introduces a P-queue KM algorithm that implements a more efficient optimization *** allocation efficiency is improved by approximately 23.83%compared to the baseline *** results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

关键词： Mobile crowdsourcing task allocation anomaly detection GAN attention mechanisms

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A calibration and compensation method for an industrial robot with high accuracy harmonic reducers

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Science China(Technological Sciences) 2024年第3期67卷 725-739页

作者： ZHAO YiYang LIU Xun CAO JiaBin LIU JinYu XU Yan YAN WeiXin School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040China

Industrial serial robots need high stiffness to keep absolute pose accuracy and meet the requirements in practical applications. However, the weak stiffness feature of robot joints and the payloads affected on robot end-effector, which will also increase the pose error of robot. Especially, the existing calibration methods often consider under no-payload condition without discussing the payload state. In this paper, we report a new industrial serial robot composed by a new harmonic reducer: Model-Y, based on high accuracy and high stiffness, and a kinematic parameter calibration algorithm which is based on a harmonic reducer forcedeformation model. To decrease the accuracy effects of payload, an iterative calibration method for kinematic parameters with payload situation was proposed. Simulation and experiments are conducted to verify the effectiveness of the proposed calibration method using the self-developed industrial serial robot. The results show a remarkably improved accuracy in absolute position and orientation with the robot's payload range. The position mean error has 70% decreased to 0.1 mm and the orientation mean error diminished to less than 0.01° after calibration with compensation. Additionally, online linear and circular tests are carried out to evaluate the position error of the robot during large-scale spatial and low-speed continuous movement. The accuracy is consistent with the previous calibration results, indicating the effectiveness and advantages of the proposed strategy in this article.

关键词： industrial serial robot Model-Y harmonic calibration stiffness model online compensation

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Digital twin-and extended reality-based telepresence for collaborative robot programming in the 6G perspective

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Digital Communications and Networks 2024年第2期10卷 315-327页

作者： Davide Calandra F.Gabriele Pratticò Alberto Cannavò Claudio Casetti Fabrizio Lamberti Politecnico di Torino Department of Control and Computer EngineeringCorso Duca Degli Abruzzi2410129TurinItaly

In the context of Industry 4.0,a paradigm shift from traditional industrial manipulators to Collaborative Robots(CRs)is ongoing,with the latter serving ever more closely humans as auxiliary tools in many production *** this scenario,continuous technological advancements offer new opportunities for further innovating robotics and other areas of next-generation *** example,6G could play a prominent role due to its human-centric view of the industrial *** particular,its expected dependability features will pave the way for new applications exploiting highly effective Digital Twin(DT)-and eXtended Reality(XR)-based *** this work,a novel application for the above technologies allowing two distant users to collaborate in the programming of a CR is *** approach encompasses demanding data flows(e.g.,point cloud-based streaming of collaborating users and robotic environment),with network latency and bandwidth *** obtained by analyzing this approach from the viewpoint of network requirements in a setup designed to emulate 6G connectivity indicate that the expected performance of forthcoming mobile networks will make it fully feasible in principle.

关键词： 6G Digital twin Telepresence Collaborative robots Industry 4.0 Augmented reality Virtual reality Point cloud streaming

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A generic optimization and learning framework for Parkinson disease via speech and handwritten records

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10673-10693页

作者： Yousif, Nada R. Balaha, Hossam Magdy Haikal, Amira Y. El-Gendy, Eman M. Computer and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Parkinson’s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features;and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches. © 2022, The Author(s).

关键词： Feature extraction

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

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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

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

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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

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Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

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Intelligent Automation & Soft Computing 2023年第8期37卷 1963-1981页

作者： Zhizhong Liu Bin Zhou Lingqiang Meng Guangyu Huang School of Computer and Control Engineering Yantai UniversityYantai264005China

Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment *** challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion ***,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level *** improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and *** we fuse the unimodal features into bimodal features using the bimodal attention fusion ***,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal ***,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level *** on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.

关键词： Multimdoal sentiment analysis BiGRU attention mechanism features-level fusion hybrid fusion strategy

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Part Deviation Correction Method Based on Geometric Feature Recognition

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International Journal of Crowd Science 2023年第3期7卷 113-119页

作者： Zhang, Guoqing Sun, Hongbo School of Computer and Control Engineering Yantai University Yantai264005 China

To realize the automatic loading process of parts, one of the core tasks is to identify the geometric contour of the part’s surface and the angular direction. Since the angular direction of each part is not the same when it arrives at the loading position, for example, there are two same types of parts with the same pattern, when they arrive at the loading position, the pattern on one part may be on the right side of the part surface, and the pattern on the other part may be on the left side of the part surface, the gripper of the mechanical arm needs to rotate above the parts in order to grab the parts during each loading process. If the rotation angle is wrong, there will be an impact between the gripper and the parts. Therefore, in order to solve the problem of different angles, this paper proposes a method of parts deviation correction based on geometric features. In this work, firstly, the acquired image is preprocessed, the image background is separated, and the geometric features of the parts are obtained. Then edge detection is used to obtain the set of edge pixels to obtain the contour in the image. Finally, the image moment and measurement model are used to output angular direction. Through 500 repeated detection experiments, the results show that this method can perform better angular direction correction. The maximum angular direction difference is 0.073°, which is 0.856° and 1.793° higher than the Least square method and Hough transform circle detection accuracy, respectively. The average detection time is 1.89 s and is 0.336 s and 1.39 s less than the Least square method and Hough transform circle detection, which meets the requirements of industrial applications. © The author(s) 2023.

关键词： Hough transforms

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Modeling and Verification of Simulation-Oriented Digital Selves

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International Journal of Crowd Science 2023年第2期7卷 87-96页

作者： Wang, Zhaotong Sun, Hongbo School of Computer and Control Engineering Yantai University Yantai264005 China

Networked life has now become one of our major life forms. In social networks, each individual has its own attributes and certain functions, which makes the current network present characteristics that the previous network did not have. The existing research believes that the structure and attributes of individuals in a network are the same, and they are in a single network at the same time. However, individuals in any social network may be in different networks at the same time and thus exhibit different behaviors, and such individuals are called digital selves. In this paper, we propose a simulation-oriented modeling method for digital selves, which allows them to be in multiple networks at the same time and to have their own decision-making mechanisms. The model consists of six parts, namely, pattern, affecter, decider, executor, monitor, and connector. After the verification of three simulation experiments, namely coevolutions, ecological structure evolution of an e-commerce market, and multi-information coevolution spreading, the model can be well applied in various scenarios, which verifies its feasibility and applicability. © The author(s) 2023.

关键词： Decision making

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