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IEEE International Conference on Electronics and Communication engineering (ICECE)

作者： Chunlan Luo Xiaoxia Zhu College of Computer and Control Engineering Minjiang University Fuzhou Fujian China

ISBN: (纸本)9781728188577

In the context of informatization and big data, the demands for the reliability and real-time of information transmission in wireless communication channels are increasing. Quadratic residue (QR) codes have a high prospect on error correction for reliable data conveyance over channel with noise. This paper proposes an optimized algebraic scheme for decoding (71, 36, 11) QR code for correcting up to 5 errors on one-case-by-one-case basis. This scheme is mainly to improve its algebraic decoding algorithm based on the Lin’s algorithm, only in this paper, the Lin’s algorithm is called LTC71 algorithm. The key technology is to use the algebraic structure of QR code and give a new discriminant condition by mathematical derivation, which can quickly detect whether there are four errors in the code. Simulation results show that the proposed decoding scheme achieves the same bit error-rate performance as LTC71 algorithm, and the decoding time is reduced by about 28.72% owing to the reduced complexity of the decoder when there are four errors in the code.

关键词： Wireless communication Simulation Information processing Numerical simulation Real-time systems Mathematical models Decoding

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lncRNA-Disease Association Prediction Based On Weight Matrix And Projection Score

Research Square

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Research Square 2021年

作者： Wang, Bo Zhang, Chao Du, Xiao-Xin Zhang, Jian-Fei College of Computer and Control Engineering Qiqihar University Qiqihar161006 China

Background: with the development of medical science, lncRNA, originally considered as a noise gene, has been found to participate in a variety of biological activities. Nowadays, more and more studies show that lncRNA is involved in various human diseases, such as gastric cancer, prostate cancer, lung cancer, etc. However, obtaining lncRNA-disease association only through biological experiments not only costs manpower and material resources, but also gains little. Therefore, it is very important to develop effective computational models for predicting lncRNA-disease association. Results: In this paper, a new lncRNA-disease association prediction model LDAP-WMPS based on weight distribution and projection score is proposed. Based on the existing research results of disease semantic similarity, the integrated lncRNA similarity matrix and the integrated disease similarity matrix are calculated according to the disease semantic similarity and the association information between data. On this basis, the weight algorithm is combined with the improved projection algorithm to predict the lncRNA-disease association through the known lncRNA-miRNA association and miRNA-disease association. The simulation results show that under the loocv framework, the AUC of LDAP-WMPS can reach 0.8822. Better than the latest results. Through the case study of adenocarcinoma and colorectal cancer, it is proved that LDAP-WMPS can effectively infer lncRNA-disease association. Conclusions: The simulation results show that LDAP-WMPS has good prediction performance, which is an important supplement to the research of lncRNA-disease association prediction without lncRNA-disease association data. © 2021, CC BY.

关键词： Diseases

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Optimizing machine learning algorithms to enhance blood donation for liver patients: a case study

Optimizing machine learning algorithms to enhance blood dona...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Maad M. Mijwil El-Sayed M. El-kenawy Indu Bala Mohammad Aljanabi Mostafa Abotaleb Abdelhameed Ibrahim Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood.

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Modeling and control of Intermediate Point Temperature of A 1000-WM Ultra Supercritical Thermal Power Plant 40

Modeling and Control of Intermediate Point Temperature of A ...

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40th Chinese control Conference, CCC 2021

作者： Peng, Chen Xxx Shanghai University School of Mechatronic Engineering and Automation Shanghai201900 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Nanjing University of Posts and Telecommunications College of Automation and College of Artificial Intelligence Nanjing210023 China

ISBN: (纸本)9789881563804

This paper proposes an improved fuzzy particle swarm optimization algorithm (FPSO) to model intermediate point temperature. In FPSO, the plant-wide operation region with high nonlinearity is divided into several local regions by clustering algorithm. Following an improved particle swarm optimization algorithm is used to obtain local linear models. Then, fuzzy strategy is introduced to fuse the local models to get the model of the whole system. Besides, an improved generalized predictive control (GPC) is introduced to improve the control performance based on the identified modes. Finally, the simulation results show that the proposed modeling and control method can achieve accurate model identification and effective control of the intermediate point temperature. © 2021 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Clustering algorithms

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Energy Consumption Prediction for Manufacturing in Industrial IoT Based on Heterogeneous GNN

Energy Consumption Prediction for Manufacturing in Industria...

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IEEE International Conference on Communications in China (ICCC)

作者： Xuan Su Mingyan Li Qihao Li Chao Chen Songtao Guo Xu Wang College of Mechanical and Vehicle Engineering Chongqing University Chongqing P. R. China College of Computer Science Chongqing University Chongqing P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China College of Communication Engineering Jilin University Changchun P. R. China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Energy consumption prediction plays a crucial role in green manufacturing for industry, providing a theoretical foundation for energy-saving planning, workshop scheduling, and process optimization. However, owing to the processing complexity of mechanical products and the multiplicity of production information, previous methods can only achieve prediction for individual devices, which are quite restrictive to collaborative manufacturing planning. To deal with product-level energy management, this paper first proposes a heterogeneous manufacturing association graph (HetMG) which contains various manufacturing elements including product projects, decomposed orders and processing devices. Then, with HetMG, an energy consumption prediction method, HetEP, is introduced based on heterogeneous graph neural network (GNN), which incorporates Relational Graph Convolutional Networks (R-GCNs), and Long Short-Term Memory (LSTM) to mine the spatial-temporal relation of heterogeneous elements during the manufacturing process. Moreover, Graph Attention Network (GAT) is utilized in HetEP to adaptively learn the graph-structured data so that informative representations of each element can be obtained. The experimental results based on a manufacturing dataset from a generator factory demonstrate that the proposed HetEP yields superior prediction performance compared to the device-oriented prediction methods in terms of prediction accuracy.

关键词： Representation learning Performance evaluation Energy consumption Green manufacturing Job shop scheduling Prediction methods Production facilities

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A Multi-Discriminator Weighted Adversarial Network for Cross-Domain Unknown Wind Turbine Fault Diagnosis

A Multi-Discriminator Weighted Adversarial Network for Cross...

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IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Yinghua Han Huaiyuan Qi Fangyuan Si Kangping Li Qiang Zhao School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Control Engineering Notheastern University at Qinhuangdao Qinhuangdao China Department of Electrical Engineering Tsinghua University Beijing China The College of Smart Energy Shanghai Jiao Tong University Shanghai China

The practical cross-domain fault diagnosis methods in wind turbines based on transfer learning have been widely studied, where most of the existing transfer learning methods assume that test data and training data have the same label space. However, this assumption is not realistic in practical wind turbines because the data in the target domain wind turbine often presents new fault categories during the testing phase, which can affect the accuracy of the model. A multi-discriminator weighted adversarial network (MWAN) is proposed here to handle the above problem. The proposed method can automatically identify outliers in the source domain and unknown fault classes in the target domain by introducing a hybrid weighting balanced strategy and using an auxiliary discriminator to weigh source and target domain samples. Adversarial training is introduced to achieve shared class-level alignment in the two domains. Experiments verify that the proposed method outperforms state-of-the-art methods for wind turbine fault diagnosis applications.

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An Improved Machine Learning Technique with Effective Heart Disease Prediction System

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computers, Materials & Continua 2021年第12期69卷 4169-4181页

作者： Mohammad Tabrez Quasim Saad Alhuwaimel Asadullah Shaikh Yousef Asiri Khairan Rajab Rihem Farkh Khaled Al Jaloud College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death *** heart disease is challenging because it requires substantial experience and *** research studies have found that the diagnostic accuracy of heart disease is *** coronary heart disorder determines the state that influences the heart valves,causing heart *** indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep *** work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial *** first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)*** this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was *** CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods *** proposed method offers an illustrative framework that helps predict heart attacks with high *** proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

关键词： Machine learning deep recurrent neural network effective heart disease prediction framework

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Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation Network

arXiv

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arXiv 2024年

作者： Li, Shaokai Ji, Yixuan Song, Peng Sun, Haoqin Zheng, Wenming School of Computer and Control Engineering Yantai University Yantai264005 China Business School Xuzhou University of Technology Xuzhou221018 China College of Computer Science Nankai University Tianjin300350 China Key Laboratory of Child Development and Learning Science of Ministry of Education Southeast University Nanjing210096 China

In this paper, we propose a novel deep inductive transfer learning framework, named feature distribution adaptation network, to tackle the challenging multi-modal speech emotion recognition problem. Our method aims to use deep transfer learning strategies to align visual and audio feature distributions to obtain consistent representation of emotion, thereby improving the performance of speech emotion recognition. In our model, the pre-trained ResNet-34 is utilized for feature extraction for facial expression images and acoustic Mel spectrograms, respectively. Then, the cross-attention mechanism is introduced to model the intrinsic similarity relationships of multi-modal features. Finally, the multi-modal feature distribution adaptation is performed efficiently with feed-forward network, which is extended using the local maximum mean discrepancy loss. Experiments are carried out on two benchmark datasets, and the results demonstrate that our model can achieve excellent performance compared with existing ones. Our code is available at https://***/shaokai1209/FDAN. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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InterTracker: Discovering and Tracking General Objects Interacting with Hands in the Wild

arXiv

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arXiv 2023年

作者： Shao, Yanyan Ye, Qi Luo, Wenhan Zhang, Kaihao Chen, Jiming College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen518107 China College of Engineering Computing & Cybernetics Australian National University CanberraACT2601 Australia

Understanding human interaction with objects is an important research topic for embodied Artificial Intelligence and identifying the objects that humans are interacting with is a primary problem for interaction understanding. Existing methods rely on frame-based detectors to locate interacting objects. However, this approach is subjected to heavy occlusions, background clutter, and distracting objects. To address the limitations, in this paper, we propose to leverage spatio-temporal information of hand-object interaction to track interactive objects under these challenging cases. Without prior knowledge of the general objects to be tracked like object tracking problems, we first utilize the spatial relation between hands and objects to adaptively discover the interacting objects from the scene. Second, the consistency and continuity of the appearance of objects between successive frames are exploited to track the objects. With this tracking formulation, our method also benefits from training on large-scale general object-tracking datasets. We further curate a video-level hand-object interaction dataset for testing and evaluation from 100DOH. The quantitative results demonstrate that our proposed method outperforms the state-of-the-art methods. Specifically, in scenes with continuous interaction with different objects, we achieve an impressive improvement of about 10% as evaluated using the Average Precision (AP) metric. Our qualitative findings also illustrate that our method can produce more continuous trajectories for interacting objects. Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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