检索结果-内蒙古大学图书馆

System for Detection and Quantitative Evaluation of Compensatory Movement in Post-Stroke Patients Based on Wearable Sensor and machine learning Algorithm

引用

IEEE SENSORS JOURNAL 2024年第14期24卷 22830-22842页

作者： Ding, Kangjia Wang, Jiping Wang, Xiaojun Zhou, Linfu Xiong, Daxi Guo, Liquan Univ Sci & Technol China Sch Biomed Engn Suzhou Div Life Sci & Med Hefei 230052 Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Suzhou 215163 Peoples R China Suzhou Xiangcheng Peoples Hosp Suzhou 215131 Peoples R China Nanjing Med Univ Affiliated Hosp 1 Dept Resp & Crit Care Med Nanjing 211166 Peoples R China

During upper limb rehabilitation training and assessment, compensatory movements can significantly impede the recovery of motor function in stroke survivors. Current methods for detecting these movements primarily rely on visual sensors, torso restraint belts, and pressure pads. However, these approaches present challenges such as obstacle occlusion, complex operation, and an inability to provide a quantitative assessment of compensatory movements. To address these issues, we developed a system for detecting and quantitatively evaluating compensatory movements, utilizing wearable sensors and machine learning algorithms. We recruited 30 healthy volunteers and 32 stroke patients to complete seven experimental motions. Their movement data were recorded using three inertial measurement units (IMUs) placed on the upper limbs and sternum. After preprocessing the data, six machine learning algorithms [K-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)] were employed to train a multilabel classification model for detecting and quantitatively evaluating compensatory movements. The algorithm with the highest performance was selected as the system's classifier. The results show that the proposed system improves the number and accuracy of compensatory movement detection and achieves accurate classification and detection of seven compensatory movements (average accuracy is 95.7%, F1-score is 96.8%). Furthermore, the KNN classifier classified compensatory movements as mild, moderate, and severe according to the different severity of the compensatory movements and obtained an average accuracy of 93.7%, which proved the reliability and responsiveness of the system. This system is applicable to stroke patients in clinical, home, and community-based rehabilitation settings, enabling a quantitative assessment of compensatory movements.

关键词： Stroke (medical condition) Sensors Shoulder Motors Data models Compensation detection machine learning algorithms quantitative evaluation wearable sensors quantitative evaluation wearable sensors

来源：评论

学校读者我要写书评

暂无评论

Self-Powered Wireless Devices and machine learning Enable Local Humidity Monitoring

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 10369-10374页

作者： Zhou, Yuankai Cui, Xin Zhang, Yan Univ Elect Sci & Technol China Sch Phys Chengdu 610054 Peoples R China Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 100083 Peoples R China Univ Chinese Acad Sci Coll Nanosci & Technol Beijing 100049 Peoples R China

Self-powered wireless sensors are a promising design paradigm for Internet of Things (IoT) devices that need to enable continuous, real-time environmental monitoring using energy harvesting devices. However, currently available energy harvesting devices such as the triboelectric nanogenerator (TENG), generally have either millimetre-scale environmental monitoring devices or self-powered wireless transmitters, but not both. Typical low energy density of environmental energy has limited self-powered wireless sensors' long-term operation to laboratory settings and prevented their widespread and largescale deployment. Here we have developed a battery-free wireless moisture meter with simple structure and high accuracy. Our device is based on off-the-shelf parts suitable for various sensor and artificial intelligence applications. In the detection process, the light signal from the droplet-powered triboelectric nanogenerator is detected by the machine learning algorithm, and the humidity level detection accuracy is up to 97.3%.

关键词： Humidity Wireless communication Sensors Wireless sensor networks Optical sensors machine learning machine learning algorithms Triboelectric nanogenerator (TENG) environmental monitoring battery-free wireless sensor artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Wireless Edge Caching and Content Popularity Prediction Using machine learning

引用

IEEE CONSUMER ELECTRONICS MAGAZINE 2024年第4期13卷 32-41页

作者： Krishnendu, S. Bharath, B. N. Bhatia, Vimal Nebhen, Jamel Dobrovolny, Michal Ratnarajah, Tharmalingam Indian Inst Technol Indore Dept Elect Engn Indore India Indian Inst Technol Dharwad Dept Elect Engn Dharwad India Indian Inst Technol Indore Indore India Univ Hradec Kralove Hradec Kralove Czech Republic Prince Sattam Bin Abdulaziz Univ Alkharj Saudi Arabia Univ Hradec Kralove Appl Informat Hradec Kralove Czech Republic Univ Edinburgh Inst Digital Commun Edinburgh Midlothian Scotland

The ever pervasive growth in information services and technology has resulted in the outbreak of demand for data in the wireless networks. This has made the network operators to ponder over the imminent difficulties, such as computing capabilities and fronthaul-backhaul link capacities. Hence, to bridge the gap between the cloud capacity and requirement of mobile services by the network edges, edge computing, and caching techniques have been gaining more and more attention from researchers across the world. Further, motivated by the successful applications of machine learning (ML) in solving complex and dynamic problems, in this article, it has been used to advance edge caching capabilities. The proposed ML-based algorithms have been evaluated and proved to have better performance compared with the existing conventional algorithms. The mean squared error (mse), for the proposed deep learning (DL) algorithm, is 20-times less than the existing algorithms and while comparing with the simple neural network, the gain in mse for the proposed DL algorithm is observed around 27%. Similarly for the federated learning-based caching algorithm, the average cache hit gain is of the order of $10<<^>>algorithms$, hence demonstrating the benefit of the proposed algorithms. In addition, opportunities and challenges for a promising upcoming future of ML in edge computing and content popularity prediction have also been discussed.

关键词： Q-learning Wireless networks machine learning algorithms Device-to-device communication Prediction algorithms Markov processes Consumer electronics

来源：评论

学校读者我要写书评

暂无评论

UAV Swarm Cooperative Target Search: A Multi-Agent Reinforcement learning Approach

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 568-578页

作者： Hou, Yukai Zhao, Jin Zhang, Rongqing Cheng, Xiang Yang, Liuqing Tongji Univ Sch Software Engn Shanghai 200092 Peoples R China Peking Univ Sch Elect Beijing 100871 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Internet Things Thrust & Intelligent Transporta T Guangzhou 510000 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

The development of machine learning and artificial intelligence algorithms, as well as the progress of unmanned aerial vehicle swarm technology, has significantly enhanced the intelligence and autonomy of unmanned aerial vehicles in search missions, resulting in greater efficiency when searching unknown areas. However, as search scenarios become more complex, the existing unmanned aerial vehicle swarm search method lacks scalability and efficient cooperation. Furthermore, due to the increasing scale of search scenarios, the accuracy and real-time performance of global information are difficult to ensure, necessitating the provision of local information. This paper focuses on the large-scale search scenario and split it to provide both local and global information for running unmanned aerial vehicle swarm search algorithms. Since the search environment is often unknown, dynamic, and complex, it requires adaptive decision-making in a constantly changing environment, which is suitable for modeling as a Markov decision process. Considering the sequential-based scenario, we propose a distributed collaborative search method based on a multi-agent reinforcement learning algorithm, which can operate efficiently in complex and large-scale scenarios. Additionally, the proposed method can utilize a convolutional neural network to process high-dimensional map data with almost no loss of the structure information. Experimental results demonstrate that the proposed method can collaboratively search unknown areas, avoid collisions and repetitions, and find all targets faster compared with the benchmarks.

关键词： Autonomous aerial vehicles Task analysis Search problems Collaboration Scalability Real-time systems machine learning algorithms Unmanned aerial vehicles multi-agent reinforcement learning distributed search algorithm Markov decision process

来源：评论

学校读者我要写书评

暂无评论

Advanced machine learning-based modeling of interfacial tension in the crude oil-brine-diethyl ether system: Insights into the effects of temperature and salinity

引用

JOURNAL OF MOLECULAR LIQUIDS 2024年 404卷

作者： Mohammadi, Amir Keradeh, Mahsa Parhizgar Keshavarz, Alireza Farrokhrouz, Mohsen Sahand Univ Technol Fac Petr & Nat Gas Engn POB 51335-1996 Tabriz Iran Edith Cowan Univ Sch Engn Joondalup WA 6027 Australia

Solvent injection, a well-established method for enhanced oil recovery (EOR), has demonstrated significant improvements in oil recovery when compared with conventional water flooding techniques. The interfacial tension (IFT) is pivotal in determining the displacement efficiency and overall performance of innovative techniques like dimethyl ether-enhanced waterflooding (DEW), which has gained substantial attention in recent years. In this study, following laboratory measurements of IFT, six advanced machine learning (ML) techniques were employed: Generalized Linear Model (GLM), Generalized Additive Model (GAM), Random Forest (RF), Support Vector machine (SVM), Extreme Gradient Boosting (XGBoost), and Boosted Regression Tree (BRT) to model the IFT in both oil-brine and oil-brine-diethyl ether (DEE) systems. The analysis is based on an extensive dataset comprising 7,017 data points for oil-brine and 6,949 data points for oil-brine-DEE systems obtained from experimental studies. The findings indicate that the developed RF model excels in predicting IFT, boasting a remarkable coefficient of determination (R2 = 0.99) along with the lowest root mean squared error (RMSE = 0.2), mean squared error (MSE = 0.04), and mean absolute error (MAE = 0.13). The study underscores the significance of optimizing salinity levels to achieve the most substantial reduction in IFT. This reduction is attributed to the enhanced migration of polar components, such as asphaltene molecules, to the interface of the oil-brine system. Moreover, the research highlights a synergistic decrease in IFT when both DEE and soluble ions are present, resulting in the lowest IFT at around 2 mN/m in 40,000 ppm salinity (S2) at 70 degrees C (T3). This indicates that the adsorption of DEE at the water-oil interface forms a layer capable of adsorbing ions, thereby enhancing the layer's thickness. As a result, the oil-solvent-ion layer becomes thicker compared to the oil-ion layer, leading to the maximum decrease i

关键词： Diethyl ether Interfacial tension machine learning algorithms Mutual solvent EOR

来源：评论

学校读者我要写书评

暂无评论

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

引用

ENVIRONMENTAL TOXICOLOGY 2024年第5期39卷 2908-2926页

作者： Xu, Qi-Tong Qiang, Jian-Kun Huang, Zhi-Ye Jiang, Wan-Ju Cui, Xi-Mao Hu, Ren-Hao Wang, Tao Yi, Xiang-Lan Li, Jia-Yuan Yu, Zuoren Zhang, Shun Du, Tao Liu, Jinhui Jiang, Xiao-Hua Tongji Univ Shanghai East Hosp Sch Med Dept Gastrointestinal Surg Shanghai Peoples R China Tongji Univ Sch Med Key Lab Arrhythmias Minist Educ China Shanghai Peoples R China Nanjing Med Univ Affiliated Hosp 1 Dept Gynecol Nanjing Peoples R China Tongji Univ Shanghai East Hosp Sch Med Dept Gastrointestinal Surg Shanghai 200092 Peoples R China

BackgroundColorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis *** retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic *** integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC *** current study highlights the potential of integrating machine learning models to enhance the pr

关键词： colorectal cancer (CRC) experimental validation machine learning algorithms prognostic signature programmed cell death (PCD)

来源：评论

学校读者我要写书评

暂无评论

Large-scale prediction of solar irradiation, shading impacts, and energy generation on building Façade through urban morphological indicators: A machine learning approach

引用

ENERGY AND BUILDINGS 2024年 323卷

作者： Zhao, Hongying Liu, Chengyang Yang, Rebecca Jing Sun, Chayn RMIT Univ Sch Property Construct & Project Management Solar Energy Applicat Lab Melbourne Australia RMIT Univ Sch Sci Melbourne Australia

Incorporating Building-integrated Photovoltaics (BIPVs) into urban environments offers a sustainable approach by transforming building exteriors into renewable energy sources, especially by utilizing the extensive vertical surfaces in cities. While individual building applications have been extensively studied, there is a lack of research on BIPV potential at the urban scale due to the complexity of urban fabrics. Therefore, this study presents a novel method for predicting large-scale solar irradiation and energy generation on building surfaces using urban morphological indicators through machine learning models. Using an Australian city as a case study, a hierarchical two-level machine learning model is developed to predict the solar potential for building fa & ccedil;ade PV systems. The model incorporates 13 urban morphological predictors to determine average shading height and unshaded solar irradiance. By combining these predictions with wall datasets and different fa & ccedil;ade BIPV efficiencies, the proposed method predicts unshaded electricity generation. The results showcase high efficiency and accuracy in the prediction of the shading height, solar irradiance, and electricity generation. This innovative approach aids urban planners in considering large-scale surface BIPVs into urban development.

关键词： Building PV Solar potential Urban morphology machine learning algorithms Decision making

来源：评论

学校读者我要写书评

暂无评论

Analyzing machine learning Algorithm Performance in Predicting Student Academic Performance in Data Structures and algorithms Based on Lifestyles

Analyzing Machine Learning Algorithm Performance in Predicti...

引用

International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Elisa V. Malasaga Angelo C. Arguson John Kenneth F. Andales Shaneth C. Ambat Rhonnel S. Paculanan May Florence San Pablo Computer Science FEU Institute of Technology Manila Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

This research study employed machine learning algorithm in This research study employed a machine learning algorithm in predicting student academic performance in the Data Structures and Algorithm (DSA) course which is based on student lifestyle to analyze the factors that affect the high or low performance result. A total number of 251 Bachelor of Science in Computer Science (BSCS) students participated in the study where 207 or 82% were male and 44 or 18% were female. A oneshot case study was conducted that led to data collection through the administration of an online survey on former enrollees of the said course. The dataset was extracted with 43 features and was analyzed using Python on Jupyter Notebook. Randomly selected 70% of these, 176 observations, are used to train the classifier models. The remaining 30%, 75 observations, were used as the test data. In order to classify academic performance students, eight machine learning algorithms were applied based on random forest (RF), decision tree (DT), support vector machines (SVM), K-nearest neighbors (KNN), logistic regression (LR), Gaussian Naive Bayes (GNB), stochastic gradient descent (SGD), and perceptron. Although SGD and Perceptron classifier models show comparably low classification performances, both random forest and decision tree classifiers provided the highest metric performance. The study indicated that the lifestyles of students contributed to whether the student performance became high or low in their grade performance.

关键词： Support vector machines Surveys Logistic regression machine learning algorithms Gaussian processes Prediction algorithms Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Electronic Skin: Opportunities and Challenges in Convergence with machine learning

引用

ANNUAL REVIEW OF BIOMEDICAL ENGINEERING 2024年第1期26卷 331-355页

作者： Koo, Ja Hoon Lee, Young Joong Kim, Hye Jin Matusik, Wojciech Kim, Dae-Hyeong Jeong, Hyoyoung Sejong Univ Dept Semicond Syst Engn Seoul South Korea Sejong Univ Inst Semicond & Syst IC Seoul South Korea MIT Comp Sci & Artificial Intelligence Lab Cambridge MA USA MIT Dept Elect Engn & Comp Sci Cambridge MA USA Inst for Basic Sci Korea Ctr Nanoparticle Res Seoul South Korea Seoul Natl Univ Inst Chem Proc Sch Chem & Biol Engn Seoul South Korea Seoul Natl Univ Dept Mat Sci & Engn Seoul South Korea Seoul Natl Univ Interdisciplinary Program Bioengn Seoul South Korea Univ Calif Davis Dept Elect & Comp Engn Davis CA USA

Recent advancements in soft electronic skin (e-skin) have led to the development of human-like devices that reproduce the skin's functions and physical attributes. These devices are being explored for applications in robotic prostheses as well as for collecting biopotentials for disease diagnosis and treatment, as exemplified by biomedical e-skins. More recently, machine learning (ML) has been utilized to enhance device control accuracy and data processing efficiency. The convergence of e-skin technologies with ML is promoting their translation into clinical practice, especially in healthcare. This review highlights the latest developments in ML-reinforced e-skin devices for robotic prostheses and biomedical instrumentations. We first describe technological break-throughs in state-of-the-art e-skin devices, emphasizing technologies that achieve skin-like properties. We then introduce ML methods adopted for control optimization and pattern recognition, followed by practical applications that converge the two technologies. Lastly, we briefly discuss the challenges this interdisciplinary research encounters in its clinical and industrial transition.

关键词： electronic skins machine learning algorithms robotic skins electronic prostheses wearable electronics skin-mounted electronics

来源：评论

学校读者我要写书评

暂无评论

Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised machine learning Approach

引用

IEEE ACCESS 2024年 12卷 91127-91139页

作者： Alshawki, Mohammed B. Najm, Ihab Ahmed Hamoud, Alaa Khalaf Univ Kufa Dept Comp Sci Najaf 54003 Iraq Eotvos Lorand Univ Dept Comp Algebra H-1053 Budapest Hungary Univ Tikrit Dept Math Tikrit 43000 Iraq Univ Basrah Dept Cybersecur Basrah 61004 Iraq

With the emergence of 5G technology, congestion control has become a vital challenge to be addressed in order to have efficient communication. There are several congestion control models that have been proposed to control and predict the possible congestion in 5G technology. However, finding the optimal congestion control model is an important yet challenging task. In this paper, we examine the supervised and unsupervised machine learning approaches to the task of predicting the possible node that causes congestion in the 5G environment. Due to the huge variance in the domains of the data set columns, measuring the prediction's consistency was not an easy task. During our study, we tested twenty-six supervised and seven clustering algorithms. Finally, and based on the performance criteria, we have identified the best five algorithms out of the studied algorithms.

关键词： 5G mobile communication machine learning algorithms Prediction algorithms Classification algorithms Clustering algorithms Random forests Predictive models Unsupervised learning Supervised learning machine learning congestion control 5G supervised ML unsupervised ML

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：