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Intelligent wheelchair human-robot interactive system based on human posture recognition

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第8期38卷 4353-4363页

作者： Han, Yali Zhou, Lei Jiang, Weijia Wang, Gairan Nanjing Inst Technol Sch Mech Engn Nanjing 211167 Peoples R China

A human posture interaction system guaranteed by algorithmic control is proposed to improve the convenience and control accuracy of wheelchairs. Initially, a system is designed based on microelectromechanical systems (MEMS) technology that incorporates a dual-inertial measurement unit to efficiently capture data on the user's body posture. Subsequently, a K-means clustering algorithm is implemented in the system to analyze and recognize the user's body position in real-time. This condition allows the system to deduce the user's intention and react accordingly. Finally, the control system is validated through identification control experiments. Experiment results demonstrate a success rate above 97 %, thereby suggesting the reliability of the posture recognition algorithm.

关键词： Posture recognition Intelligent wheelchair clustering algorithm Sensor application

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Spatial logic of scientific research: an exploratory and quantitative analysis of 42 scientific research buildings sited in America and Europe

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ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 2024年第4期20卷 850-867页

作者： Zhang, Xinwei Cui, Tong Lan, Jun Shijiazhuang Tiedao Univ Sch Architecture & Art Shijiazhuang Peoples R China Univ Chinese Acad Sci Ctr Architecture Res & Design Beijing Peoples R China 17 Beierhuan East Rd Shijiazhuang Hebei Peoples R China

Since the nineteenth-century devolution of natural philosophy into disciplines, scientific activity has expanded from single-disciplined research to comprehensive research. The flow of information between different research teams makes scientific research and its places increasingly social, which is no longer limited to the closed laboratory workbench. Reviewing the literature, we found that the development of scientific research buildings experienced a transition from efficiency to openness. And the new organization of research communities and its demand for space put forward higher requirements for scientific research buildings. To deeply explore the design trends of scientific research buildings at this stage, the sample database is established using space syntax and K-means clustering algorithm software to quantitatively analyze spatial layouts of 42 scientific research buildings worldwide from the perspective of sociology and innovation, clustered into five categories. Each category shows a different tendency in terms of accessibility, visibility, and the proportion of public space, and the different spatial configurations and openness somehow reflect the attitude of the research organization or group towards the use of space.

关键词： Scientific research buildings space syntax spatial configuration clustering algorithm

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Improvements to the IntiGIS Model Related to the clustering of Consumers for Rural Electrification 8th

Improvements to the IntiGIS Model Related to the Clustering ...

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8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023

作者： Torres-Pérez, Mirelys Peña Abreu, Marieta Domínguez, Javier University of Las Tunas Avenida 30 de Noviembre S/N Reparto Aurora Las Tunas75100 Cuba University of Informatics Sciences Carretera a San Antonio de los Baños Km 2 ½ Torrens La Habana Boyeros19370 Cuba CIEMAT Centro de la Moncloa Complutense 40 Madrid28040 Spain

ISBN: (纸本)9783031495519

Providing access to electricity in rural areas remains a challenge in many developing countries, where the lack of infrastructure, low population density, and high costs hinder the implementation of conventional electrification schemes. In this context, off grid solutions (microgrid and individual systems) have emerged as a promising solution, allowing the integration of local renewable energy resources and providing electricity to communities that are not connected to the main power grid. The IntiGIS model has been proposed as a tool to allow the evaluation and comparison of the various electrification technology options. However, some limitations and challenges have been identified in the original model, particularly related to the level of consumer aggregation and the distribution network layout. In this paper, we present improvements to the IntiGIS model related to the clustering of consumers for rural electrification, based on a modified agglomerative clustering algorithm and a set of performance metrics to form the clusters. We also present a comparison with the IntiGIS II version in a case study in Guamá, Santiago de Cuba to demonstrate the effectiveness of the proposed improvements. The results show that the modified IntiGIS model can generate clusters that meet the technical and economic requirements for microgrid and grid extensions systems, with better accuracy and efficiency than the original model. These improvements can contribute to the implementation of sustainable and reliable electricity access in rural areas, promoting social and economic development in these regions. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： clustering algorithm Energy Access Geospatial Analysis IntiGIS Rural Electrification Semisupervised Learning Sustainable Development

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A Simplified Approach to Recognize Vortex-Induced Vibration Response Using Machine Learning

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STRUCTURAL ENGINEERING INTERNATIONAL 2024年第4期34卷 670-682页

作者： Yan, Zhengxi Zheng, Shixiong Yang, Fengfan Tai, Xueyang Chen, Zhiqiang Southwest Jiaotong Univ Sch Civil Engn Chengdu Peoples R China China MCC5 Grp Corp Ltd Chengdu Peoples R China Chengdu Univ Coll Architecture & Civil Engn Chengdu Peoples R China

The vortex-induced vibration (VIV) problem has been of critical concern for the wind-resistance of long-span bridges. Usually there are four types of approach for VIV studies: wind tunnel tests, field monitoring, computational fluid dynamics and mathematical models. However, traditional approaches have shown some limitations, such as high cost and low efficiency. In order to improve the efficiency and accuracy of VIV studies, this article has taken the VIV problem of a split three-box girder in a cable-stayed and cooperative suspension system bridge as an instance, and conducted a series of VIV wind tunnel tests. An approach based on machine learning is described that is able to serve as a complement to the wind tunnel tests. The proposed approach involves two steps: firstly, based on the dataset produced by wind tunnel tests, a clustering algorithm is introduced to separate the VIV signals automatically from other vibrations. Then, an artificial neural network is utilized to recognize the VIV response and aerodynamic force in the lock-in region directly. It is shown that the clustering algorithm can be a good tool for the recognition of VIV signals. Moreover, the proposed artificial neural network models show good ability for recognizing VIV amplitude and aerodynamic lift force.

关键词： vortex-induced vibration (VIV) split three-box girder machine learning clustering algorithm artificial neural network (ANN)

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Hardware Trojan Detection via Synthetic Sample Optimization and clustering Enhancement 12

Hardware Trojan Detection via Synthetic Sample Optimization ...

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12th International Conference on Information Systems and Computing Technology

作者： Lu, Kailun Lu, Jiahao Shanghai Univ Elect Power Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350379860;9798350379877

With the globalization of integrated circuit design, the risks of intellectual property theft and hardware Trojan (HT) insertion have become increasingly prevalent. This paper addresses the challenges of sample imbalance and limitations of single detection models in HT detection. To tackle sample imbalance, Borderline Synthetic Minority Over-sampling Technique (Borderline-SMOTE) generates synthetic samples, which are further optimized by a genetic algorithm to improve classifier performance. Additionally, clustering algorithm is combined with supervised learning to enhance feature representation and improve detection accuracy. Light Gradient Boosting Machine (LightGBM) was chosen for its superior performance, achieving an average true positive rate (TPR) of 90.45%. Experimental results demonstrate that this method surpasses existing HT detection techniques.

关键词： Hardware trojan Synthetic samples Genetic algorithm clustering algorithm Supervised learning

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Dynamic Regularized Adaptive Cluster Optimization (DRACO) for Quantitative Cardiac Cine MRI in Complex Arrhythmias

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JOURNAL OF MAGNETIC RESONANCE IMAGING 2025年第1期61卷 248-262页

作者： Ming, Zhengyang Pogosyan, Arutyun Christodoulou, Anthony G. Finn, J. Paul Ruan, Dan Nguyen, Kim-Lien UCLA VA Greater Angeles Healthcare Syst 11301 Wilshire Blvd MC 111E Los Angeles CA 90073 USA UCLA David Geffen Sch Med 200 Med PlazaB269 Los Angeles CA 90095 USA Univ Calif Los Angeles Phys & Biol Med Grad Program Los Angeles CA USA UCLA David Geffen Sch Med Dept Radiol Sci Los Angeles CA USA UCLA David Geffen Sch Med Div Cardiol Los Angeles CA USA VA Greater Angeles Healthcare Syst Los Angeles CA USA Univ Calif Los Angeles Dept Bioengn Los Angeles CA USA UCLA David Geffen Sch Med Dept Radiat Oncol Los Angeles CA USA

Background: Irregular cardiac motion can render conventional segmented cine MRI nondiagnostic. clustering has been proposed for cardiac motion binning and may be optimized for complex arrhythmias. Purpose: To develop an adaptive cluster optimization method for irregular cardiac motion, and to generate the corresponding time-resolved cine images. Study Type: Prospective. Subjects: Thirteen with atrial fibrillation, four with premature ventricular contractions, and one patient in sinus rhythm. Field Strength/Sequence: Free-running balanced steady state free precession (bSSFP) with sorted golden-step, reference real-time sequence. Assessment: Each subject underwent both the sorted golden-step bSSFP and the reference Cartesian real-time imaging. Golden-step bSSFP images were reconstructed using the dynamic regularized adaptive cluster optimization (DRACO) method and k-means clustering. Image quality (4-point Likert scale), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge sharpness, and ventricular function were assessed. Statistical TestsPaired t-tests, Friedman test, regression analysis, Fleiss' Kappa, Bland-Altman analysis. Significance level P < 0.05. Results: The DRACO method had the highest percent of images with scores >= 3 (96% for diastolic frame, 93% for systolic frame, and 93% for multiphase cine) and the percentages were significantly higher compared with both the k-means and real-time methods. Image quality scores, SNR, and CNR were significantly different between DRACO vs. k-means and between DRACO vs. real-time. Cardiac function analysis showed no significant differences between DRACO vs. the reference real-time. Conclusion: DRACO with time-resolved reconstruction generated high quality images and has early promise for quantitative cine cardiac MRI in patients with complex arrhythmias including atrial fibrillation.

关键词： clustering algorithm atrial fibrillation arrhythmia golden-step acquisition total variation quantitative MRI

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clustering Product Development Project Organization From the Perspective of Social Network Analysis

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2022年第6期69卷 2482-2496页

作者： Yang, Qing Yang, Na Browning, Tyson R. Jiang, Bin Yao, Tao Univ Sci & Technol Beijing Sch Econ & Management Beijing 100083 Peoples R China Texas Christian Univ Neeley Sch Business Ft Worth TX 76129 USA Depaul Univ Driehaus Coll Business Chicago IL 60604 USA Penn State Univ Harold & Inge Marcus Dept Ind & Mfg Engn University Pk PA 16802 USA

In product development (PD) organizations, coordinating technical dependencies among teams with different expertise in overlapping processes is a fundamental challenge. This article takes a more sophisticated approach than prior methodologies to improve coordination via organizational clustering, by accounting for both team structural and attribute similarity from the perspective of social network analysis. We built models to quantify the impact of the overlapping processes on the interaction strength among PD teams, which we then used to construct structural similarity by combining tie strength and social cohesion among teams via the design structure matrix. To evaluate the organization network, we propose social embeddedness-related centrality indices within (intracluster) and across (intercluster) team groupings. To facilitate knowledge sharing, we base team attribute similarity on product- and process-related expertise among teams. We integrate the modularity index and an improved silhouette index to find an optimal number of clusters, which we then incorporate with team similarity measures as inputs to a spectral clustering algorithm. An industrial example illustrates the proposed model. The clustering results reinforce several managerial practices but also yield new insights, such as how to measure similarity among teams based on organizational network characteristics and how structural and attribute similarities impact the optimal organizational structure.

关键词： Organizations clustering algorithms Social networking (online) Product development Indexes Complexity theory Analytical models clustering algorithm design structure matrix (DSM) organization design product development (PD) project management social network analysis (SNA)

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Sensor-validated simulations predict fracture healing outcomes in an ovine model

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RESULTS IN ENGINEERING 2025年 25卷

作者： Feist, Alicia Hetreau, Carla Ernst, Manuela Varga, Peter Schwarzenberg, Peter AO Res Inst Davos CH-7270 Davos Switzerland

Fracture management remains a significant clinical challenge, with nonunions occurring in approximately 5 % of fractures. In tibial shaft fractures 1 in 4 patients are considered unhealed after 5 months and a reoperation rate of 12 % is reported. By predicting healing outcomes, prognostic fracture healing simulations could assist in treatment selection and enable early intervention in the event of complications but have lacked comprehensive ground truth validation. This study validated a mechanoregulatory fracture healing simulation algorithm against continuous in vivo sensor data from an ovine preclinical study with eight animals, including a delayed union and a nonunion case. Tibia osteotomies were fixed with locking plates and a strain sensor was attached to the plate to monitor healing in vivo. Subject-specific finite element models, derived from CT post-operative scans, were used with the simulation algorithm, which updated material properties through iterative strain-based tissue differentiation. Specimens were grouped into different healing categories using an unsupervised clustering algorithm, distinguishing cases towards union, delayed union, and nonunion. Significant differences between the predicted healing trajectories were identified, enabling the correct classification of the healing outcomes between union, delayed union and non-union cases. Overall, this study demonstrated successful application of the healing algorithm with validation of the simulation results to healing outcomes. The potential of the simulation to predict healing patterns and to be used as a tool for non-union risk assessment was illustrated. Further refinements are necessary before the healing simulation can be implemented in clinical settings, requiring future research.

关键词： clustering algorithm Fracture healing Fuzzy logic Healing simulations Nonunion Sensors

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A Self-learning clustering algorithm Based on clustering Coefficient

A Self-learning Clustering Algorithm Based on Clustering Coe...

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15th International Conference on Web Information Systems Engineering (WISE)

作者： Zhong, MingJie Ding, ZhiJun Sun, HaiChun Wang, PengWei Tongji Univ Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai 200092 Peoples R China Univ Pisa Dept Comp Sci I-56127 Pisa Italy

ISBN: (纸本)9783319117492;9783319117485

This paper presents a novel clustering algorithm based on clustering coefficient. It includes two steps: First, k-nearest-neighbor method and correlation convergence are employed for a preliminary clustering. Then, the results are further split and merged according to intra-class and inter-class concentration degree based on clustering coefficient. The proposed method takes correlation between each other in a cluster into account, thereby improving the weakness existed in previous methods that consider only the correlation with center or core data element. Experiments show that our algorithm performs better in clustering compact data elements as well as forming some irregular shape clusters. It is more suitable for applications with little prior knowledge, e. g. hotspots discovery.

关键词： clustering algorithm clustering coefficient self-learning clustering

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Pythagorean fuzzy information processing based on centroid distance measure and its applications

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 236卷

作者： Sun, Gang Wang, Mingxin Guizhou Univ Commerce Comp & Informat Engn Coll Guiyang 550014 Peoples R China Hunan Inst Technol Sch Math & Phys Hengyang 421002 Peoples R China

By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages.

关键词： Pythagorean fuzzy number (PFN) Centroid distance measure clustering algorithm Ranking method Multi criteria decision making (MCDM) method

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