Mixed precision block-Jacobi preconditioner: algorithms, performance evaluation and feature analysis

Falling will cause significant health risks to the elderly. Wearable fall detection system will effectively reduce the risk of fall-related complications, and improve the quality of life and well-being of the elderly. The behavior analysis of falls and the research progress of fall detection through the architecture of wearable fall detection equipment for the elderly are introduced. Furthermore, the procedures of the fall detection system for the elderly such as sensor data acquisition and preprocess, feature extraction and analysis, classification algorithm, performance evaluation and the classification technology are also introduced. In addition, the current research work from several aspects, such as classification technology, comparison and statistical analyzation, and summary and prospect are introduced for some meaningful references.

Every year, traffic congestion and traffic accidents have been rapidly increasing in proportion to increasing number of vehicles. Although the roadway design and signal system have been improved to relieve traffic congestion, traffic casualties and property damage do not decrease. The traffic accident is a serious issue of society because vehicle is a primary means of transportation. This paper develops a real-time traffic accident detection system (RTADS): This system helps us to cope with accidents and discover the causes of traffic accident by detecting the accident. We gathered video data recorded at several intersections and used them to detect accidents at different intersections which have different traffic flow and intersection design. However, because the data gathered from intersections have incompleteness, uncertainty and complicated causal dependency between them, we construct probability-based networks which calculate based on the probability for correct accident detection. This system instantly sends the detected result to managers using accident alarm system. RTADS features real time accident detection and analysis of the cause of accidents. In performance evaluation, the proposed system achieved a detection rate of 97% with a correct detection rate of 92% and a false alarm rate of 0.77%.

Under the dual effects of the rapid growth of tunnel mileage and operating years, the application and research of tunnel crack identification based on machine vision are increasing with the vigorous development of machine vision. However, due to the complex environment in tunnels, it is difficult to quickly obtain tunnel lining cracks via computer visions in the tunnel. Therefore, this paper presents the design of a fast acquisition system with the geometric feature analysis for tunnel lining cracks, which has been integrated into a tunnel fast inspection vehicle with a machine vision module. Through the research on the image acquisition system of the tunnel lining, the parameter selection of the crack shooting hardware system is determined, and the fast calculation method of shooting parameters is proposed. The geometric characteristic analysis of the tunnel lining crack image is employed to calculate crack width and determine the optimal gray value of crack extraction. Field tests have been conducted in the highway tunnels in Zhejiang and Yunnan provinces in China and the result indicates that the proposed approach yields much better performance in the detection efficiency, whose time of detection is only 1%, and the number of personnel required is only 40% of the traditional pure manual method. Compared with similar systems, it also has significant advantages in crack resolution and detection speed. This research provides a means of rapid acquisition of tunnel cracks and laying a foundation for the evaluation of the service performance of the tunnel.

In the first place, this thesis, based on an analysis of the features of the logistics system of the fresh and live agriculture products, the affecting factors of its performance and influencing mechanism, considering the specificity of the whole fresh and live agriculture products logistics system, constructs an indicating system on the evaluation of logistics system performance of the fresh and live agriculture products. Secondly, based on an analysis of the applicability of the analytic network process, this paper, analyses the dependent network process relationship between different indicators in the evaluation system of performance of the fresh and live agriculture products logistics system establishes a network construction of the fresh and live agriculture products and confirms partial weight and overall weight. Practically, through a survey of the relative departments and professors in Chang Tai in Fujian province, this paper, with an empirical analysis of the software Super Decision, raised some managerial suggestions which can the improve the performance.

The binding entropy is an important thermodynamic quantity which has numerous applications in studies of the biophysical process, and configurational entropy is often one of the major contributors in it. Therefore, its accurate estimation is important, though it is challenging mostly due to sampling limitations, anharmonicity, and multimodality of atomic fluctuations. The present work reports a Neighbor Approximated Maximum Information Spanning Tree (A-MIST) method for conformational entropy and presents its performance and computational advantage over conventional Mutual Information Expansion (MIE) and Maximum Information Spanning Tree (MIST) for two protein-ligand binding cases: indirubin-5-sulfonate to Plasmodium falciparum Protein Kinase 5 (PfPK5) and P. falciparum RON2-peptide to P. falciparum Apical Membrane Antigen 1 (PfAMA1). Important structural regions considering binding configurational entropy are identified, and physical origins for such are discussed. A thorough performance evaluation is done of a set of four entropy estimators (Maximum Likelihood (ML), Miller-Madow (MM), Chao-Shen (CS), and James and Stein shrinkage (JS)) with known varying degrees of sensitivity of the entropy estimate on the extent of sampling, each with two schemes for discretization of fluctuation data of Degrees of Freedom (DFs) to estimate Probability Density Functions (PDFs). Our comprehensive evaluation of influences of variations of parameters shows Neighbor Approximated MIE (A-MIE) outperforms MIE in terms of convergence and computational efficiency. In the case of A-MIE/MIE, results are sensitive to the choice of root atoms, graph search algorithm used for the Bond-Angle-Torsion (BAT) conversion, and entropy estimator, while A-MIST/MIST are not. A-MIST yields binding entropy within 0.5 kcal/mol of MIST with only 20-30% computation. Moreover, all these methods have been implemented in an OpenMP/MPI hybrid parallel C++11 code, and also a python package for data preprocessing and entropy contribution analysis is developed and made available. A comparative analysis of features of current implementation and existing tools is also presented.

Manual identification and classification of ornamental flower varieties is time-consuming and highly dependent onindividual expertise, resulting in identification errors that impact the production value chain and operational efficiency ofthe horticulture industry. This research aims to implement an automated classification system for three types ofornamental flowers (sunflower, rose, and tulip) using K-Means Clustering method with visual feature analysis to improveidentification accuracy and computational efficiency. The research methodology includes acquisition of 210 high-qualitybalanced flower images (70 samples per class), preprocessing with RGB to HSV color space transformation, segmentationusing K-Means with k=3, and extraction of 10 multi-dimensional features encompassing morphology, color, and GLCMtexture. The dataset was divided into 80% training and 20% testing using stratified sampling with K-Fold CrossValidation. Performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The researchresults demonstrate overall accuracy of 88.89% with sunflower achieving F1-score of 0.98 (0% error), rose 0.86 (14.3%error), and tulip 0.85 (19% error). Aspect ratio, solidity, and mean red channel intensity proved to be the mostdiscriminative features. Misclassification predominantly occurred in the rose-tulip pair (71.4%) due to red spectrum coloroverlap and morphological variation. K-Means algorithm demonstrated optimal balance between accuracy,computational efficiency (0.3s/image), and interpretability, although it has limitations on low feature separability. Thisstudy is limited to a small dataset (210 images) and controlled conditions, requiring real-world validation for bettergeneralization

To accurately analyse and evaluate the online learning situation of MOOC learners, this article focuses on MOOC learners and proposes innovative evaluation methods. First, using techniques such as association feature extraction and adaptive mining, the online learning behaviour data of MOOC learners are collected. Second, by analysing in detail the behavioural data of course access, video learning, homework submission, and daily grades, the external performance characteristics of MOOC learners' online learning behaviour can be quantitatively analysed. Finally, a multi-level fuzzy comprehensive evaluation method is used to construct a learning performance evaluation system, which quantitatively evaluates learning performance by setting evaluation factor sets, weight allocation, and comment sets. The test results show that the proposed method exhibits the highest feature analysis accuracy of 98% and performance evaluation accuracy of 95%.

This article presents the analysis and results of analytical, calculation, and design solutions for the development of piston engines for aircraft (tractors, automobiles, etc.). The authors propose an original design of a W-shaped engine with trailer connecting rods and made a comparative analysis of design features. The main feature of the W-shaped engine design is a significant reduction in length, and, consequently, the length of the crankshaft. As known, the manufacturing of internal combustion engine crankshafts is a long, complex, and expensive procedure requiring a number operations for its mechanical and heat treatment. An important aspect of crankshaft design is the calculation of mechanical tension, torsional vibrations, and the hydraulic performance evaluation of the main and connecting rod bearing, including engine balancing as a whole. At the end of the article the operating schemes of the engine section are presented (the section consists of four pistons, two main and two connecting rods bearing kinematically connected to one crankpin). The design differences from the classical schemes of crank-connecting mechanism (CCM) allow us to design the W-shaped engine in accordance with the technical requirements. For example, maximum power can be achieved by using variant “A”, with an increase in the power and heat stress of the parts of the cylinder-piston group (CPG), when the section is organized according to variants “B” or “C”, the loading of CCM parts is increased, but the loading of CPG parts is reduced. Considering that a W-shaped engine with trailed connecting rods has a same piston stroke in main and side cylinders and the workflow pattern in each cylinder is identical and is moved at the crankshaft rotation angle – which allows the engine to be designed according to variants above – the performance characteristics of such an engine are significantly expanded due to electronic control. The authors consider continuation of the work in the implementation of a full set of calculation studies taking into account the design features of the engine with a W-shaped design of CCM.

Toward Formal Safety and Performance Evaluation of GNSS-based Railway Localisation Function

In this paper we propose a novel local feature analysis and feature level fusion technique for detecting tampering or forgery for facial-biometric based on-line access control scenarios. The local features are extracted by analysing facial image data in the chrominance colour space and hue-saturation colour space. A feature level fusion of local features consisting of hue and saturation gradients with global features obtained from principal component analysis showed that a significant improvement in performance can be achieved in detecting tampered or forged images from genuine images in low bandwidth online streaming video access control contexts. The performance evaluation of the proposed fusion technique for a multimodal facial video corpus showed that an equal error rate of less than 1% could be achieved with feature level fusion of local features and global features.

Research on Frequency Modulation Performance Evaluation and Optimization Strategies for New Energy Power Stations Based on Multi Scenario Feature Analysis

Performance evaluation of implicit smartphones authentication via sensor-behavior analysis