Relevant bibliographies by topics / Semi Average Methods

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Academic literature on the topic 'Semi Average Methods'

Author: Grafiati
Published: 4 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Semi Average Methods"

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Barbalho, Fernando D., Gabriela F. N. da Silva, and Klebber T. M. Formiga. "Average Rainfall Estimation: Methods Performance Comparison in the Brazilian Semi-Arid." Journal of Water Resource and Protection 06, no. 02 (2014): 97–103. http://dx.doi.org/10.4236/jwarp.2014.62014.

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Machfiroh, Ines Saraswati, Widiya Astuti Alam Sur, and Robby Tri Pangestu. "TREND SEMI AVERAGE AND LEAST SQUARE IN FORECASTING YAMAHA MOTORCYCLE SALES." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 1 (2022): 343–54. http://dx.doi.org/10.30598/barekengvol16iss1pp341-352.

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This study compared the Semi Average and Least Square methods to determine the sales trend of Yamaha motorcycles in obtaining the best method for predicting motorcycle sales at CV Surya Prima Pelaihari. Mean Absolute Percentage Error (MAPE) was used to determine the accuracy of the Semi Average and Least Square methods in predicting the sales of CV Surya Prima Pelaihari motorbikes. Semi Average method was based on the MAPE value of 43.96%. The Least Square method has a MAPE value of 31.89%. The comparison of MAPE values shows that the Least Square method provides better predicting results because of the lower MAPE value. Therefore, the Least Square method was used to predict sales at CV Surya Prima Pelaihari. A more accurate output can be obtained than the Semi Average method.
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Ni, Nyoman Supuwiningsih. "DISTRIBUTION TREN OF SENIOR HIGH SCHOOL AS ORIGIN SCHOOL OF COLLEGE STUDENT STIKOM BALI BASE GIS." International Journal of Engineering Technologies and Management Research 6, no. 12 (2020): 78–88. https://doi.org/10.5281/zenodo.3604621.

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GIS (Geographic Information System) is an information system that can store and integrate spatial data and non-spatial data can be used for interactive mapping of STIKOM Student School origin in Denpasar. During this time the spread of origin of high school / vocational school / equivalent STIKOM Bali students has never been mapped to find out the trend of increase or decrease in the number of origin of STIKOM Bali student schools from 2013-2018 and predict the number of students in accordance with the origin of schools in the city of Denpasar. This study aims to provide information to the management of STIKOM Bali regarding the distribution trends of the interests of prospective students to continue to tertiary level, especially STIKOM Bali. This research will collaborate between statistical science and the concept of GIS (Geographic Information System). Statistically the number of STIKOM Bali students is based on the origin of schools in Denpasar City and predicts it for the next 3 years using a trend analysis of semi-average methods (Semi Average Methods) as a material for evaluating the performance of STIKOM Bali management in improving the performance of campus promotions. This method makes trends by finding the average group of data which consists of grouping data into 2 parts, calculating average arithmetic, calculating the difference, formulating the value of change and making equations for subsequent trends. The results of these calculations are mapped with the concept of GIS (Geographic Information System) using ArcView as software to implement that integrates spatial data with non-spatial data.
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Wang, Jiaxin, Chris H. Q. Ding, Sibao Chen, Chenggang He, and Bin Luo. "Semi-Supervised Remote Sensing Image Semantic Segmentation via Consistency Regularization and Average Update of Pseudo-Label." Remote Sensing 12, no. 21 (2020): 3603. http://dx.doi.org/10.3390/rs12213603.

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Image segmentation has made great progress in recent years, but the annotation required for image segmentation is usually expensive, especially for remote sensing images. To solve this problem, we explore semi-supervised learning methods and appropriately utilize a large amount of unlabeled data to improve the performance of remote sensing image segmentation. This paper proposes a method for remote sensing image segmentation based on semi-supervised learning. We first design a Consistency Regularization (CR) training method for semi-supervised training, then employ the new learned model for Average Update of Pseudo-label (AUP), and finally combine pseudo labels and strong labels to train semantic segmentation network. We demonstrate the effectiveness of the proposed method on three remote sensing datasets, achieving better performance without more labeled data. Extensive experiments show that our semi-supervised method can learn the latent information from the unlabeled data to improve the segmentation performance.
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Huang, Shuhui, Baohong Zhu, Yongan Zhang, Hongwei Liu, Shuaishuai Wu, and Haofeng Xie. "Microstructure Comparison for AlSn20Cu Antifriction Alloys Prepared by Semi-Continuous Casting, Semi-Solid Die Casting, and Spray Forming." Metals 12, no. 10 (2022): 1552. http://dx.doi.org/10.3390/met12101552.

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Antifriction alloys such as AlSn20Cu are key material options for sliding bearings used in machinery. Uniform distribution and a near-equiaxed granularity tin phase are generally considered to be ideal characteristics of an AlSn20Cu antifriction alloy, although these properties vary by fabrication method. In this study, to analyze the variation of the microstructure with the fabrication method, AlSn20Cu alloys are prepared by three methods: semi-continuous casting, semi-solid die casting, and spray forming. Bearing blanks are subsequently prepared from the fabricated alloys using different processes. Morphological information, such as the total area ratio and average particle diameter of the tin phase, are quantitatively characterized. For the tin phase of the AlSn20Cu alloy, the deformation and annealing involved in semi-continuous casting leads to a prolate particle shape. The average particle diameter of the tin phase is 12.6 µm, and the overall distribution state is related to the deformation direction. The tin phase of AlSn20Cu alloys prepared by semi-solid die casting presents both nearly spherical and strip shapes, with an average particle diameter of 9.6 µm. The tin phase of AlSn20Cu alloys prepared by spray forming and blocking hot extrusion presents a nearly equilateral shape, with an average particle diameter of 6.2 µm. These results indicate that, of the three preparation methods analyzed in this study, semi-solid die casting provides the shortest process flow time, whereas a finer and more uniform tin-phase structure may be obtained using the spray-forming process. The semi-solid die casting method presents the greatest potential for industrial application, and this method therefore presents a promising possibility for further optimization.
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Ahmed, Huda Yahya, and Munaf Yousif Hmood. "Comparison of Some Semi-parametric Methods in Partial Linear Single-Index Model." Journal of Economics and Administrative Sciences 27, no. 130 (2021): 170–84. http://dx.doi.org/10.33095/jeas.v27i130.2207.

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The research dealt with a comparative study between some semi-parametric estimation methods to the Partial linear Single Index Model using simulation. There are two approaches to model estimation two-stage procedure and MADE to estimate this model. Simulations were used to study the finite sample performance of estimating methods based on different Single Index models, error variances, and different sample sizes , and the mean average squared errors were used as a comparison criterion between the methods were used. The results showed a preference for the two-stage procedure depending on all the cases that were used
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Sanqoor, Afra Naqib, Fatma Altamimi, Naeema Aljanahi, et al. "Comparative Study of Two Semi-automated Forensic DNA Extraction Methods." Arab Journal of Forensic Sciences and Forensic Medicine 5, no. 2 (2023): 180–90. http://dx.doi.org/10.26735/yskr7711.

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Automation in forensic DNA analysis is crucial for analysts to reduce time, improve results, and decrease risk of contamination. With the variety of commercially available automated DNA extraction systems, comes the need for end-users to be informed of what they provide and what they might lack. Thus, this study aimed to evaluate the efficiency of two semi-automated DNA extraction systems used for forensic DNA analysis: Automate Express™ and Hamilton Microlab STAR™ system, for four parameters; reproducibility, stability, sensitivity and contamination. Overall, the results indicated that both semi-automated systems performed similarly in providing robust and reproducible DNA results while maintaining good capability to overcome PCR inhibition with low risk of contamination. The two semi-automated systems showed higher DNA recovery than organic extraction using phenol-chloroform by 22% for semen and 7% for blood samples. In addition, three sample types, blood, saliva, semen were tested to compare the two systems (total samples n=100). Overall, the data showed the average DNA recovery for Hamilton was higher than the DNA recovery by Automate Express™ for the blood and semen sample types indicating better performance of the Hamilton Microlab STAR™ in terms of recovery and sensitivity level.
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Wu, Lang, Dazhi Zhang, Boying Wu, and Xiong Meng. "Fifth-Order Mapped Semi-Lagrangian Weighted Essentially Nonoscillatory Methods Near Certain Smooth Extrema." Journal of Applied Mathematics 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/127624.

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Fifth-order mapped semi-Lagrangian weighted essentially nonoscillatory (WENO) methods at certain smooth extrema are developed in this study. The schemes contain the mapped semi-Lagrangian finite volume (M-SL-FV) WENO 5 method and the mapped compact semi-Lagrangian finite difference (M-C-SL-FD) WENO 5 method. The weights in the more common scheme lose accuracy at certain smooth extrema. We introduce mapped weighting to handle the problem. In general, a cell average is applied to construct the M-SL-FV WENO 5 reconstruction, and the M-C-SL-FD WENO 5 interpolation scheme is proposed based on an interpolation approach. An accuracy test and numerical examples are used to demonstrate that the two schemes reduce the loss of accuracy and improve the ability to capture discontinuities.
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Moraci, N., M. C. Mandaglio, and D. Ielo. "Analysis of the internal stability of granular soils using different methods." Canadian Geotechnical Journal 51, no. 9 (2014): 1063–72. http://dx.doi.org/10.1139/cgj-2014-0006.

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The knowledge of the internal stability of granular soils is a key factor in the design of granular or geotextile filters. To evaluate the internal stability of granular soils, different semi-empirical methods are generally used. Nevertheless, the results of these methods, on the same soil, can lead to different internal stability evaluations. In this paper, to evaluate the reliability of the semi-empirical methods available in literature, the internal stability of different granular soils, reconstituted by the authors and by other researchers, has been studied by means of theoretical and experimental approaches. In particular, the theoretical analysis of the internal stability was performed using the Simulfiltr method, developed recently by the authors, while the experimental evaluation of the internal stability was carried out by means of long-term filtration tests. The comparison of the internal stability analysis performed by means of semi-empirical, theoretical, and experimental methods showed that the semi-empirical methods are not always reliable. Therefore, on the base of these results, a new chart, in terms of minimum slope Smin (%) of the grain-size distribution and of average value of finer percentage F, has been proposed to evaluate the internal stability of granular soils.
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Chumachenko, Olena, and Kirill Riazanovskiy. "Semi-supervised Segmentation of Medical Images." Electronics and Control Systems 3, no. 81 (2024): 22–29. http://dx.doi.org/10.18372/1990-5548.81.18986.

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This article is devoted to the development of a method (algorithm) of medical image segmentation based on semi-supervised learning. Semi-supervised learning methods are shown to have significant potential for improving medical image segmentation through effective use of unlabeled data. However, challenges remain in adapting these methods to the specific characteristics of medical images, such as high variability, class imbalance, and the presence of noise and artifacts. To overcome these difficulties, it is proposed to integrate several approaches (consistency regularization, pseudo-labeling, average teacher model) into a single structure. To increase the robustness and generalizability of the model for different imaging methods, we include industry-specific data supplements tailored to the unique characteristics and challenges of each method. Large-scale experiments on magnetic resonance imaging, computed tomography, and optical coherence tomography datasets demonstrate that the proposed framework significantly outperforms fully supervised and individual semisupervised learning methods, especially in scenarios with low data labeling.
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Books on the topic "Semi Average Methods"

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Araújo, Ana Cláudia Vaz de. Síntese de nanopartículas de óxido de ferro e nanocompósitos com polianilina. Brazil Publishing, 2021. http://dx.doi.org/10.31012/978-65-5861-120-2.

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In this work magnetic Fe3O4 nanoparticles were synthesized through the precipitation method from an aqueous ferrous sulfate solution under ultrasound. A 23 factorial design in duplicate was carried out to determine the best synthesis conditions and to obtain the smallest crystallite sizes. Selected conditions were ultrasound frequency of 593 kHz for 40 min in 1.0 mol L-1 NaOH medium. Average crystallite sizes were of the order of 25 nm. The phase obtained was identified by X-ray diffractometry (XRD) as magnetite. Scanning electron microscopy (SEM) showed polydisperse particles with dimensions around 57 nm, while transmission electron microscopy (TEM) revealed average particle diameters around 29 nm, in the same order of magnitude of the crystallite size determined with Scherrer’s equation. These magnetic nanoparticles were used to obtain nanocomposites with polyaniline (PAni). The material was prepared under exposure to ultraviolet light (UV) or under heating, from dispersions of the nanoparticles in an acidic solution of aniline. Unlike other synthetic routes reported elsewhere, this new route does not utilize any additional oxidizing agent. XRD analysis showed the appearance of a second crystalline phase in all the PAni-Fe3O4 composites, which was indexed as goethite. Furthermore, the crystallite size decreases nearly 50 % with the increase in the synthesis time. This size decrease suggests that the nanoparticles are consumed during the synthesis. Thermogravimetric analysis showed that the amount of polyaniline increases with synthesis time. The nanocomposite electric conductivity was around 10-5 S cm-1, nearly one order of magnitude higher than for pure magnetite. Conductivity varied with the amount of PAni in the system, suggesting that the electric properties of the nanocomposites can be tuned according to their composition. Under an external magnetic field the nanocomposites showed hysteresis behavior at room temperature, characteristic of ferromagnetic materials. Saturation magnetization (MS) for pure magnetite was ~ 74 emu g-1. For the PAni-Fe3O4 nanocomposites, MS ranged from ~ 2 to 70 emu g-1, depending on the synthesis conditions. This suggests that composition can also be used to control the magnetic properties of the material.
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Book chapters on the topic "Semi Average Methods"

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Bartolucci, Francesco, Alessandro Cardinali, and Fulvia Pennoni. "A Generalized Moving Average Convergence/Divergence for Testing Semi-strong Market Efficiency." In Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89824-7_18.

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Dobša, Jasminka, and Henk A. L. Kiers. "Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_14.

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AbstractIn the paper we propose a method for representation of documents in a semantic lower-dimensional space based on the modified Reduced k-means method which penalizes clusterings that are distant from classification of training documents given by experts. Reduced k-means (RKM) enables simultaneously clustering of documents and extraction of factors. By projection of documents represented in the vector space model on extracted factors, documents are clustered in the semantic space in a semi-supervised way (using penalization) because clustering is guided by classification given by experts, which enables improvement of classification performance of test documents. Classification performance is tested for classification by logistic regression and support vector machines (SVMs) for classes of Reuters-21578 data set. It is shown that representation of documents by the RKM method with penalization improves the average precision of classification by SVMs for the 25 largest classes of Reuters collection for about 5,5% with the same level of average recall in comparison to the basic representation in the vector space model. In the case of classification by logistic regression, representation by the RKM with penalization improves average recall for about 1% in comparison to the basic representation.
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Chourasia, Basant Kumar, and S. C. Mullick. "Study of Second Figure of Merit of Box Type Solar Cookers by Semi-Log Plot Method Using Instantaneous and Averaged Values of Climatic Variables." In Proceedings of ISES World Congress 2007 (Vol. I – Vol. V). Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75997-3_396.

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"Prediction Theory for Autoregressive-Moving Average Processes." In Readings In Unobserved Components Models, edited by Andrew C. Harvey and Tommaso Proietti. Oxford University PressOxford, 2005. http://dx.doi.org/10.1093/oso/9780199278657.003.0002.

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Abstract In the statistical time series literature, the dominant approach to prediction theory is that developed by Wiener and Kolmogorov and exposited, for example, by Whittle (1963). This “classical” statistical theory relates to purely non-deterministic stationary processes, and is based on the autocovariance function of the relevant variables. Both in theoretical work and in practical implementation, the auto- covariance function is commonly specified by postulating models for the relevant processes, and so is expressed as a function of model parameters. Most often, linear autoregressive-moving average (ARMA) models are chosen. Attention is usually restricted to linear least squares (l.l.s.) methods, and the theory delivers the linear combination of observed values that minimizes the mean square error of prediction. The set of observed values is usually assumed to extend from the indefinite past, that is, to comprise a semi-infinite sample.
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Ziemek, Daniel, and Christoph Brockel. "Network-Driven Analysis Methods and their Application to Drug Discovery." In Handbook of Research on Computational and Systems Biology. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-491-2.ch013.

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Drug discovery and development face tremendous challenges to find promising intervention points for important diseases. Any therapeutic agent targeting such an intervention point must prove its efficacy and safety in patients. Success rates measured from first studies in human to registration average around 10% only. Over the last decade, massive knowledge on biological systems has been accumulated and genome-scale primary data are produced at an ever increasing rate. In parallel, methods to use that knowledge have matured. This chapter will present some of the problems facing the pharmaceutical industry and elaborate on the current state of network-driven analysis methods. It will focus especially on semi-quantitative methods that are applicable to large-scale data analysis and point out their potential use in many relevant drug discovery challenges.
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Thomas, George, Timothy Wilmot, Steve Szatmary, Dan Simon, and William Smith. "Evolutionary Optimization of Artificial Neural Networks for Prosthetic Knee Control." In Efficiency and Scalability Methods for Computational Intellect. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3942-3.ch007.

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This chapter discusses closed-loop control development and simulation results for a semi-active above-knee prosthesis. This closed-loop control is a delta control that is added to previously developed open-loop control. The control signal consists of two hydraulic valve settings. These valves control a rotary actuator that provides torque to the prosthetic knee. Closed-loop control using artificial neural networks (ANNs) are developed, which is an intelligent control method. The ANNs are trained with biogeography-based optimization (BBO), which is a recently developed evolutionary algorithm. This research contributes to the field of evolutionary algorithms by demonstrating that BBO is successful at finding optimal solutions to real-world, nonlinear, time varying control problems. The research contributes to the field of prosthetics by showing that it is possible to find effective closed-loop control signals for a newly proposed semi-active hydraulic knee prosthesis. The research also contributes to the field of ANNs; it shows that they are able to mitigate some of the effects of noise and disturbances that will be common in normal operation of a prosthesis and that they can provide better robustness and safer operation with less risk of stumbles and falls. It is demonstrated that ANNs are able to improve average performance over open-loop control by up to 8% and that they show the greatest improvement in performance when there is high risk of stumbles.
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Haarbrandt Birger, Schwartze Jonas, Gusew Nathalie, Seidel Christoph, and Haux Reinhold. "Primary Care Providers' Acceptance of Health Information Exchange Utilizing IHE XDS." In Studies in Health Technology and Informatics. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-276-9-106.

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We assessed primary care providers' perception of a health information exchange system (HIE) based on IHE XDS. The HIE will be part of a regional health network in the metropolitan area of Braunschweig, Lower Saxony, Germany. An application enabling access to medical documents in an XDS Affinity Domain was developed. We examined usability and factors related to user acceptance. User perception was probed using system usability scale (SUS) and semi-structured interviews. The evaluation was performed on 7 participants. The SUS showed an above average usability with a median score of 77.5. During interviews, participants submitted suggestions for additional features and improvement of usability. Poor integration of functionality into existing workflows was most frequently criticized. While usability was well perceived by primary care providers, challenges remain in adoption of XDS based IHE. To speed up document access in time-critical domains, we suggest use of complementary methods, enabling directed communication flows.
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Ho, Tien Xuan, and Tham My Duong. "The Correlation between Students' Learning Engagement and Their Academic Achievement." In Advances in Educational Technologies and Instructional Design. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6497-0.ch010.

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The inclusion in English language education was evaluated by examining the relationship between academic achievement and high school students' engagement in EFL classrooms in this study. The grade point average (GPA) of students and the four components of their involvement—the cognitive, behavioral, affective, and agentic—were explored by the mixed-methods approach. The GPAs of 188 eleventh graders and levels of engagement in the EFL classrooms, were gathered through a closed-ended questionnaire. In-depth information was also obtained through a semi-structured interview. The results revealed that students frequently engaged in all four aspects of learning in EFL classes; however, the agentic component had the least impact on students' engagement. There was a noticeable correlation between the students' academic achievement and their learning engagement. The agentic dimension was closely linked to the affective aspect, which experienced the greatest influence. The study also offers some recommendations for teachers and learners to improve the quality in teaching and learning English.
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Młyński, Rafał, and Emil Kozłowski. "Speech perception by level-dependent hearing protectors users in impulse noise conditions." In New techniques and methods for noise and vibration measuring, assessing and reducing. Digital Monograph. Central Institute for Labour Protection – National Research Institute, 2022. http://dx.doi.org/10.54215/noise_control_2022_a_digital_monograph_mlynski_r_kozlowski_e.

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The aim of this study was to check the functionality of level-dependent hearing protectors in impulse noise conditions in terms of speech perception by their users. Measurements were conducted in semi-anechoic chamber under acoustic conditions that reflect the situation present at shooting range. Impulse noise previously recorded in real conditions and 20-word lists were emitted from loudspeaker sets. The study included nine different level-dependent hearing protectors, including eight earmuffs and one earplug. Each hearing protector was tested by 25 people in both passive and level-dependent mode. Two out of nine models of hearing protectors provide better speech intelligibility in level-dependent than in passive mode, on average by nearly 10 percentage points. The changes between operating modes were not statistically significant for the remaining seven hearing protectors. In level-dependent mode, there is little differentiation in speech intelligibility values between hearing protectors. In passive mode, the speech intelligibility values are more differentiated. Higher attenuation is associated with poorer speech intelligibility. The use of level-dependent hearing protectors is not restricted by any significant impairment of speech intelligibility in the presence of impulse noise. Among hearing protectors operating in level-dependent mode there are those that do not impair speech intelligibility in the presence of acoustic impulses, compared to passive protection, but also those that improve it.
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Yang, Honglan, Dawei Zhang, Tohir A. Bozorov, et al. "Transgenic Technology can Accelerate Cotton Breeding: Transgenic ScALDH21 Cotton Significantly Improve Drought Tolerance in Southern and Northern Xinjiang." In Cotton [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.103833.

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Aldehyde dehydrogenases (ALDHs) contribute to cellular protection against oxidative stress. These enzymes are crucial to organisms’ ability to cope with environmental stress. The ALDH21 gene was introduced into upland cotton (Gossypium hirsutum L.) from desiccant-tolerant Syntrichia caninervis moss, created stable genetic transgenic lines. As a result, drought tolerance is increased and yield penalty is reduced in those transgenic lines. The first study to demonstrate overexpression of ALDH21 enhances drought tolerance in cotton under multi-location field experiments is presented here. Cotton genotypes containing ScALDH21 exhibit significant morphological, physiological, and economic benefits. ScALDH21 functions in the physiology of cotton plants to protect them by scavenging ROS and reducing osmotic stress. The yield of transgenic cotton in northern Xinjiang showed up to 10% improvement under full irrigation and up to 18% improvement in deficit irrigation conditions on fields with purple clay loam soils. Additionally, transgenic cotton can be grown in sandy loam soil in southern Xinjiang with an average yield increase of 40% on different irrigation levels in the desert-oasis ecotone. Using ScALDH21 as a candidate gene for cotton improvement in arid and semi-arid regions was demonstrated. In addition, we assessed different irrigation protocols and optimized irrigation methods with minimal water requirements for ScALDH21-transgenic cotton that could be used in production agriculture.
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Conference papers on the topic "Semi Average Methods"

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Xu, Bingbing, Huawei Shen, Qi Cao, Keting Cen, and Xueqi Cheng. "Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/267.

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Graph convolutional networks gain remarkable success in semi-supervised learning on graph-structured data. The key to graph-based semisupervised learning is capturing the smoothness of labels or features over nodes exerted by graph structure. Previous methods, spectral methods and spatial methods, devote to defining graph convolution as a weighted average over neighboring nodes, and then learn graph convolution kernels to leverage the smoothness to improve the performance of graph-based semi-supervised learning. One open challenge is how to determine appropriate neighborhood that reflects relevant information of smoothness manifested in graph structure. In this paper, we propose GraphHeat, leveraging heat kernel to enhance low-frequency filters and enforce smoothness in the signal variation on the graph. GraphHeat leverages the local structure of target node under heat diffusion to determine its neighboring nodes flexibly, without the constraint of order suffered by previous methods. GraphHeat achieves state-of-the-art results in the task of graph-based semi-supervised classification across three benchmark datasets: Cora, Citeseer and Pubmed.
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Jiang, Zhenyu, Moustafa El-Gindy, and Donald Streit. "Ride Comfort of Five-Axle Tractor/Semi-Trailer." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-1202.

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Abstract The issue of ride comfort for vehicle operations has recently generated considerable interest especially in heavy vehicle systems since long-distance drivers are more likely to experience high levels of vibration. This paper introduces the general concept of vibration-related health problems, discusses ride comfort assessment criteria and methods, and then focuses on the methodology of using computer simulation to analyze ride comfort. The computer-based ride comfort model can be divided into three sub-models: vehicle model, driver/seat model, and road profile input model. Several vehicle models and driver/seat models are reviewed and detailed modeling techniques are introduced. A five-axle tractor/semi-trailer/driver combination ride comfort simulation model is developed in this paper using the software DADS. Both four-spring tandem suspension and independent air spring suspension are studied. Road profiles are assumed as static zero mean Gaussian random process. Vertical acceleration at the interface between seat and driver body is obtained from simulation results. Power spectral density and root mean square (RMS) vertical acceleration are calculated based on simulation results. RMS acceleration at ISO classified good and average roads are compared with ISO 8-hour fatigue vibration limit. It is found that RMS acceleration of this particular vehicle simulated in this paper is below the ISO 8-hour fatigue limit for both good and average roads when traveling at the speed of fifty miles per hour. This implies a good ride comfort. Axle dynamic load coefficients (DLC) are calculated for four suspension configurations that are combinations of air springs and steel springs. Results show that large DLC doesn’t necessarily indicate bad ride quality.
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Yoo, Jaemin, Hyunsik Jeon, and U. Kang. "Belief Propagation Network for Hard Inductive Semi-Supervised Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/580.

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Given graph-structured data, how can we train a robust classifier in a semi-supervised setting that performs well without neighborhood information? In this work, we propose belief propagation networks (BPN), a novel approach to train a deep neural network in a hard inductive setting, where the test data are given without neighborhood information. BPN uses a differentiable classifier to compute the prior distributions of nodes, and then diffuses the priors through the graphical structure, independently from the prior computation. This separable structure improves the generalization performance of BPN for isolated test instances, compared with previous approaches that jointly use the feature and neighborhood without distinction. As a result, BPN outperforms state-of-the-art methods in four datasets with an average margin of 2.4% points in accuracy.
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Pham, Minh, Craig A. Knoblock, Muhao Chen, Binh Vu, and Jay Pujara. "SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/488.

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Error detection is one of the most important steps in data cleaning and usually requires extensive human interaction to ensure quality. Existing supervised methods in error detection require a significant amount of training data while unsupervised methods rely on fixed inductive biases, which are usually hard to generalize, to solve the problem. In this paper, we present SPADE, a novel semi-supervised probabilistic approach for error detection. SPADE introduces a novel probabilistic active learning model, where the system suggests examples to be labeled based on the agreements between user labels and indicative signals, which are designed to capture potential errors. SPADE uses a two-phase data augmentation process to enrich a dataset before training a deep learning classifier to detect unlabeled errors. In our evaluation, SPADE achieves an average F1-score of 0.91 over five datasets and yields a 10% improvement compared with the state-of-the-art systems.
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5

Garro, Giuseppe T. V., and Chris K. Mechefske. "Experimental and Computational Methods for Investigating Automotive Door Closure Sounds." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85628.

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The focus of this investigation was to examine the acoustic trends present during operation of an automotive door closure at two impact speeds through experimental methods. Transient sound pressure of five different door closure mechanisms were collected in a semi-anechoic chamber using a three-element condenser microphone array. Post-processing methodologies such as Sound Pressure Level versus 1/3 Octave band and Continuous Wavelet Transform computations were conducted. These procedures provided an in-depth analysis on the overall generated sound in addition to identifying which frequencies dominate the response at specific impact events during latch operation. Computational model analyses of the closure system using Rigid Body Dynamic and Explicit Dynamic methods using ANSYS to obtain a clearer understanding of the latch component interactions. Recorded average sound pressure level, frequency decomposition, and impact reaction forces are presented in addition to the notable trends between both impacting speeds.
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Zhu, Guogang, Xuefeng Liu, Xinghao Wu, et al. "Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/290.

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Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model. In FSSL, the heterogeneous data can introduce prediction bias into the model, causing the model's prediction to skew towards some certain classes. Existing FSSL methods primarily tackle this issue by enhancing consistency in model parameters or outputs. However, as the models themselves are biased, merely constraining their consistency is not sufficient to alleviate prediction bias. In this paper, we explore this bias from a Bayesian perspective and demonstrate that it principally originates from label prior bias within the training data. Building upon this insight, we propose a debiasing method for FSSL named FedDB. FedDB utilizes the Average Prediction Probability of Unlabeled Data (APP-U) to approximate the biased prior. During local training, FedDB employs APP-U to refine pseudo-labeling through Bayes' theorem, thereby significantly reducing the label prior bias. Concurrently, during the model aggregation, FedDB uses APP-U from participating clients to formulate unbiased aggregate weights, thereby effectively diminishing bias in the global model. Experimental results show that FedDB can surpass existing FSSL methods. The code is available at https://github.com/GuogangZhu/FedDB.
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Lenner, R., P. Ryjacek, and M. Sýkora. "Resistance Models for Semi-Probabilistic Assessment of Historic Steel Bridges." In IABSE Symposium, Wroclaw 2020: Synergy of Culture and Civil Engineering – History and Challenges. International Association for Bridge and Structural Engineering (IABSE), 2020. http://dx.doi.org/10.2749/wroclaw.2020.1061.

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<p>The mechanical properties of historic metal materials exhibit a considerable scatter dependent on periods of construction and the region of a producer. Assessments of historic metal bridges then need to be based on measurements and tests. The use of non- or minor-destructive tests (NDTs) is often preferred over to destructive tests (DTs) to reduce the cost of structural survey. This contribution explores the measurement errors associated with common NDT hardness techniques and quantifies uncertainties in design (assessment) values of resistance. When deriving the partial factor, the uncertainty in geometry and model uncertainty is considered along with the variability of a material property and measurement error. Numerical studies reveal the effects of measurement error and model uncertainty (bending, buckling) on assessment values of resistance. A unity mean and coefficient of variation of 12% might be adopted for the measurement uncertainty of the hardness methods under study as a first approximation. On average, the true assessment resistance is by ~15% larger than that based on a NDT survey. Model uncertainty affects the partial factor for resistance of historic metal bridges.</p>
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Thamarai Kannan, Harish Kumar, and John B. Ferris. "Discretization-Based Semi-Active Suspension Control Using Road Preview Data." In Automotive Technical Papers. SAE International, 2024. http://dx.doi.org/10.4271/2024-01-5087.

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<div class="section abstract"><div class="htmlview paragraph">While semi-active suspensions help improve the ride comfort and road-holding capacity of the vehicle, they tend to be reactive and thus leave a lot of room for improvement. Incorporating road preview data allows these suspensions to become more proactive rather than reactive and helps achieve a higher level of performance. A lot of preview-based control algorithms in literature tend to require high computational effort to arrive at the optimal parameters thus making it difficult to implement in real time. Other algorithms tend to be based upon lookup tables, which classify the road input into different categories and hence lose their effectiveness when mixed types of road profiles are encountered that are difficult to classify. Thus, a novel MPC (model predictive control)-based algorithm is developed which is easy to implement online and more responsive to the varying road profiles that are encountered by the vehicle. The efficacy of the algorithm is tested against a numerical methods-based control algorithm that can determine the maximum possible ride comfort achieved using semi-active dampers capable of altering their damping characteristics every 0.01 s. Results indicated that the proposed strategy is quite effective in providing holistic improvement in the sprung mass motion, achieving on average 69% of the maximum ride comfort possible with a fraction of the computational effort.</div></div>
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Keyes, Edward, and Jason Abt. "An Advanced Integrated Circuit Analysis System." In ISTFA 2006. ASM International, 2006. http://dx.doi.org/10.31399/asm.cp.istfa2006p0398.

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Abstract Historically, the extraction of circuitry from an integrated circuit was normally within the abilities of the average FA laboratory and could be accomplished with little more than an optical microscope and film camera. Dramatic increases in the level of integration and number of metal interconnect levels coupled with shrinking feature sizes have rendered these techniques obsolete. This paper describes techniques and methods for the fast, semi-automated extraction of detailed circuit schematics from modern, nanometer scale integrated circuits.
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Keprate, Arvind, R. M. Chandima Ratnayake, and Shankar Sankararaman. "Comparing Different Metamodelling Approaches to Predict Stress Intensity Factor of a Semi-Elliptic Crack." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-62333.

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This paper examines the applicability of the different meta-models (MMs) to predict the Stress Intensity Factor (SIF) of a semi-elliptic crack propagating in topside piping, as an inexpensive alternative to the Finite Element Methods (FEM). Five different MMs, namely, multi-linear regression (MLR), second order polynomial regression (PR-2) (with interaction), Gaussian process regression (GPR), neural networks (NN) and support vector regression (SVR) have been tested. Seventy data points (SIF values obtained by FEM) are used to train the aforementioned MMs, while thirty data points are used as the testing points. In order to compare the accuracy of the MMs, four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (AAE), and Coefficient of Determination (R2) are used. Although PR-2 emerged as the best fit, GPR was selected as the best MM for SIF determination due to its capability of calculating the uncertainty related to the prediction values. The aforementioned uncertainty representation is quite valuable, as it is used to adaptively train the GPR model, which further improves its prediction accuracy.
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