Academic literature on the topic 'Occupant size classification'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Occupant size classification.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Occupant size classification"
1
Schewe, Frederik, Hao Cheng, Alexander Hafner, Monika Sester, and Mark Vollrath. "Occupant Monitoring in Automated Vehicles: Classification of Situation Awareness Based on Head Movements While Cornering." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (2019): 2078–82. http://dx.doi.org/10.1177/1071181319631048.
Full textAbstract:
We tested whether head-movements under automated driving can be used to classify a vehicle occupant as either situation-aware or unaware. While manually cornering, an active driver’s head tilt correlates with the road angle which serves as a visual reference, whereas an inactive passenger’s head follows the g-forces. Transferred to partial/conditional automation, the question arises whether aware occupant’s head-movements are comparable to drivers and if this can be used for classification. In a driving-simulator-study (n=43, within-subject design), four scenarios were used to generate or deteriorate situation awareness (manipulation checked). Recurrent neural networks were trained with the resulting head-movements. Inference statistics were used to extract the discriminating feature, ensuring explainability. A very accurate classification was achieved and the mean side rotation-rate was identified as the most differentiating factor. Aware occupants behave more like drivers. Therefore, head-movements can be used to classify situation awareness in experimental settings but also in real driving.
2
Krishnan, Deepu, Scott Kelly, and Yohan Kim. "A Meta-Analysis Review of Occupant Behaviour Models for Assessing Demand-Side Energy Consumption." Energies 15, no. 3 (2022): 1219. http://dx.doi.org/10.3390/en15031219.
Full textAbstract:
Occupant behaviour plays a significant role in shaping the dynamics of energy consumption in buildings, but the complex nature of occupant behaviour has hindered a deeper understanding of its influence. A meta-analysis was conducted on 65 published studies that used data-driven quantitative assessments to assess energy-related occupant behaviour using the Knowledge Discovery and Data Mining (KDD) framework. Hierarchical clustering was utilised to categorise different modelling techniques based on the intended outcomes of the model and the types of parameters used in various models. This study will assist researchers in selecting the most appropriate parameters and methods under various data constraints and research questions. The research revealed two distinct model categories being used to study occupant behaviour-driven energy consumption, namely (i) occupancy status models and (ii) energy-related behaviour models. Multiple studies have identified limitations on data collection and privacy concerns as constraints of modelling occupant behaviour in residential buildings. The “regression model” and its variants were found to be the preferred model types for research that models “energy-related behaviour”, and “classification models” were found to be preferable for modelling “occupancy” status. There were only limited instances of data-driven studies that modelled occupant behaviour in low-income households, and there is a need to generate region-specific models to accurately model energy-related behaviour.
3
Senger, R. S., and M. N. Karim. "Variable Site-Occupancy Classification of N-Linked Glycosylation Using Artificial Neural Networks." Biotechnology Progress 21, no. 6 (2005): 1653–62. http://dx.doi.org/10.1021/bp0502375.
Full text
4
Burgos, Julian M., and John K. Horne. "Characterization and classification of acoustically detected fish spatial distributions." ICES Journal of Marine Science 65, no. 7 (2008): 1235–47. http://dx.doi.org/10.1093/icesjms/fsn087.
Full textAbstract:
AbstractBurgos, J. M., and Horne, J. K. 2008. Characterization and classification of acoustically detected fish spatial distributions. – ICES Journal of Marine Science, 65: 1235–1247. High-resolution, two-dimensional measurements of aquatic-organism density are collected routinely during echo integration trawl surveys. School-detection algorithms are commonly used to describe and analyse spatial distributions of pelagic and semi-pelagic organisms observed in echograms. This approach is appropriate for species that form well-defined schools, but is limited when used for species that form demersal layers or diffuse pelagic shoals. As an alternative to metrics obtained from school-detection algorithms, we used landscape indices to quantify and characterize spatial heterogeneity in density distributions of walleye pollock (Theragra chalcogramma). Survey transects were divided into segments of equal length and echo integrated at a resolution of 20 m (horizontal) and 1 m (vertical). A series of 20 landscape metrics was calculated in each segment to measure occupancy, patchiness, size distribution of patches, distances among patches, acoustic density, and vertical location and dispersion. Factor analysis indicated that the metric set could be reduced to four factors: spatial occupancy, aggregation, packing density, and vertical distribution. Cluster analysis was used to develop a 12-category classification typology for distribution patterns. Visual inspection revealed that spatial patterns of segments assigned to each type were consistent, but that there was considerable overlap among types.
5
Li, Wei, Libo Cao, Lingbo Yan, Chaohui Li, Xiexing Feng, and Peijie Zhao. "Vacant Parking Slot Detection in the Around View Image Based on Deep Learning." Sensors 20, no. 7 (2020): 2138. http://dx.doi.org/10.3390/s20072138.
Full textAbstract:
Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.
6
Ostermann, Adrian, Yann Fabel, Kim Ouan, and Hyein Koo. "Forecasting Charging Point Occupancy Using Supervised Learning Algorithms." Energies 15, no. 9 (2022): 3409. http://dx.doi.org/10.3390/en15093409.
Full textAbstract:
The prediction of charging point occupancy enables electric vehicle users to better plan their charging processes and thus promotes the acceptance of electromobility. The study uses Adaptive Charging Network data to investigate a public and a workplace site for predicting individual charging station occupancy as well as overall site occupancy. Predicting individual charging point occupancy is formulated as a classification problem, while predicting total occupancy is formulated as a regression problem. The effects of different feature sets on the predictions are investigated, as well as whether a model trained on data of all charging points per site performs better than one trained on the data of a specific charging point. Reviewed studies so far, however, have failed to compare these two approaches to benchmarks, to use more than one algorithm, or to consider more than one site. Therefore, the following supervised machine-learning algorithms were applied for both tasks: linear and logistic regression, k-nearest neighbor, random forest, and XGBoost. Further, the model results are compared to three different naïve approaches which provide a robust benchmark, and the two training approaches were applied to two different sites. By adding features, the prediction quality can be increased considerably, which resulted in some models performing better than the naïve approaches. In general, models trained on data of all charging points of a site perform slightly better on median than models trained on individual charging points. In certain cases, however, individually trained models achieve the best results, while charging points with very low relative charging point occupancy can benefit from a model that has been trained on all data.
7
López-Medina, Miguel Ángel, Macarena Espinilla, Chris Nugent, and Javier Medina Quero. "Evaluation of convolutional neural networks for the classification of falls from heterogeneous thermal vision sensors." International Journal of Distributed Sensor Networks 16, no. 5 (2020): 155014772092048. http://dx.doi.org/10.1177/1550147720920485.
Full textAbstract:
The automatic detection of falls within environments where sensors are deployed has attracted considerable research interest due to the prevalence and impact of falling people, especially the elderly. In this work, we analyze the capabilities of non-invasive thermal vision sensors to detect falls using several architectures of convolutional neural networks. First, we integrate two thermal vision sensors with different capabilities: (1) low resolution with a wide viewing angle and (2) high resolution with a central viewing angle. Second, we include fuzzy representation of thermal information. Third, we enable the generation of a large data set from a set of few images using ad hoc data augmentation, which increases the original data set size, generating new synthetic images. Fourth, we define three types of convolutional neural networks which are adapted for each thermal vision sensor in order to evaluate the impact of the architecture on fall detection performance. The results show encouraging performance in single-occupancy contexts. In multiple occupancy, the low-resolution thermal vision sensor with a wide viewing angle obtains better performance and reduction of learning time, in comparison with the high-resolution thermal vision sensors with a central viewing angle.
8
Cai, Lei, Jincheng Zhou, and Dan Wang. "Improving temporal smoothness and snapshot quality in dynamic network community discovery using NOME algorithm." PeerJ Computer Science 9 (July 18, 2023): e1477. http://dx.doi.org/10.7717/peerj-cs.1477.
Full textAbstract:
The goal of dynamic community discovery is to quickly and accurately mine the network structure for individuals with similar attributes for classification. Correct classification can effectively help us screen out more desired results, and it also reveals the laws of dynamic network changes. We propose a dynamic community discovery algorithm, NOME, based on node occupancy assignment and multi-objective evolutionary clustering. NOME adopts the multi-objective evolutionary algorithm MOEA/D framework based on decomposition, which can simultaneously decompose the two objective functions of modularization and normalized mutual information into multiple single-objective problems. In this algorithm, we use a Physarum-based network model to initialize populations, and each population represents a group of community-divided solutions. The evolution of the population uses the crossover and mutation operations of the genome matrix. To make the population in the evolution process closer to a better community division result, we develop a new strategy for node occupancy assignment and cooperate with mutation operators, aiming at the boundary nodes in the connection between the community and the connection between communities, by calculating the comparison node. The occupancy rate of the community with the neighbor node, the node is assigned to the community with the highest occupancy rate, and the authenticity of the community division is improved. In addition, to select high-quality final solutions from candidate solutions, we use a rationalized selection strategy from the external population size to obtain better time costs through smaller snapshot quality loss. Finally, comparative experiments with other representative dynamic community detection algorithms on synthetic and real datasets show that our proposed method has a better balance between snapshot quality and time cost.
9
Stetsky, S. V., A. O. Zheltaya, and E. A. Dorozhkina. "Functional zoning in urban planning and architectural design." E3S Web of Conferences 402 (2023): 09005. http://dx.doi.org/10.1051/e3sconf/202340209005.
Full textAbstract:
The article discusses a variety of issues, concerning functional zoning. These questions are being considered, starting from the wide-scale urban territories and up to small private plots and dwellings. The urban planning and design are being analyzed in accordance to the main classification features, i.e., in accordance to the number of population, planning schemes and planning structures. It is noted, that in the long run, the principles of zoning are highly dependent in the above-mentioned points of a master planning. The functional zoning of individual houses or flats are being considered with nearly the same method adopted. A dwelling division to functional zones depends upon its size, number of occupants, personal desires, etc.
10
Quero, Javier, Matthew Burns, Muhammad Razzaq, Chris Nugent, and Macarena Espinilla. "Detection of Falls from Non-Invasive Thermal Vision Sensors Using Convolutional Neural Networks." Proceedings 2, no. 19 (2018): 1236. http://dx.doi.org/10.3390/proceedings2191236.
Full textAbstract:
In this work, we detail a methodology based on Convolutional Neural Networks (CNNs) to detect falls from non-invasive thermal vision sensors. First, we include an agile data collection to label images in order to create a dataset that describes several cases of single and multiple occupancy. These cases include standing inhabitants and target situations with a fallen inhabitant. Second, we provide data augmentation techniques to increase the learning capabilities of the classification and reduce the configuration time. Third, we have defined 3 types of CNN to evaluate the impact that the number of layers and kernel size have on the performance of the methodology. The results show an encouraging performance in single-occupancy contexts, with up to 92 % of accuracy, but a 10 % of reduction in accuracy in multiple-occupancy. The learning capabilities of CNNs have been highlighted due to the complex images obtained from the low-cost device. These images have strong noise as well as uncertain and blurred areas. The results highlight that the CNN based on 3-layers maintains a stable performance, as well as quick learning.
Dissertations / Theses on the topic "Occupant size classification"
1
Schold, Elizabeth K. "Using a custom landscape classification to understand the factors driving site occupancy by a rapidly declining migratory songbird." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5587.
Full textAbstract:
Land cover classifications are useful in a broad range of ecological applications, yet publicly available classifications are not always useful for the needs of specific projects. Custom classifications are always a possibility, however, they can be financially or computationally out of reach for many researchers. Here we present a custom 1m resolution land cover classification created using freely available imagery and a random forest classification approach. This classification detected shrub cover more accurately and at a finer resolution than previous classifications. With the creation of this map, we were then able to examine landscape factors influencing occupancy dynamics of the golden-winged warbler, a rapidly declining shrubland specialist, at two ecologically relevant scales. Our findings indicate that shrub cover is important in predicting warbler occupancy and persistence at scales relevant to nesting, while forest characteristics are important at scales relevant to foraging and fledgling dispersal.
Book chapters on the topic "Occupant size classification"
1
"Managing Centrarchid Fisheries in Rivers and Streams." In Managing Centrarchid Fisheries in Rivers and Streams, edited by Jan-Michael Hessenauer, Kevin Wehrly, Danielle Forsyth Kilijanczyk, Todd Wills, and Troy Zorn. American Fisheries Society, 2019. http://dx.doi.org/10.47886/9781934874523.ch3.
Full textAbstract:
<em>Abstract.</em>—Black bass <em> Micropterus </em>spp. are among the most popular sportfish species in North America, however, managers currently have insufficient information about patterns and drivers of their distribution. Tradeoffs exist with the spatial scale at which to collect information associated with the distribution of species. Local-scale data are expensive to collect, but are likely directly associated with occupancy of a particular site. Landscape-scale data are increasingly available, are easily scaled and linked to site occupancy, but management often does not occur at this scale. We utilized site-specific habitat data collected as part of a statewide stream sampling program from 2002 to 2013 to generate presence–absence data for Smallmouth Bass <em> M. dolomieu</em>. We used site specific local-scale, landscape-scale, and a combination of local- and landscape-scale data to create random forest classification tree models of Smallmouth Bass presence and absence. All three models had similar total error rates ranging from 13.9% to 16.6%. Overall rates of success and error did not differ among the three models (<EM> P </EM>= 0.95). The model using only landscape-scale data were used to predict the presence or absence of Smallmouth Bass for all Michigan stream segments and had an error rate of 17% based on an independent data validation. These data suggest that our approach has utility for predicting Smallmouth Bass occurrence in Michigan streams by identifying important habitat features associated with Smallmouth Bass occurrence. This approach could be extended to understand the distribution of other black bass species in locations where distributional data may be more limited.
2
Liu, Kunxiang, and Baoyun Wang. "Gully Identification of Debris Flow Disaster Based on Knowledge Distillation." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220043.
Full textAbstract:
Convolutional neural networks have been applied in the field of remote sensing image classification. For convolutional neural networks with shallower layers and simpler structures, the accuracy of the recognition of debris flow gully images is not ideal, while the number of layers is deeper and the structure is relatively simple. More complex neural networks often consume a lot of system resources and are difficult to deploy on the user side. Aiming at this problem, an optimized convolutional neural network method is proposed. First, through multiple sets of comparative experiments, select Resnet101 and Resnet18 models with good image classification performance; then, use the characteristics of debris flow gully images to pre-train the deeper and more complex Resnet101 model; finally, through the method of knowledge distillation, The trained “knowledge” is extracted into the Resnet18 model to achieve the effect of improving accuracy while reducing system resource occupation. Experimental data shows that after using knowledge distillation, the accuracy and sensitivity of the Resnet18 model are increased by 2.36 and 1.72 percentage points, respectively, and the image processor occupancy is reduced by 37 percentage points compared with the Resnet101 model.
Conference papers on the topic "Occupant size classification"
1
Lichtenberg, Glen, and Kirsten Carr. "Performance Evaluation of Occupant Classification Systems." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60132.
Full textAbstract:
The new FMVSS 208 Federal Regulation requires restraint systems to focus on occupants other than the 50th percentile male. The new focus includes small adults and children. As a result, restraint systems may need to perform differently for several occupant classes, thereby creating a need for occupant classification systems (OCS). A typical regulation compliance strategy is to suppress the restraint system when a child occupies the front passenger seat and to enable the restraints when an adult occupies the seat. The regulation provides specific weight and height ranges to define these classes of seat occupants. The evolution of OCS technologies produced a need for test methodologies and objective metrics to measure classification system capability. The application of the statistical one-sided tolerance interval to OCS systems has proven invaluable in measuring classification performance and driving system improvements. The one-sided tolerance method is based on a single continuous variable, such as weight. A single common threshold, or tolerance limit, is used to compare two competing populations, such as 6-year-old versus 5th percentile female populations. Output of the method produces graphics demonstrating reliability as a function of potential threshold that objectively characterizes a system’s classification performance level. This paper also discusses the importance of applying the one-sided tolerance interval method to performance data that captures the noise sources that impact system performance. For occupant classification systems, noise sources include differences in test subjects’ sizes, how they sit in the seat, and how the seat is set-up. This paper also discusses the importance of sample size selection. Two methods of determining a sample size are presented. The first method uses the one-sided tolerance interval method equation directly. The second method simulates a noise source and selects a sample size where the noise standard deviation converges to its population variance. Once the mean, standard deviation, and sample size for each test case is known, the proposed method computes the reliability of each test case evaluated for a range of potential thresholds. A review of the resulting reliability curves characterizes classification performance. If an acceptable range of thresholds exists, the resulting range is referred to as a “threshold window.” System improvements can be directed toward those test cases that constrain the “threshold window.” This paper proposes a statistical method that can provide a solid measure of the robust capability of an OCS that classifies based on a single continuous variable (such as weight) to distinguish between occupant classes. This statistical method enables the careful balance necessary in setting thresholds.
2
Leiss, Peter, Christopher Roche, Marcus Mazza, and Roland Hoover. "Automotive Seat Back Strength Testing of 19 Front Seats." In ASME 2024 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/imece2024-142194.
Full textAbstract:
Abstract Automotive front seat back strength in rearward loading has been researched and reported on for over fifty years. In rear impacts, the seat back provides the primary means of restraint to the occupant as the force of the collision causes the occupant to move rearward, away from their seatbelt webbing. Seat strength requirements for vehicles Certified to be sold in the United States are prescribed in Federal Motor Vehicle Safety Standard (FMVSS) 207. If the seat belt is attached to the seat frame, commonly referred to as an All Belts to Seat (ABTS) design, the seat must also meet the seat belt anchorage requirements of FMVSS 210. The strength requirements of automotive seats have remained unchanged for several decades. This paper describes the fabrication of a wooden body block, first designed and used in static seat back strength testing in the 1960’s, and a hydraulically actuated testing rig with instrumentation recording synchronized force and displacement measurements. A six-seat repeatability study was undertaken where six seats of the same design were tested and the force versus displacement curves were analyzed for consistency prior to testing 19 different seat designs. Results of the current testing are added to previously reported seat back strength test results to identify trends in automotive seat back strength. The 19 seats tested were selected in groups by their parent vehicle sales classification. Seventeen seats from vehicles sold in the United States D segment (mid-size) sedan, luxury sedan, and pickup-truck classifications were identified, seats procured, and tested. Also tested were seats installed in two different models of vehicles built by the same vehicle manufacturer, one was designed and manufactured by a long-standing Tier One automotive supplier of seats and interior trim components, the other was designed and manufactured in-house by the Original Equipment Manufacturer (OEM). One of the tested seat frames has features in the seat back bracket that are designed to control the deflection in rear impacts. The design of this feature changed during the production run of the seat frame design. Seats with the original and modified bracket features were tested. Maximum force and the force versus deflection curves are compared between the Tier One and OEM designed seats and the original and modified seat back brackets. The results of this study show the test fixture produced repeatable results. The study adds to the available seat back strength test results reported in previous literature; adding these results to previous testing shows how seat back strength has changed throughout the past 60 years.
3
Belwadi, Aditya, and King H. Yang. "Near Side Lateral Impacts and Aortic Injury: A Parametric Study." In ASME 2011 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2011. http://dx.doi.org/10.1115/sbc2011-54019.
Full textAbstract:
Traumatic rupture of the aorta (TRA) remains the second most common cause of death associated with motor vehicle crashes after brain injury. On an average, nearly 8,000 people die annually in the United States due to blunt injury to the aorta. It is observed that more than 80% of occupants who suffer an aortic injury die at the scene due to exsanguination into the chest cavity. TRA and blunt aortic injury (BAI) are leading causes of death in high-speed blunt impact trauma. More specific injuries that fall under these classifications include myocardial contusion (MC), traumatic aortic disruption (TAD), sternal fracture (SF), flail chest (FC) and tracheobronchial disruption (TBD) (Swan et al. 2001). Smith and Chang (1986) reported on 387 cases of blunt traumatic death in vehicular crashes and found that aortic injury was second only to head injury as the leading cause of death. Burkhart et al. (2001) reviewed 242 autopsy cases with fatal BAI and concluded that in most cases aortic injury was accompanied by head injury, rib fractures and/or hepatic trauma.