Academic literature on the topic 'Occupancy Models Detection-nondetection data'
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Journal articles on the topic "Occupancy Models Detection-nondetection data"
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Rossman, Sam, Charles B. Yackulic, Sarah P. Saunders, Janice Reid, Ray Davis, and Elise F. Zipkin. "DynamicN-occupancy models: estimating demographic rates and local abundance from detection-nondetection data." Ecology 97, no. 12 (December 2016): 3300–3307. http://dx.doi.org/10.1002/ecy.1598.
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Ngo, Chung D., Hai P. Dang, Nghiep T. Hoang, and Binh Van Ngo. "Factors Associated with Detection Probability and Site Occupancy of the Long-Tailed Skink (Eutropis longicaudata) in the Aluoi Area, Central Vietnam." Russian Journal of Herpetology 28, no. 2 (May 3, 2021): 67–72. http://dx.doi.org/10.30906/1026-2296-2021-28-2-67-72.
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Lizard species are rarely detected with perfect accuracy, regardless of the method employed. Nondetection of a species at a site does not necessarily mean the species was absent unless the detection probability was 100%. We assessed the influence of site covariates (less disturbed habitat and disturbed habitat) and sample covariates (temperature, humidity, rainfall) on the occupancy of Eutropis longicaudata in the Aluoi area, central Vietnam. Based on detection/nondetection data over nine visits at 40 less disturbed sites and 39 sites with disturbed habitats, the distribution of E. longicaudata was estimated using site occupancy models. From the best model, we estimated a site occupancy probability of 0.595, a 12.05% increase over the naive occupancy of 0.531 at which E. longicaudata skinks were actually observed. The site covariate of the less disturbed habitat was an important determinant of site occupancy, which was not associated with the variable of disturbance habitats. In the combined AIC model weight, p(precipitation), p(temperature), and p(humidity) have 92%, 36%, and 21% of the total, respectively; providing evidence that environmental conditions (especially precipitation) were important sample covariates in modelling detection probabilities of E. longicaudata. In terms of occupancy probability, the combined weight for the ψ(less disturbed habitat) model and the ψ(disturbed habitat) model were 60% and 32%, respectively. Our results substantiate the importance of incorporating detection and occupancy probabilities into studies of habitat relationships and suggest that the less disturbed habitat associated with weather conditions influence the occupancy of E. longicaudata in central Vietnam.
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Lee, Jooyoung, Jihye Byun, Jaedeok Lim, and Jaeyun Lee. "A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method." Journal of Advanced Transportation 2020 (December 2, 2020): 1–8. http://dx.doi.org/10.1155/2020/8870211.
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High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling.
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Holcomb, Jordan M., Colin P. Shea, and Nathan A. Johnson. "Cumulative Spring Discharge and Survey Effort Influence Occupancy and Detection of a Threatened Freshwater Mussel, the Suwannee Moccasinshell." Journal of Fish and Wildlife Management 9, no. 1 (February 2, 2018): 95–105. http://dx.doi.org/10.3996/052017-jfwm-042.
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AbstractFreshwater mussels (Unionidae) are among the most imperiled groups of organisms in the world, and the lack of information regarding species distributions, life-history characteristics, and ecological and biological requirements may limit the protection of remaining mussel populations. We examined the influence of hydrologic factors on the occurrence of the Suwannee Moccasinshell Medionidus walkeri, a federally threatened freshwater mussel species, endemic to the Suwannee River Basin in Georgia and Florida. We also evaluated the influence of survey effort on detection of Suwannee Moccasinshell during field surveys. We compiled all recent (2013–2016) mussel survey records in the Suwannee River Basin. We calculated cumulative discharge contributed by upstream springs for each of 220 survey locations. We combined the spring discharge predictor variable with Suwannee Moccasinshell detection and nondetection data from each survey location to develop a suite of occupancy models. Modeling results indicated that detection of Suwannee Moccasinshell during surveys was strongly and positively related to survey effort. Modeling results also indicated that sites with cumulative spring discharge inputs exceeding ∼28 cubic meters per second were most likely (i.e., predicted occupancy probabilities >0.5) to support Suwannee Moccasinshell populations. However, occupancy declined in the lowermost reaches of the Suwannee mainstem despite high spring discharge inputs, presumably due to greater tidal influences and differences in physicochemical habitat conditions. Historical localities where Suwannee Moccasinshell has presumably been extirpated are all devoid of springs in their upstream watersheds. We hypothesize that springs may buffer extremely tannic, and at times polluted, surface waters, in addition to maintaining adequate flows during periods of drought, thereby promoting the persistence of Suwannee Moccasinshell populations. Our study suggests that springs are a critical resource for Suwannee Moccasinshell and may be more important for conservation planning than was previously recognized.
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Santos, Rodrigo A. L., Mário Mota-Ferreira, Ludmilla M. S. Aguiar, and Fernando Ascensão. "Predicting wildlife road-crossing probability from roadkill data using occupancy-detection models." Science of The Total Environment 642 (November 2018): 629–37. http://dx.doi.org/10.1016/j.scitotenv.2018.06.107.
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Lepard, Clara C., Remington J. Moll, Jonathon D. Cepek, Patrick D. Lorch, Patricia M. Dennis, Terry Robison, and Robert A. Montgomery. "The influence of the delay-period setting on camera-trap data storage, wildlife detections and occupancy models." Wildlife Research 46, no. 1 (2019): 37. http://dx.doi.org/10.1071/wr17181.
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Context The use of camera traps in ecological research has grown exponentially over the past decade, but questions remain about the effect of camera-trap settings on ecological inference. The delay-period setting controls the amount of time that a camera trap is idle between motion-activated triggers. Longer delay periods may potentially extend battery life, reduce data-storage requirements, and shorten data-analysis time. However, they might result in lost data (i.e. missed wildlife detections), which could bias ecological inference and compromise research objectives. Aims We aimed to examine the effect of the delay period on (1) the number of camera-trap triggers, (2) detection and site-occupancy probabilities for eight mammalian species that varied in size, movement rate and commonness and (3) parameter estimates of habitat-based covariates from the occupancy models for these species. Methods We deployed 104 camera traps for 4 months throughout an extensive urban park system in Cleveland, Ohio, USA, using a spatially random design. Using the resultant data, we simulated delay periods ranging from 10s to 60min. For each of these delay periods and for each of our eight focal species, we calculated the number of camera-trap triggers and the parameter estimates of hierarchical Bayesian occupancy models. Key results A simulated increase in the delay period from 10s to 10min decreased the number of triggers by 79.6%, and decreased detection probability and occupancy probability across all species by 1.6% and 4.4% respectively. Further increases in the delay period (i.e. from 10 to 60min) resulted in modest additional reductions in the number of triggers and detection and occupancy probabilities. Variation in the delay period had negligible effects on the qualitative interpretations of habitat-based occupancy models for all eight species. Conclusions Our results suggest that delay-period settings ranging from 5 to 10min can drastically reduce data-storage needs and analysis time without compromising inference resulting from occupancy modelling for a diversity of mammalian species. Implications Broadly, we provide guidance on designing camera-trap studies that optimally trade-off research effort and potential bias, thereby increasing the utility of camera traps as ecological research tools.
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Koshkina, Vira, Yan Wang, Ascelin Gordon, Robert M. Dorazio, Matt White, and Lewi Stone. "Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection." Methods in Ecology and Evolution 8, no. 4 (April 2017): 420–30. http://dx.doi.org/10.1111/2041-210x.12738.
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Ngo, Binh Van, Ya-Fu Lee, and Chung D. Ngo. "Detection Probability and Site Occupancy of the Granular Spiny Frog (Quasipaa verrucospinosa) in the Tropical Rain Forests of Bach Ma National Park, Central Vietnam." Russian Journal of Herpetology 27, no. 1 (March 21, 2020): 26–32. http://dx.doi.org/10.30906/1026-2296-2020-27-1-26-32.
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Amphibian species are rarely detected with perfect accuracy, regardless of the method employed. A large-scale assessment for Quasipaa verrucospinosa occupancy was conducted at 35 sites in the primary forest and 42 sites in the secondary forest of Bach Ma National Park, central Vietnam. Based on the detection data for each site, the distribution of Q. verrucospinosa was estimated in different habitat types using occupancy models. From the best model among all performed models, we estimated a site occupancy probability of 0.576 that was higher than the naive occupancy estimate of 0.403 and a 43.1% increase over the site proportion at which Q. verrucospinosa was actually observed. The site covariate of the primary forest was an important determinant of site occupancy, which was not associated with the variable of secondary forest. In a combined AIC model weight: the p(temperature), p(humidity), and p(precipitation) models have 47.3, 67.1, and 90.9% of the total, respectively; providing evidence that aforementioned environmental conditions were important sample covariates in modelling detection probabilities of Q. verrucospinosa. Our results substantiate the importance of incorporating detection and occupancy probabilities into studies of habitat relationships and suggest that the primary forests associated with weather conditions influence the site occupancy of Q. verrucospinosa in Bach Ma National Park, central Vietnam.
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Murn, Campbell, and Graham J. Holloway. "Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys." Royal Society Open Science 3, no. 10 (October 2016): 160368. http://dx.doi.org/10.1098/rsos.160368.
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Species occurring at low density can be difficult to detect and if not properly accounted for, imperfect detection will lead to inaccurate estimates of occupancy. Understanding sources of variation in detection probability and how they can be managed is a key part of monitoring. We used sightings data of a low-density and elusive raptor (white-headed vulture Trigonoceps occipitalis ) in areas of known occupancy (breeding territories) in a likelihood-based modelling approach to calculate detection probability and the factors affecting it. Because occupancy was known a priori to be 100%, we fixed the model occupancy parameter to 1.0 and focused on identifying sources of variation in detection probability. Using detection histories from 359 territory visits, we assessed nine covariates in 29 candidate models. The model with the highest support indicated that observer speed during a survey, combined with temporal covariates such as time of year and length of time within a territory, had the highest influence on the detection probability. Averaged detection probability was 0.207 (s.e. 0.033) and based on this the mean number of visits required to determine within 95% confidence that white-headed vultures are absent from a breeding area is 13 (95% CI: 9–20). Topographical and habitat covariates contributed little to the best models and had little effect on detection probability. We highlight that low detection probabilities of some species means that emphasizing habitat covariates could lead to spurious results in occupancy models that do not also incorporate temporal components. While variation in detection probability is complex and influenced by effects at both temporal and spatial scales, temporal covariates can and should be controlled as part of robust survey methods. Our results emphasize the importance of accounting for detection probability in occupancy studies, particularly during presence/absence studies for species such as raptors that are widespread and occur at low densities.
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van Strien, A. J., J. J. A. Dekker, M. Straver, T. van der Meij, L. L. Soldaat, A. Ehrenburg, and E. van Loon. "Occupancy dynamics of wild rabbits (Oryctolagus cuniculus) in the coastal dunes of the Netherlands with imperfect detection." Wildlife Research 38, no. 8 (2011): 717. http://dx.doi.org/10.1071/wr11050.
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Context Wild rabbits are considered a key species in the coastal dunes of the Netherlands, but populations have collapsed as a result of viral diseases. Aim We studied to what extent population collapse led to local extinction and whether recolonisation of empty patches in the dunes happened. Methods We investigated occupancy dynamics using data of 245 transects where rabbits were surveyed in 1984–2009. Dynamic site-occupancy models were used to analyse the data. These models adjust for imperfect detection to avoid bias in occupancy-trend estimation. Key results The decline of the rabbit population has resulted in many local extinctions, especially in woodland and in the northern part of the coastal dunes. Most transects along grassland and mixed vegetation have recently been reoccupied. The recovery of woodland occupancy is slow, probably not because of limited dispersal capacity of rabbits, but because the quality of woodland habitats is poor. Detection probability of rabbits varied considerably over the years and among habitat types, indicating the necessity of taking detection into account. Rabbits were slightly better detected when it was cloudy, windy and rainy and when lunar phase approached new moon. Conclusion Extinction and recolonisation of habitat patches varied considerably among habitat types. Implications The current slow recolonisation hampers the recovery of rabbit populations in woodland habitats in the Dutch coastal dunes. Furthermore, monitoring rabbit occupancy should take imperfect detection into account to avoid biased results.
Book chapters on the topic "Occupancy Models Detection-nondetection data"
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Najeh, Houda, Christophe Lohr, and Benoit Leduc. "Real-Time Human Activity Recognition in Smart Home on Embedded Equipment: New Challenges." In Lecture Notes in Computer Science, 125–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09593-1_10.
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AbstractBuilding Energy Management (BEM) and monitoring systems should not only consider HVAC systems and building physics but also human behaviors. These systems could provide information and advice to occupants about the significance of their practices with regard to the current state of a dwelling. It is also possible to provide services such as assistance to the elderly, comfort and health monitoring. For this, an intelligent building must know the daily activities of its residents and the algorithms of the smart environment must track and recognize the activities that the occupants normally perform as part of their daily routine. In the literature, deep learning is one of effective supervised learning model and cost-efficient for real-time HAR, but it still struggles with the quality of training data (missing values in time series and non-annotated event), the variability of data, the data segmentation and the ontology of activities. In this work, recent research works, existing algorithms and related challenges in this field are firstly highlighted. Then, new research directions and solutions (performing fault detection and diagnosis for drift detection, multi-label classification modeling for multi-occupant classification, new indicators for training data quality, new metrics weighted by the number of representations in dataset to handle the issue of missing data and finally language processing for complex activity recognition) are suggested to solve them respectively and to improve this field.
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Fatema, Nuzhat, and Hasmat Malik. "Data-Driven Occupancy Detection Hybrid Model Using Particle Swarm Optimization Based Artificial Neural Network." In Metaheuristic and Evolutionary Computation: Algorithms and Applications, 283–97. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7571-6_13.
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Kéry, Marc, and Michael Schaub. "Estimation of Occupancy and Species Distributions from Detection/Nondetection Data in Metapopulation Designs Using Site-Occupancy Models." In Bayesian Population Analysis using WinBUGS, 413–61. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-12-387020-9.00013-4.
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V. Ngo, Binh, and Ya-Fu Lee. "Ecology of the Granular Spiny Frog Quasipaa verrucospinosa (Amphibia: Anura - Dicroglossidae) in Central Vietnam." In Protected Area Management - Recent Advances. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.99656.
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We conducted a large-scale assessment at 35 primary forest sites and 42 secondary forest sites in Bach Ma National Park, central Vietnam, using the detection/non-detection data for each site over multiple visits, to quantify the site proportions that were occupied by granular spiny frogs (Quasipaa verrucospinosa). We additionally investigated the effect of site covariates (primary versus secondary forests) and sample covariates (temperature, humidity, and precipitation) to examine the environmental needs that may be incorporated for conserving rain forest amphibians in Vietnam. From the best model among all candidate models, We estimated a site occupancy probability of 0.632 that was higher than the naïve occupancy estimate of 0.403 and a 57% increase over the proportion of sites at which frogs were actually observed. The primary forest variable was an important determinant of site occupancy, whereas occupancy was not associated with the variable of secondary forest. In a combined AIC model weight, the detection model p (temperature, humidity, precipitation) included 90.9% of the total weight, providing clear evidence that environmental conditions were important sample covariates in modeling detection probabilities of granular spiny frogs. Our results substantiate the importance of incorporating occupancy and detection probabilities into studies of habitat relationships and suggest that the primary forest factor associated with environmental conditions influence the occupancy of granular spiny frogs.
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"Conservation, Ecology, and Management of Catfish: The Second International Symposium." In Conservation, Ecology, and Management of Catfish: The Second International Symposium, edited by CHRISTOPHER J. WOOD and ROBERT B. NICHOLS. American Fisheries Society, 2011. http://dx.doi.org/10.47886/9781934874257.ch25.
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<em>Abstract</em>.—The Carolina madtom <em>Noturus</em><em> furiosus</em> is a rare fish endemic to the Tar and Neuse River basins of North Carolina. Surveys over the past three decades suggest declines in its distribution and abundance. We conducted 60 surveys at 30 sites with historical survey records in April–August of 2007 to assess the current status of the Carolina madtom. Data were compared to historical records to detect any temporal change in occurrence. We also estimated the proportion of sites occupied (occupancy) and detection probabilities for a subset of sites with the computer software package PRESENCE using repeat detection/nondetection data. Additionally, we examined aspects of the general biology and population structure of the Carolina madtom (e.g., spawning period, size structure, catch per unit effort). Results indicate a significant decrease in occurrence in the Neuse River basin (χ<sup>2</sup> = 41.6, <em>p</em> < 0.05). Frequencies of occurrence decreased from 0.80 to 0.13 between 1960s and 2007 data. A robust population was detected at only one site surveyed in the Neuse River basin. No significant temporal change in occurrence was seen in the Tar River basin (χ<sup>2</sup>= 0, <em>p</em> = 1). Occupancy estimates generated from PRESENCE were similar to observed frequencies of occurrence due to high detection probabilities. Spawning and nesting behaviors were observed from mid-May through early July. Catch-per-unit-effort data and length-frequencies suggest strong recruitment in most Tar River basin populations and in one Neuse River basin population. Conservation measures are needed throughout the range of the Carolina madtom and especially in the Neuse River basin where there is a high risk of extirpation.
Conference papers on the topic "Occupancy Models Detection-nondetection data"
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Hand, Rebecca, Ian Cleland, and Chris Nugent. "A Comparison of Feature Extraction Methods Applied to Thermal Sensor Binary Image Data to Classify Bed Occupancy." In 24th Irish Machine Vision and Image Processing Conference. Irish Pattern Recognition and Classification Society, 2022. http://dx.doi.org/10.56541/qlzv1440.
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Low-resolution thermal sensing technology is suitable for sleep monitoring due to being light invariant and privacy preserving. Feature extraction is a critical step in facilitating robust detection and tracking, therefore this paper compares a blob analysis approach of extracting statistical features to several common feature descriptor algorithm approaches (SURF and KAZE). The features are extracted from thermal binary image data for the purpose of detecting bed occupancy. Four common machine learning models (SVM, KNN, DT and NB) were trained and evaluated using a leave-one-subject-out validation method. The SVM trained with feature descriptor data achieved the highest accuracy of 0.961.
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Wang, Chenli, and Hohyun Lee. "Economical and Non-Invasive Residential Human Presence Sensing via Temperature Measurement." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88211.
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Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption in United States, encompassing about 25% of total residential energy usage. A significant portion of energy is wasted by unnecessary operation, such as overheating/overcooling or operation without occupants. Wasteful behaviors will consume twice the amount of energy compared to energy conscious behaviors. Many market programmable thermostats exist to address this problem, however, difficulties in persistent programming of such products and lack of understanding of underlying physics prevent users from achieving tangible impact. Hence, fully autonomous energy control system is desirable to engage as many people into energy conscious behaviors as possible. Occupancy measurement is necessary components to enable fully autonomous control. Occupancy information can save energy by automatically turn off the HVAC system when the building is not occupied, or floats to a more energy-efficient setback temperature when the activity level is low. A number of existing sensor solutions available on the market include Passive Infrared (PIR), ultrasonic, Bluetooth/GPS, and CO2 sensors, but these are either too expensive, not user-friendly, or limited in detection scope. These sensors are also incapable of detecting whether or not the occupant is an animal or a human. The work in this paper proposes an economical, reliable, non-invasive package to both detect human presence in a residence of a wide variety of geometries at the time and predict future occupancy pattern, by utilizing temperature sensors. To accomplish this, thermal sensors will be attached to both ends of door handles to collect the temperature data. This data will allow us to create a schedule to identify human activity leaving and exiting the space. At the same time, we will be collecting the skin temperature to determine the human activity level for better identification of the thermal comfort zone for occupants. The prediction model for occupancy pattern will be developed from previous data by using machine learning algorithm. For verification, experimental setup was built to verify our model by comparing actual human presence data from a house with the measured and predicted occupancy pattern from the temperature sensors. Future steps include implementing a data fusion scheme into the model to combine information from multiple types of sensors.
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Gomez Ortega, Jose Luis, Liangxiu Han, and Nicholas Bowring. "A Novel Dynamic Hidden Semi-Markov Model (D-HSMM) for Occupancy Pattern Detection from Sensor Data Stream." In 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS). IEEE, 2016. http://dx.doi.org/10.1109/ntms.2016.7792429.
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Chinta, Kiran Kumar, and Fred Barez. "Driver Distraction Detection and Recognition." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24474.
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Abstract Statistics have shown that the main reason for traffic accidents is human error. Modern vehicles are equipped to protect occupants in the event of a crash. The latest advanced vehicles come with driver behavior monitoring systems in recent years, and many have been proven to be effective systems in the prevention of accidents. However, these systems do not provide a complete solution and can only detect driver fatigue or driver distraction. This project aims to build an AI model for sensing the distraction of drivers and identifying the kind of distraction using the Kinect sensor and the Brio camera and reorient driver’s attention on driving. For this, the system is divided into three sub-segments; calling arm position (arms up or down, arms right or left), facial expressions (blinking and mouth), and head orientation. Each segment develops important info for gauging the distraction of a driver based on the depth mapping of data and color from the Kinect sensor and Brio camera respectively. Testing on a driving simulator is completed on 4 different drivers of diverse ethnicity, sex, and age along with over 240 mins of recorded material. Since all the segments were recorded and prepared separately, they can further be taken to build different outcomes and can be implemented for real car systems.
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Fong, N. K., and C. H. Ko. "Study of Smoke Stratification in Atrium Halls Using Scale Model." In ASME 2009 Heat Transfer Summer Conference collocated with the InterPACK09 and 3rd Energy Sustainability Conferences. ASMEDC, 2009. http://dx.doi.org/10.1115/ht2009-88430.
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Nowadays, atrium building is very popular because it can provide extensity and attraction to the users even if they are inside the enclosed environment. Similar to other buildings, fire safety is one of the major concerns especially the atrium are linked to shopping arcades. The major challenge is to control the smoke movement in the case of fire and maintain a stable smoke layer clear height to allow sufficient time for the occupants to evacuate from the building. Therefore, an efficient smoke management system (SMS) is necessary. For the SMS to function properly, the smoke behaviour inside the atrium must be studied. One of the phenomena affecting the operation of the SMS is smoke stratification. That is, due to the vertical temperature gradient inside the atrium, a thermally stratified environment is formed and smoke will not be able to reach the smoke detectors/smoke outlets in the ceiling. In the past decade, various studies were conducted to study the smoke filling process in the atrium. Only a few studies were carried out to study smoke stratification in atrium. This paper attempted to study the factors leading to the development of thermally stratified environment in an atrium and the formation of smoke stratification under the ceiling space of an atrium building using scale model. These factors included the temperature of the smoke plume, the air temperature under the ceiling, the configuration of roof ceiling and the ambient air temperature. Two types of ceiling configurations such as a cuboid and a triangular prism are used. Data concerning the ceiling air temperature, smoke plume temperature, effect of different ceiling configuration and maximum smoke layer height in a thermally stratified environment are collected. Comparisons are conducted with the calculated results from National Fire Protection Association (NFPA) 92B equations. With all these information, better design criteria of smoke detection system, SMS in an atrium building can be developed. Finally, the experimental results can be used to investigate the discrepancies between the experimental measurement and the calculated results from NFPA 92B equations. Put abstract text here.
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de Borba, Thiago, Ondrej Vaculin Ondrej Vaculin, and Parth Patel Parth Patel. "Concept of a Vehicle Platform for Development and Testing of Low-Speed Automated Driving Functions." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-acm-118.
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"The development of automated driving functions belongs among the most complex tasks, which have been focused on by academia and industry in the recent decade. The advantages of automated driving are enabled by achievements in many levels, such as the development of sensors, object tracking algorithms, car2x communication, planning and decision, and many others. Automated driving should contribute to a higher level of safety by reducing human driving errors, reduced congestion, CO2 emissions by optimizing the traffic flow, and finally lower stress and higher comfort for the vehicle occupants. It will also bring a new business model in the area of mobility. These benefits are possible due to the wide development of new technologies and the posterior tests required for validation on the system and sub-system levels. This paper presents an open vehicle platform for the development and testing of automated driving functions and their applications. They range from driving functions for SAE level 2, in which the driver is present and must monitor the environment to higher levels such as automated valet parking. Another application field is connected with the teleoperated driving based on efficient c2i communication. The platform is based on a production vehicle Renault Twizy extended with a drive-by-wire system for actuation and control of longitudinal and lateral dynamics connected through the low-level drive-by-wire controller by CAN bus to the control devices. The open vehicle interface enables to approach of the actuators from different HW and AW configurations. The universal sensor mounts on the modified car body enable flexible modification of sensor configuration. The vehicle can be equipped with a LIDAR and up to 8 cameras and can also be extended with radars, GNSS, and ultrasonic sensors. It also contains an in-vehicle computer, which processes, in real-time, all the detection data of the environment and outputs the desired commands to the drive by wire control unit. The open software interface, in this case, an open-source autonomous driving stack, provides predefined modules for perception, decision, and planning. Additionally, it allows users to implement their own modules. In the perception module, the LIDAR is responsible for localization, through prerecorded point cloud maps, detection, and tracking of objects in the surrounding. For instance, it is also possible to perform sensor fusion with camera data for object recognition. With this data, the system provides the best trajectory and can avoid unexpected obstacles. After all, the software outputs a set of velocity, angular velocity, wheel angle, and curvature, which is transferred by CAN bus to the drive-by-wire low-level controller. Then, the low-level controller sends the commands to the actuators."