Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19最新文献

{"title":"Dataset","authors":"R. Cardell-Oliver, C. Hübner, M. Leopold, Jason Beringer","doi":"10.1145/3359427.3361912","DOIUrl":"https://doi.org/10.1145/3359427.3361912","url":null,"abstract":"Badland landscapes exhibit high erosion rates and represent the main source of fine sediments in some catchments. Advances in High Resolution Topographic methods allow analysis of topographic changes at high temporal and spatial scales. We apply the Mapping Geomorphic Processes in the Environment (MaGPiE) algorithm to infer the main geomorphic process signatures operating in two sub-humid badlands with contrasting morphometric attributes located in the Southern Pyrenees. By interrogating a five-year dataset of seasonal and annual topographic changes, we examine the variability of geomorphic processes at multiple temporal scales. The magnitude of geomorphic processes is linked to landform attributes and meteorological variables. Morphometric differences between both adjacent badlands allow analysing the role of landform attributes on main geomorphic process re-shaping landscapes subjected to the same external forcing (i.e. rainfall and temperature). The dominant geomorphic process signatures observed in both badlands are different, despite their close proximity and same rainfall and temperature regimes. Process signatures determining surface lowering in the gentle-sloping south-facing badland, characterised by lower connectivity and more vegetation cover, are driven by surface runoff-based processes, both diffuse, causing Sheet Washing , and concentrated, determining Cutting and Filling and Rilling and Gullying . The steeper and more connected north-facing slopes of the other badland are re-shaped by means of gravitational processes with Mass Wasting dominating topographic changes. In terms of processes determining surface raising, both Mass Wasting and Cutting and Filling are most frequently observed in both badlands. There is a clear near-balanced feedback between both surface-raising and lowering processes that becomes unbalanced at larger temporal scales due to the thresholds overcoming, as the volume associated with surface lowering becomes higher than that associated with raising-based processes. Rainfall variables control surface flow processes while those variables associated with low temperature have a significant relation with mass movement-based processes and other localised processes as Regolith Cohesion Loss . Finally, our results point out as morphometry (slope and connectivity) together with vegetation cover are key factors determining geomorphic processes and associated topographic changes. Our observations demonstrate that surveys at seasonal temporal scales allow depiction of specific geomorphological processes preparing and detaching sediments from the slopes, while annual temporal scales are required to estimate average values of erosion (i.e. denudation), masking the geomorphic processes responsible of long-term changes. in","PeriodicalId":121087,"journal":{"name":"Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124582967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset","authors":"Romain Jacob, Reto Da Forno, Roman Trüb, Andreas Biri, Lothar Thiele","doi":"10.1145/3359427.3361910","DOIUrl":"https://doi.org/10.1145/3359427.3361910","url":null,"abstract":"Energy harvesting is finding widespread use as a long-term energy supply for the Internet of Things (IoT). The dependency of these devices on the spatially and temporally variable environment further complicates reliable application design. We present a long-term indoor solar harvesting dataset to support the modeling, analysis, calibration and evaluation of energy harvesting systems. More than 2 years of joint high accuracy power and ambient condition traces were collected at 6 diverse indoor locations. The dataset provides a solid foundation for the design and validation of energy prediction, energy management and run-time adaptation schemes. The detailed description of the measurement setup and the resulting dataset is accompanied by the public release of the dataset, as well as the hardware design of the measurement platform and code examples for post-processing the dataset in R and Python.","PeriodicalId":121087,"journal":{"name":"Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19","volume":"280 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122938765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset","authors":"J. K. Brinke, N. Meratnia","doi":"10.1145/3359427.3361908","DOIUrl":"https://doi.org/10.1145/3359427.3361908","url":null,"abstract":"We describe and analyze a dataset that comprises horse movement. Data was collected during horse riding sessions and when the horses freely roamed the pasture over 7 days. The dataset comprises 1.8 million 2-second data samples from 18 individual horses, of which 93303 samples from 11 subjects were labeled. Sensor devices were attached to a collar around the neck of the horses while the orientation was not fixed. The devices contained a 3-axis accelerometer, gyroscope, and magnetometer that were sampled at 100 Hz. To demonstrate how this dataset can be used, we evaluated a Naive Bayes classifier with leave-one-out validation. Our results show that a performance of 90 % accuracy can be achieved using only the 3D acceleration vector as input. Furthermore, we demonstrate the effect of increased complexity, parameter tuning, and class balancing on classification performance and identify open research challenges. The complete dataset is available online with open access at the 4TU.Centre for Research Data [9].","PeriodicalId":121087,"journal":{"name":"Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116091932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset","authors":"Gabe Fierro, Sriharsha Guduguntla, D. Culler","doi":"10.1145/3359427.3361922","DOIUrl":"https://doi.org/10.1145/3359427.3361922","url":null,"abstract":"Semantic metadata standards for buildings such as Brick and Project Haystack show promise in enabling wide-scale deployment of energy-efficiency measures and advanced building management technologies. However, techniques for converting existing diverse and idiosyncratic forms of building metadata to these standard forms is an area of active research. To encourage and facilitate research into the development and evaluation of such techniques, we are releasing an open dataset of metadata pulled from real building management systems, containing attributes for 103,064 points over 92 buildings. In addition, we are releasing an open-source tool for scraping and cleaning metadata from building management systems (BMS) for contribution to the dataset.","PeriodicalId":121087,"journal":{"name":"Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19","volume":"31 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125704194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset","authors":"Anooshmita Das, J. Schwee, Emil Stubbe Kolvig-Raun, M. Kjærgaard","doi":"10.1145/3359427.3361909","DOIUrl":"https://doi.org/10.1145/3359427.3361909","url":null,"abstract":"The ability to accurately estimate the presence and counts of occupants inside a monitored area has many notable and compelling applications, e.g., efficient space utilization, intelligent building operations, comfortable indoor environment, safety, and evacuation. Significant research on design approaches and methods have been implemented to extrapolate rich information on occupant behavior and actions. However, these methods are evaluated based on a subset of divergent data collected from heterogeneous sensing modalities available in instrumented public buildings. Comprehensive baseline datasets are lacking which can serve for benchmarking and performance comparison despite the potential inconsistencies. In this paper, we present a dataset collected from three 3D Stereo Vision Cameras for 13 full days, with a total of 5,485,350 readings and 1242 estimated occupants in the summer of 2019 from a public building. The dataset contains occupancy presence and trajectory data. The dataset can serve for benchmarking and foster data-driven research in occupant presence and behavior modeling. This dataset would help to extract the inter-relationships of different factors influencing occupant behavior and trajectory patterns. Furthermore, high fidelity information about actions can be extrapolated from the presented dataset.","PeriodicalId":121087,"journal":{"name":"Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116460920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}