scholarly journals Estimating Number and Dwell Time of Visitors in a Large-scale Indoor Space from HVAC Data and Its Evaluation in Different Seasons

2021 ◽  
Vol 3 (1) ◽  
pp. 25-41
Author(s):  
Keisuke Tsunoda ◽  
Naoki Arai ◽  
Kazuaki Obana
Keyword(s):  
Air Conditioning ◽  
Dwell Time ◽  
Large Scale ◽  
Time Window ◽  
Co2 Concentration ◽  
Sensor Data ◽  
Hvac System ◽  
Indoor Space ◽  
Different Seasons ◽  
Concentration Changes

The aim of this paper is to estimate the number and dwell time of visitors in a large-scale indoor space or room with a common heating-ventilation-air conditioning (HVAC) system that includes sensors for CO2 and indoor temperature in any season. Previous studies tried to estimate the number and dwell time of visitors from CO2 concentration in small rooms with or without a HVAC system. However, in a large-scale indoor space with large air-conditioning and ventilation systems, the number and dwell time of visitors are difficult to estimate for three reasons: 1) CO2 concentration changes much more slowly than the number and dwell time of visitors and with a delay, 2) the difference in changes is affected by the amount of ventilation, and 3) this difference may be affected by operation of HVAC, which is affected by seasonal climate. To solve these problems, we proposed partial modeling with a variable time window. This method can make a partial estimation model that automatically corresponds to differences in the change speed between two variables: visitors and CO2 concentration. We demonstrate the effectiveness of our proposal using measured sensor data in summer, fall, and winter to clarify its feasibility in different seasons in Japan.

scholarly journals Hydrochemical Characterisation of High-Fluoride Groundwater and Development of a Conceptual Groundwater Flow Model Using a Combined Hydrogeological and Hydrochemical Approach on an Active Volcano: Mount Meru, Northern Tanzania

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2159
Author(s):  
George Bennett ◽  
Jill Van Reybrouck ◽  
Ceven Shemsanga ◽  
Mary Kisaka ◽  
Ines Tomašek ◽  
...  
Keyword(s):  
Drinking Water ◽  
Large Scale ◽  
Active Volcano ◽  
Aquifer System ◽  
Groundwater Samples ◽  
Northern Tanzania ◽  
Different Seasons ◽  
High Fluoride ◽  
Arusha National Park ◽  
The Impact

This study characterises high-fluoride groundwater in the aquifer system on the flanks of Mount Meru, focusing on parts of the flanks that were only partially or not at all covered by previous research. Additionally, we analyse the impact of rainwater recharge on groundwater chemistry by monitoring spring discharges during water sampling. The results show that the main groundwater type in the study area is NaHCO3 alkaline groundwater (average pH = 7.8). High F− values were recorded: in 175 groundwater samples, the concentrations range from 0.15 to 301 mg/L (mean: 21.89 mg/L, median: 9.67 mg/L), with 91% of the samples containing F− values above the WHO health-based guideline for drinking water (1.5 mg/L), whereas 39% of the samples have Na+ concentrations above the WHO taste-based guideline of 200 mg/L. The temporal variability in F− concentrations between different seasons is due to the impact of the local groundwater recharge. We recommend that a detailed ecohydrological study should be carried out for the low-fluoride springs from the high-altitude recharge areas on the eastern and northwestern flanks of Mount Meru inside Arusha National Park. These springs are extracted for drinking purposes. An ecohydrological study is required for the management of these springs and their potential enhanced exploitation to ensure the sustainability of this water extraction practice. Another strategy for obtaining safe drinking water could be to use a large-scale filtering system to remove F− from the groundwater.

scholarly journals A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 400 ◽  
Author(s):  
Zelin Nie ◽  
Feng Gao ◽  
Chao-Bo Yan
Keyword(s):  
Optimization Model ◽  
Air Conditioning ◽  
State Of The Art ◽  
Hvac Systems ◽  
Practical Significance ◽  
Hvac System ◽  
Bilinear Model ◽  
Optimization And Control ◽  
And Control ◽  
Slow Timescale

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

scholarly journals Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110131
Author(s):  
Junfeng Wu ◽  
Li Yao ◽  
Bin Liu ◽  
Zheyuan Ding ◽  
Lei Zhang
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Unified Framework ◽  
Diagnosis System ◽  
Learning Framework ◽  
Task Learning ◽  
Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

scholarly journals Hydrology influences breeding time in the white-throated dipper

BMC Ecology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Anna L. K. Nilsson ◽  
Thomas Skaugen ◽  
Trond Reitan ◽  
Jan Henning L’Abée-Lund ◽  
Marlène Gamelon ◽  
...  
Keyword(s):  
Climate Change ◽  
River Discharge ◽  
Large Scale ◽  
Time Window ◽  
River System ◽  
Open Water ◽  
Local Conditions ◽  
Environmental Indices ◽  
Groundwater Levels ◽  
Breeding Time

Abstract Background Earlier breeding is one of the strongest responses to global change in birds and is a key factor determining reproductive success. In most studies of climate effects, the focus has been on large-scale environmental indices or temperature averaged over large geographical areas, neglecting that animals are affected by the local conditions in their home ranges. In riverine ecosystems, climate change is altering the flow regime, in addition to changes resulting from the increasing demand for renewable and clean hydropower. Together with increasing temperatures, this can lead to shifts in the time window available for successful breeding of birds associated with the riverine habitat. Here, we investigated specifically how the environmental conditions at the territory level influence timing of breeding in a passerine bird with an aquatic lifestyle, the white-throated dipper Cinclus cinclus. We relate daily river discharge and other important hydrological parameters, to a long-term dataset of breeding phenology (1978–2015) in a natural river system. Results Dippers bred earlier when winter river discharge and groundwater levels in the weeks prior to breeding were high, and when there was little snow in the catchment area. Breeding was also earlier at lower altitudes, although the effect dramatically declined over the period. This suggests that territories at higher altitudes had more open water in winter later in the study period, which permitted early breeding also here. Unexpectedly, the largest effect inducing earlier breeding time was territory river discharge during the winter months and not immediately prior to breeding. The territory river discharge also increased during the study period. Conclusions The observed earlier breeding can thus be interpreted as a response to climate change. Measuring environmental variation at the scale of the territory thus provides detailed information about the interactions between organisms and the abiotic environment.

scholarly journals Geographic Aspects of Temperature and Concentration Feedbacks in the Carbon Budget

2010 ◽  
Vol 23 (3) ◽  
pp. 775-784 ◽  
Author(s):  
G. J. Boer ◽  
V. Arora
Keyword(s):  
Negative Feedback ◽  
Carbon Concentration ◽  
Carbon Budget ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Climate Feedback ◽  
Climate Modelling ◽  
Emission Scenarios ◽  
Temperature Feedback ◽  
Concentration Changes

Abstract The geographical distribution of feedback processes in the carbon budget is investigated in a manner that parallels that for climate feedback/sensitivity in the energy budget. Simulations for a range of emission scenarios, made with the Canadian Centre for Climate Modelling and Analysis (CCCma) earth system model (CanESM1), are the basis of the analysis. Anthropogenic CO2 emissions are concentrated in the Northern Hemisphere and provide the forcing for changes to the atmospheric carbon budget. Transports redistribute the emitted CO2 globally where local feedback processes act to enhance (positive feedback) or suppress (negative feedback) local CO2 amounts in response to changes in CO2 concentration and temperature. An increased uptake of CO2 by the land and ocean acts to counteract increased atmospheric CO2 concentrations so that “carbon–concentration” feedbacks are broadly negative over the twenty-first century. Largest values are found over land and particularly in tropical regions where CO2 acts to fertilize plant growth. Extratropical land also takes up CO2 but here the effect is limited by cooler temperatures. Oceans play a lesser negative feedback role with comparatively weak uptake associated with an increase in the atmosphere–ocean CO2 gradient rather than with oceanic biological activity. The effect of CO2-induced temperature increase is, by contrast, to increase atmospheric CO2 on average and so represents an overall positive “carbon–temperature” feedback. Although the average is positive, local regions of both positive and negative carbon–temperature feedback are seen over land as a consequence of the competition between changes in biological productivity and respiration. Positive carbon–temperature feedback is found over most tropical land while mid–high-latitude land exhibits negative feedback. There are also regions of positive and negative oceanic carbon–temperature feedback in the eastern tropical Pacific. The geographical patterns of carbon–concentration and carbon–temperature feedbacks are comparatively robust across the range of emission scenarios used, although their magnitudes are somewhat less robust and scale nonlinearly as a consequence of the large CO2 concentration changes engendered by the scenarios. The feedback patterns deduced nevertheless serve to illustrate the localized carbon feedback processes in the climate system.

scholarly journals Using autonomous video to estimate the bottom-contact area of longline trap gear and presence–absence of sensitive benthic habitat

2018 ◽  
Vol 75 (5) ◽  
pp. 797-812 ◽  
Author(s):  
Beau Doherty ◽  
Samuel D.N. Johnson ◽  
Sean P. Cox
Keyword(s):  
Contact Area ◽  
Large Scale ◽  
Marine Protected Area ◽  
Cold Water ◽  
Sensor Data ◽  
Benthic Habitat ◽  
Commercial Fishing ◽  
Depth Sensor ◽  
Quality Of Data ◽  
Bottom Contact

Bottom longline hook and trap fishing gear can potentially damage sensitive benthic areas (SBAs) in the ocean; however, the large-scale risks to these habitats are poorly understood because of the difficulties in mapping SBAs and in measuring the bottom-contact area of longline gear. In this paper, we describe a collaborative academic–industry–government approach to obtaining direct presence–absence data for SBAs and to measuring gear interactions with seafloor habitats via a novel deepwater trap camera and motion-sensing systems on commercial longline traps for sablefish (Anoplopoma fimbria) within SGaan Kinghlas – Bowie Seamount Marine Protected Area. We obtained direct presence–absence observations of cold-water corals (Alcyonacea, Antipatharia, Pennatulacea, Stylasteridae) and sponges (Hexactinellida, Demospongiae) at 92 locations over three commercial fishing trips. Video, accelerometer, and depth sensor data were used to estimate a mean bottom footprint of 53 m2 for a standard sablefish trap, which translates to 3200 m2 (95% CI = 2400–3900 m2) for a 60-trap commercial sablefish longline set. Our successful collaboration demonstrates how research partnerships with commercial fisheries have potential for massive improvements in the quantity and quality of data needed for conducting SBA risk assessments over large spatial and temporal scales.

Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation

2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Model Comparison ◽  
Drilling Fluid ◽  
Sensor Data ◽  
False Alarms ◽  
Suitable Model ◽  
Lost Circulation

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.

scholarly journals A Hybrid Processing System for Large-Scale Traffic Sensor Data

IEEE Access ◽  
2015 ◽  
Vol 3 ◽  
pp. 2341-2351 ◽  
Author(s):  
Zhuofeng Zhao ◽  
Weilong Ding ◽  
Jianwu Wang ◽  
Yanbo Han
Keyword(s):  
Large Scale ◽  
Processing System ◽  
Sensor Data
Sign in / Sign up

Export Citation Format

Share Document