scholarly journals Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5497 ◽  
Author(s):  
Beril Sirmacek ◽  
Maria Riveiro
Keyword(s):  
Computer Vision ◽  
Low Cost ◽  
Elderly Care ◽  
Sensor Data ◽  
Low Resolution ◽  
Feature Classification ◽  
Advantages And Disadvantages ◽  
The People ◽  
Heat Sensor ◽  
Occupancy Prediction

Solving the challenge of occupancy prediction is crucial in order to design efficient and sustainable office spaces and automate lighting, heating, and air circulation in these facilities. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects, which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors; however, they have not addressed nor compensated for such heat artifacts. Therefore, in this paper, we presented a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We used a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We proposed two novel workflows to predict the occupancy; one that is based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we used several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyzed noise resources that affect the heat sensor data. The experiments show that the feature classification based method gives high accuracy when the data are clean from noise artifacts. However, when there are noise artifacts, the computer vision based method can compensate for those artifacts providing robust results. Because the computer vision based method requires an empty room recording, the feature classification based method should be chosen either when there is no expectancy of seeing noise artifacts in the data or when there is no empty recording available. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

scholarly journals Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices

2020 ◽  
Author(s):  
Beril Sirmacek ◽  
Maria Riveiro
Keyword(s):  
Low Cost ◽  
Elderly Care ◽  
Sensor Data ◽  
Low Resolution ◽  
Multiple Sensors ◽  
Advantages And Disadvantages ◽  
The People ◽  
Energy Consuming ◽  
Heat Sensor ◽  
Occupancy Prediction

In order to design efficient and sustainable office spaces and to automate lighting, heating and air circulation in these facilities, solving the challenge of occupancy prediction is crucial. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors, however, they have not addressed nor compensate for such heat artifacts. Therefore, in this paper, we present a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We use a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We propose two novel workflows to predict the occupancy; one based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we use several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyze noise resources which affect the heat sensor data. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

scholarly journals Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1036
Author(s):  
Simon Arvidsson ◽  
Marcus Gullstrand ◽  
Beril Sirmacek ◽  
Maria Riveiro
Keyword(s):  
Energy Consumption ◽  
Low Cost ◽  
Sensor Data ◽  
Low Resolution ◽  
Real Time Processing ◽  
Privacy Concerns ◽  
Sensor Field ◽  
Heat Sensor ◽  
Occupancy Prediction ◽  
The Impact

Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and ventilation systems through considering the occupancy and activity information might reduce energy consumption by more than 50%. However, it is difficult to use high-resolution sensors and cameras for occupancy prediction due to privacy concerns. In this paper, we propose a novel solution for predicting occupancy using multiple low-cost and low-resolution heat sensors. We suggest two different methods for fusing and processing the data captured from multiple heat sensors and we use a Convolutional Neural Network for predicting occupancy. We conduct experiments to assess both the performance of the proposed solutions and analyze the impact of sensor field view overlaps on the prediction results. In summary, our experimental results show that the implemented solutions show high occupancy prediction accuracy and real-time processing capabilities.

scholarly journals Mediation from the Penalogical Viewpoint

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Mohammad Hossein Hajaryan ◽  
Iraj Golduzian ◽  
Ehsan Hajarian
Keyword(s):  
Low Cost ◽  
Criminal Liability ◽  
Status Quo ◽  
Judicial System ◽  
Legal Punishment ◽  
Advantages And Disadvantages ◽  
The People ◽  
The Status ◽  
Settlement Of Disputes

Due to increasing population and heaviness of the judicial system's burden and high expense of referring to the courts for the people of society, the judicial system tries to settle disputes through traditional and low cost ways. Mediation is a body which has long history in settlement of disputes between persons but the use of mediation has advantages and disadvantages. As mediation cost is lower than cost of referring to court, the parties will prefer to use this body. Result of mediation creates liability for the criminal and although this liability has no predetermined legal punishment, it is criminal liability which is enacted by the nongovernmental persons under supervision of government for the status quo of the dispute. Punishment has had different function at different times. It sometimes had authoritarian function and was controlled by the states and sometimes had preventive-corrective function and aims to protect people against the offender's behavior. This article attempts to show purpose of the mediation result in societies. Key words:Mediation, Penal, Penology

scholarly journals Normative Aspects of Nudging in the International Sphere

AJIL Unbound ◽  
2021 ◽  
Vol 115 ◽  
pp. 263-267
Author(s):  
Doron Teichman ◽  
Eyal Zamir
Keyword(s):  
Energy Consumption ◽  
Organ Donation ◽  
Low Cost ◽  
National Level ◽  
The Past ◽  
The People ◽  
Posthumous Organ Donation ◽  
Regulatory Problems

The use of nudges—“low-cost, choice-preserving, behaviorally informed approaches to regulatory problems”—has become quite popular at the national level in the past decade or so. Examples include changing the default concerning employees’ saving for retirement in a bid to encourage such saving; altering the default about consent to posthumous organ donation to increase the supply of organs for transplantation; and informing people about other people's energy consumption to spur them to reduce theirs. Nudges are therefore used to promote the welfare of the people being nudged, and of society at large. However, the use of nudges has sparked a lively normative debate. When turning to the international arena, new arguments for and against nudges can be raised. This essay focuses on the normative aspects of using nudges in the international arena.

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2944
Author(s):  
Benjamin James Ralph ◽  
Marcel Sorger ◽  
Benjamin Schödinger ◽  
Hans-Jörg Schmölzer ◽  
Karin Hartl ◽  
...  
Keyword(s):  
Open Source ◽  
User Interfaces ◽  
Industrial Revolution ◽  
Low Cost ◽  
Material Behavior ◽  
Sensor Data ◽  
Management Tool ◽  
Special Focus ◽  
Programming Environments ◽  
Metal Processing

Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.

scholarly journals Reinforcement of Recycled Aggregate by Microbial-Induced Mineralization and Deposition of Calcium Carbonate—Influencing Factors, Mechanism and Effect of Reinforcement

Crystals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 887
Author(s):  
Chunhua Feng ◽  
Buwen Cui ◽  
Haidong Ge ◽  
Yihong Huang ◽  
Wenyan Zhang ◽  
...  
Keyword(s):  
Calcium Carbonate ◽  
Global Economy ◽  
Low Cost ◽  
High Water ◽  
Strengthening Mechanism ◽  
Recycled Aggregate ◽  
Strengthening Effect ◽  
Calcium Carbonate Precipitation ◽  
Environmental Friendliness ◽  
Advantages And Disadvantages

Recycled aggregate is aggregate prepared from construction waste. With the development of a global economy and people’s attention to sustainable development, recycled aggregate has shown advantages in replacing natural aggregate in the production of concrete due to its environmental friendliness, low energy consumption, and low cost. Recycled aggregate exhibits high water absorption and a multi-interface transition zone, which limits its application scope. Researchers have used various methods to improve the properties of recycled aggregate, such as microbially induced calcium carbonate precipitation (MICP) technology. In this paper, the results of recent studies on the reinforcement of recycled aggregate by MICP technology are synthesized, and the factors affecting the strengthening effect of recycled aggregate are reviewed. Moreover, the strengthening mechanism, advantages and disadvantages of MICP technology are summarized. After the modified treatment, the aggregate performance is significantly improved. Regardless of whether the aggregate was used in mortar or concrete, the mechanical properties of the specimens were clearly improved. However, there are some issues regarding the application of MICP technology, such as the use of an expensive culture medium, a long modification cycle, and untargeted mineralization deposition. These difficulties need to be overcome in the future for the industrialization of regenerated aggregate materials via MICP technology.

scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

scholarly journals A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 683
Author(s):  
José L. Escalona ◽  
Pedro Urda ◽  
Sergio Muñoz
Keyword(s):  
Computer Vision ◽  
Inertial Sensors ◽  
Vision System ◽  
Accurate Method ◽  
Sensor Data ◽  
Measuring System ◽  
Railway Vehicles ◽  
Track Geometry ◽  
Video Cameras ◽  
Measuring Unit

This paper describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder. The kinematic description, that is borrowed from the multibody dynamics analysis of railway vehicles used in computer simulation codes, is used to calculate the relative motion between the vehicle and the track, and also for the computer vision system and its calibration. The multibody framework is thus used to find the formulas that are needed to calculate the track irregularities (gauge, cross-level, alignment and vertical profile) as a function of sensor data. The TGMS has been experimentally tested in a 1:10 scaled vehicle and track specifically designed for this investigation. The geometric irregularities of a 90 m-scale track have been measured with an alternative and accurate method and the results are compared with the results of the TGMS. Results show a good agreement between both methods of calculation of the geometric irregularities.

scholarly journals BIM and IoT Sensors Integration: A Framework for Consumption and Indoor Conditions Data Monitoring of Existing Buildings

2021 ◽  
Vol 13 (8) ◽  
pp. 4496
Author(s):  
Giuseppe Desogus ◽  
Emanuela Quaquero ◽  
Giulia Rubiu ◽  
Gianluca Gatto ◽  
Cristian Perra
Keyword(s):  
Energy Consumption ◽  
Low Cost ◽  
Application Programming Interface ◽  
Visual Programming ◽  
Information Modeling ◽  
Sensor Data ◽  
Time Data ◽  
Existing Building ◽  
Building Maintenance ◽  
Indoor Conditions

The low accessibility to the information regarding buildings current performances causes deep difficulties in planning appropriate interventions. Internet of Things (IoT) sensors make available a high quantity of data on energy consumptions and indoor conditions of an existing building that can drive the choice of energy retrofit interventions. Moreover, the current developments in the topic of the digital twin are leading the diffusion of Building Information Modeling (BIM) methods and tools that can provide valid support to manage all data and information for the retrofit process. This paper shows the aim and the findings of research focused on testing the integrated use of BIM methodology and IoT systems. A common data platform for the visualization of building indoor conditions (e.g., temperature, luminance etc.) and of energy consumption parameters was carried out. This platform, tested on a case study located in Italy, is developed with the integration of low-cost IoT sensors and the Revit model. To obtain a dynamic and automated exchange of data between the sensors and the BIM model, the Revit software was integrated with the Dynamo visual programming platform and with a specific Application Programming Interface (API). It is an easy and straightforward tool that can provide building managers with real-time data and information about the energy consumption and the indoor conditions of buildings, but also allows for viewing of the historical sensor data table and creating graphical historical sensor data. Furthermore, the BIM model allows the management of other useful information about the building, such as dimensional data, functions, characteristics of the components of the building, maintenance status etc., which are essential for a much more conscious, effective and accurate management of the building and for defining the most suitable retrofit scenarios.

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