Data-driven virtual sensing for spatial distribution of temperature and humidity

Kowli, Anupama; Rani, Vinita; Sanap, Mayur

doi:10.1016/j.jobe.2022.105726

Cited by 7 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Virtual sensing techniques have recently gained great attention in many research fields. As proved in several application areas such as transportation system [4], computer science [5], chemistry [6], automation [7], and building automation [8], they are a viable solution for lowering the cost of sensors networks. Furthermore, they enable accurate measurements to be obtained at critical locations where sensors are not easy to install.…”

Section: Introductionmentioning

confidence: 99%

Output-only response mapping of bridges for dynamic response estimation of gusset plate using convolutional neural networks

Dabbaghchian,

Eshkevari,

Marasco

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Structural health monitoring of bridges aims to provide an assessment of the condition of the structure, using collected structural response. The efficacy of the methods usually is constrained by the number and spatial distribution of sensors. Developing methods to map the response from known to unknown locations has been a challenging yet interesting area of study in recent years. In this research, we propose and study a novel framework to estimate and reconstruct the dynamic response of bridges at the connection level from the vibration response at global locations. The bridge is considered as a dynamic system in which vehicle excitations are the input and responses at sensor locations are the outputs. The proposed method studies an output-only problem and the input is considered unknown. The response at two types of output locations of the bridge, one at a global and one at the connection detail level are used to learn the dynamic relationship between the time signals via convolutional neural networks. This model-free framework is validated through a finite element simulation to reconstruct the strain response at the gusset plate of a truss bridge from the responses at other nodes along the bridge.

show abstract

Section: Introductionmentioning

confidence: 99%

Output-only response mapping of bridges for dynamic response estimation of gusset plate using convolutional neural networks

Dabbaghchian,

Eshkevari,

Marasco

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Hence, the modernization of control systems and sensors has enabled the development of this research area [16,17]. The data collected via these systems have various applications, including physical space analysis [18], biodynamics [19], and virtual sensors [20].…”

Section: Introductionmentioning

confidence: 99%

Exploring Spatial Patterns in Sensor Data for Humidity, Temperature, and RSSI Measurements

Botero-Valencia

Pérez

Hernández-García

et al. 2023

Data

View full text Add to dashboard Cite

The Internet of Things (IoT) is one of the fastest-growing research areas in recent years and is strongly linked to the development of smart cities, smart homes, and factories. IoT can be defined as connecting devices, sensors, and physical objects that can collect and transmit data across a network, enabling increased automation and better decision-making. In several IoT applications, humidity and temperature are some of the most used variables for adjusting system configurations and understanding their performance because they are related to various physical processes, human comfort, manufacturing processes, and 3D printing, among other things. In addition, one of the biggest problems associated with IoT is the excessive production of data, so it is necessary to develop methodologies to optimize the process of collecting information. This work presents a new dataset comprising almost 55 million values of temperature, relative humidity, and RSSI (Received Signal Strength Indicator) collected in two indoor spaces for longer than 3915 h at 10 s intervals. For each experiment, we captured the information from 13 previously calibrated sensors suspended from the ceiling at the same height and with a known relative position. The proposed dataset aims to contribute a benchmark for evaluating indoor temperature and humidity-controlled systems. The collected data allow the validation and improvement of the acquisition process for IoT applications.

show abstract

Artificial Intelligence-Aided Life-Cycle Assessment Strategies

Marasco,

Eshkevari,

Dabbaghchian

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Data-driven virtual sensing for spatial distribution of temperature and humidity

Cited by 7 publications

References 32 publications

Output-only response mapping of bridges for dynamic response estimation of gusset plate using convolutional neural networks

Output-only response mapping of bridges for dynamic response estimation of gusset plate using convolutional neural networks

Exploring Spatial Patterns in Sensor Data for Humidity, Temperature, and RSSI Measurements

Artificial Intelligence-Aided Life-Cycle Assessment Strategies

Contact Info

Product

Resources

About