Digital Twin-Based Assessment Framework for Energy Savings in University Classroom Lighting

In this paper, a digital twin-based assessment framework is proposed to determine which energy-saving technologies and strategies will work best in existing buildings. The proposed framework is based on a digital twin that integrates the existing building’s hardware system, the building’s operational schedule database, and a probabilistic model of occupant behavior. A digital model was constructed based on field measurements and database integration for a case study involving nine university buildings and 55 classrooms. As a result, in the classrooms involved in the case study, the lighting was turned on in the absence of occupants for an average of 10.7 h a day. The results indicate that it is very important to turn off the lights after the last hour of use in university classrooms in South Korea and that it is possible to reduce power consumption by more than 60% by employing an off strategy involving a passive infrared sensor or manager. Additionally, LED lighting in most classrooms is over-designed, which indicates that 46% of the energy consumed can be saved by adjusting the luminance level to an appropriate range

Selecting Disaster Waste Transportation Routes to Reduce Overlapping of Transportation Routes after Floods

Disasters have been a major subject of research considering damages caused in terms of losses of lives and properties and the functionality of critical services in cities. Floods generate large amounts of waste, causing several functional deteriorations, such as disrupted transportation, water supply, and wastewater management. Hence, it is necessary to establish an effective plan to secure urban resilience during the disaster response and recovery phases. This study proposes a method to reduce overlaps between disaster waste transportation routes and other emergency response activities after floods in the response and recovery phases. The network analysis of a geographic information system was used to analyze the supplying routes of evacuation, rescue/aid, hospital transportation, and police services for each disaster phase to reduce the overlapping of routes. The results showed that by using the proposed method, the average length of the disaster waste transportation routes increased by 25.29% and 9.80% in the response and recovery phases, respectively, whereas the length of the sections overlapping with the routes providing critical services decreased by 47.49% and 55.57% in the response and recovery phases, respectively. We believe that the proposed method identifies new corresponding key issues to establish disaster waste management plans to secure urban resilience after a disaster

Selecting Disaster Waste Transportation Routes to Reduce Overlapping of Transportation Routes after Floods

Disasters have been a major subject of research considering damages caused in terms of losses of lives and properties and the functionality of critical services in cities. Floods generate large amounts of waste, causing several functional deteriorations, such as disrupted transportation, water supply, and wastewater management. Hence, it is necessary to establish an effective plan to secure urban resilience during the disaster response and recovery phases. This study proposes a method to reduce overlaps between disaster waste transportation routes and other emergency response activities after floods in the response and recovery phases. The network analysis of a geographic information system was used to analyze the supplying routes of evacuation, rescue/aid, hospital transportation, and police services for each disaster phase to reduce the overlapping of routes. The results showed that by using the proposed method, the average length of the disaster waste transportation routes increased by 25.29% and 9.80% in the response and recovery phases, respectively, whereas the length of the sections overlapping with the routes providing critical services decreased by 47.49% and 55.57% in the response and recovery phases, respectively. We believe that the proposed method identifies new corresponding key issues to establish disaster waste management plans to secure urban resilience after a disaster

Load Prediction Algorithm Applied with Indoor Environment Sensing in University Buildings

Recently, building automation system (BAS) and building energy management system (BEMS) technologies have been applied to efficiently reduce the energy consumption of buildings. In addition, studies on utilizing large quantities of building data have been actively conducted using artificial intelligence and machine learning. However, the high cost and installation difficulties limit the use of measuring devices to sense the indoor environment of all buildings. Therefore, this study developed a comprehensive indoor environment sensor module with relatively inexpensive sensors to measure the indoor environment of a university building. In addition, an algorithm for predicting the load in real time through machine learning based on indoor environment measurement is proposed. When the reliability of the algorithm for predicting the number of occupants and load according to the indoor CO2 concentration was quantitatively assessed, the mean squared error (MSE), root mean square deviation (RMSD), and mean absolute error (MAE) were calculated to be 23.1, 4.8, and 2.5, respectively, indicating the high accuracy of the algorithm. Since the sensor used in this study is economical and can be easily applied to existing buildings, it is expected to be favorable for the dissemination of load prediction technology

Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production

Unmanned aerial vehicles (UAVs) have been employed to perform aerial surveys in many industries owing to their versatility, relatively low cost, and efficiency. Ground control points (GCPs) are used for georeferencing to ensure orthophoto geolocation/positioning accuracy. In this study, we investigate the impact of the number and distribution of GCPs on the accuracy of orthophoto production based on images acquired by UAVs. A test site was selected based on regulatory requirements, and several scenarios were developed considering the specifications of the UAVs used in this study. The locations of GCPs were varied to obtain the results. Based on the results obtained for different numbers of GCPs per unit area and distribution of GCPs, it is shown that UAV-based platforms can be more extensively utilized in a range of applications. The findings of this study will significantly impact the development process of GCP automation algorithms and enable a more cost-effective approach when determining target sites for UAV-based orthophoto production

IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety

Fires usually occur in homes because of carelessness and changes in environmental conditions. They cause threats to the residential community and may result in human death and property damage. Consequently, house fires must be detected early to prevent these types of threats. The immediate notification of a fire is the most critical issue in domestic fire detection systems. Fire detection systems using wireless sensor networks sometimes do not detect a fire as a consequence of sensor failure. Wireless sensor networks (WSN) consist of tiny, cheap, and low-power sensor devices that have the ability to sense the environment and can provide real-time fire detection with high accuracy. In this paper, we designed and evaluated a wireless sensor network using multiple sensors for early detection of house fires. In addition, we used the Global System for Mobile Communications (GSM) to avoid false alarms. To test the results of our fire detection system, we simulated a fire in a smart home using the Fire Dynamics Simulator and a language program. The simulation results showed that our system is able to detect early fire, even when a sensor is not working, while keeping the energy consumption of the sensors at an acceptable level

Context-Aware Mobile Sensors for Sensing Discrete Events in Smart Environment

Over the last few decades, several advancements in the field of smart environment gained importance, so the experts can analyze ideas for smart building based on embedded systems to minimize the expense and energy conservation. Therefore, propelling the concept of smart home toward smart building, several challenges of power, communication, and sensors’ connectivity can be seen. Such challenges distort the interconnectivity between different technologies, such as Bluetooth and ZigBee, making it possible to provide the continuous connectivity among different objects such as sensors, actuators, home appliances, and cell phones. Therefore, this paper presents the concept of smart building based on embedded systems that enhance low power mobile sensors for sensing discrete events in embedded systems. The proposed scheme comprises system architecture that welcomes all the mobile sensors to communicate with each other using a single platform service. The proposed system enhances the concept of smart building in three stages (i.e., visualization, data analysis, and application). For low power mobile sensors, we propose a communication model, which provides a common medium for communication. Finally, the results show that the proposed system architecture efficiently processes, analyzes, and integrates different datasets efficiently and triggers actions to provide safety measurements for the elderly, patients, and others

Exploiting Small World Problems in a SIoT Environment

Internet of Things (IoT) has been at the center of attention among researchers for the last two decades. Their aim was to convert each real-world object into a virtual object. Recently, a new idea of integrating the Social Networking concept into the Internet of Things has merged and is gaining popularity and attention in the research society due to its vast and flexible nature. It comprises of the potential to provide a platform for innovative applications and network services with efficient and effective manners. In this paper, we provide the sustenance for the Social Internet of Things (SIoT) paradigm to jump to the next level. Currently, the SIoT technique has been proven to be efficient, but heterogeneous smart devices are growing exponentially. This can develop a problematic scenario while searching for the right objects or services from billions of devices. Small world phenomena have revealed some interesting facts and motivated many researchers to find the hidden links between acquaintances in order to reach someone across the world. The contribution of this research is to integrate the SIoT paradigm with the small world concept. By integrating the small world properties in SIoT smart devices, we empower the Smart Social Agent (SSA). The Smart Social Agent ensures the finding of appropriate friends (i.e., the IoT devices used by our friend circle) and services that are required by the user, without human intervention. The Smart Social Agent can be any smart device in SIoTs, e.g., mobile phones

In Situ Monitoring of Individual Plasmonic Nanoparticles Resolves Multistep Nanoscale Sulfidation Reactions Hidden by Ensemble Average

The generation of complex nanostructures to obtain novel characteristics and improved performance has been achieved by coupling multiple nanoscale reactions. Because reactions at the nanometer scale directly govern the morphology of nanostructures, understanding the reaction mechanism is critical to precisely control the morphology and, eventually, the physicochemical properties of the materials. However, because of the ensemble-average effect, investigating the reaction mechanism at the bulk level does not provide sufficient information. In this study, we investigated the overall sulfidation reaction mechanism that occurred on individual silver nanocubes in real time at high temperature. Using the single-particle dark-field imaging technique, three discrete steps of the sulfidation reaction were clearly resolved in the profiles of the plasmon peak shift and the intensity change of individual particles according to time progress: (I) reactant diffusion to the silver surface by passing through a ligand barrier, (II) silver sulfide formation by C-S bond cleavage of cysteine molecules, and (III) diffusion of silver atoms in the silver sulfide layer until the complete formation of silver sulfide. By a combination of simulation and control experiments, physical constants were derived for each step, which is completely hidden in the ensemble measurements. Each individual nanoparticle exhibited a large variation of physical values, such as the reaction rate constant and diffusivity, mainly resulting from the intrinsic structural heterogeneity. Dark-field microscopy image processing based on surface plasmon scattering would be helpful to analyze the reaction kinetics and understand the reaction mechanisms of the numerous multistep nanoscale reactions in real time with high spatial and temporal resolutions under actual reaction conditions. © 2019 American Chemical Society.1