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1 |
Digital Twin-driven approach to improving energy efficiency of indoor lighting based on computer vision and dynamic BIM
رویکرد دیجیتال دوقلو برای بهبود بهره وری انرژی در روشنایی داخلی بر اساس بینایی کامپیوتر و BIM پویا-2022 Intelligent lighting systems and surveillance systems have become an important part of intelligent buildings. However, the current intelligent lighting system generally adopts independent sensor control and
does not perform multi-source heterogeneous data fusion with other digital systems. This paper fully
considers the linkage between the lighting system and the surveillance system and proposes a digital
twin lighting (DTL) system that mainly consists of three parts. Firstly, a visualized operation and maintenance (VO&M) platform for a DTL system was established based on dynamic BIM. Secondly, the environment perception, key-frame similarity judgment, and multi-channel key-frame cut and merge
mechanism were utilized to preprocess the video stream of the surveillance system in real-time.
Lastly, pedestrians detected using YOLOv4 and the ambient brightness perceived by the environment
perception mechanism were transmitted to the cloud database and were continuously read by the
VO&M platform. The intent here was to aid timely adaptive adjustment of the digital twin and realistic
lighting through the internet. The effectiveness of the proposed method was verified by experimenting
with a surveillance video stream for 14 days. The key results of the experiments are as follows: (1) the
accuracy rate of intelligent decision control reached 95.15%; (2) energy consumption and electricity costs
were reduced by approximately 79%; and (3) the hardware cost and energy consumption of detection
equipment and the time and cost of operation and maintenance (O&M) were greatly reduced.
keywords: Computer vision | Digital Twin | Dynamic BIM | Energy-efficient buildings | Intelligent lighting control |
مقاله انگلیسی |
2 |
A holistic approach to health and safety monitoring: Framework and technology perspective
رویکردی جامع برای نظارت بر سلامت و ایمنی: چارچوب و دیدگاه فناوری-2022 Existing H&S monitoring methods are manual, cumbersome, time consuming and issues with
safety compliance and use of PPE remain a concern. With the existing manual H&S processes,
there are significant delays and, in some cases, even failure to report incidences, resulting in no or
slow improvements to safety. This paper proposes a prototype PPE access monitoring system
which combines smart PPE and an indoor/outdoor personnel location monitoring system. The
paper also proposes a generic framework to be used for smart gateway services within a
manufacturing site to augment and enable smart PPE, separating areas of high and low risk. The
prototype automated PPE detection gate presents a practical use of the framework and demon-
strates a suitable method for assessing workforce/visitor PPE compliance. Its secondary function
is to act as a location waypoint system to support other location tracking methods, identified in
the literature and throughout the testing protocol. The system could be further adapted to support
augmented personnel within the Operator 4.0 paradigm to improve site safety, monitoring and
control. keywords: اینترنت اشیا | تجهیزات حفاظت فردی | اپراتور 4.0 | انطباق با PPE | چارچوب | RFID | Internet of things | Personal protective equipment | Operator 4.0 | PPE compliance | Framework | RFID |
مقاله انگلیسی |
3 |
A Survey of Indoor Location Technologies, Techniques and Applications in Industry
بررسی فن آوری ها، تکنیک ها و کاربردهای مکان داخلی در صنعت-2022 The recent academic research surrounding indoor positioning systems (IPS) and indoor location-
based services (ILBS) are reviewed to establish the current state-of-the-art for IPS and ILBS. This
review is focused on the use of IPS / ILBS for cyber-physical systems to support secure and safe
asset management (including people as assets), exploring the potential applications of IPS for
industry as suggested in the literature. Current application areas in industry are presented,
separated into physical item and human traceability applications for context. The literature are
reviewed to identify gaps in the ILBS development for industrial applications, future research
needs to focus a development framework to enable scalable solutions for industry. The key gaps
identified in the literature are: (i) a lack of pathways to extend IPS research into an ILBS, (ii) no
end-to-end ILBS have been developed and (iii) no framework has been reported that outlines the
information pathways from sensor data collection and location information to an established
ILBS. The technologies reviewed are presented in a comparison table (Table 1) intended as a
reference for selecting technologies for future systems based on requirements. The techniques
used to extract location information from each of the technologies identified are also explored
stating current accuracy and aligning the techniques with their suitable technologies.
1. Introduction keywords: موقعیت داخلی | اینترنت اشیا | سیستم های حسگر | خدمات مبتنی بر مکان داخلی | سیستم های فیزیکی سایبری | Indoor location | Internet of things | Sensor systems | Indoor location based services | Cyber physical systems |
مقاله انگلیسی |
4 |
Smart sensors network for accurate indirect heat accounting in apartment buildings
سنسورهای هوشمند شبکه برای حسابداری دقیق غیر مستقیم در ساختمان های آپارتمان-2021 A new method for accurate indirect heat accounting in apartment buildings has been recently
developed by the Centre Suisse d’Electronique et de Microtechnique (CSEM). It is based on a data
driven approach aimed to the smart networking of any type of indirect heat allocation devices,
which can provide, for each heat delivery point of an apartment building, measurements or es-
timations of the temperature difference between the heat transfer fluid and the indoor environ-
ment. The analysis of the data gathered from the devices installed on the heating bodies, together
with the measurements of the overall building heat consumption provided by direct heat
metering, allows the evaluation of the characteristic thermal model parameters of heating bodies
at actual installation and working conditions. Thus overcoming the negative impact on accuracy
of conventional indirect heat accounting due to off-design operation, in which these measurement
systems normally operate. The method has been tested on conventional heat cost allocators
(HCA), and on innovative smart radiator thermostatic valves developed by CSEM. The evaluations
were carried out at the centralized heating system mock-up of the Istituto Nazionale di Ricerca
Metrologica (INRIM), and also in a real building in Neuchatel, Switzerland. The method has
proven to be an effective tool to improve the accuracy of indirect heat metering systems;
compared to conventional HCA systems, the error on the individual heating bill is reduced by
20%–50%. keywords: شبکه سنسورهای هوشمند | حسابداری غیر مستقیم | اندازه گیری حرارت | مخزن هزینه های حرارتی | سیستم های گرمایش متمرکز | Smart sensors network | Indirect heat accounting | Heat metering | Heat cost allocators | Centralized heating systems |
مقاله انگلیسی |
5 |
On the relevance of the metadata used in the semantic segmentation of indoor image spaces
ارتباط فراداده های مورد استفاده در تقسیم بندی معنایی فضاهای تصویر داخلی-2021 The study of artificial learning processes in the area of computer vision context has mainly focused on achieving a fixed output target rather than on identifying the underlying processes as a means to develop solutions capable of performing as good as or better than the human brain. This work reviews the well-known segmentation efforts in computer vision. However, our primary focus is on the quantitative evaluation of the amount of contextual information provided to the neural network. In particular, the information used to mimic the tacit information that a human is capable of using, like a sense of unambiguous order and the capability of improving its estimation by complementing already learned information. Our results show that, after a set of pre and post- processing methods applied to both the training data and the neural network architecture, the predictions made were drastically closer to the expected output in comparison to the cases where no contextual additions were provided. Our results provide evidence that learning systems strongly rely on contextual information for the identification task process. Keywords: Deep learning | U-net | Semantic segmentation | Metadata preprocessing | Fully convolutional network | Indoor scenes |
مقاله انگلیسی |
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Evaluating the effectiveness of biometric sensors and their signal features for classifying human experience in virtual environments
ارزیابی اثربخشی سنسورهای بیومتریک و ویژگی های سیگنال آنها برای طبقه بندی تجربه انسان در محیط های مجازی-2021 Built environments play an essential role in our day-to-day lives since people spend more than 85% of their times indoors. Previous studies at the conjunction of neuroscience and architecture confirmed the impact of architectural design features on varying human experience, which propelled researchers to study the improvement of human experience in built environments using quantitative methods such as biometric sensing. However, a notable gap in the knowledge persists as researchers are faced with sensors that are commonly used in the neuroscience domain, resulting in a disconnect regarding the selection of effective sensors that can be used to measure human experience in designed spaces. This issue is magnified when considering the variety of sensor signal features that have been proposed and used in previous studies. This study builds on data captured during a series of user studies conducted to measure subjects’ physiological responses in designed spaces using the combination of virtual environments and biometric sensing. This study focuses on the data analysis of the collected sensor data to identify effective sensors and their signal features in classifying human experience. To that end, we used a feature attribution model (i.e., SHAP), which calculates the importance of each signal feature in terms of Shapley values. Results show that electroencephalography (EEG) sensors are more effective as compared to galvanic skin response (GSR) and photoplethysmogram (PPG) (i.e., achieving the highest SHAP values among the three at 3.55 as compared to 0.34 for GSR and 0.21 for PPG) when capturing human experience in alternate designed spaces. For EEG, signal features calculated from the back channels (occipital and parietal areas) were found to possess comparable effectiveness as the frontal channel (i.e., have similar mean SHAP values per channel). In addition, frontal and occipital asymmetry were found to be effective in identifying human experience in designed spaces. Keywords: Architectural design | Feature attribution | Data-driven methods | Human experience | Virtual environments |
مقاله انگلیسی |
7 |
Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings
یادگیری تقویتی عمیق برای بهینه سازی کنترل دمای داخلی و مصرف انرژی گرمایشی در ساخت-2020 In this work, Deep Reinforcement Learning (DRL) is implemented to control the supply water temperature
setpoint to terminal units of a heating system. The experiment was carried out for an office building
in an integrated simulation environment. A sensitivity analysis is carried out on relevant hyperparameters
to identify their optimal configuration. Moreover, two sets of input variables were considered for
assessing their impact on the adaptability capabilities of the DRL controller. In this context a static and
dynamic deployment of the DRL controller is performed. The trained control agent is tested for four different
scenarios to determine its adaptability to the variation of forcing variables such as weather conditions,
occupant presence patterns and different indoor temperature setpoint requirements. The
performance of the agent is evaluated against a reference controller that implements a combination of
rule-based and climatic-based logics. As a result, when the set of variables are adequately selected a heating
energy saving ranging between 5 and 12% is obtained with an enhanced indoor temperature control
with both static and dynamic deployment. Eventually the study proves that if the set of input variables
are not carefully selected a dynamic deployment is strictly required for obtaining good performance. Keywords: Deep reinforcement learning | Building adaptive control | Energy efficiency | Temperature control | HVAC |
مقاله انگلیسی |
8 |
Study on the motion law of aerosols produced by human respiration under the action of thermal plume of different intensities
مطالعه در مورد قانون حرکات ذرات معلق در هوا که توسط تنفس انسان تحت اثر ستون های حرارتی با شدت های مختلف تولید می شود-2020 Predicting influence of human thermal plume on the diffusion of respiration-produced particles is an important
issue for improving indoor air quality through eliminating infectious microbes efficiently. In this study, the Large
Eddy Simulation was utilized to predict the effects of thermal plume of different intensities on particle diffusion.
Three postures of the human body model and three room temperatures were considered. The results show that
the convective heat transfer coefficient on the surface of the human body varies greatly with different postures.
The coefficient is the largest when the model is in sitting posture, leading to the greatest heat transfer rate.
Meanwhile, the thermal plume generated by bending the thigh increases the size of the facial thermal plume in
horizon direction. The increase of the difference between indoor temperature and skin temperature causes an
increase of the convective heat transfer of the manikin, leading to stronger airflow in front of the face. The
thicker and faster the human thermal plume is, the more difficult it is penetrated by aerosols produced by nasal
breathing, finally resulting in most particles distributed within 0.2m thick under the roof. Keywords: Thermal plume | Large eddy simulation| Aerosol | Nasal breathing | Computational fluid dynamics |
مقاله انگلیسی |
9 |
Comfort evaluation of seasonally and daily used residential load insmart buildings for hottest areas via predictive mean vote method
ارزیابی راحتی ساختمانهای بار مسکونی فصلی و روزانه برای گرمترین مناطق با استفاده از روش پیش بینی میانگین رای گیری-2020 tIn this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mam-dani (BPSOFMAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonallyused appliances are considered for the analysis of the efficient energy management through these con-trollers. Energy management is performed using the two Demand Side Management (DSM) strategies:load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of thedaily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances,i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are appliedin this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of thehottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occu-pancy, price, decision control variables, priority and length of operation times of the appliances, whereasthe output parameters are: energy consumption, cost and thermal and appliance usage comfort. More-over, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger’spredictive mean vote method. The comfort is further investigated by incorporating the renewable energysources, i.e., photovoltaic systems. Simulation results show the effectiveness of the proposed controllersas compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energyconsumption and cost of the proposed scenario. Keywords:Energy management | Thermal comfort | Appliance usage comfort | Fuzzy logic | Fuzzy inference systems |
مقاله انگلیسی |
10 |
A new wellbore fluid load diagnosing model based on the energy conservation law
یک مدل تشخیصی بار مایع چاه جدید بر اساس قانون حفظ انرژی-2020 Liquid load in the borehole would affect the rate stability of gas well adversely. However, the existing liquid load detection techniques are
somewhat limited by practical application. For the purpose of clarifying the liquid-carrying mechanism of two-phase flow in the wellbore with a
higher liquid rate, it is necessary to accurately diagnose the presence of liquid loading in the wellbore and reasonably formulate the production
measures of the gas well. On the basis of previous studies, this paper established a new model for diagnosing the liquid-carrying conditions of
gaseliquid two-phase vertical pipe flow according to the law of energy conservation. Then, by comparing with field practice statistics and indoor
experimental data, the accuracy of the new model was verified. Finally, the new model was applied to analyze the liquid-carrying condition of
one certain liquid-producing gas well. And the following research results were obtained. First, when the liquid production rate is lower, the
critical liquid-carrying gas rate calculated by the new model is much lower than the calculation result of the Turner model. Second, with the
increase of the liquid production rate, the critical liquid-carrying gas rate calculated by the new model increases gradually. And the higher the
pressure is, the more obvious the increase of the critical liquid-carrying gas rate is. Third, from the perspective of flow pattern, the gaseliquid
two-phase vertical pipe flow can be divided into 5 kinds, including bubble flow, slug flow, transition flow, wavy flow and annular mist flow. When
the two-phase flow is transition flow, wavy flow or annular mist flow, there is no liquid loading in the wellbore. In conclusion, the calculation
result of the new model is basically accordant with field practice statistics and indoor experimental data, and its diagnostic conclusion conforms
to the actual situations. Obviously, this model is of universality and provides a theoretical support for the diagnosis of liquid-carrying condition
and the prevention of fluid loading in liquid-producing gas wells. Keywords: Liquid-producing gas well | Liquid carrying mechanism | Flow pattern | Wellbore | Diagnosis on liquid loading | Critical liquid-carrying gas rate | Energy conservation |
مقاله انگلیسی |