scholarly journals Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 696
Author(s):  
Eun Ji Choi ◽  
Jin Woo Moon ◽  
Ji-hoon Han ◽  
Yongseok Yoo
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Thermal Environment ◽  
The Body ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Body Parts ◽  
Joint Location

The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

scholarly journals A Product Pose Tracking Paradigm Based on Deep Points Detection

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 112
Author(s):  
Loukas Bampis ◽  
Spyridon G. Mouroutsos ◽  
Antonios Gasteratos
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Visible Spectrum ◽  
The Body ◽  
Autonomous Control ◽  
Pose Tracking ◽  
Computer Aided ◽  
Manufacturing Procedure ◽  
Infrared Cameras ◽  
Representative Points

The paper at hand presents a novel and versatile method for tracking the pose of varying products during their manufacturing procedure. By using modern Deep Neural Network techniques based on Attention models, the most representative points to track an object can be automatically identified using its drawing. Then, during manufacturing, the body of the product is processed with Aluminum Oxide on those points, which is unobtrusive in the visible spectrum, but easily distinguishable from infrared cameras. Our proposal allows for the inclusion of Artificial Intelligence in Computer-Aided Manufacturing to assist the autonomous control of robotic handlers.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

scholarly journals ADAPTIVE HOUSE DESIGN AND PEOPLE’S HABITS IN ACHIEVING THERMAL COMFORT IN GAYO HIGHLAND ACEH, INDONESIA

2021 ◽  
Vol 8 (1) ◽  
pp. 23
Author(s):  
Erna Meutia ◽  
Laina Hilma Sari
Keyword(s):  
Thermal Comfort ◽  
Thermal Environment ◽  
The Body ◽  
Mental Condition ◽  
Human Adaptation ◽  
Cold Environment ◽  
Adaptive Thermal Comfort ◽  
Aceh Province ◽  
Lower Temperature ◽  
House Design

The Gayo Highland is one of the districts in Aceh Province, Sumatra. Due to the topography, this area has a lower  temperature compared than the flat and coastal areas in Aceh. The thermal comfort that is felt is based on a person's mental condition and how he expresses his satisfaction with his thermal environment. In other words, it shows how humans adapt to their thermal environment. Thermal comfort based on human adaptation is known as adaptive thermal comfort. The form of dwelling for the Gayo Highland community has shifted and changed from traditional dwelling to Transitional and Modern forms that influence the Gayo Highland community's adaptation to achieve thermal comfort. Therefore, this paper aims to investigate the house design in Gayo highland in providing warmth to the occupants naturally in the cold environment. Another aim of this study is to investigate the people's habits in warming up the body to deal with the low air temperature in the area.  This study shows how the local people adapt themselves through the house element and daily habit to gain the internal thermal comfort.

scholarly journals Field Study Of A Radiant Heating System For Sleep Thermal Comfort And Potential Energy Saving

2021 ◽  
Author(s):  
Christopher L. K. Wang
Keyword(s):  
Potential Energy ◽  
Thermal Comfort ◽  
Energy Saving ◽  
Energy Savings ◽  
Thermal Environment ◽  
Residential Buildings ◽  
Thermal Conditions ◽  
Heating System ◽  
The Body ◽  
Radiant Temperature

As sleep is unconscious, the traditional definition of thermal comfort with conscious judgment does not apply. In this thesis sleep thermal comfort is defined as the thermal condition which enables sleep to most efficiently rejuvenate the body and mind. A comfort model was developed to stimulate the respective thermal environment required to achieve the desired body thermal conditions and a new infrared sphere method was developed to measure mean radiant temperature. Existing heating conditions according to building code conditions during sleeping hours was calculated to likely overheat a sleeping person and allowed energy saving potential by reducing nighttime heating set points. Experimenting with existing radiantly and forced air heated residential buildings, it was confirmed that thermal environment was too hot for comfortable sleep and that the infrared sphere method shows promise. With the site data, potential energy savings were calculated and around 10% of energy consumption reduction may be achieved during peak heating.

scholarly journals Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages

2019 ◽  
Vol 11 (13) ◽  
pp. 1584 ◽  
Author(s):  
Yang Chen ◽  
Won Suk Lee ◽  
Hao Gan ◽  
Natalia Peres ◽  
Clyde Fraisse ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Detection System ◽  
Growth Cycle ◽  
Aerial Images ◽  
Accurate Estimation ◽  
Yield Prediction ◽  
Distribution Maps ◽  
Detection And Counting

Strawberry growers in Florida suffer from a lack of efficient and accurate yield forecasts for strawberries, which would allow them to allocate optimal labor and equipment, as well as other resources for harvesting, transportation, and marketing. Accurate estimation of the number of strawberry flowers and their distribution in a strawberry field is, therefore, imperative for predicting the coming strawberry yield. Usually, the number of flowers and their distribution are estimated manually, which is time-consuming, labor-intensive, and subjective. In this paper, we develop an automatic strawberry flower detection system for yield prediction with minimal labor and time costs. The system used a small unmanned aerial vehicle (UAV) (DJI Technology Co., Ltd., Shenzhen, China) equipped with an RGB (red, green, blue) camera to capture near-ground images of two varieties (Sensation and Radiance) at two different heights (2 m and 3 m) and built orthoimages of a 402 m2 strawberry field. The orthoimages were automatically processed using the Pix4D software and split into sequential pieces for deep learning detection. A faster region-based convolutional neural network (R-CNN), a state-of-the-art deep neural network model, was chosen for the detection and counting of the number of flowers, mature strawberries, and immature strawberries. The mean average precision (mAP) was 0.83 for all detected objects at 2 m heights and 0.72 for all detected objects at 3 m heights. We adopted this model to count strawberry flowers in November and December from 2 m aerial images and compared the results with a manual count. The average deep learning counting accuracy was 84.1% with average occlusion of 13.5%. Using this system could provide accurate counts of strawberry flowers, which can be used to forecast future yields and build distribution maps to help farmers observe the growth cycle of strawberry fields.

The Thermal Comfort of Sedentary Workers

1987 ◽  
Vol 1 (2) ◽  
pp. 74-77 ◽  
Author(s):  
S C Foo ◽  
WO Phoon
Keyword(s):  
Thermal Comfort ◽  
Air Temperature ◽  
Thermal Environment ◽  
Thermal Sensation ◽  
Tropical Climate ◽  
Climatic Conditions ◽  
The Body ◽  
Office Workers ◽  
Preferred Temperature

Two hundred and eighty-five Office workers were surveyed and the micro-climatic conditions in which they worked were measured to evaluate their preferred temperature. About 78% of workers considered the natural tropical climate uncomfortable. However, 76% to 87% of workers in airconditioned Offices approved of their thermal environment if its temperature ranged from 21°C to 27°C. Many workers who felt that the temperature produced a neutral thermal sensation in the body as a whole, tended to complain that their heads were too warm and at the same time their limbs too cool. About 60% of workers in airconditioned Offices were exposed to an air temperature of less than 24°C. Present data suggest that an air temperature of 27°C would be comfortable for more than 80% of workers.

scholarly journals An image-based gangrene disease classification

2020 ◽  
Vol 10 (6) ◽  
pp. 6001
Author(s):  
Pramod Sekharan Nair ◽  
Tsrity Asefa Berihu ◽  
Varun Kumar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Image Features ◽  
The Body ◽  
Disease Classification ◽  
Support Vector ◽  
Body Parts ◽  
Intermediate Layers ◽  
The Given

Gangrene disease is one of the deadliest diseases on the globe which is caused by lack of blood supply to the body parts or any kind of infection. The gangrene disease often affects the human body parts such as fingers, limbs, toes but there are many cases of on muscles and organs. In this paper, the gangrene disease classification is being done from the given images of high resolution. The convolutional neural network (CNN) is used for feature extraction on disease images. The first layer of the convolutional neural network was used to capture the elementary image features such as dots, edges and blobs. The intermediate layers or the hidden layers of the convolutional neural network extracts detailed image features such as shapes, brightness, and contrast as well as color. Finally, the CNN extracted features are given to the Support Vector Machine to classify the gangrene disease. The experiment results show the approach adopted in this study performs better and acceptable.

scholarly journals Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Maria Bibi ◽  
Muhammad Kashif Hanif ◽  
Muhammad Umer Sarwar ◽  
Muhammad Irfan Khan ◽  
Shouket Zaman Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Maximum Temperature ◽  
Economic Losses ◽  
Asian Citrus Psyllid ◽  
Average Maximum ◽  
Average Minimum Temperature ◽  
Network Approaches

Asian citrus psyllid, Diaphorina citri Kuwayama (Liviidae: Hemiptera) is a menacing and notorious pest of citrus plants. It vectors a phloem vessel-dwelling bacterium Candidatus Liberibacter asiaticus, which is a causative pathogen of the serious citrus disease known as Huanglongbing. Huanglongbing disease is a major bottleneck in the export of citrus fruits from Pakistan. It is being responsible for huge citrus economic losses globally. In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus-growing regions of Pakistan. The pest prediction models can be used for proper applications of pesticides only when needed for reducing the environmental and cost impacts of pesticides. Pearson’s correlation analysis was performed to find the relationship between different predictor (abiotic and biotic) variables and pest infestation rate on citrus plants. Multiple linear regression, random forest regressor, and deep neural network approaches were compared to predict population dynamics of Asian citrus psyllid. In comparison with other regression techniques, a deep neural network-based prediction model resulted in the least root mean squared error values while predicting egg, nymph, and adult populations.

Sign in / Sign up

Export Citation Format

Share Document