Integration of Multisensor data and Deep Learning for realtime Occupancy Detection for Building Environment Control Strategies

This paper analyses the status of aquiculture in China and gives out some of its potential problems. In order to over these problems, Industrial fieldbus and Intranet technology are used in this paper to achieve the hardware and software design, as well as control strategies for factory aquaculture. It applies WEB server, database server and browser to establish the management platform for environment control and production process. The whole system was successfully verified at Zhenjiang production base. Through a real-time control of dissolved oxygen, temperature and PH in pond, this system stabilizes these parameters at each own optimum values, and dramatically improves the overall productivity. The test results show that this system is easy-operated and user friendly, it provides a direct and practical measure for aquiculture, and saves energy as well.

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DeepClass: edge based class occupancy detection aided by deep learning and image cropping

Twelfth International Conference on Digital Image Processing (ICDIP 2020) ◽

10.1117/12.2572948 ◽

2020 ◽

Cited By ~ 1

Author(s):

Rita Tse ◽

Lorenzo Monti ◽

Marcus Im ◽

Silvia Mirri ◽

Giovanni Pau ◽

...

Keyword(s):

Deep Learning ◽

Occupancy Detection ◽

Edge Based

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Automation for Water and Nitrogen Deficit Stress Detection in Soilless Tomato Crops Based on Spectral Indices

Horticulturae ◽

10.3390/horticulturae4040047 ◽

2018 ◽

Vol 4 (4) ◽

pp. 47 ◽

Cited By ~ 2

Author(s):

Angeliki Elvanidi ◽

Nikolaos Katsoulas ◽

Constantinos Kittas

Keyword(s):

Crop Production ◽

Control Strategies ◽

Classification Tree ◽

Management Strategies ◽

Total Sample ◽

Sensor Data ◽

Limiting Factors ◽

Tree Model ◽

Environment Control ◽

The Impact

Water and nitrogen deficit stress are some of the most important growth limiting factors in crop production. Several methods have been used to quantify the impact of water and nitrogen deficit stress on plant physiology. However, by performing machine learning with hyperspectral sensor data, crop physiology management systems are integrated into real artificial intelligence systems, providing richer recommendations and insights into implementing appropriate irrigation and environment control management strategies. In this study, the Classification Tree model was used to group complex hyperspectral datasets in order to provide remote visual results about plant water and nitrogen deficit stress. Soilless tomato crops are grown under varying water and nitrogen regimes. The model that we developed was trained using 75% of the total sample dataset, while the rest (25%) of the data were used to validate the model. The results showed that the combination of MSAVI, mrNDVI, and PRI had the potential to determine water and nitrogen deficit stress with 89.6% and 91.4% classification accuracy values for the training and testing samples, respectively. The results of the current study are promising for developing control strategies for sustainable greenhouse production.

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Validation, optimisation and comparison of carbon dioxide-based occupancy estimation algorithms

Indoor and Built Environment ◽

10.1177/1420326x19871332 ◽

2019 ◽

Vol 29 (6) ◽

pp. 820-834

Author(s):

Felix Nienaber ◽

Sebastian Wolf ◽

Mark Wesseling ◽

Davide Calì ◽

Dirk Müller ◽

...

Keyword(s):

Carbon Dioxide ◽

Air Conditioning ◽

Control Strategies ◽

Fixed Time ◽

Mass Balance Equation ◽

Estimation Algorithms ◽

Detection Algorithms ◽

Occupancy Detection ◽

Occupancy Estimation ◽

The Given

The operation of heating, cooling and air-conditioning (HVAC) in buildings often adheres to fixed time schedules. However, associating HVAC schedules to the occupant’s presence patterns can save a significant amount of energy, reducing operation periods to the required minimum. Therefore, automated occupancy estimation provides valuable input to efficient building control strategies. This work discusses the validation and adjustment for two carbon dioxide-based occupancy detection algorithms based on data from ten multi-person offices. Both methods are based on a carbon dioxide mass balance equation. However, they follow two different philosophies. One model is deterministic and includes a more detailed representation of the system, whereas the other model includes stochastic elements and was based on fewer assumptions. Both approaches show similar and promising results. The advantages and drawbacks of each method are reviewed. Furthermore, adjustments of the algorithms to the given conditions and possible future improvements are discussed.

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