scholarly journals Development of a “smart greenhouse” based on the model “plant–environment–situation–control”

2020 ◽  
pp. 49-64
Author(s):  
Mihail Grif ◽  
◽  
Baurzhan Belgibaev ◽  
Amantur Umarov ◽  
◽  
...  
Keyword(s):  
Black Box ◽  
Model Soil ◽  
Hardware System ◽  
Plant Environment ◽  
Box Models ◽  
Closed Type ◽  
National University ◽  
Complex Differential Equations ◽  
Computational Resources ◽  
And Control

The greenhouse is a closed-type agroecological system in which energy processes are strictly determined by the technological process of growing plants, taking into account the influence of the environment. As you know, greenhouse models are divided into two types: white box models and black box models. The well-known model of the “Soil-Plant-Atmosphere” system belongs to the first type, built on the physical principles of thermo-, hydro- and gas dynamics. They consist of several complex differential equations that use numerous coefficients and parameters that are known in advance. Such models are cumbersome and require large computational resources and time-consuming. The proposed model of the system “Plant-Environment-Situation-Management” is a practical analogue of the well-known model “Soil-Plant-Atmosphere”. The main difference of this model is that it refers to a black box model, which is an approximation of the observed processes and allows you to describe processes based on experimental data. On the basis of the “Plant-Environment-Situation-Management” model, the software and hardware system “Smart Greenhouse” was developed, which is a human-machine system with a rational separation of the functions of preparation (computer) and decision-making (Man). It allows you to control and control the growth and development of the plant during the growing season, taking into account the influence of environmental conditions. The system is implemented and used in the greenhouse of the al-Farabi Kazakh National University.

scholarly journals Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Mohammad Reza Davahli ◽  
Waldemar Karwowski ◽  
Krzysztof Fiok ◽  
Thomas Wan ◽  
Hamid R. Parsaei
Keyword(s):  
Artificial Intelligence ◽  
Rating Scale ◽  
Black Box ◽  
Healthcare Industry ◽  
Emergency Plans ◽  
Controlling System ◽  
Box Models ◽  
Key Dimensions ◽  
Value Model ◽  
And Control

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained six key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

scholarly journals Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

2021 ◽  
Author(s):  
Mohammad Reza Davahli ◽  
Waldemar Karwowski ◽  
Krzysztof Fiok ◽  
Thomas T.H. Wan ◽  
Hamid R Parsaei
Keyword(s):  
Artificial Intelligence ◽  
Rating Scale ◽  
Black Box ◽  
Healthcare Industry ◽  
Emergency Plans ◽  
Controlling System ◽  
Box Models ◽  
Key Dimensions ◽  
Value Model ◽  
And Control

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained 6 key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

scholarly journals Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6749
Author(s):  
Reda El Bechari ◽  
Stéphane Brisset ◽  
Stéphane Clénet ◽  
Frédéric Guyomarch ◽  
Jean Claude Mipo
Keyword(s):  
Branch And Bound ◽  
Prediction Error ◽  
Global Solutions ◽  
Black Box ◽  
High Fidelity ◽  
Electromagnetic Devices ◽  
Box Models ◽  
Element Simulation ◽  
The Cost ◽  
Black Box Models

Metamodels proved to be a very efficient strategy for optimizing expensive black-box models, e.g., Finite Element simulation for electromagnetic devices. It enables the reduction of the computational burden for optimization purposes. However, the conventional approach of using metamodels presents limitations such as the cost of metamodel fitting and infill criteria problem-solving. This paper proposes a new algorithm that combines metamodels with a branch and bound (B&B) strategy. However, the efficiency of the B&B algorithm relies on the estimation of the bounds; therefore, we investigated the prediction error given by metamodels to predict the bounds. This combination leads to high fidelity global solutions. We propose a comparison protocol to assess the approach’s performances with respect to those of other algorithms of different categories. Then, two electromagnetic optimization benchmarks are treated. This paper gives practical insights into algorithms that can be used when optimizing electromagnetic devices.

Design of Solar-Wind Complementary Grid-Connected Power Generation Monitoring System for Teaching

2011 ◽  
Vol 383-390 ◽  
pp. 3628-3632
Author(s):  
Rong Xia Sun ◽  
Jian Li Wang ◽  
Pan Pan Huang ◽  
Jian Kang ◽  
Xiao Feng Chen ◽  
...  
Keyword(s):  
Power Generation ◽  
Solar Wind ◽  
Monitoring System ◽  
Experimental Teaching ◽  
Hardware System ◽  
New Energy ◽  
Power Enterprise ◽  
Field Device ◽  
And Control ◽  
Monitoring Application

With the development of new energy industry, the technicians in the area of solar-wind complementary grid-connected power generation are urgently needed. For this reason, the monitoring system of 10kW solar-wind complementary grid-connected power generation was designed. Hardware system includes field device, communication network and monitoring host. Software design includes operation monitoring, application analysis, video surveillance, information issuing. It realizes functions of supervise and control, equipment events and alarm, report forms and print, energy management and forecasting, remote monitoring and so on. This research can be used to demonstrate experimental teaching in high shool and train power enterprise technicians.

An overview of deep learning techniques

2018 ◽  
Vol 66 (9) ◽  
pp. 690-703 ◽  
Author(s):  
Michael Vogt
Keyword(s):  
Deep Learning ◽  
Single Layer ◽  
Visual Image ◽  
Image Understanding ◽  
Number Of Layers ◽  
Box Models ◽  
Learning Techniques ◽  
Engineering Work ◽  
Computational Resources ◽  
Black Box Models

Abstract Deep learning is the paradigm that profoundly changed the artificial intelligence landscape within only a few years. Although accompanied by a variety of algorithmic achievements, this technology is disruptive mainly from the application perspective: It considerably pushes the border of tasks that can be automated, changes the way products are developed, and is available to virtually everyone. Subject of deep learning are artificial neural networks with a large number of layers. Compared to earlier approaches with ideally a single layer, this allows using massive computational resources to train black-box models directly on raw data with a minimum of engineering work. Most successful applications are found in visual image understanding, but also in audio and text modeling.

Creating Dynamic Pretrade Models: Beyond the Black Box

2018 ◽  
Keyword(s):  
Functional Form ◽  
Information Leakage ◽  
Black Box ◽  
Sensitivity Analyses ◽  
Stock Selection ◽  
Market Participants ◽  
Box Models ◽  
Portfolio Managers ◽  
Black Box Models ◽  
Shed Light

We provide a framework for investment managers to create dynamic pretrade models. The approach helps market participants shed light on vendor black-box models that often do not provide any transparency into the model’s functional form or working mechanics. In addition, this allows portfolio managers to create consensus estimates based on their own expectations, such as forecasted liquidity and volatility, and to incorporate firm proprietary alpha estimates into the solution. These techniques allow managers to reduce overdependency on any one black-box model, incorporate costs into the stock selection and portfolio optimization phase of the investment cycle, and perform “what-if” and sensitivity analyses without the risk of information leakage to any outside party or vendor.

scholarly journals The Role of Textualisation and Argumentation in Understanding the Machine Learning Process

2017 ◽  
Author(s):  
Kacper Sokol ◽  
Peter Flach
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Spatial Perception ◽  
Black Box ◽  
High Dimensional ◽  
Box Models ◽  
Machine Learning Applications ◽  
Black Box Models ◽  
Machine Learning Models

Understanding data, models and predictions is important for machine learning applications. Due to the limitations of our spatial perception and intuition, analysing high-dimensional data is inherently difficult. Furthermore, black-box models achieving high predictive accuracy are widely used, yet the logic behind their predictions is often opaque. Use of textualisation -- a natural language narrative of selected phenomena -- can tackle these shortcomings. When extended with argumentation theory we could envisage machine learning models and predictions arguing persuasively for their choices.

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