The assessment of a smart system in hydroponic vertical farming via fuzzy MCDM methods

2021 ◽  
pp. 1-12
Author(s):  
A. Cagri Tolga ◽  
Murat Basar
Keyword(s):  
Technology Selection ◽  
Smart System ◽  
Smart Systems ◽  
Vertical Farming ◽  
Extra Food ◽  
Cultivable Land ◽  
Selection For ◽  
Set Up ◽  
Global Population ◽  
Global Food

By 2050, the global population is estimated to rise to over 9 billion people, and the global food need is expected to ascend 50%. Moreover, by cause of climate change, agricultural production may decrease by 10%. Since cultivable land is constant, multi-layered farms are feasible alternatives to yield extra food from the unit land. Smart systems are logical options to assist production in these factory-like farms. When the amount of food grown per season is assessed, a single indoor hectare of a vertical farm could deliver yield equal to more than 30 hectares of land consuming 70% less water with nearly zero usage of pesticides. In this study, we evaluated technology selection for three vertical farm alternatives via MCDM methods. Even though commercial vertical farms are set up in several countries, area is still fresh and acquiring precise data is difficult. Therefore, we employed fuzzy logic as much as possible to overcome related uncertainties. WEDBA (Weighted Euclidean Distance Based Approximation) and MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) methods are employed to evaluate alternatives.

EEG electrodes selection for emotion recognition independent of stimulus presentation paradigms

2021 ◽  
pp. 1-17
Author(s):  
Naveen Masood ◽  
Humera Farooq
Keyword(s):  
Emotion Recognition ◽  
Stimulus Presentation ◽  
Setup Time ◽  
Majority Voting ◽  
Emotional States ◽  
Smart Systems ◽  
Eeg Data ◽  
Recognition Systems ◽  
Selection For ◽  
Set Up

Most of the electroencephalography (EEG) based emotion recognition systems rely on single stimulus to evoke emotions. EEG data is mostly recorded with higher number of electrodes that can lead to data redundancy and longer experimental setup time. The question “whether the configuration with lesser number of electrodes is common amongst different stimuli presentation paradigms” remains unanswered. There are publicly available datasets for EEG based human emotional states recognition. Since this work is focused towards classifying emotions while subjects are experiencing different stimuli, therefore we need to perform new experiments. Keeping aforementioned issues in consideration, this work presents a novel experimental study that records EEG data for three different human emotional states evoked with four different stimuli presentation paradigms. A methodology based on iterative Genetic Algorithm in combination with majority voting has been used to achieve configuration with reduced number of EEG electrodes keeping in consideration minimum loss of classification accuracy. The results obtained are comparable with recent studies. Stimulus independent configurations with lesser number of electrodes lead towards low computational complexity as well as reduced set up time for future EEG based smart systems for emotions recognition

scholarly journals Patent Analysis on the Development of the Shale Petroleum Industry Based on a Network of Technological Indices

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6746
Author(s):  
Jong-Hyun Kim ◽  
Yong-Gil Lee
Keyword(s):  
Petroleum Industry ◽  
Development Pattern ◽  
Directional Drilling ◽  
Patent Data ◽  
Smart System ◽  
Smart Systems ◽  
Complex Development ◽  
Technological Developments ◽  
Technological Indices ◽  
Degree Of Convergence

This study investigated the technological developments in the shale petroleum industry by analyzing patent data using a network of technological indices. The technological developments were promoted by the beginning of the shale industry, and after the first five years, it showed a more complex development pattern with the convergence of critical technologies. This paper described progress in the shale petroleum technologies as changes in relatedness networks of technological components. The relatedness represents degree of convergence between technological components, and betweenness centrality of network represents priority of technological components. In the results, the progress of the critical technologies such as directional drilling, increasing permeability, and smart systems, were actively carried out from 2012 to 2016. Especially, unconverged technology of increasing permeability and the converged technology of directional drilling and smart system has been intensively developed. Some technological components of the critical technologies are more significant in the form of converged technology.

Market Segmentation and Product Technology Selection for Remanufacturable Products

2005 ◽  
Vol 51 (8) ◽  
pp. 1193-1205 ◽  
Author(s):  
Laurens G. Debo ◽  
L. Beril Toktay ◽  
Luk N. Van Wassenhove
Keyword(s):  
Market Segmentation ◽  
Technology Selection ◽  
Product Technology ◽  
Selection For

Food security in the 21st century and in the role of biotechnology

foresight ◽  
1999 ◽  
Vol 1 (5) ◽  
pp. 399-412 ◽  
Author(s):  
Per Pinstrup‐Andersen ◽  
Marc J. Cohen
Keyword(s):  
Developing Countries ◽  
Food Security ◽  
Food Production ◽  
Supply Side ◽  
Adequate Diet ◽  
The World ◽  
Productivity Gains ◽  
Global Population ◽  
Global Food

Although global food production has consistently kept pace with population growth, the gap between food production and demand in certain parts of the world is likely to remain. More than 800 million people in developing countries lack access to a minimally adequate diet. Continued productivity gains are essential on the supply side, because global population will increase by 73 million people a year over the next two decades. In this article we assess the current global food situation, look at the prospects through to the year 2020, and outline the policies needed to achieve food security for all. Emphasis is on the role that agricultural biotechnology might play in reaching this goal.

Appropriate decision making in energy technology selection for developing countries: The Life Cycle Assessment approach

2021 ◽  
pp. 83-89
Author(s):  
Abeer Ali Khan
Keyword(s):  
Decision Making ◽  
Developing Countries ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Technology Selection ◽  
Energy Technology ◽  
High Demand ◽  
Environmentally Conscious ◽  
Assessment Approach ◽  
Selection For

As the high demand of energy of the developing countries is met by importing energy and different energy technology, it has become increasingly necessary to discuss the environmental impacts throughout the life cycle of those technologies and make better decisions. Developed in the late 1960s, Life Cycle Assessment (LCA) has become a wide-ranging tool used to determine impacts of products or systems over several environmental and resource issues. The LCA approach has become more prevalent in research, industry and policy with growing concern for the environment. Therefore, the aim of this paper is to introduce the use of LCA in the decision-making process while selecting an energy technology. In this way, more environmentally conscious decisions will be made as LCAs can provide a better basis for this process.

Geological Adaptive Cutterhead Selection for EPB Shield Based on BP Neural Network

2014 ◽  
Vol 607 ◽  
pp. 118-123
Author(s):  
Lai Kuang Lin ◽  
Yi Min Xia ◽  
Fei He ◽  
Qing Song Mao ◽  
Kui Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Earth Pressure ◽  
Training Model ◽  
Structure Design ◽  
Pressure Balance ◽  
Selection For ◽  
Earth Pressure Balance ◽  
Set Up ◽  
Epb Shield

In view of complex and fuzziness of geological adaptive cutterhead selection for earth pressure balance (EPB) shield, a cutterhead selection method based on BP neural network is put forward. Considering the structure characteristics of EPB shield cutterhead, typical cutterhead types are classified and summarized based on cutterhead topology structure and number of spokes. After analyzing the determinants of cutterhead selection, one-to-many mapping relation between cutterhead type and geological parameters is put forward, and then core geologic parameters related to cutterhead selection are concluded. The feasibility of using neural network method to choose the cutterhead type is analyzed, and a BP neural network training model for cutterhead selection is set up and tested in testing sample data. The result shows that the selected cutterhead and the construction cutterhead are basically consistent. The feasibility of this method is proved and it can be theoretical basis for the cutterhead structure design which will improve scientific of cutterhead selection.

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