(1) The document summarizes a research project that aims to optimize energy harvesting using piezoelectric materials. (2) It discusses using ant colony optimization algorithms to simulate and predict human behavior patterns to maximize energy collection from piezoelectric devices. (3) The initial simulation results show that the ant colony approach finds shorter paths over time through a process of probabilistic decision-making informed by pheromone signals, though challenges remain in applying it to real-world three-dimensional environments and energy harvesting applications.

1. Research Project1
Optimization of energy harvesting devices
using piezoelectric material
11-12-2017
Research Project1
Materials and Processes of Sustainable Energetics
Kota Taharaguchi

2. Research Project1
• Introduction and Purpose
• Overview of my research
• Simulation
• Condition
• Result
• Conclusion
• Resources
1/16
Outline

3. Research Project1
We need to come up with new method to produce sustainable energy
2/16
Introduction -Energy issues-
Increase of green house gas Exhaustion of fossil fuels
Atmospheric CO2
All-time high amount:
407.27ppm (July, 2017)
High energy demand :
In 2040, the demand could be
1.5 times in the world
We need sustainable energy New method to produce energy

4. Research Project1 3/16
Introduction -Energy harvesting-
According to the institute of physics:
 Energy harvesting ,or energy scavenging, is a process
that captures small amount of energy that would
otherwise be lost as heat, light, sound, vibration or
movement

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Introduction -Piezoelectricity -
Piezoelectric Material
 Crystals, Ceramics and biological matter
↑ It exists in nature
Piezoelectric characters
 Electric Potential Field → Mechanical Strain: Actuator
 Mechanical Field → Electrical Potential Field: Sensor

6. Research Project1 5/16
Purpose
How can we optimize to produce energy by piezoelectricity?
→ My research purpose is to classify the effective way for piezoelectricity
 Human behavior prediction
 Best material combination of
energy harvesting
 Energy use optimization
Quartz, Berlinite, Rochelle salt, Dry bone
Pedestrian, sports, Living activities
To eliminate energy loss of other energy resources
Human
Behavior
Material
Decreasing
Energy loss

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Overview of my research
Human
Behavior
Material
Decreasing
Energy loss
Improvement of energy
harvesting model
Evaluation of this
model

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Simulation -Condition-
Observation and record
 Sensor, Beacon, Raspberry Pi, Bluetooth
Algorithm
 Ant colony optimization algorithms, Simulated Annealing,
Genetic algorithm
Machine learning
 Big data + Neural network
How can we predict human behavior to get much energy?

9. Research Project1 8/16
Simulation -Condition-
Ant colony optimization algorithms(ACO)
 probabilistic technique for solving computational
problems which can be reduced to finding good paths
through graphs(Wikipedia)
 Shortest route is found using pheromone trail which
ants deposit whenever they travel, as a form of indirect
communication

10. Research Project1 9/16
Simulation -Condition-
Ant colony optimization algorithms(ACO)
①Ant foraging - Co-operative search by pheromone trails
②When the ants in the shorter direction find a food, they carry and start returning back
Following their pheromone trails
③Because of evaporation of pheromone, the shortest path can be much stronger
④Over time, this positive feed back process prompts all ants to choose the shorter path

11. Research Project1 10/16
Simulation -Condition-
Ant colony optimization algorithms(ACO)
Moving probability
the amount of pheromone
Evaluate the value
https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms

12. Research Project1 11/16
Simulation -Condition-
Ant colony optimization algorithms(ACO)
 Number of destination：11
 Number of ants: 10000
 Start position: [0, 0]
 Alpha(pheromone): 1
 Beta(Movement):5
 Ρ(Ratio of pheromone evaporation) : 0.95
 Python anaconda3-4.0.0

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Simulation -Result-
Ant colony optimization algorithms(ACO)
Position X
PositionY
Position X
Start position End position
Before implementation After implementation

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 Since there are many parameters, it might be hard to
apply for the real situation
 The path depends on the number of ants mainly. It
needs to define the best number of ants
 At this time, I just focus on 2-D dimension, but also
need to think about 3-D dimension
13/16
Challenge

15. Research Project1 14/16
Conclusion
 Optimization of piezoelectricity must be needed for
obtaining a new way to produce energy
 ACO is a recently proposed metaheuristic approach for
solving optimization problems
 ACO shows the shortest path, but depends on the
parameter, the result might change slightly
 For ACO, environment search is important to obtain
good results

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• https://qiita.com/EnsekiTT/items/9b13ceba391216687f42
• https://en.wikipedia.org/wiki/Ant_colony_optimization_algo
rithms
• http://shiropen.com/2017/11/17/29625
• https://www.slideshare.net/iesli/20140163-senooken-
masterthesishearing
• http://www.cgc-japan.com/energy.html
Resources

17. Research Project1
Thank you for your attention!
Any questions?