529 research outputs found
Solar-powered aquaponics prototype as sustainable approach for food production
This paper presents the establishment of a solar-powered aquaponics prototype as a sustainable, cost
effective and environmentally sound approach for food production. In this study, a prototype bench
top aquaponics rig with an integrated 20 W solar panel were fabricated for the cultivation of red
Hybrid Tilapia (Oreochromis spp.) and leaf mustard (Brassica juncea). The size of the fish tank is about
29.5L and serves as the base for the setup. Additionally, the hydroponic grower compartment (0.45 m
(L) ๏ฟฝ 0.32 m (W) ๏ฟฝ 0.13 m (H)) was stacked on top of the fish tank and was filled with LECA media
bed for the plant growth. Two important operating parameters were studied. First, the amount of energy
produced by the solar panel and the energy consumption by the water pump used in the setup. Secondly,
the resultant effects from fish cultivation and plants growth on the water qualities and nitrification effi๏ฟฝciency of the aquaponics unit. The aquaponics unit was operated for a month and the values of pH, tem๏ฟฝperature, and ammonia level were measured to be within the range of 6.4โ7.2, 27.1โ31.7 ๏ฟฝC, and
1 mg๏ฟฝL๏ฟฝ1
, respectively. Survival rate for fish was about 75% with specific growth rate (SGR) of 3.75%
per day and food conversion ratio of about 1.15. A slight nutrient deficiency was evident and plants
showed a healthy growth with height gain as high as 5 cm was achieved. Despite raining season, our data
shows that the energy produced via 20 W solar panel enabled the unit to run at night without depending
on local electricity for nearly two hours. Clearly, a larger solar panel is needed for longer operation.
Nevertheless, the study has proven the potential of operating a low cost aquaponics setup using renew๏ฟฝable energy for a sustainable food production method
A review of task allocation methods for UAVs
Unmanned aerial vehicles, can offer solutions to a lot of problems, making it crucial to research more and improve the task allocation methods used. In this survey, the main approaches used for task allocation in applications involving UAVs are presented as well as the most common applications of UAVs that require the application of task allocation methods. They are followed by the categories of the task allocation algorithms used, with the main focus being on more recent works. Our analysis of these methods focuses primarily on their complexity, optimality, and scalability. Additionally, the communication schemes commonly utilized are presented, as well as the impact of uncertainty on task allocation of UAVs. Finally, these methods are compared based on the aforementioned criteria, suggesting the most promising approaches
๋์๋๋ฌ์ ๊ณ ๋ คํ ๋ณต์ ๋ฌด์ธ๊ธฐ ์๋ฌดํ ๋น ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2017. 8. ๊น์ ๋จ.๋ฌด์ธํญ๊ณต๊ธฐ์ ์์จ๋นํ ๊ธฐ์ ์ด ์ฑ์ํจ์ ๋ฐ๋ผ ๋ฌด์ธํญ๊ณต๊ธฐ์ ์๊ตฌ๋๋ ์๋ฌด์ ๋ณต์ก๋์ ์ ๋ฐ๋๊ฐ ์ฆ๊ฐํ๊ณ ์๋ค. ์ต๊ทผ์๋ ๋จ์ผ ๋ฌด์ธํญ๊ณต๊ธฐ์ ์ํ ๊ฐ์์ ์ฐฐ ์๋ฌด์์ ๋์๊ฐ ๋ค์์ ๋ฌด์ธํญ๊ณต๊ธฐ์ ํ๋ ฅ์ ์ธ ์๋ฌด์ํ ๋ฅ๋ ฅ์ ๊ดํ ์ฐ๊ตฌ๊ฐ ํ๋ฐํ ์ํ๋๊ณ ์๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๋ฌด์ธํญ๊ณต๊ธฐ์ ํ์
์ ์ํ ์ ์ฌ๋ ฅ์ ์ต๋ํ์ผ๋ก ํ์ฉํ๊ธฐ ์ํ์ฌ ๋ค์์ ๋ฌด์ธํญ๊ณต๊ธฐ๊ฐ ๋์์ ์ํํด์ผ ํ๋ ์๋ฌด๋ฅผ ๊ณ ๋ คํ์๋ค. ์ด๋ฌํ ์๋ฌด๋ก๋ ์ํ๋๊ฐ ๋์ ๋ฐฉ์ด ์์คํ
์ ๋์์ ๊ณต๊ฒฉํ๋ ์๋ฌด, ๋์ ์ฌ๋์ง์ญ์ ๋ค์์ ๋ฌด์ธ๊ธฐ๊ฐ ๋์์ ์์, ๋ฌผํ์ง์, ๊ตฌ์กฐ ๋ฑ์ ์ํํ๋ ์๋ฌด, ๊ทธ๋ฆฌ๊ณ ๋ฌด๊ฑฐ์ด ๋ฌผ์ฒด๋ฅผ ๋ค์์ ๋ฌด์ธํญ๊ณต๊ธฐ๊ฐ ํ๋ ฅํ์ฌ ์์กํ๋ ์๋ฌด ๋ฑ์ ๊ณ ๋ คํ ์ ์๋ค. ์ด์ ๊ฐ์ด ๋ณต์กํ ์๋ฌด๋ฅผ ๊ด๋ฆฌํ๊ธฐ ์ํด ์ง์ ์กฐ์ข
์ฌ๋ ๋ค์์ ๋ฌด์ธํญ๊ณต๊ธฐ๋ฅผ ๊ด์ ํ์ฌ์ผ ํ๋ฉฐ, ์ด ๊ณผ์ ์์ ๊ณผ๋ํ ์
๋ฌด๋ถํ๋ ์กฐ์ข
์ฌ ์ค์๋ฅผ ์ ๋ฐํ์ฌ ์๋ฌด์ํ ํจ์จ์ ํ๋ก ์ด์ด์ง ์ ์๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ๋ค์ ๋ฌด์ธํญ๊ณต๊ธฐ์ ๋์๋๋ฌ์ ๊ณ ๋ คํ ํ๋ ฅ ์๋ฌดํ ๋น ๋ฌธ์ ๋ฅผ ์ ์๊ณํ๋ฒ์ผ๋ก ์ ์ํํ๊ณ , ์ค์์ง์คํ ์๋ฌดํ ๋น ๋ฐฉ์๊ณผ ๋ถ์ฐํ ์๋ฌดํ ๋น ๋ฐฉ์์ ์ฐ๊ตฌํ์๋ค. ๋ฌด์ธํญ๊ณต๊ธฐ๋ก๋ถํฐ ์์ง๋ ์ ๋ณด๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ต์ ์ ๊ฐ๊น์ด ์๋ฌดํ ๋น์ ๊ฒฐ์ ํ๋ ์ค์์ง์คํ ์๋ฌดํ ๋น ๋ฐฉ์์ผ๋ก๋ ๋ชจ๋ ํด ๊ณต๊ฐ์ ํ์ํ์ฌ ์ต์ ํด๋ฅผ ๊ณ์ฐํ๋ ๋ฐฉ์, ๊ฒฝํ์ ์ธ ๋ฒ์น์ ํตํด ์ ์ํ๊ฒ ํด๋ฅผ ๊ฒฐ์ ํ๋ ๋ฐฉ์, ๊ทธ๋ฆฌ๊ณ ๋ฉํ ํด๋ฆฌ์คํฑ ๊ธฐ๋ฒ์ ์ผ์ข
์ธ ๊ตฐ์ง ์ต์ ํ ๊ธฐ๋ฒ์ ํ์ฉํ๋ ๋ฐฉ์์ ์ ์ํ์๋ค. ๋ถ์ฐํ ์๋ฌดํ ๋น ๋ฐฉ์์ผ๋ก๋ ๊ฐ๋ณ ๋ฌด์ธํญ๊ณต๊ธฐ๋ ๋ชจ๋ ๋ฌด์ธํญ๊ณต๊ธฐ๊ฐ ์๋ ์ด์ ๋ฌด์ธํญ๊ณต๊ธฐ๋ค๊ณผ๋ง ์ ๋ณด๋ฅผ ๊ต๋ฅํ๊ณ , ์ด๋ฅผ ํตํ์ฌ ์์จ์ ์ผ๋ก ์๋ฌด๋ฅผ ํ ๋นํ๋ ๊ธฐ๋ฒ์ ์ ์ํ์๋ค. ์ ํ๋ ํต์ ๋ฐ๊ฒฝ์ ๋ฐ๋ฅธ ์ค์๊ฐ ๋คํธ์ํฌ ์์๋ณํ ์ํฉ์ ๊ณ ๋ คํ๊ธฐ ์ํ์ฌ ์ง๊ฒฐ์ง ๊ฐ๋
์ ๋์
ํ์์ผ๋ฉฐ, ์ฐ๊ฒฐ๋ ๋คํธ์ํฌ ์ํฉ์ ๋ํ์ฌ ์๋ ด์ฑ๊ณผ ํ์ฅ์ฑ์ ๋ถ์ํ์๋ค. ์ ์ํ ๊ธฐ๋ฒ๋ค์ ์ฑ๋ฅ์ ๊ฒ์ฆํ๊ธฐ ์ํ์ฌ ์ ๋๊ณต๋ง ์ ์์์ ์๋๋ฆฌ์ค์ ๋ํ ์์น ์๋ฎฌ๋ ์ด์
์ ์ํํ๊ณ , ์ ์ํ ๊ธฐ๋ฒ ๊ฐ์ ์ฑ๋ฅ์ ๋น๊ต ๋ถ์ํ์๋ค.With increasing demand for unmanned aerial vehicles (UAVs) in military and civilian areas, coordination of multiple UAVs is expected to play a key role in complex missions. As the number of agents and tasks increases, however, a greater burden is imposed on ground operators, which may cause safety issues and performance degradation accomplishing the mission. In particular, the operation requiring temporal and spatial cooperation by UAVs is significantly difficult.
This dissertation proposes autonomous task allocation algorithms for cooperative timing missions with simultaneous spatial/temporal involvement of multiple agents. After formulating the task allocation problem into integer programming problems in view of UAVs and tasks, centralized and distributed algorithms are proposed. In the centralized approach, an algorithm to find an optimal solution that minimizes the time to complete all the missions is introduced. Since the exact algorithm is time intensive, heuristic algorithms working in a greedy manner are proposed. A metaheuristic approach is also considered to find a near-optimal solution within a feasible duration. In the distributed approach, market-based task allocation algorithms are designed. The mathematical convergence and scalability analyses show that the proposed algorithms have a polynomial time complexity. The baseline algorithms for a connected network are then extended to address time-varying network topology including isolated sub-networks due to a limited communication range. The performance of the proposed algorithms is demonstrated via Monte Carlo simulations for a scenario involving the suppression of enemy air defenses.Chapter 1 Introduction 1
1.1 Motivation and Objective 1
1.2 Literature Survey 3
1.2.1 Vehicle Routing Problem 3
1.2.2 Centralized and Distributed Control 4
1.2.3 Centralized Control: Optimal Coalition Formation Problem 5
1.2.4 Distributed Control 8
1.3 Research Contribution 10
1.3.1 Systematic Problem Formulation 10
1.3.2 Design of a Centralized TA Algorithm for a Cooperative Timing Mission 11
1.3.3 Design of a Distributed TA Algorithm for a Cooperative Timing Mission 11
1.4 Dissertation Organization 12
Chapter 2 Problem Statement 13
2.1 Assumptions 13
2.2 Agent-based Formulation 15
2.3 Task-based Formulation 19
2.4 Simplified Form of Task-based Formulation 21
Chapter 3 Centralized Task Allocation 23
3.1 Assumptions 23
3.2 Exact Algorithm 24
3.3 Agent-based Sequential Greedy Algorithm: A-SGA 26
3.4 Task-based Sequential Greedy Algorithm: T-SGA 28
3.5 Agent-based Particle Swarm Optimization: A-PSO 30
3.5.1 Preliminaries on PSO 30
3.5.2 Particle Encoding 33
3.5.3 Particle Refinement 33
3.5.4 Score Calculation Considering DAG Constraint 34
3.6 Task-based Particle Swarm Optimization: T-PSO 38
3.6.1 Particle Encoding 38
3.6.2 Particle Refinement 39
3.7 Numerical Results 41
Chapter 4 Distributed Task Allocation 49
4.1 Assumptions 50
4.2 Project Manager-oriented Coalition Formation Algorithm : PCFA 51
4.3 Task-oriented Coalition Formation Algorithm: TCFA 63
4.4 Modified Greedy Distributed Allocation Protocol: Modified GDAP 68
4.5 Properties 71
4.5.1 Convergence 71
4.5.2 Scalability 72
4.5.3 Performance 75
4.5.4 Comparison with GDAP 76
4.6 TA Algorithm in Dynamic Environment 79
4.6.1 Challenges in Dynamic Environment 79
4.6.2 Assumptions 79
4.6.3 Distributed TA Architecture in Dynamic Environment 80
4.6.4 Rally Point 85
4.6.5 Convergence 87
4.6.6 Deletion of Duplicated Allocation 87
4.7 Numerical Results 88
4.7.1 Scalability 88
4.7.2 Application: SEAD Scenario 94
4.7.3 Discussion 106
Chapter 5 Conclusions 107
5.1 Concluding Remarks 107
5.1.1 Problem Statement 107
5.1.2 Centralized Task Allocation 107
5.1.3 Distributed Task Allocation 108
5.2 Future Research 110
Abstract (in Korean) 125Docto
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