Approximate Path Searching Method for Single-Satellite Observation and Transmission Task Planning Problem

Mathematical Problems in Engineering ◽

10.1155/2017/7304506 ◽

2017 ◽

Vol 2017 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Shuang Peng ◽

Hao Chen ◽

Jun Li ◽

Ning Jing

Keyword(s):

Optimal Solution ◽

Satellite Observation ◽

Experimental Result ◽

Observation System ◽

Planning Problem ◽

Data Sets ◽

Task Planning ◽

Ground Station ◽

Label Setting Algorithm ◽

Searching Method

Satellite task planning not only plans the observation tasks to collect images of the earth surface, but also schedules the transmission tasks to download images to the ground station for users’ using, which plays an important role in improving the efficiency of the satellite observation system. However, most of the work to our knowledge, scheduling the observation and transmission tasks separately, ignores the correlation between them in resource (e.g., energy and memory) consumption and acquisition. In this paper, we study the single-satellite observation and transmission task planning problem under a more accurate resource usage model. Two preprocessing strategies including graph partition and nondominated subpaths selection are used to decompose the problem, and an improved label-setting algorithm with the lower bound cutting strategy is proposed to maximize the total benefit. Finally, we compare the proposed method with other three algorithms based on three data sets, and the experimental result shows that our method can find the near-optimal solution in much less time.

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Optimum Distribution System Expansion Planning Incorporating DG Based on N-1 Criterion for Sustainable System

Sustainability ◽

10.3390/su13126708 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6708

Author(s):

Hamza Mubarak ◽

Nurulafiqah Nadzirah Mansor ◽

Hazlie Mokhlis ◽

Mahazani Mohamad ◽

Hasmaini Mohamad ◽

...

Keyword(s):

Power Systems ◽

Distribution System ◽

Industrial Revolution ◽

Optimal Solution ◽

Search Space ◽

Planning Problem ◽

System Expansion ◽

Optimum Distribution ◽

Optimal Sizing ◽

Expansion Planning

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.

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Unravelling the opposing effects of network clustering on electricity system models with high shares of renewables

10.5194/egusphere-egu21-10011 ◽

2021 ◽

Author(s):

Martha Frysztacki ◽

Jonas Hörsch ◽

Veit Hagenmeyer ◽

Tom Brown

Keyword(s):

Spatial Resolution ◽

Social Acceptance ◽

Optimal Solution ◽

Renewable Resource ◽

Optimal Investment ◽

Data Sets ◽

Network Clustering ◽

High Resolution Data ◽

Transmission Expansion ◽

Expansion Model

<p>Energy systems are typically modeled with a low spatial resolution that is based on administrative boundaries such as countries, which eases data collection and reduces computation times. However, a low spatial resolution can lead to sub-optimal investment decisions for renewable generation, transmission expansion or both. Ignoring power grid bottlenecks within regions tends to underestimate system costs, while combining locations with different renewable capacity factors tends to overestimate costs. We investigate these two competing effects in a capacity expansion model for Europe&#8217;s future power system that reduces carbon emissions by 95% compared to 1990s levels, taking advantage of newly-available high-resolution data sets and computational advances. We vary the model resolution by changing the number of substations, interpolating between a 37-node model where every country and synchronous zone is modeled with one node respectively, and a 512-node model based on the location of electricity substations. If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with higher capacity factors and thus reduces system costs by up to 10.5% compared to a low resolution model. This results in a big swing from offshore to onshore wind investment. However, if we introduce grid bottlenecks by raising the network resolution, costs increase by up to 19% as generation has to be sourced more locally where demand is high, typically at sites with worse capacity factors. These effects are most pronounced in scenarios where transmission expansion is limited, for example, by low social acceptance.</p>

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SVM-Based Deep Stacking Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33015273 ◽

2019 ◽

Vol 33 ◽

pp. 5273-5280 ◽

Cited By ~ 2

Author(s):

Jingyuan Wang ◽

Kai Feng ◽

Junjie Wu

Keyword(s):

Neural Networks ◽

High Performance ◽

Optimal Solution ◽

Data Sets ◽

Complex Data ◽

Layer By Layer ◽

Deep Network ◽

Local View ◽

Linear Svm ◽

Network Approaches

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models.

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Pareto Optimal Solution Selection for a Multi-Site Supply Chain Planning Problem Using the VIKOR and TOPSIS Methods

International Journal of Service Science Management Engineering and Technology ◽

10.4018/ijssmet.2017070102 ◽

2017 ◽

Vol 8 (3) ◽

pp. 21-39 ◽

Cited By ~ 25

Author(s):

Houssem Felfel ◽

Omar Ayadi ◽

Faouzi Masmoudi

Keyword(s):

Supply Chain ◽

Optimal Solution ◽

Quality Level ◽

Planning Problem ◽

Pareto Optimal ◽

Pareto Optimal Solutions ◽

Compromise Solution ◽

Optimal Solutions ◽

Pareto Solution ◽

Solution Approach

In this paper, a multi-objective, multi-product, multi-period production and transportation planning problem in the context of a multi-site supply chain is proposed. The developed model attempts simultaneously to maximize the profit and to maximize the product quality level. The objective of this paper is to provide the decision maker with a front of Pareto optimal solutions and to help him to select the best Pareto solution. To do so, the epsilon-constraint method is adopted to generate the set of Pareto optimal solutions. Then, the technique for order preference by similarity to ideal solution (TOSIS) is used to choose the best compromise solution. The multi-criteria optimization and compromise solution (VIKOR), a commonly used method in multiple criteria analysis, is applied in order to evaluate the selected solutions using TOPSIS method. This paper offers a numerical example to illustrate the solution approach and to compare the obtained results using TOSIS and VIKOR methods.

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Lexicographic Goal Programming and Assessment Tools for a Combinatorial Production Problem

Multi-Objective Optimization in Computational Intelligence ◽

10.4018/978-1-59904-498-9.ch006 ◽

2011 ◽

pp. 148-184 ◽

Cited By ~ 1

Author(s):

Seamus M. McGovern ◽

Surendra M. Gupta

Keyword(s):

Goal Programming ◽

Optimal Solution ◽

General Purpose ◽

Combinatorial Problems ◽

Assessment Tools ◽

Data Sets ◽

Combinatorial Search ◽

Benchmark Data ◽

Lexicographic Goal Programming ◽

Solution Techniques

NP-complete combinatorial problems often necessitate the use of near-optimal solution techniques including heuristics and metaheuristics. The addition of multiple optimization criteria can further complicate comparison of these solution techniques due to the decision-maker’s weighting schema potentially masking search limitations. In addition, many contemporary problems lack quantitative assessment tools, including benchmark data sets. This chapter proposes the use of lexicographic goal programming for use in comparing combinatorial search techniques. These techniques are implemented here using a recently formulated problem from the area of production analysis. The development of a benchmark data set and other assessment tools is demonstrated, and these are then used to compare the performance of a genetic algorithm and an H-K general-purpose heuristic as applied to the production-related application.

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Cooperative Task Assignment and Trajectory Planning of Unmanned Systems Via HFLC and PSO

Unmanned Systems ◽

10.1142/s2301385019500018 ◽

2019 ◽

Vol 07 (02) ◽

pp. 65-81 ◽

Cited By ~ 2

Author(s):

Ahmed T. Hafez ◽

Mohamed A. Kamel

Keyword(s):

Trajectory Planning ◽

Optimization Problem ◽

Fuzzy Logic Controller ◽

Hybrid Approach ◽

Task Assignment ◽

Time Discretization ◽

Planning Problem ◽

Dynamic Constraints ◽

Ground Station ◽

Novel Approach

This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.

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Sistema de observación para analizar la interacción en el juego de Boccia por equipos

Cuadernos de Psicología del Deporte ◽

10.6018/cpd.393821 ◽

2019 ◽

Vol 20 (1) ◽

pp. 37-47

Author(s):

Daniel Lapresa Ajamil ◽

Javier Pascual Laguna ◽

Javier Arana ◽

M. Teresa Anguera

Keyword(s):

Ad Hoc ◽

High Reliability ◽

Observation System ◽

Data Sets ◽

Antisocial Behaviors ◽

Observation Instrument ◽

The Social ◽

El Sistema ◽

Disability Group ◽

Generalizability Study

Se ha diseñado un instrumento de observación ad hoc, combinación de formato de campo y sistemas de categorías, que permite analizar la interacción social -conductas prosociales y antisociales- que tiene lugar en la competición por equipos en el juego de boccia. El registro y codificación de los datos se ha desarrollado mediante el software Lince. La validez de contenido del instrumento de observación ha quedado avalada por el equipo técnico de la Selección Española de boccia. Los resultados relativos a la concordancia entre los registros generados por tres observadores diferentes, calculada mediante el coeficiente Kappa de Cohen, indican una elevada fiabilidad de los datos obtenidos mediante el sistema de observación. En el seno de la teoría de la Generalizabilidad, mediante el software SAGT, se ha desarrollado el plan de medida [Jugador] [Categoría] / [Parcial], que ha permitido asegurar que, con el número de parciales analizados, se consigue una elevada fiabilidad de precisión de generalización. Además, se ha procedido a la optimización del plan de medida [Parciales] [Categorías] / [Jugador]. La operatividad del sistema de observación desarrollado ha quedado patente en los T-patterns detectados mediante el software Theme, versión 6. Edu. De los resultados obtenidos se desprende que el juego de boccia constituye un entorno favorable de elevado valor formativo para el colectivo de la discapacidad. The observation instrument was purpose-built and combines a field format with systems of categories. The observation instrument allows to analyze the social interaction -prosocial and antisocial behaviors- that takes place in team boccia competition. The content validity of the observation instrument has been guaranteed by the coaching staff of the Boccia Spanish Team. The data were coded with the Lince software programme. Cohen's Kappa coefficient obtained by comparing the data sets generated by three observers indicates a high reliability of the data. We also performed a generalizability study, [Player][Category]/[End], demonstrating the consistency of the data based on the Ends observed. The application of the optimization module for [End][Category]/[Player] facets showed us how many players would constitute an optimal sample in future studies. The practical application of the observation system was demonstrated by performing T-pattern analysis using Theme software programme. The results obtained show that boccia is a very favorable educational environment for the disability group. O instrumento de observação foi construído ad hoc e combina um formato de campo com sistemas de categorias. O instrumento de observação permite analisar a interação social - comportamentos anti-sociais e anti-sociais - que ocorre na competição de bocha em equipe. A validade de conteúdo do instrumento de observação foi garantida pela equipe técnica da Equipe Espanhola de Boccia. Os dados foram codificados com o programa de software Lince. Coeficiente Kappa de Cohen obtido pela comparação dos conjuntos de dados gerados por três observadores indica alta confiabilidade dos dados. Também realizamos um estudo de generalização [Jogador] [Categoria] / [Parcial], demonstrando a consistência dos dados com base nas extremidades observadas. A aplicação do módulo de otimização para as facetas [Parciales] [Categorias] / [Jogador] nos mostrou quantos jogadores seriam uma ótima amostra em estudos futuros. A aplicação prática do sistema de observação foi demonstrada através da análise do padrão T usando o programa de software Theme. Os resultados obtidos são desprezíveis que o jogo de bocha é constituído por um formulário de valor favorável para o colectivo da discapacidade.

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Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.365-366.194 ◽

2013 ◽

Vol 365-366 ◽

pp. 194-198 ◽

Cited By ~ 1

Author(s):

Mei Ni Guo

Keyword(s):

Genetic Algorithm ◽

Optimal Solution ◽

Mathematic Model ◽

Simulation Software ◽

Initial Population ◽

Planning Problem ◽

Improved Genetic Algorithm ◽

Routing Problem ◽

Search Speed ◽

Vehicle Path

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

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Multistage Analysis of Abnormal Human Behavior in Complex Scenes

Journal of Sensors ◽

10.1155/2019/1276438 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Lei Cai ◽

Peien Luo ◽

Guangfu Zhou

Keyword(s):

Human Behavior ◽

Warning Signal ◽

Abnormal Behavior ◽

Experimental Result ◽

Force Model ◽

Data Sets ◽

Social Force ◽

Left Behind ◽

Precise Analysis ◽

Complex Scenes

Effective abnormal human behavior analysis serves as a warning signal before emergencies. However, most abnormal human behavior detections rely on manual monitoring at present. This method is criticized for being subjective and lack of timeliness. In response to the problems above, this paper proposes a multistage analysis method of abnormal human behavior in complex scenes. This paper firstly differentiates the abnormal behavior roughly from a large monitoring area with similarity measurement applied to the social force model, and precise analysis is conducted thereafter. The multistage analysis, based on the three-frame difference algorithm, is used for intrusion, left-behind baggage detection, and motion trajectory identification. The experimental result demonstrates the superiority of the proposed method in UMV, CAVIAR, and datasets. To demonstrate the adaptability and generalization ability of the proposed method, this paper selects the CVC and JAAD driving anomaly detection data sets to test the method. Experimental results show that the proposed method is superior to the existing methods.

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