In today's world, various approaches and parameters exist for designing a plan and determining its spatial, placement. Hence, various modes for identifying crucial locations can be explored when an architectural p...
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In today's world, various approaches and parameters exist for designing a plan and determining its spatial, placement. Hence, various modes for identifying crucial locations can be explored when an architectural plan is designed in different dimensions. While designing all these modes takes considerable time, there are numerous potential applications for artificial intelligence (AI) in this domain. This study aims to compute and use an adjacency matrix to generate architectural residential plans. Additionally, it develops a plan generation algorithm in Rhinoceros software, utilizing the Grasshopper plugin to create a dataset of architectural plans. In the following step, the data was entered into a neural network to identify the architectural plan's type, furniture, icons, and use of spaces, which was achieved using YOLOv4, EfficientDet, YOLOv5, DetectoRS, and RetinaNet. The algorithm's execution, testing, and training were conducted using Darknet and PyTorch. The research dataset comprises 12,000 plans, with 70% employed in the training phase and 30% in the testing phase. The network was appropriately trained practically and precisely in relation to an average precision (AP) resulting of 91.50%. After detecting the types of space use, the main research algorithm has been designed and coded, which includes determining the adjacency matrix of architectural plan spaces in seven stages. All research processes were conducted in Python, including dataset preparation, network object detection, and adjacency matrix algorithm design. Finally, the adjacency matrix is given to the input of the proposed plan generator network, which consequently, based on the resulting adjacency, obtains different placement modes for spaces and furniture.
It has been a long-standing open problem to determine the exact randomized competitiveness of the 2-server problem, that is, the minimum competitiveness of any randomized online algorithm for the 2-server problem. For...
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It has been a long-standing open problem to determine the exact randomized competitiveness of the 2-server problem, that is, the minimum competitiveness of any randomized online algorithm for the 2-server problem. For deterministic algorithms the best competitive ratio that can be obtained is 2 and no randomized algorithm is known that improves this ratio for general spaces. For the line, Bartal et al. (1998) [2] give a 155/78 competitive algorithm, but their algorithm is specific to the geometry of the line. We consider here the 2-server problem over Cross Polytope Spaces M-24. We obtain an algorithm with competitive ratio of and show that this ratio is best possible. This algorithm gives the second non-trivial example of metric spaces with better than 2-competitive ratio. The algorithm uses a design technique called the knowledge state technique a method not specific to M-24. (C) 2010 Elsevier B.V. All rights reserved.
We study the problem of computing the k maximum sum subsequences. Given a sequence of real numbers (x(1), x(2),..., x(n)) and an integer parameter k, 1 <= k <= 1/2n(n - 1), the problem involves finding the k lar...
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We study the problem of computing the k maximum sum subsequences. Given a sequence of real numbers (x(1), x(2),..., x(n)) and an integer parameter k, 1 <= k <= 1/2n(n - 1), the problem involves finding the k largest values of Sigma(j)(l=t) x(l) for 1 <= 1 <= j <= n. The problem for fixed k = 1, also known as the maximum sum subsequence problem, has received much attention in the literature and is linear-time solvable. Recently, Bae and Takaoka presented a Theta (nk)-time algorithm for the k maximum sum subsequences problem. In this paper we design an efficient algorithm that solves the above problem in O(min{k + n log(2) n, n root k}) time in the worst case. Our algorithm is optimal for k = Omega(n log(2) n) and improves over the previously best known result for any value of the user-defined parameter k < 1. Moreover, our results are also extended to the multi-dimensional versions of the k maximum sum subsequences problem;resulting in fast algorithms as well.
designing an optimal machine trail network is a complex locational problem that requires an understanding of different machines' operations and terrain features as well as the trade-offs between various objectives...
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designing an optimal machine trail network is a complex locational problem that requires an understanding of different machines' operations and terrain features as well as the trade-offs between various objectives. With the overall goal to minimize the operational costs of the logging operation, this paper proposes a mathematical optimization model for the trail network design problem and a greedy heuristic method based on different ran-domized search scenarios aiming to find the optimal location of machine trails -with potential to reduce negative environmental impact. The network is designed so that all trees can be reached and adapted to how the machines can maneuver while considering the terrain elevation's influence. To examine the effectiveness and practical performance of the heuristic and the optimization model, it was applied in a case study on four harvest units with different topologies and shapes. The computational experiments show that the heuristic can generate solutions that outperform the solutions corresponding to conventional, manual designs within practical time limits for operational planning. Moreover, to highlight certain features of the heuristic and the parameter set-tings' effect on its performance, we present an extensive computational sensitivity analysis.
With the increased focus on Computational Thinking (CT) in education, there has been an increase in the development of learning platforms to teach CT. The current study developed an Online Inquiry-based learning platf...
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ISBN:
(纸本)9798350300543
With the increased focus on Computational Thinking (CT) in education, there has been an increase in the development of learning platforms to teach CT. The current study developed an Online Inquiry-based learning platform for Computational Thinking (CT-ONLINQ) to support CT activities using Inquiry-based Learning (IBL) pedagogy. IBL-based CT steps include algorithm design, analysis, and comparison of algorithms. The platform allows students to explore multiple solutions and provides hints as support during problem-solving activities. A 4-week experimental study was conducted to evaluate the usability of the online platform. A total of 79 9th-grade students volunteered to participate in this study to complete six activities. Subsequently, the students completed the SUS questionnaire and open-ended feedback questions. Results showed that around 80% of the students scored above the "Good" category (70-80), with a total average score of 78.45. Also, we analyzed the difference in rating scores among groups based on multiple background factors. Findings showed an average rating above 4 with no significant difference between the ratings among factors such as gender and math performance (below-above average math scores). In addition, analysis of the feedback comments showed that the platform is user-friendly, with students enthusiastic about learning coding on the platform.
This paper briefly considers tome relationships of general systems theory to algorithm theory. A general systems theory type of approach provides a framework for algorithm design. We explicate this framework, and then...
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This paper briefly considers tome relationships of general systems theory to algorithm theory. A general systems theory type of approach provides a framework for algorithm design. We explicate this framework, and then we demonstrate new insights into algorithm design which follow from the framework. Of particular importance in algorithm theory is the proper introduction of the concept of design principle and its relation to algorithm model.
Although the community of nature-inspired computing has witnessed a wide variety of metaheuristics, it often requires considerable effort to adapt them to different combinatorial optimization problems (COPs), and few ...
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Although the community of nature-inspired computing has witnessed a wide variety of metaheuristics, it often requires considerable effort to adapt them to different combinatorial optimization problems (COPs), and few studies have been devoted to reducing this burden. This paper proposes a systematic approach that consists of a set of basic steps and strategies for adapting water wave optimization (WWO), a simple and generic metaheuristic, to concrete heuristic algorithms for different COPs. Taking advantages of the generic algorithmic framework, designers can only focus on adapting the prorogation operator and the wavelength calculation method according to the combinatorial properties of the given problem, and thus easily derive efficient problem-solving algorithms. We illustrate and test our approach on the flow-shop scheduling problem (FSP), the single-objective multidimensional knapsack problem (MKP), and the multi-objective MKP, and then present an application to a machine utilization optimization problem for a large manufacturing enterprise. The results demonstrate that our approach can derive concrete algorithms that are competitive to the state-of-the-arts. Our approach also provides insights into the adaptation of other metaheuristics and the development of new metaheuristics for COPs. (C) 2019 The Author(s). Published by Elsevier B.V.
We develop an algorithm for the Dutch National Flag problem that has an adjustable integer parameter smax≧0smax≧0\operatorname{smax} \geqq 0 allowing a time/space tradeoff. (Let n be the length of the input to be or...
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We consider the problem of fitting a step function to a set of points. More precisely, given an integer k and a set P of n points in the plane, our goal is to find a step function f with k steps that minimizes the max...
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We consider the problem of fitting a step function to a set of points. More precisely, given an integer k and a set P of n points in the plane, our goal is to find a step function f with k steps that minimizes the maximum vertical distance between f and all the points in P. We first give an optimal I similar to(nlog n) algorithm for the general case. In the special case where the points in P are given in sorted order according to their x-coordinates, we give an optimal I similar to(n) time algorithm. Then, we show how to solve the weighted version of this problem in time O(nlog (4) n). Finally, we give an O(nh (2)log n) algorithm for the case where h outliers are allowed. The running time of all our algorithms is independent of k.
In adversarial situations, the input data to an algorithm could be manipulated to make the algorithm produce erroneous output or to make a wrong decision. The paper presents a formal definition and a model of algorith...
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ISBN:
(纸本)9781905305407
In adversarial situations, the input data to an algorithm could be manipulated to make the algorithm produce erroneous output or to make a wrong decision. The paper presents a formal definition and a model of algorithm manipulation from a game-theoretic point of view. algorithm manipulation is viewed as a game between a decision maker and an adversary. The decision maker runs an algorithm to make a decision, whereas the adversary manipulates the input data to his own advantage and to the disadvantage of the decision maker. The paper also proposes a method for decision making based on manipulated input. According to the method, the decision strategy and the manipulation must be in Nash equilibrium. In other words, the decision strategy is the best response to the manipulation and vice versa, the manipulation is the best response to the decision strategy.
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