scholarly journals An Efficient Geometric Search Algorithm of Pandemic Boundary Detection

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 244
Author(s):  
Zhanhao Zhang ◽  
Qifan Huang
Keyword(s):  
Infection Rate ◽  
Search Algorithm ◽  
Boundary Detection ◽  
Boundary Region ◽  
Efficient Algorithms ◽  
Minimal Size ◽  
Asymptotic Bound ◽  
Geometric Search ◽  
Testing Cost ◽  
Simulation Results

We consider a scenario where the pandemic infection rate is inversely proportional to the power of the distance between the infected region and the non-infected region. In our study, we analyze the case where the exponent of the distance is 2, which is in accordance with Reilly’s law of retail gravitation. One can test for infection but such tests are costly so one seeks to determine the region of infection while performing few tests. Our goal is to find a boundary region of minimal size that contains all infected areas. We discuss efficient algorithms and provide the asymptotic bound of the testing cost and simulation results for this problem.

scholarly journals An Adaptive Geometric Search Algorithm for Macromolecular Scaffold Selection

2017 ◽  
Author(s):  
Tian Jiang ◽  
P. Douglas Renfrew ◽  
Kevin Drew ◽  
Noah Youngs ◽  
Glenn Butterfoss ◽  
...  
Keyword(s):  
Protein Design ◽  
Active Site ◽  
Metal Binding ◽  
Search Algorithm ◽  
Three Dimensional ◽  
Side Chain ◽  
Enzyme Design ◽  
Hydrogen Binding ◽  
Performance Improvements ◽  
Geometric Search

AbstractA wide variety of protein and peptidomimetic design tasks require matching functional three-dimensional motifs to potential oligomeric scaffolds. Enzyme design, for example, aims to graft active-site patterns typically consisting of 3 to 15 residues onto new protein surfaces. Identifying suitable proteins capable of scaffolding such active-site engraftment requires costly searches to identify protein folds that can provide the correct positioning of side chains to host the desired active site. Other examples of biodesign tasks that require simpler fast exact geometric searches of potential side chain positioning include mimicking binding hotspots, design of metal binding clusters and the design of modular hydrogen binding networks for specificity. In these applications the speed and scaling of geometric search limits downstream design to small patterns. Here we present an adaptive algorithm to searching for side chain take-off angles compatible with an arbitrarily specified functional pattern that enjoys substantive performance improvements over previous methods. We demonstrate this method in both genetically encoded (protein) and synthetic (peptidomimetic) design scenarios. Examples of using this method with the Rosetta framework for protein design are provided but our implementation is compatible with multiple protein design frameworks and is freely available as a set of python scripts (https://github.com/JiangTian/adaptive-geometric-search-for-protein-design).

scholarly journals Position Tracking of ML System using CSA Based PID Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 1956-1959
Keyword(s):  
Pid Controller ◽  
Magnetic Levitation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Pid Controllers ◽  
Position Tracking ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Simulation Results

The classical proportional integral derivative (PID) controllers are still use in various applications in industry. Magnetic levitation (ML) systems are rigidly nonlinear and sometimes unstable systems. Due to inbuilt nonlinearities of ML systems, tracking of position of ML Systems is still difficult. For the tracking purpose of position, PID controller parameters are found by choosing Cuckoo Search Algorithm (CSA) of optimization. The ranges of parameters are customized by z-n method of parameters. Simulation results show the tracking of position of ML systems using conventional and optimized parameters obtained with the CSA based controller.

Crow-ENN

2020 ◽  
pp. 173-213
Author(s):  
Rehan Ullah ◽  
Abdullah Khan ◽  
Syed Bakhtawar Shah Abid ◽  
Siyab Khan ◽  
Said Khalid Shah ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Search Algorithm ◽  
Heuristic Model ◽  
Classification Problems ◽  
Elman Neural Network ◽  
Main Research ◽  
Research Activities ◽  
Proposed Model ◽  
Simulation Results

DNA sequence classification is one of the main research activities in bioinformatics on which, many researchers have worked and are working on it. In bioinformatics, machine learning can be applied for the analysis of genomic sequences like the classification of DNA sequences, comparison of DNA sequences. This article proposes a new hybrid meta-heuristic model called Crow-ENN for leukemia DNA sequences classification. The proposed algorithm is the combination of the Crow Search Algorithm (CSA) and the Elman Neural Network (ENN). DNA sequences of Leukemia are used to train and test the proposed hybrid model. Five other comparable models i.e. Crow-ANN, Crow-BPNN, ANN, BPNN and ENN are also trained and tested on these DNA sequences. The performance of models is evaluated in terms of accuracy and MSE. The overall simulation results show that the proposed model has outperformed all the other five comparable models by attaining the highest accuracy of over 99%. This model may also be used for other classification problems in different fields because it can achieve promising results.

Automatic Circuit Design of CMOS Miller OTA Using Cuckoo Search Algorithm

2020 ◽  
Vol 11 (1) ◽  
pp. 36-44
Author(s):  
Pankaj P. Prajapati ◽  
Mihir V. Shah
Keyword(s):  
Circuit Design ◽  
Computer Aided Design ◽  
Search Algorithm ◽  
Fitness Function ◽  
Large Fraction ◽  
Cuckoo Search ◽  
Cmos Technology ◽  
Experimental Simulation ◽  
Automatic Design ◽  
Simulation Results

The circuit design of the CMOS based analog part of a mixed-signal integrated circuit (IC) needs a large fraction of the overall design cycle time. The automatic design of an analog circuit is inevitable, seeing recently development of System-on-Chip (SOC) design. This brings about the need to develop computer aided design (CAD) tools for automatic design of CMOS based analog circuits. In this article, a Cuckoo Search (CS) algorithm is presented for automatic design of a CMOS Miller Operational Transconductance Amplifier (OTA). The source code of the CS algorithm is developed using the C language. The Ngspice circuit simulator has been used as a fitness function creator and evaluator. A script file is written to provide an interface between the CS algorithm and the Ngspice simulator. BSIM3v3 MOSFET models with 0.18 µm and 0.35 µm CMOS technology have been used to simulate this circuit. The simulation results of this work are presented and compared with previous works reported in the literature. The experimental simulation results obtained by the CS algorithm satisfy all desired specifications for this circuit.

scholarly journals Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 734 ◽  
Author(s):  
Hao-Xiang Chen ◽  
Ying Nan ◽  
Yi Yang
Keyword(s):  
Assignment Problem ◽  
Time Window ◽  
Search Algorithm ◽  
Task Assignment ◽  
Multiple Time ◽  
Nsga Ii ◽  
Symbiotic Organisms Search ◽  
Simulation Results ◽  
Symbiotic Organisms ◽  
Task Assignment Problem

This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs’ task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.

Design of 3D-Route Planning Based on the Improvement of Simple Genetic Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1690-1694
Author(s):  
Jian Xia Zhang ◽  
Tao Yu ◽  
Ji Ping Chen ◽  
Ying Hao Lin ◽  
Yu Meng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Scientific Research ◽  
Route Planning ◽  
Optimal Solution ◽  
Global Search ◽  
Global Search Algorithm ◽  
Simple Genetic Algorithm ◽  
Local Optimal Solution ◽  
Simulation Results

With the wide application of UAV in the scientific research,its route planning is becoming more and more important. In order to design the best route planning when UAV operates in the field, this paper mainly puts to use the simple genetic algorithm to design 3D-route planning. It primarily introduces the advantages of genetic algorithm compared to others on the designing of route planning. The improvement of simple genetic algorithm is because of the safety of UAV when it flights higher, and the 3D-route planning should include all the corresponding areas. The simulation results show that: the improvement of simple genetic algorithm gets rid of the dependence of parameters, at the same time it is a global search algorithm to avoid falling into the local optimal solution. Whats more, it can meet the requirements of the 3D-route planning design, to the purpose of regional scope and high safety.

Boundary detection in skin tumor images: An overall approach and a radial search algorithm

1990 ◽  
Vol 23 (11) ◽  
pp. 1235-1247 ◽  
Author(s):  
J.E. Golston ◽  
R.H. Moss ◽  
W.V. Stoecker
Keyword(s):  
Search Algorithm ◽  
Boundary Detection ◽  
Skin Tumor

scholarly journals Research on Cooperative Combat for Integrated Reconnaissance-Attack-BDA of Group LAVs

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Li Bing ◽  
Li Jie ◽  
He Guanglin ◽  
Li Dalin
Keyword(s):  
Finite State Machine ◽  
State Transition ◽  
Search Algorithm ◽  
State Machine ◽  
Target Area ◽  
Weapon Systems ◽  
Group System ◽  
Finite State ◽  
Simulation Results ◽  
Feasible Solutions

LAVs (loitering air vehicles) are advanced weapon systems that can loiter autonomously over a target area, detect and acquire the targets, and then attack them. In this paper, by the theory of Itô stochastic differential, a group system was analyzed. The uniqueness and continuity of the solution of the system was discussed. Afterwards the model of the system based on the state transition was established with the finite state machine automatically. At last, a search algorithm was proposed for obtaining good feasible solutions for problems. And simulation results show that model and method are effective for dealing with cooperative combat of group LAVs.

scholarly journals Positive Macroscopic Approximation for Fast Attribute Reduction

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Zheng-Cai Lu ◽  
Zheng Qin ◽  
Qiao Jing ◽  
Lai-Xiang Shan
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Attribute Reduction ◽  
Boundary Region ◽  
Feature Subset ◽  
Decision Systems ◽  
Heuristic Search Algorithms ◽  
Heuristic Search Algorithm ◽  
Single Target ◽  
Lower And Upper Approximations

Attribute reduction is one of the challenging problems facing the effective application of computational intelligence technology for artificial intelligence. Its task is to eliminate dispensable attributes and search for a feature subset that possesses the same classification capacity as that of the original attribute set. To accomplish efficient attribute reduction, many heuristic search algorithms have been developed. Most of them are based on the model that the approximation of all the target concepts associated with a decision system is dividable into that of a single target concept represented by a pair of definable concepts known as lower and upper approximations. This paper proposes a novel model called macroscopic approximation, considering all the target concepts as an indivisible whole to be approximated by rough set boundary region derived from inconsistent tolerance blocks, as well as an efficient approximation framework called positive macroscopic approximation (PMA), addressing macroscopic approximations with respect to a series of attribute subsets. Based on PMA, a fast heuristic search algorithm for attribute reduction in incomplete decision systems is designed and achieves obviously better computational efficiency than other available algorithms, which is also demonstrated by the experimental results.

Analysis and Simulation of Binary Search Algorithm

2014 ◽  
Vol 635-637 ◽  
pp. 1692-1695
Author(s):  
Zhi Long Liu
Keyword(s):  
Search Algorithm ◽  
Binary Search ◽  
Matlab Simulation ◽  
Recognition Time ◽  
Binary Search Algorithm ◽  
Simulation Results ◽  
Improved Algorithm

Some drawbacks of existing binary search algorithm has been improved to reduce the number of paging through improved reader in this paper to reduce the number of bytes for each tag and reader communication transmission, thereby reducing the improved algorithm of recognition time. At the same time, an improved binary anti-collision algorithm, and by Matlab simulation results show the advantages of the improved algorithm compared to other improved binary search algorithm.

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