Herding in Ranch Operations: An Analysis of a Search Problem

2002 ◽  
Author(s):  
Amitrajeet A. Batabyal
Keyword(s):  
Search Problem

On a 2-dimensional search problem

1993 ◽  
Vol 37 (3) ◽  
pp. 371-383 ◽  
Author(s):  
Miklós Ruszinkó
Keyword(s):  
Search Problem

scholarly journals Efficient Retrieval of Music Recordings Using Graph-Based Index Structures

Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 336-352
Author(s):  
Frank Zalkow ◽  
Julian Brandner ◽  
Meinard Müller
Keyword(s):  
Nearest Neighbor ◽  
Response Times ◽  
Negative Impact ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Search Problem ◽  
Index Structures ◽  
Music Retrieval ◽  
Retrieval Systems ◽  
Music Recordings

Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the K-d tree, which has already been used in music retrieval systems. As an alternative, we propose to use a modern graph-based index, denoted as Hierarchical Navigable Small World (HNSW) graph. As our main contribution, we explore its potential in the context of a cross-version music retrieval application. In particular, we report on systematic experiments comparing graph- and tree-based index structures in terms of the retrieval quality, disk space requirements, and runtimes. Despite the fact that the HNSW index provides only an approximate solution to the nearest neighbor search problem, we demonstrate that it has almost no negative impact on the retrieval quality in our application. As our main result, we show that the HNSW-based retrieval is several orders of magnitude faster. Furthermore, the graph structure also works well with high-dimensional index items, unlike the tree-based structure. Given these merits, we highlight the practical relevance of the HNSW graph for music information retrieval (MIR) applications.

EXPRESS: Baseline Correction Based on a Search Algorithm from Artificial Intelligence

2020 ◽  
pp. 000370282097751
Author(s):  
Xin Wang ◽  
Xia Chen
Keyword(s):  
Artificial Intelligence ◽  
Objective Function ◽  
Least Squares ◽  
Least Squares Method ◽  
Search Algorithm ◽  
Absolute Error ◽  
Search Problem ◽  
Baseline Correction ◽  
Current Spectrum ◽  
Data Points

Many spectra have a polynomial-like baseline. Iterative polynomial fitting (IPF) is one of the most popular methods for baseline correction of these spectra. However, the baseline estimated by IPF may have substantially error when the spectrum contains significantly strong peaks or have strong peaks located at the endpoints. First, IPF uses temporary baseline estimated from the current spectrum to identify peak data points. If the current spectrum contains strong peaks, then the temporary baseline substantially deviates from the true baseline. Some good baseline data points of the spectrum might be mistakenly identified as peak data points and are artificially re-assigned with a low value. Second, if a strong peak is located at the endpoint of the spectrum, then the endpoint region of the estimated baseline might have significant error due to overfitting. This study proposes a search algorithm-based baseline correction method (SA) that aims to compress sample the raw spectrum to a dataset with small number of data points and then convert the peak removal process into solving a search problem in artificial intelligence (AI) to minimize an objective function by deleting peak data points. First, the raw spectrum is smoothened out by the moving average method to reduce noise and then divided into dozens of unequally spaced sections on the basis of Chebyshev nodes. Finally, the minimal points of each section are collected to form a dataset for peak removal through search algorithm. SA selects the mean absolute error (MAE) as the objective function because of its sensitivity to overfitting and rapid calculation. The baseline correction performance of SA is compared with those of three baseline correction methods: Lieber and Mahadevan–Jansen method, adaptive iteratively reweighted penalized least squares method, and improved asymmetric least squares method. Simulated and real FTIR and Raman spectra with polynomial-like baselines are employed in the experiments. Results show that for these spectra, the baseline estimated by SA has fewer error than those by the three other methods.

Constrained optimal terrain following/threat avoidance trajectory planning using network flow

2014 ◽  
Vol 118 (1203) ◽  
pp. 523-539 ◽  
Author(s):  
R. Zardashti ◽  
A. A. Nikkhah ◽  
M. J. Yazdanpanah
Keyword(s):  
Cost Function ◽  
Trajectory Planning ◽  
Network Flow ◽  
Equations Of Motion ◽  
Minimum Cost ◽  
Masking Effect ◽  
Search Problem ◽  
Starting Point ◽  
Terrain Following ◽  
Threat Avoidance

AbstractThis paper focuses on the trajectory planning for a UAV on a low altitude terrain following/threat avoidance (TF/TA) mission. Using a grid-based approximated discretisation scheme, the continuous constrained optimisation problem into a search problem is transformed over a finite network. A variant of the Minimum Cost Network Flow (MCNF) to this problem is then applied. Based on using the Digital Terrain Elevation Data (DTED) and discrete dynamic equations of motion, the four-dimensional (4D) trajectory (three spatial and one time dimensions) from a starting point to an end point is obtained by minimising a cost function subject to dynamic and mission constraints of the UAV. For each arc in the grid, a cost function is considered as the combination of the arc length, fuel consumption and flight time. The proposed algorithm which considers dynamic and altitude constraints of the UAV explicitly is then used to obtain the feasible trajectory. The resultant trajectory can increase the survivability of the UAV using the threat region avoidance and the terrain masking effect. After obtaining the feasible trajectory, an improved algorithm is proposed to smooth the trajectory. The numeric results are presented to verify the capability of the proposed approach to generate admissible trajectory in minimum possible time in comparison to the previous works.

An FPTAS for a vector subset search problem

2014 ◽  
Vol 8 (3) ◽  
pp. 329-336 ◽  
Author(s):  
A. V. Kel’manov ◽  
S. M. Romanchenko
Keyword(s):  
Search Problem ◽  
Subset Search

scholarly journals On Quantum Algorithm for Binary Search and Its Computational Complexity

2015 ◽  
Vol 22 (03) ◽  
pp. 1550019 ◽  
Author(s):  
S. Iriyama ◽  
M. Ohya ◽  
I.V. Volovich
Keyword(s):  
Computational Complexity ◽  
Time Complexity ◽  
Quantum Algorithm ◽  
Binary Search ◽  
Search Problem

A new quantum algorithm for the search problem and its computational complexity are discussed. Its essential part is the use of the so-called chaos amplifier, [8, 9, 10, 13]. It is shown that for the search problem containing [Formula: see text] objects time complexity of the method is polynomial in [Formula: see text].

The Rendezvous Search Problem

1995 ◽  
Vol 33 (3) ◽  
pp. 673-683 ◽  
Author(s):  
Steve Alpern
Keyword(s):  
Search Problem
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