Algorithm Research Papers

A Distance Metric for Uneven Clusters of Unsupervised K-Means Clustering Algorithm

In this paper, we propose a new distance metric for the K-means clustering algorithm. Applying this metric in clustering a dataset, forms unequal clusters. This metric leads to a larger size for a cluster with a centroid away from the origi...

Symbolic regression in materials science

Advanced DFE, MLD, and RDE Equalization Techniques for Enhanced 5G mm-Wave A-RoF Performance at 60 GHz

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical lin...

Small Object Detection Algorithm Based on Improved YOLOv8 for Remote Sensing

Due to the limitations of small targets in remote sensing images, such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accurac...

Experimental methodology for obtaining sound absorption coefficients

Objective: the authors propose a new methodology for estimating sound absorption coefficients using genetic algorithms. Methodology: sound waves are generated and conducted along a rectangular silencer. The waves are then attenuated by the ...

Optimization of False Target Jamming against UAV Detection

Unmanned aerial vehicles (UAVs) have been widely used for target detection in modern battlefields. From the viewpoint of the opponents, false target jamming is an effective approach to decrease the UAV detection ability or probability, but ...

A computational tool for conceptual design and optimization of planetary rovers

The design process of a Mars rover is driven by multiple design constraints, namely overall mass, power consumption and volume (dimensions). Various systems, such as mobility, manipulation, handling, power, thermal, communication, navigatio...

Towards Efficient Function Optimisation with Quantum–Classical Hybrid Search

Finding the global minimum of a function with many local extrema is well known to be a challenging computational task. We propose a hybrid quantum–classical algorithm for this optimisation problem, based on a combination of Grover quantum s...

A Secure, Intelligent, and Smart-Sensing Approach for Industrial System Automation and Transmission over Unsecured Wireless Networks

In Industrial systems, Supervisory control and data acquisition (SCADA) system, the pseudo-transport layer of the distributed network protocol (DNP3) performs the functions of the transport layer and network layer of the open systems interc...

Quantum Algorithms for Compositional Natural Language Processing

We propose a new application of quantum computing to the field of natural language processing. Ongoing work in this field attempts to incorporate grammatical structure into algorithms that compute meaning. In (Coecke, Sadrzadeh and Clark, 2...

MFSM: Chinese-English sentence alignment based on multi- feature self-attention mechanism fusion

Bilingual parallel corpora is a very important basic resource in the research field of natural language processing based on statistics. There are cross alignment and empty alignment in Chinese-English bilingual text, it is easy to affect th...

A fault location method for secondary virtual circuits in smart substations based on the integration of GAE and GCN

Abstract The secondary virtual circuits in smart substations enable digital and networked information exchange between devices. However, their extensive logical connections, complex structure, and implicit interdependencies pose significant...

Integrating NLP to Enhance Algorithmic Identification of Metastatic and Castration‐Resistant Prostate Cancer in Large Claims‐Based Studies

ABSTRACT Purpose Accurate classification of prostate cancer (PC) disease states defined by the presence or absence of metastasis and castration resistance (CRPC) is critical but challenging in population‐based research. As chart review is n...

A Refutation of Finite-State Language Models through Zipf’s Law for Factual Knowledge

We present a hypothetical argument against finite-state processes in statistical language modeling that is based on semantics rather than syntax. In this theoretical model, we suppose that the semantic properties of texts in a natural langu...

Optimization for Computer Vision

Kernel Methods in Computer Vision

Over the last years, kernel methods have established themselves as powerful tools for computer vision researchers as well as for practitioners. In this tutorial, we give an introduction to kernel methods in computer vision from a geometric ...

Online visual tracking by integrating spatio‐temporal cues

The performance of online visual trackers has improved significantly, but designing an effective appearance‐adaptive model is still a challenging task because of the accumulation of errors during the model updating with newly obtained resul...

DAPS3D: Domain Adaptive Projective Segmentation of 3D LiDAR Point Clouds

LiDARs are one of the key sources of reliable environmental ranging information for autonomous vehicles. However, segmentation of 3D scene elements (roads, buildings, people, cars, etc.) based on LiDAR point clouds has limitations. On the o...

Evaluation of Indoor Thermal Environments Using a Novel Predicted Mean Vote Model Based on Artificial Neural Networks

The assessment of indoor thermal environments is crucial to achieving thermal comfort and energy efficiency. However, the inaccurate evaluation and strong nonlinear variations of thermal comfort parameters limit engineering designs. Therefo...

Boosting Neural Networks

Boosting is a general method for improving the performance of learning algorithms. A recently proposed boosting algorithm, AdaBoost, has been applied with great success to several benchmark machine learning problems using mainly decision tr...