scholarly journals Lost Person Search Area Prediction Based on Regression and Transfer Learning Models

2021 ◽  
Vol 10 (2) ◽  
pp. 80
Author(s):  
Ljiljana Šerić ◽  
Tomas Pinjušić ◽  
Karlo Topić ◽  
Tomislav Blažević
Keyword(s):  
Transfer Learning ◽  
Initial Point ◽  
Search And Rescue ◽  
Gps Tracking ◽  
Second Phase ◽  
Data Sets ◽  
Rescue Service ◽  
Mountain Rescue ◽  
Person Search ◽  
Archive Data

In this paper, we propose a methodology and algorithms for search and rescue mission planning. These algorithms construct optimal areas for lost person search having in mind the initial point of planning and features of the surrounding area. The algorithms are trained on previous search and rescue missions data collected from three stations of the Croatian Mountain Rescue Service. The training was performed in two training phases and having two data sets. The first phase was the construction of a regression model of the speed of walking. This model predicts the speed of walking of a rescuer who is considered a well-trained and motivated person since the model is fitted on a dataset made of GPS tracking data collected from Mountain Rescue Service rescuers. The second phase is the calibration of the model for lost person speed of walking prediction with transfer learning on lost person data. The model is used in the simulation of walking in all directions to predict the maximum area where a person can be located. The performance of the algorithms was analysed with respect to a small dataset of archive data of real search and rescue missions that was available and results are discussed.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

scholarly journals Self-Adaptive K-Means Based on a Covering Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Yiwen Zhang ◽  
Yuanyuan Zhou ◽  
Xing Guo ◽  
Jintao Wu ◽  
Qiang He ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Real Data ◽  
Second Phase ◽  
Data Sets ◽  
Number Of Clusters ◽  
Large Scale Data ◽  
Long Time ◽  
Two Phases ◽  
Selection Of

The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

FT-MDnet: A Deep-Frozen Transfer Learning Framework for Person Search

2021 ◽  
pp. 1-1
Author(s):  
Ronghua Hu ◽  
Tian Wang ◽  
Yi Zhou ◽  
Hichem Snoussi ◽  
Abel Cherouat
Keyword(s):  
Transfer Learning ◽  
Learning Framework ◽  
Person Search ◽  
Frozen Transfer

Dimensionality Reduction with Unsupervised Feature Selection and Applying Non-Euclidean Norms for Classification Accuracy

2011 ◽  
pp. 91-109
Author(s):  
Amit Saxena ◽  
John Wang
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Fitness Function ◽  
Synthetic Data ◽  
Feature Subset Selection ◽  
Second Phase ◽  
Data Sets ◽  
Feature Subset ◽  
K Nearest Neighbor ◽  
Two Phase

This paper presents a two-phase scheme to select reduced number of features from a dataset using Genetic Algorithm (GA) and testing the classification accuracy (CA) of the dataset with the reduced feature set. In the first phase of the proposed work, an unsupervised approach to select a subset of features is applied. GA is used to select stochastically reduced number of features with Sammon Error as the fitness function. Different subsets of features are obtained. In the second phase, each of the reduced features set is applied to test the CA of the dataset. The CA of a data set is validated using supervised k-nearest neighbor (k-nn) algorithm. The novelty of the proposed scheme is that each reduced feature set obtained in the first phase is investigated for CA using the k-nn classification with different Minkowski metric i.e. non-Euclidean norms instead of conventional Euclidean norm (L2). Final results are presented in the paper with extensive simulations on seven real and one synthetic, data sets. It is revealed from the proposed investigation that taking different norms produces better CA and hence a scope for better feature subset selection.

Military and Civilian Air Rescue in Italy

1985 ◽  
Vol 1 (1) ◽  
pp. 72-72
Author(s):  
Corrado Manni ◽  
Gaetano Rotondo
Keyword(s):  
Air Force ◽  
Armed Forces ◽  
Civil Defense ◽  
Search And Rescue ◽  
Air Rescue ◽  
Red Cross ◽  
Public And Private ◽  
Rescue Service ◽  
Emergency Transport ◽  
Injured Patients

In contrast to other European countries, Italy lacks a Civil Defense Organization. Air Rescue is a task for the Italian Air Force Search and Rescue (S.A.R.) organization. It may also draw, if necessary, on the cooperation of the other Armed Forces and State Corps, the Merchant Navy, civilian organizations, the Italian Red Cross (C.R.I.), and the Mountain Rescue Service of the Italian Alpine Club (C.A.I.). The S.A.R. units intervene at the request of civil, state, public and private, national and international organizations. The tasks currently performed by the S.A.R. in Italy include search and rescue of civilian and military air crews lost at sea or over land and of shipwrecked survivors; emergency transport of doctors and supplies to the seriously sick or injured patients from ships at sea; inaccessible localities, earthquakes, floods, and other disasters.

scholarly journals Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4408 ◽  
Author(s):  
Hyun-Myung Cho ◽  
Heesu Park ◽  
Suh-Yeon Dong ◽  
Inchan Youn
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Mental Arithmetic ◽  
Small Data ◽  
Data Sets ◽  
Learning Method ◽  
Data Set ◽  
Electrocardiogram Signals ◽  
Proposed Model ◽  
Small Data Set

The goals of this study are the suggestion of a better classification method for detecting stressed states based on raw electrocardiogram (ECG) data and a method for training a deep neural network (DNN) with a smaller data set. We suggest an end-to-end architecture to detect stress using raw ECGs. The architecture consists of successive stages that contain convolutional layers. In this study, two kinds of data sets are used to train and validate the model: A driving data set and a mental arithmetic data set, which smaller than the driving data set. We apply a transfer learning method to train a model with a small data set. The proposed model shows better performance, based on receiver operating curves, than conventional methods. Compared with other DNN methods using raw ECGs, the proposed model improves the accuracy from 87.39% to 90.19%. The transfer learning method improves accuracy by 12.01% and 10.06% when 10 s and 60 s of ECG signals, respectively, are used in the model. In conclusion, our model outperforms previous models using raw ECGs from a small data set and, so, we believe that our model can significantly contribute to mobile healthcare for stress management in daily life.

scholarly journals Global Minimization of Nonsmooth Constrained Global Optimization with Filled Function

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Wei-xiang Wang ◽  
You-lin Shang ◽  
Ying Zhang
Keyword(s):  
Original Problem ◽  
Initial Point ◽  
Solution Algorithm ◽  
Global Minimization ◽  
Second Phase ◽  
Constrained Global Optimization ◽  
Filled Function ◽  
Minimization Problems ◽  
Global Optimizer ◽  
Two Phases

A novel filled function is constructed to locate a global optimizer or an approximate global optimizer of smooth or nonsmooth constrained global minimization problems. The constructed filled function contains only one parameter which can be easily adjusted during the minimization. The theoretical properties of the filled function are discussed and a corresponding solution algorithm is proposed. The solution algorithm comprises two phases: local minimization and filling. The first phase minimizes the original problem and obtains one of its local optimizers, while the second phase minimizes the constructed filled function and identifies a better initial point for the first phase. Some preliminary numerical results are also reported.

Selected aspects of modelling the system on the example of search and rescue service

2018 ◽  
Vol 120 ◽  
pp. 39-48
Author(s):  
Norbert Chamier-Gliszczyński ◽  
Jerzy Fiuk
Keyword(s):  
Search And Rescue ◽  
Operating Costs ◽  
System Model ◽  
Time Of Arrival ◽  
International Convention ◽  
Rescue Service ◽  
Full Analysis ◽  
Maritime Search And Rescue

The article is an attempt to present the aspect of modelling the system on the example of air maritime rescue interpreted as SAR service (Search and Rescue). The necessity and need for the existence of the SAR service results from the International Convention on Maritime Search and Rescue signed by Poland. An important element of the structure of the SAR service are its individual elements (eg. location of air bases, etc.). The time of arrival in places far away from the currently existing SAR air service bases is so significant that it may cause the failure of the action. The question arises whether increasing the number of airbases gives a chance to reduce the system's operating costs, increase its efficiency and thereby raise the level of safety at sea and in the area of responsibility of the SAR service. For the full analysis of the functioning of the SAR service it is necessary to undertake optimization studies. Due to the complexity of the SAR service, it should be considered in systemic categories. Authors interpreting aviation SAR service in the form of a system whose mapping is the SAR system model undertakes research aimed at optimizing the operation of this service.

Sign in / Sign up

Export Citation Format

Share Document