scholarly journals Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chun Liu ◽  
Jian Li
Keyword(s):  
Time Window ◽  
Time Windows ◽  
Target Object ◽  
Variable Time ◽  
Ship Detection ◽  
Detection Model ◽  
Maritime Surveillance ◽  
Model Training ◽  
Direction Judgment ◽  
Deep Learning Model

Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. YOLOv3 pretraining model is used for model training with sample images for ship detection. The ship detection model is built by adjusting and optimizing parameters. Combining the target HSV color histogram features and LBP local features’ target, object recognition and selection are realized by using the deep learning model due to its efficiency in extracting object characteristics. Since tracking targets are subject to drift and jitter, a self-correction network that composites both direction judgment based on regression and target counting method with variable time windows is designed, which better realizes automatic detection, tracking, and self-correction of moving object numbers in water. The method in this paper shows stability and robustness, applicable to the automatic analysis of waterway videos and statistics extraction.

scholarly journals Ship Detection from X-Band SAR Images Using M2Det Deep Learning Model

2020 ◽  
Vol 10 (21) ◽  
pp. 7751
Author(s):  
Seong-Jae Hong ◽  
Won-Kyung Baek ◽  
Hyung-Sup Jung
Keyword(s):  
Deep Learning ◽  
Learning Process ◽  
Intensity Difference ◽  
Sar Images ◽  
Ship Detection ◽  
Detection Model ◽  
Learning Techniques ◽  
Vessel Detection ◽  
Intensity Images ◽  
Deep Learning Model

Synthetic aperture radar (SAR) images have been used in many studies for ship detection because they can be captured without being affected by time and weather. In recent years, the development of deep learning techniques has facilitated studies on ship detection in SAR images using deep learning techniques. However, because the noise from SAR images can negatively affect the learning of the deep learning model, it is necessary to reduce the noise through preprocessing. In this study, deep learning vessel detection was performed using preprocessed SAR images, and the effects of the preprocessing of the images on deep learning vessel detection were compared and analyzed. Through the preprocessing of SAR images, (1) intensity images, (2) decibel images, and (3) intensity difference and texture images were generated. The M2Det object detection model was used for the deep learning process and preprocessed SAR images. After the object detection model was trained, ship detection was performed using test images. The test results are presented in terms of precision, recall, and average precision (AP), which were 93.18%, 91.11%, and 89.78% for the intensity images, respectively, 94.16%, 94.16%, and 92.34% for the decibel images, respectively, and 97.40%, 94.94%, and 95.55% for the intensity difference and texture images, respectively. From the results, it can be found that the preprocessing of the SAR images can facilitate the deep learning process and improve the ship detection performance. The results of this study are expected to contribute to the development of deep learning-based ship detection techniques in SAR images in the future.

A Task Conflict Detection Model of Logistics Distribution

2012 ◽  
Vol 452-453 ◽  
pp. 736-740
Author(s):  
Qing Kui Cao ◽  
Qian Zhang
Keyword(s):  
Time Window ◽  
Time Windows ◽  
Distribution Center ◽  
Task Conflict ◽  
Scheduling Problem ◽  
Temporal Constraint ◽  
Logistics Distribution ◽  
Detection Model ◽  
Constraint Network ◽  
Multiple Tasks

Based on the time window in the vehicle scheduling problem(VSP), we propose the concept of time windows of delivery tasks(TWDT) in the distribution center, and describe the TWDT in a kind of three-tuple. Transferring time windows constraint of multiple tasks to a simple temporal constraint network, we simplify the problem of checking the time constraint of multiple tasks to the problem of checking the temporal constraint network’s consistency. According to the characteristic of delivery task and simple temporal constraint network, we proposed the model of time window conflict checking of delivery task.

scholarly journals A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

2021 ◽  
Author(s):  
Hongguang Wu ◽  
Yuelin Gao ◽  
Wanting Wang ◽  
Ziyu Zhang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Ant Colony ◽  
Routing Problem ◽  
Multiple Strategies ◽  
Hard Time ◽  
Mutation Operation

AbstractIn this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

scholarly journals The last-mile vehicle routing problem with delivery options

OR Spectrum ◽  
2021 ◽  
Author(s):  
Christian Tilk ◽  
Katharina Olkis ◽  
Stefan Irnich
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Resource Constraints ◽  
Time Window ◽  
Time Windows ◽  
Service Level ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Common Location ◽  
Delivery Options

AbstractThe ongoing rise in e-commerce comes along with an increasing number of first-time delivery failures due to the absence of the customer at the delivery location. Failed deliveries result in rework which in turn has a large impact on the carriers’ delivery cost. In the classical vehicle routing problem (VRP) with time windows, each customer request has only one location and one time window describing where and when shipments need to be delivered. In contrast, we introduce and analyze the vehicle routing problem with delivery options (VRPDO), in which some requests can be shipped to alternative locations with possibly different time windows. Furthermore, customers may prefer some delivery options. The carrier must then select, for each request, one delivery option such that the carriers’ overall cost is minimized and a given service level regarding customer preferences is achieved. Moreover, when delivery options share a common location, e.g., a locker, capacities must be respected when assigning shipments. To solve the VRPDO exactly, we present a new branch-price-and-cut algorithm. The associated pricing subproblem is a shortest-path problem with resource constraints that we solve with a bidirectional labeling algorithm on an auxiliary network. We focus on the comparison of two alternative modeling approaches for the auxiliary network and present optimal solutions for instances with up to 100 delivery options. Moreover, we provide 17 new optimal solutions for the benchmark set for the VRP with roaming delivery locations.

scholarly journals Impact of COVID-19 pandemic on HIV viremia: a single-center cohort study in northern Italy

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Ilaria Izzo ◽  
Canio Carriero ◽  
Giulia Gardini ◽  
Benedetta Fumarola ◽  
Erika Chiari ◽  
...  
Keyword(s):  
Time Window ◽  
Time Windows ◽  
Continuum Of Care ◽  
Northern Italy ◽  
Hiv Diagnosis ◽  
Hiv Patients ◽  
Before And After ◽  
Missed Visits ◽  
Hiv Continuum Of Care ◽  
The Impact

Abstract Background Brescia Province, northern Italy, was one of the worst epicenters of the COVID-19 pandemic. The division of infectious diseases of ASST (Azienda Socio Sanitaria Territoriale) Spedali Civili Hospital of Brescia had to face a great number of inpatients with severe COVID-19 infection and to ensure the continuum of care for almost 4000 outpatients with HIV infection actively followed by us. In a recent manuscript we described the impact of the pandemic on continuum of care in our HIV cohort expressed as number of missed visits, number of new HIV diagnosis, drop in ART (antiretroviral therapy) dispensation and number of hospitalized HIV patients due to SARS-CoV-2 infection. In this short communication, we completed the previous article with data of HIV plasmatic viremia of the same cohort before and during pandemic. Methods We considered all HIV-patients in stable ART for at least 6 months and with at least 1 available HIV viremia in the time window March 01–November 30, 2019, and another group of HIV patients with the same two requisites but in different time windows of the COVID-19 period (March 01–May 31, 2020, and June 01–November 30, 2020). For patients with positive viremia (PV) during COVID-19 period, we reported also the values of viral load (VL) just before and after PV. Results: the percentage of patients with PV during COVID-19 period was lower than the previous year (2.8% vs 7%). Only 1% of our outpatients surely suffered from pandemic in term of loss of previous viral suppression. Conclusions Our efforts to limit the impact of pandemic on our HIV outpatients were effective to ensure HIV continuum of care.

Study on Vehicle Logistics Routing Path Problem with Time Windows Based on Particle Swarm Optimization

2014 ◽  
Vol 687-691 ◽  
pp. 5161-5164
Author(s):  
Lian Zhou Gao
Keyword(s):  
Particle Swarm Optimization ◽  
World Economy ◽  
Time Window ◽  
Time Windows ◽  
Particle Swarm ◽  
Modern Logistics ◽  
Swarm Optimization ◽  
Logistics Industry ◽  
Search Speed ◽  
Simulation Results

As the development of world economy, how to realize the reasonable vehicle logistics routing path problem with time window constrain is the key issue in promoting the prosperity and development of modern logistics industry. Through the research of vehicle logistics routing path 's demand, particle swarm optimization with a novel particle presentation is designed to solve the problem which is improved, effective and adept to the normal vehicle logistics routing. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability.

Lexical and sentential priming in competition: Implications for two-stage theories of lexical access

2001 ◽  
Vol 22 (2) ◽  
pp. 191-215 ◽  
Author(s):  
ARTURO E. HERNANDEZ ◽  
CHRISTINE FENNEMA-NOTESTINE ◽  
CARE UDELL ◽  
ELIZABETH BATES
Keyword(s):  
Lexical Access ◽  
Time Window ◽  
Early Stage ◽  
Time Windows ◽  
Unrelated Word ◽  
Two Stage ◽  
Sentential Context ◽  
Lexical Priming ◽  
Priming Word ◽  
Stage Theories

This article presents a new method that can compare lexical priming (word–word) and sentential priming (sentence–word) directly within a single paradigm. We show that it can be used to address modular theories of word comprehension, which propose that the effects of sentence context occur after lexical access has taken place. Although lexical priming and sentential priming each occur very quickly in time, there should be a brief time window in which the former is present but the latter is absent. Lexical and sentential priming of unambiguous words were evaluated together, in competing and converging combinations, using time windows designed to detect an early stage where lexical priming is observed but sentential priming is not. Related and unrelated word pairs were presented visually, in rapid succession, within auditory sentence contexts that were either compatible or incompatible with the target (the second word in each pair). In lexical decision, the additive effects of lexical priming and sentential priming were present under all temporal conditions, although the latter was always substantially larger. In cross-modal naming, sentential priming was present in all temporal conditions; lexical priming was more fragile, interacting with timing and sentential congruence. No evidence was found for a stage in which lexical priming is present but sentential priming is absent – a finding that is difficult to reconcile with two-stage models of lexical versus sentential priming. We conclude that sentential context operates very early in the process of word recognition, and that it can interact with lexical priming at the earliest time window.

scholarly journals A single‐shot autocorrelator for the ultraviolet with a variable time window

1990 ◽  
Vol 61 (3) ◽  
pp. 998-1003 ◽  
Author(s):  
S. Szatmári ◽  
F. P. Schäfer ◽  
J. Jethwa
Keyword(s):  
Time Window ◽  
Single Shot ◽  
Variable Time
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