scholarly journals Collision Prevention Algorithm for Fishing Vessels Using mmWAVE Communication

2020 ◽  
Vol 8 (2) ◽  
pp. 115 ◽  
Author(s):  
Myoung-Ki Lee ◽  
Young-Soo Park
Keyword(s):  
Communication Technology ◽  
Continuous Monitoring ◽  
Wireless Access ◽  
False Alarms ◽  
Maritime Traffic ◽  
Fishing Vessels ◽  
Dangerous Situation ◽  
Alarm Signaling ◽  
Collision Prevention

This study leveraged the millimeter wireless access in vehicular environments (mmWAVE) communication technology to reflect the maneuvering characteristics of small fishing vessels and constructed a collision prevention algorithm that can be applied relatively easily. The algorithm was verified through simulation and actual ship experiments. The algorithm had four components: detection of vessels within three miles; identification of dangerous vessels by applying the time to the closest point of approach (TCPA) and distance at the closest point of approach (DCPA) criteria; continuous monitoring of maritime traffic risk; and incremental alarm signaling. The simulations and experiments confirmed that the alarm was generated incrementally in accordance with the distance to a dangerous situation, with no false alarms. Thus, the proposed algorithm offers potential to enhance the safety of small fishing vessels.

scholarly journals Congestion-Aware Signaling Aggregation and Barring Scheme for LTE Based Machine-to-Machine Network

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Fangmin Xu ◽  
Chao Qiu ◽  
Pengbiao Wang ◽  
Xiaokai Liu
Keyword(s):  
Theoretical Analysis ◽  
Communication Technology ◽  
Cellular Network ◽  
Access Network ◽  
Wireless Access ◽  
System Efficiency ◽  
M2m Communication ◽  
Machine To Machine ◽  
Core Networks ◽  
Congestion Aware

With the recently progress of Machine-to-Machine (M2M) communication technology, especially the enormous M2M devices and unique service of M2M, some challenges are emerging to the traditional wireless access and core networks, especially the congestion problem due to simultaneously bursty M2M service. Following this paradigm, the purpose of this paper is to support and optimize the signaling aggregation and barring of M2M services based on cellular network. With LTE network being the example access network, a congestion-aware signaling aggregation and barring scheme is designed considering the various requirements of M2M services and the congestion situation in the network entity. Theoretical analysis and experimental simulations show that this scheme can improve the system efficiency and greatly alleviate the signaling congestion, especially for the bursty M2M service.

scholarly journals Incorporating Negative Sample Training for Ship Detection Based on Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 684 ◽  
Author(s):  
Lianru Gao ◽  
Yiqun He ◽  
Xu Sun ◽  
Xiuping Jia ◽  
Bing Zhang
Keyword(s):  
Detection Methods ◽  
False Alarms ◽  
Negative Sample ◽  
Training Strategy ◽  
Ship Detection ◽  
Maritime Traffic ◽  
Effective Training ◽  
The Absolute ◽  
Optical Satellite Images ◽  
Selection Of

While ship detection using high-resolution optical satellite images plays an important role in various civilian fields—including maritime traffic survey and maritime rescue—it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea–land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters. In this study, Faster Region-based Convolutional Neural Network (Faster R-CNN) is applied to detect ships without the need for land masking. We propose an effective training strategy for the Faster R-CNN by incorporating a large number of images containing only terrestrial regions as negative samples without any manual marking, which is different from the selection of negative samples by targeted way in other detection methods. The experiments using Gaofen-1 satellite (GF-1), Gaofen-2 satellite (GF-2), and Jilin-1 satellite (JL-1) images as testing datasets under different ship detection conditions were carried out to evaluate the effectiveness of the proposed strategy in the avoidance of false alarms on land. The results show that the method incorporating negative sample training can largely reduce false alarms in terrestrial areas, and is superior in detection performance, algorithm complexity, and time consumption. Compared with the method based on sea–land segmentation, the proposed method achieves the absolute increment of 70% of the F1-measure, when the image contains large land area such as the GF-1 image, and achieves the absolute increment of 42.5% for images with complex harbors and many coastal ships, such as the JL-1 images.

scholarly journals COLLISION PREVENTION IN SINGAPORE STRAIT BY USING TIMED PETRI NET

Transport ◽  
2019 ◽  
Vol 35 (3) ◽  
pp. 273-282
Author(s):  
Rino Bošnjak ◽  
Danko Kezić ◽  
Pero Vidan ◽  
Zvonko Kavran
Keyword(s):  
Petri Nets ◽  
Warning System ◽  
Traffic Density ◽  
Visual Object ◽  
Timed Petri Nets ◽  
Traffic System ◽  
Maritime Traffic ◽  
Collision Prevention ◽  
Probability Of Collision ◽  
Singapore Strait

The problem of maritime traffic in Singapore Strait is traffic density, also the probability of collision, which is increased beside the existing Vessel Traffic System (VTS). The paper discusses the synthesis early warning system, or automatic crossing supervisor for Singapore Strait by using the Timed Petri Nets (TPN). Authors proposes dividing the strait in zones, so called crossings, where routes are crossing and in which the number of ships must be limited. The maximum number of vessels in period of time of the highest traffic density through the crossings are determined. Derived constraints are used for synthesis of crossing supervisor. The authors uses Petri nets to make model Singapore Strait, and use P-invariant method to syntheses crossing supervisor, which limits the number of ships in all critical crossings. Finally, the author verified derived supervisor by using Visual Object Net ++ programme for computer simulation. With the aid of Transas nautical simulator, the traffic in the strait is analysed.

Guard Robot Alarm System Design Based on GPRS

2010 ◽  
Vol 43 ◽  
pp. 217-220 ◽  
Author(s):  
Li Ning Sun ◽  
Mao Hai Li ◽  
Tao Chen
Keyword(s):  
Mobile Phone ◽  
System Design ◽  
Wireless Access ◽  
Alarm System ◽  
Domestic Environment ◽  
Dangerous Situation ◽  
Terminal Equipment

This paper designs guard robot alarm system using GPRS technology. An interesting communications infrastructure for remotely accessing is offered based on GPRS, which can control and interact with robots in an integrated and highly portable manner. In recent years the GPRS network provides wireless access of internet for mobile phone which becomes the most important and common terminal equipment. Therefore, the alarm information is designed to send to the user’s mobile phone through GPRS networks, and man-made intervention is added to the system, thus a more reliable and convenient alarm system is implemented. The system mainly consists of two parts: detection of abnormal and dangerous situation in domestic environment and sending the alarm information as MMS form to the user’s phone via GPRS networks.

scholarly journals A Review over AI Methods Developed for Maritime Awareness Systems

2020 ◽  
Vol XXIII (2) ◽  
pp. 287-299
Author(s):  
Pohontu Alexandru
Keyword(s):  
Artificial Intelligence ◽  
Safety Management ◽  
Vital Role ◽  
Data Sources ◽  
Maritime Surveillance ◽  
Maritime Traffic ◽  
Multiple Data ◽  
Collision Prevention ◽  
Traffic Security ◽  
Illegal Activities

Due to their operations against illegal activities, maritime threats or collision prevention analysis, maritime surveillance plays a vital role in maritime traffic security and safety management. Today's maritime surveillance and awareness systems can integrate multiple data sources like: coastal, HFSWR and SAR radars, AIS or satellite imagery; and this process produces massive amounts of data. That available data can be processed, with the use of Artificial Intelligence (AI) methods and algorithms, to automatically monitor the maritime traffic and its implications in safety, security, economy and environment. This paper's purpose is to briefly reveal current AI techniques that have been researched and deployed in the industry, and to seize the opportunity of implementing them.

A Test Result on the Performance of Next Generation ITS Communication in the Railway Environment

2017 ◽  
Author(s):  
Daekyo Shin ◽  
Soohyun Jang ◽  
Pusik Park
Keyword(s):  
Wireless Communication ◽  
Communication Technology ◽  
High Speed ◽  
Base Station ◽  
Wireless Access ◽  
Operating Costs ◽  
Next Generation ◽  
Safe Operation ◽  
Communication Performance ◽  
Test Result

Recently the demand of wireless communications technology in railway field has increased for the purpose of safe operation and economical construction and reducing operating costs. The real implementation test was carried out in order to apply the next generation ITS wireless communication technology (WAVE: Wireless Access Vehicular Environment) to the railway. WAVE communication is suitable for high-speed environment, have a reliable wireless communication performance. This paper presents the results of test about installing the WAVE base station / WAVE Terminal for applying to the railway and measure the performance evaluation conducted.

Picture Recognition Errors in the Differential Diagnosis of Alzheimer’s Disease

2019 ◽  
Vol 30 (3) ◽  
pp. 157-168
Author(s):  
Helmut Hildebrandt ◽  
Jana Schill ◽  
Jana Bördgen ◽  
Andreas Kastrup ◽  
Paul Eling
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Clinical Diagnosis ◽  
Response Bias ◽  
Recognition Task ◽  
Discriminatory Power ◽  
False Alarms ◽  
Picture Recognition ◽  
Recognition Errors

Abstract. This article explores the possibility of differentiating between patients suffering from Alzheimer’s disease (AD) and patients with other kinds of dementia by focusing on false alarms (FAs) on a picture recognition task (PRT). In Study 1, we compared AD and non-AD patients on the PRT and found that FAs discriminate well between these groups. Study 2 served to improve the discriminatory power of the FA score on the picture recognition task by adding associated pairs. Here, too, the FA score differentiated well between AD and non-AD patients, though the discriminatory power did not improve. The findings suggest that AD patients show a liberal response bias. Taken together, these studies suggest that FAs in picture recognition are of major importance for the clinical diagnosis of AD.

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