A technique to evaluate field traffic patterns of sugarcane harvester using Autopilot System

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

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Trash shredder mounted on chopped sugarcane harvester, model John Deere 3520

Sugar Industry ◽

10.36961/si17921 ◽

2016 ◽

pp. 713-719

Author(s):

Jorge L.M. Neves ◽

Natália de C.T. Calori ◽

Reinaldo C.M. Pimenta ◽

Celso Aparecido Sarto ◽

Thales H.Y. Noleto

Keyword(s):

Particle Size ◽

Technology Center ◽

Increasing Trend ◽

Tillage Operation ◽

Sugarcane Harvester ◽

Vegetable Material

With the increasing trend of mechanized harvesting of green sugarcane the trash residue needs to be used. Leaving large amounts of trash remaining on the field can sometimes lead to problems such as delay of sprouting of tillers, increased levels of leafhopper infestation and difficulty of tillage operation. Windrowing of trash after harvesting can minimize these problems. To eliminate the operation of trash windrowing and to facilitate the trash decomposition, the Sugarcane Technology Center, CTC (Centro de Tecnologia Canavieira), Brazil developed a trash shredder system for a primary extractor of John Deere chopper sugarcane harvester. Using rotating knives all vegetable material passing through the primary extractor is chopped, thereby reducing the particle size. After some adjustments, preliminary tests were carried out to prove the efficiency of chopping and sugarcane cleaning. The machine produces a much smaller trash particle size than conventional harvesters. The system shows promise in shredding trash either for use at the mill or for dispersion on the field.

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Bridge Damage Detection Approach Using a Roving Camera Technique

Sensors ◽

10.3390/s21041246 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1246 ◽

Cited By ~ 1

Author(s):

Darragh Lydon ◽

Myra Lydon ◽

Rolands Kromanis ◽

Chuan-Zhi Dong ◽

Necati Catbas ◽

...

Keyword(s):

Low Cost ◽

Normal Operation ◽

Laboratory Model ◽

Traffic Patterns ◽

Operational Conditions ◽

Bridge Model ◽

Detection Approach ◽

Bridge Damage ◽

Computer Vision Technology ◽

Climate Events

Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations.

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Changes in Passengers’ Travel Behavior Due to COVID-19

Sustainability ◽

10.3390/su13147974 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7974

Author(s):

Dong-Gyun Ku ◽

Jung-Sik Um ◽

Young-Ji Byon ◽

Joo-Young Kim ◽

Seung-Jae Lee

Keyword(s):

Travel Behavior ◽

Occupancy Rate ◽

Traffic Patterns ◽

Similar Time ◽

Usage Rate ◽

Contagious Nature ◽

Passenger Volume ◽

Service Rates ◽

Time Periods ◽

Usage Rates

The COVID-19 outbreak in 2020 has changed the way people travel due to its highly contagious nature. In this study, changes in the travel behavior of passengers due to COVID-19 in the first half of 2020 were examined. To determine whether COVID-19 has affected the use of transportation by passengers, paired t-tests were conducted between the passenger volume of private vehicles in Seoul prior to and after the pandemic. Additionally, the passenger occupancy rate of different modes of transportation during the similar time periods were compared and analyzed to identify the changes in monthly usage rate for each mode. In the case of private vehicles and public bicycles, the usage rates have recovered or increased when compared to those of before the pandemic. Conversely, bus and rail passenger service rates have decreased from the previous year before the pandemic. Furthermore, it is found that existing bus and rail users have switched to the private auto mode due to COVID-19. Based on the results, traffic patterns of travelers after the outbreak and implications responding to the pandemic are discussed.

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Examining Online Traffic Patterns to Popular Direct-To-Consumer Websites for Evaluation and Treatment of Erectile Dysfunction

Sexual Medicine ◽

10.1016/j.esxm.2020.100289 ◽

2021 ◽

Vol 9 (1) ◽

pp. 100289

Author(s):

Joel J. Wackerbarth ◽

Richard J. Fantus ◽

Annie Darves-Bornoz ◽

Marah C. Hehemann ◽

Brian T. Helfand ◽

...

Keyword(s):

Erectile Dysfunction ◽

Traffic Patterns ◽

Direct To Consumer ◽

Online Traffic

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A Latency-Optimized Network-on-Chip with Rapid Bypass Channels

Micromachines ◽

10.3390/mi12060621 ◽

2021 ◽

Vol 12 (6) ◽

pp. 621

Author(s):

Wenheng Ma ◽

Xiyao Gao ◽

Yudi Gao ◽

Ningmei Yu

Keyword(s):

Great Reduction ◽

Network On Chip ◽

Traffic Patterns ◽

Single Cycle ◽

High Bandwidth ◽

On Chip

Network-on-Chips with simple topologies are widely used due to their scalability and high bandwidth. The transmission latency increases greatly with the number of on-chip nodes. A NoC, called single-cycle multi-hop asynchronous repeated traversal (SMART), is proposed to solve the problem by bypassing intermediate routers. However, the bypass setup request of SMART requires additional pipeline stages and wires. In this paper, we present a NoC with rapid bypass channels that integrates the bypass information into each flit. In the proposed NoC, all the bypass requests are delivered along with flits at the same time reducing the transmission latency. Besides, the bypass request is unicasted in our design instead of broadcasting in SMART leading to a great reduction in wire overhead. We evaluate the NoC in four synthetic traffic patterns. The result shows that the latency of our proposed NoC is 63.54% less than the 1-cycle NoC. Compared to SMART, more than 80% wire overhead and 27% latency are reduced.

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Entropy Based Features Distribution for Anti-DDoS Model in SDN

Sustainability ◽

10.3390/su13031522 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1522

Author(s):

Raja Majid Ali Ujjan ◽

Zeeshan Pervez ◽

Keshav Dahal ◽

Wajahat Ali Khan ◽

Asad Masood Khattak ◽

...

Keyword(s):

Network Security ◽

False Positive ◽

Denial Of Service ◽

Network Services ◽

Detection Accuracy ◽

Data Sets ◽

Traffic Patterns ◽

Average Accuracy ◽

Ddos Detection ◽

Quantitative Results

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.

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