Soil compaction by machine traffic and least limiting water range related to soybean yield

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Traffic soil compaction of an oxisol related to soybean development and yield

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Impacto do tráfego de máquinas na qualidade física do solo e produtividade de milho em Argissolo

Este estudo teve o objetivo de avaliar o impacto do tráfego de máquinas na qualidade física do solo e produtividade de milho em Argissolo. O delineamento experimental foi inteiramente casualizado, com seis tratamentos e oito repetições representadas por parcelas de 14 m². Os tratamentos foram: T0) sem tráfego; T1*) uma passada de trator de 3,0 t, uma ao lado da outra; T1) uma passada; T2) duas passadas; T4) quatro passadas; T8) oito passadas de um trator de 8,0 t. Utilizou-se o milho (Zea mays L.) híbrido master, que foi avaliado quanto à produtividade de grãos. No solo, foram avaliados a porosidade, a densidade do solo, a resistência à penetração, o intervalo hídrico ótimo e a densidade do solo relativa. O tráfego de máquinas compactou o solo até 0,25 m de profundidade e reduziu a produtividade de milho em até 22%. O intervalo hídrico ótimo diminuiu com o aumento do tráfego de máquinas indicando decréscimo da qualidade física do solo para o milho. A densidade do solo relativa limitante à produtividade de milho é de 0,89.

O tráfego de máquinas agrícolas e as propriedades físicas, hídricas e mecânicas de um Latossolo dos Cerrados

No Brasil, o tráfego contínuo e inadequado de rodados de máquinas e a ação da soleira dos implementos sobre áreas agrícolas na região dos Cerrados têm provocado alterações dos atributos físicos e mecânicos dos solos. Com este entendimento, este estudo teve por objetivo avaliar a influência do rodado traseiro e da soleira de implementos agrícolas, usualmente utilizados na região dos Cerrados, sobre a compressibilidade de um Latossolo Vermelho distrófico típico. Os ensaios foram realizados em parcelas preparadas com arado de discos, arado de aivecas, grade aradora e semeadora/adubadora, que, desde novembro de 1994 até à data da amostragem, novembro de 1999, estiveram sob o sistema plantio direto. As alterações na compressibilidade foram avaliadas pela quantificação das pressões de preconsolidação, assim como por algumas propriedades físicas e hídricas (densidade do solo, porosidade e condutividade hidráulica do solo saturado) no momento do preparo e depois da colheita. No momento do preparo, logo depois da passada do rodado e antes que o implemento mobilizasse o solo, o solo foi avaliado em superfície (SP - 0,00 a 0,05 m) e na profundidade média de trabalho (PMT - 0,24 a 0,27 m). A influência da soleira dos implementos foi avaliada logo depois do corte, na profundidade de trabalho abaixo da soleira de cada implemento (PT-SI). Depois da colheita, as amostragens foram repetidas, o que permitiu verificar a influência dos tráfegos subseqüentes. A intensidade de tráfego do rodado e a ação da soleira dos implementos alteraram a compressibilidade, a densidade do solo, a porosidade e condutividade hidráulica do solo saturado do Latossolo Vermelho distrófico nas profundidades: superficial (SP), profundidade média de trabalho (PMT) e profundidade de corte dos implementos (PT-SI). de maneira geral, a passada do rodado traseiro aumentou os valores de pressão de preconsolidação (sigmap) do solo na superfície (SP), enquanto o tráfego subseqüente, necessário ao cultivo, elevou esses valores em subsuperfície, tanto na PMP como em PT-SI. A passada do rodado elevou e reduziu a densidade do solo (Ds) e a macroporosidade, respectivamente, tendo sido o arado de aivecas o implemento que mais modificou essas propriedades. As soleiras dos órgãos ativos do arado de discos e da grade aradora foram as que mais elevaram a pressão de preconsolidação (sigmap), consolidando, portanto, a estrutura do solo na profundidade PT-SI. Com exceção da semeadora/adubadora, a soleira dos órgãos ativos dos demais implementos avaliados reduziram significativamente a condutividade hidráulica do solo saturado (Ks) na profundidade PT-SI, tendo o arado de aivecas se mostrado o implemento mais impactante, seguido da grade aradora e arado de discos.

Avaliação dos atributos físicos e da compressibilidade de um nitossolo vermelho distroférrico sob sistemas de plantio direto, preparo convencional e mata nativa

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Traffic assignment. Predictive curves and their application, including machine procedures developed in the Detroit traffic study.

Cover title.

A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.

Probabilistic graphical models for semi-supervised traffic classification

Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features. Copyright © 2010 ACM.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Advances in Model-Based Traffic Vision

Model based vision allows use of prior knowledge of the shape and appearance of specific objects to be used in the interpretation of a visual scene; it provides a powerful and natural way to enforce the view consistency constraint. A model based vision system has been developed within ESPRIT VIEWS: P2152 which is able to classify and track moving objects (cars and other vehicles) in complex, cluttered traffic scenes. The fundamental basis of the method has been previously reported. This paper presents recent developments which have extended the scope of the system to include (i) multiple cameras, (ii) variable camera geometry, and (iii) articulated objects. All three enhancements have easily been accommodated within the original model-based approach

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.