An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Recommended System for Optimizing Battery Energy Management with Floating Car Data

Atualmente, os veículos pesados que transportam mercadoria sensível à temperatura utilizam sistemas de refrigeração ruidosos e com elevado consumo de combustível. Para combater estas desvantagens, está a ser instalado um sistema capaz de recuperar e produzir energia elétrica durante as travagens e a partir de painéis fotovoltaicos. Esta energia é armazenada num conjunto de baterias para, posteriormente, alimentar o sistema frigorífico em modo elétrico. Adicionalmente, estão a ser recolhidos dados em tempo real sobre o comportamento do veículo e do sistema.Tendo em conta que toda a energia disponível durante a condução está condicionada por diversas variáveis de operação, é fulcral extrair conhecimento a partir da análise dos dados recolhidos, identificando padrões que possam otimizar a produção e gestão da energia preditivamente. Este processo de extração de conhecimento inclui seleção e avaliação dos dados a recolher, construção do modelo preditivo do sistema e estudo da sua aplicação. Assim sendo, num dado momento, tendo em conta não só as métricas recolhidas da viagem atual, mas também de dados históricos de um dado percurso, será possível ao sistema de gestão de energia instalado no camião decidir qual a melhor ação a tomar de forma a otimizar a energia produzida sem causar stress ao sistema.Nowadays, heavy vehicles that transport temperature-sensitive goods, generally use a fuel-needy dedicated diesel engine. Towards solving this problem, an energy management system (EMS) capable of producing energy on-board of the vehicle is being developed. This recovery is possible due to the regenerative braking (RB) functionality, which consists in converting kinetic energy to electrical energy during a slowdown. The recovered energy is then stored in a set of batteries that supplies the refrigeration system when needed, allowing it to run in electrical mode. Using data retrieved from the vehicle's operation and this management system, an opportunity towards intelligently using the regenerative braking functionality emerges. By introducing an intelligence layer on the energy management system, a decision on applying the RB functionality could be made based on the trip's energetic potential. This decision will optimize the battery usage and reduce the load and wear on the EMS components.In order to calculate the energetic potential of a certain route, an estimation of the road is needed. This document presents context information and different approaches towards this end. In the modeling approach recommended and implemented, a route is divided in several spatial segments and each segment is categorized among three pre-defined classes. A classification model is used to predict traffic historical data as input. By using this modeling approach based on travel times, information on traffic flow and intersection queues are incorporated and by calculating the most likely sequence of states, a estimation of the road ahead is made.Using the information of the modeled path, when the RB systems detects a situation where the functionality can be applied, a decision will be made by weighting the energetic potential of the path ahead and the energy need. When the algorithm sees fit, a higher torque may be applied to the generator, which will result in a larger quantity of energy recovered. Since this causes stress to the system, this functionality needs a robust intelligence layer

A study on stryi-icnos potatorum and pisum sativum as natural coagulants for meat food processing wastewater

Slow maintained load test is widely used by contractors in Malaysia to ensure the driven pile could accommodate the design load of the structure. Slow maintained load test is a test to determine load-settlement curve and pile capacity for a period of time using conventional load test. Conventional static pile load test equipment is large in size thus making it heavier and takes a long time to install. In addition, it consumes a lot of space which causes congestion at construction sites. Therefore, the objective of this thesis is to conduct a conventional load test by replacing the pile kentledge load with anchorage and reaction pile. Preparations of ten designs comprising six commercial designs were reviewed. In addition, four proposed designs were suggested for the setup. Final design was produced based on its safety factors and criteria referred via literature review. The test frame consists of reaction frame with four reaction helical pile with two helixes per reaction pile. The deformation shapes, safety factor, stress, and strain of the design and finite element of the model has been analysed with the use of SolidWorks and Pia.xis 30 software. SolidWorks software emphasizes on the model load-deflection relationship while Plaxis 30 ensures a correlation of reaction between pile uplift force and soil. Then, the model was tested on site to determine the relationship between physical load­deflection and pile-soil uplift force. The results of uplift force and displacement for numerical and physical test were nearly identical which increment of load­displacement graph pattern. The higher the uplift force, the higher the displacement obtained. In conclusion, the result obtained and the design may be considered as a guideline for future application of sustainable slow maintained pile load test

The future of laboratory medicine - A 2014 perspective.

Predicting the future is a difficult task. Not surprisingly, there are many examples and assumptions that have proved to be wrong. This review surveys the many predictions, beginning in 1887, about the future of laboratory medicine and its sub-specialties such as clinical chemistry and molecular pathology. It provides a commentary on the accuracy of the predictions and offers opinions on emerging technologies, economic factors and social developments that may play a role in shaping the future of laboratory medicine