Adaptive neuro-fuzzy module for inertial navigation system/global positioning system integration utilising position and velocity updates with real-time cross-validation

Recently, methods based on artificial intelligence (AI) have been suggested to provide reliable positioning information for different land vehicle navigation applications. The majority of these applications utilise both the global positioning system (GPS) and the inertial navigation system (INS). These AI modules were trained to mimic the latest vehicle dynamics so that, in case of GPS outages, the system relies on INS and the recently updated AI module to provide the vehicle position. Several neural networks and neuro-fuzzy techniques were implemented in real-time in a de-centralised fashion and provided acceptable accuracy for short GPS outages. It was reported that these methods provided poor positioning accuracy during relatively long GPS outages. In order to prevail over this limitation, this study optimises the Al-based INS/GPS integration schemes utilising adaptive neuro-fuzzy inference system with performing, in real-time, both GPS position and velocity updates. In addition, a holdout cross validation method during the update procedure was utilised in order to ensure generalisation of the model. The proposed system is tested using differential GPS and both navigational and tactical grades INS field test data obtained from a land vehicle experiment. The results showed that the effectiveness of the proposed system over both the existing Al-based and the conventional INS/GPS integration techniques, especially during long GPS outages. This method may have one limitation related to the unusual significant changes of the vehicle dynamics between the update and the prediction stages of operation which may influence the overall positioning accuracy.     

Sensor Integration for Satellite-Based Vehicular Navigation Using Neural Networks

Land vehicles rely mainly on global positioning system (GPS) to provide their position with consistent accuracy. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. In order to overcome this problem, GPS is usually combined with inertial sensors mounted inside the vehicle to obtain a reliable navigation solution, especially during GPS outages. This letter proposes a data fusion technique based on radial basis function neural network (RBFNN) that integrates GPS with inertial sensors in real time. A field test data was used to examine the performance of the proposed data fusion module and the results discuss the merits and the limitations of the proposed technique     

Adaptive Fuzzy Prediction of Low-Cost Inertial-Based Positioning Errors

Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position.     

Measurements of the thermal diffusivity of linear-medium-density-polyethylene/aluminium composites using a transient comparative method

Metal-powder/polymer composites are an interesting class of material because their physical properties may, within limits, be selected to match a particular application. It is therefore important to be able to measure and model the physical properties of these composites. The effective diffusivity of linear-medium-density-polyethylene/aluminium composites was measured for a range of volume fractions using a simple, transient comparative method. Effective thermal conductivity data were calculated from the effective thermal diffusivity data. The effective thermal conductivity data were modelled well by the EMT equation.     

Anti-schistosomal IgE and its relation to gastrointestinal allergy in breast-fed infants of Schistosoma mansoni infected mothers

To study the relationship between presence of gastrointestinal allergic manifestations in breast-fed infants and presence of IgE against Schistosoma mansoni antigens, sixty breast-fed infants of S. mansoni infected mothers were selected. Of them, thirty infants were suffering from manifestations of gastrointestinal allergy (patients) and the other thirty were not suffering from such manifestations (controls). Levels of IgE against S. mansoni adult worm antigen (AWA), soluble egg antigen (SEA) and cercarial antigen (CA) were determined, by ELISA, in sera of these infants. There was significant association between presence of allergic manifestations and presence of IgE against AWA (P = 0.018), SEA (P < 0.001) and CA (P = 0.002). Also, concentration of IgE against AWA was significantly higher in patients group than the control group (P = 0.024). IgE against AWA showed significant negative correlation with haemoglobin concentration (P = 0.009) and serum albumin level (P = 021) and significant positive correlation with absolute eosinophilic count (P = 0.005). Also, IgE against CA showed significant negative correlation with haemoglobin concentration (p = 0.047) and serum albumin level (0 = 0.036). It was concluded that gastrointestinal allergy in breast-fed infants of S. mansoni infected mothers may be due to hypersensitivity of Schistosoma mansoni antigens present in mothers' milk. Schistosoma mansoni should be investigated and treated in mothers from endemic localities when their breast-fed infants are suffering from manifestations suggestive of gastrointestinal allergy.     

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence (AI) techniques. In this article, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to forecast the inflow for the Nile River at Aswan High Dam (AHD) on monthly basis. A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database. The ANFIS model divides the input space into fuzzy sub-spaces and maps the output using a set of linear functions. A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance. The performance of the ANFIS model is compared to a recently developed artificial neural networks (ANN) model. The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model. It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD.     

Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements

The Nile River is considered the main life artery for so many African countries especially Egypt. Therefore, it is of the essence to preserve its water and utilize it very efficiently. Developing inflow-forecasting model is considered the technical way to effectively achieve such preservation. The hydrological system of the Nile River under consideration has several dams and barrages that are equipped with control gates. The improvement of these hydraulic structures’ criteria for operation can be assessed if reliable forecasts of inflows to the reservoir are available. Recently, the authors developed a forecasting model for the natural inflow at Aswan High Dam (AHD) based on Artificial Intelligence (AI). This model was developed based on the historical inflow data of the AHD and successfully provided accurate inflow forecasts with error less than 10%. However, having several forecasting models based on different types of data increase the level of confidences of the water resources planners and AHD operators. In this study, two forecasting model approach based on Radial Basis Function Neural Network (RBFNN) method for the natural inflow at AHD utilizing the stream flow data of the monitoring stations upstream the AHD is developed. Natural inflow data collected over the last 30 years at four monitoring stations upstream AHD were used to develop the model and examine its performance. Inclusive data analysis through examining cross-correlation sequences, water traveling time, and physical characteristics of the stream flow data have been developed to help reach the most suitable RBFNN model architecture. The Forecasting Error (FE) value of the error and the distribution of the error are the two statistical performance indices used to evaluate the model accuracy. In addition, comprehensive comparison analysis is carried out to evaluate the performance of the proposed model over those recently developed for forecasting the inflow at AHD. The results of the current study showed that the proposed model improved the forecasting accuracy by 50% for the low inflow season, while keep the forecasting accuracy in the same range for the high inflow season.     

Synthesis of C‐H‐O Symbiosis Networks

The concept of synthesizing carbon, hydrogen, and oxygen (C‐H‐O) SYmbiosis Networks (CHOSYNs) for the design of eco‐industrial parks is introduced. Within a CHOSYN, compounds containing C‐H‐O are exchanged, converted, separated, mixed, and allocated. The use of C‐H‐O as the basis for integration creates numerous opportunities for synergism because C, H, and O are the primary building blocks for many industrial compounds that can be exchanged and integrated. A particularly attractive feature of the CHOSYN framework is its ability to use atomic‐based targets to establish benchmarks for the design of macroscopic systems involving multiple processes. Several structural representations, benchmarking, and optimization formulations are developed to embed potential CHOSYN configurations of interest and to synthesize cost‐effective networks. A case study with several scenarios is solved to demonstrate the new concept and tools. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1242–1262, 2015     

THE EFFECT OF LOW TEMPERATURE OXIDATION ON THE CHEMICAL AND PHYSICAL PROPERTIES OF MALTENES AND ASPHALTENES DERIVED FROM HEAVY OIL

Abstract

The chemical changes that occur when the maltene and asphaltene fractions (separated from heavy oil) are subjected to low temperature oxidation (LTO) in the presence and absence of water have been investigated by a combination of classical separation techniques and analytical pyrolysis. In general, it is observed that water has a mitigating effect on the destructive nature of LTO. A detailed analysis of the pyrolytic products suggests that the presence of water reduces the ease with which oxygen reacts with sulfides to give sulfones and thereby supresses the formation of coke. An analysis of the data indicates that most of the coke produced results from LTO of the asphaltenes; only a small portion originates in the maltenes.     

Performance evaluation of a non-linear error model for underwater range computation utilizing GPS sonobuoys

Deployed from an airborne platform or a surface vessel, arrays of GPS sonobuoys can be used to efficiently track and localize submarines. The range of the target of interest can be monitored with the deployed sonobuoys. However, the accuracy deteriorates when the target is on the detection range of only one sonobuoy. The objective of this research is to improve the range computation of the target of interest by establishing a non-linear error model for range error using adaptive neuro-fuzzy inference systems (ANFIS), which has the capabilities of dealing with data of high level of uncertainty and the advantage of being based on neural computation. Furthermore, the performance of the proposed model is examined with both experimental real field data and contact-level simulation data considering different scenarios for both the array of GPS sonobuoys and the target. The results discuss merits and the limitations of the proposed method.