Behaviour Prediction Framework

We analyze the behavior of the Italian electricity market with an agent-based model. In particular, we are interested in testing the assumption that the market participants are fully rational in the economical sense. To this aim, we extend a previous model by considering a wider class of cases. After checking that the new model is a correct generalization of the existing model, we compare three optimization methods to implement the agents rationality and we verify that the model exhibits a very good fit to the real data. This leads us to conclude that our model can be used to predict the behavior of this market.

Summary A major industrial challenge in computer-supported product development today is to establish corporate development across enterprises. Corporative distributed product development and engineering processes require an extensive use of advance information technology. The support to distributed processes and to the management of distributed product data and other engineering information resources is of special importance. This article describes an approach to the integration of the product information and product ...

In recent years, extensive research and applications have demonstrated that a more accurate and stable indoor positioning system (IPS) can be realized through the complementarity of different technologies. However, improving the accuracy of a single positioning technology would significantly impact the subsequent fusion. In this paper, we proposed a smartphone positioning system based on pedestrian dead reckoning (PDR) fusing with a floor estimation module (FEM) and a directional particle filter (PF) extracted by the idea of a regular PF to improve positioning accuracy. Generally, particles are generated by circle-shaped distribution by a regular PF, leading to producing some useless particles that waste computing resources. To tackle this issue, our proposed directional PF uses the particle more effectively by adjusting the distribution shape of particles with an estimated moving direction. Comprehensive experiments are conducted to verify the positioning accuracy and robustness of the proposed method. The experimental data show that the positioning error of our proposed method is only 1.53 m in a multi-floor path with a total length of 306 m. INDEX TERMS Directional particle filtering, indoor positioning system, pedestrian dead reckoning, smartphone-based.

In recent years, extensive research and applications have demonstrated that a more accurate and stable indoor positioning system (IPS) can be realized through the complementarity of different technologies. However, improving the accuracy of a single positioning technology would significantly impact the subsequent fusion. In this paper, we proposed a smartphone positioning system based on pedestrian dead reckoning (PDR) fusing with a floor estimation module (FEM) and a directional particle filter (PF) extracted by the idea of a regular PF to improve positioning accuracy. Generally, particles are generated by circle-shaped distribution by a regular PF, leading to producing some useless particles that waste computing resources. To tackle this issue, our proposed directional PF uses the particle more effectively by adjusting the distribution shape of particles with an estimated moving direction. Comprehensive experiments are conducted to verify the positioning accuracy and robustness of the proposed method. The experimental data show that the positioning error of our proposed method is only 1.53 m in a multi-floor path with a total length of 306 m. INDEX TERMS Directional particle filtering, indoor positioning system, pedestrian dead reckoning, smartphone-based.

This paper deals with the problem of estimating the hard iron bias affecting the measurements provided by a tri-axial magnetometer. The estimation is carried out using measurements from a magnetometer and a gyroscope mounted on the same inertial measurement unit. The described solutions ensure finite-time estimation performance and robustness against measurements noise. The effectiveness of the proposed solutions is proved by experimental tests carried out using real inertial sensors.

Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other positionestimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72 • as compared with the azimuth-and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper. INDEX TERMS Indoor positioning system (IPS), pedestrian dead reckoning (PDR), heading estimation, indoor navigation, Android-based smartphone, quaternion, Kalman filter, sensor fusion.

The objective of the research presented in this paper is to develop an algorithm for predicting behaviour of complex technical systems in an uncertain working environment. System s dynamic behaviour modelling and simulation should help to develop new and improve existing architectures of complex technical systems by mapping in both directions, from the structural to the behavioural domain and vice versa. The algorithm for predicting behaviour during system s architecture development consists of several operations shortly described in this paper. The proposed algorithm was verified on an example of a complex technical system  an air-handling unit.

The objective of the research presented in this paper is to develop an approach modeling and predicting the nonlinear behaviour of complex technical systems in the operating environment, based on a presentation of the system’s dynamic behaviour and recording of such behaviour by using the rules. This approach should help develop new architectures of nonlinear complex systems and improve existing ones, by mapping in both directions, from the structural to the behavioural domain and vice versa. Verification of this approach is presented on the example of a load lifting device.

The objective of the research presented in this paper is to develop an algorithm for predicting behaviour of complex technical systems in an uncertain working environment. System s dynamic behaviour modelling and simulation should help to develop new and improve existing architectures of complex technical systems by mapping in both directions, from the structural to the behavioural domain and vice versa. The algorithm for predicting behaviour during system s architecture development consists of several operations shortly described in this paper. The proposed algorithm was verified on an example of a complex technical system  an air-handling unit.

Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other positionestimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72 • as compared with the azimuth-and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper. INDEX TERMS Indoor positioning system (IPS), pedestrian dead reckoning (PDR), heading estimation, indoor navigation, Android-based smartphone, quaternion, Kalman filter, sensor fusion.

With the increase in the number of smart phone users worldwide there comes a high demand for smart phone based location systems. Within the project “Indoor positioning using inertial measurement units” an android application has been developed wherein the position of the smart phone user with respect to the floor environment is determined. This has been achieved using the smart phone sensors step detector and rotation vector which act as inbuilt inertial measurement unit (IMU) in smart phones. The algorithm used is “Pedestrian Dead Reckoning “(PDR).

Data-driven inertial navigation is the task of estimating of positions and orientations of a moving subject from a sequence of Inertial Measurement Unit (IMU) sensor measurements. Inertial navigation is a quintessential technology due to low cost, low energy consumption and low operating constraints of IMU sensor. However, sensor errors have forced research on inertial navigation to be limited to highly constrained use cases. We leverage on the power of machine learning and big data to loosen such constraints and estimate natural human motion in the wild. More concretely, we define our problem as estimation of relative horizontal positions and heading direction of a moving subject using the IMU sensor measurements from his phone. This research propose 1) a new benchmark containing more than 40 hours of IMU sensor data from 100 human subjects with ground-truth 3D trajectories under natural human motions; 2) novel neural inertial navigation architectures, making significant improvements for challenging motion cases; and 3) qualitative and quantitative evaluations of the competing methods over three inertial navigation benchmarks. We share the code and data to promote further research on our project website http://ronin.cs.sfu.ca

With the continuous development of infrared technology and its extensive usage in the military, the infrared radiation attitude measurement technology has received more and more attention. Aiming at the compensation requirement for complex infrared background interference during the rotating missile infrared attitude measurement, the theoretical research on the dual-band infrared rotating missile attitude measurement is carried out. The mechanism and interference characteristics of the earth infrared radiation are analyzed according to the flying missile movement characteristics. The atmosphere propagation in 3~5μm and 8~14μm infrared radiation bands is studied, and the dual-band infrared radiation attitude measurement model is established. Then, the influence of cloud infrared radiation interference on the dual-band infrared radiation sensor is analyzed and a nighttime dual-band infrared radiation attitude measurement difference compensation algorithm is proposed to obtain the compensated attitude angle parameter. The results of semi-physical experiments show that the compensated roll angle error is within ±0.6° and the pitch angle error is within ±1°. The dual-band infrared radiation attitude measurement difference compensation algorithm can effectively eliminate the effects of cloud infrared radiation interference and improve the anti-interference ability of infrared radiation attitude measurement.

Currently, Pedestrian Dead Reckoning (PDR) systems are becoming more attractive in market of indoor positioning. This is mainly due to the development of cheap and light Micro Electro-Mechanical Systems (MEMS) on smartphones and less requirement of additional infrastructures in indoor areas. However, it still faces the problem of drift accumulation and needs the support from external positioning systems. Vision-aided inertial navigation, as one possible solution to that problem, has become very popular in indoor localization with satisfied performance than individual PDR system. In the literature however, previous studies use fixed platform and the visual tracking uses feature-extraction-based methods. This paper instead contributes a distributed implementation of positioning system and uses deep learning for visual tracking. Meanwhile, as both inertial navigation and optical system can only provide relative positioning information, this paper contributes a method to integrate digital map with real geographical coordinates to supply absolute location. This hybrid system has been tested on two common operation systems of smartphones as iOS and Android, based on corresponded data collection apps respectively, in order to test the robustness of method. It also uses two different ways for calibration, by time synchronization of positions and heading calibration based on time steps. According to the results, localization information collected from both operation systems has been significantly improved after integrating with visual tracking data.

Indoor tracking and navigation (ITN) mainly depend on indoor localization. An impulse radio ultra-wideband (IR-UWB) is the most advanced technology for precision indoor localization. Besides its precision, IR-UWB also has low complex hardware, low power consumption, and a flexible data rate that makes it the ideal candidate for ITN. However, two significant challenges impede the achievement of highresolution accuracy and optimum performance: non-line-of-sight (NLOS) channel condition and multipath propagation. To enhance the performance under these conditions, the ranging error is estimated and corrected using parameters' uncertainties. The uncertainties in the channel's parameters have a relationship with the error, and these uncertainties are induced due to NLOS and MPP propagation conditions. The parameters are collected in real-time experimental setups in two different environments. A proposed fuzzy inference model utilizes these uncertainties and the relationship to estimate ranging errors. The model is evaluated, and its performance is gauged in terms of residual ranging error cumulative distribution, root mean square error and outage probability parameters using experimental measurements and compared with the state-of-the-art work. Moreover, the proposed fuzzy model is evaluated for computational complexity in terms of execution time and compared with the state-of-the-art work. The time is estimated on the targeted embedded system. The experimental and simulated results show that the proposed model effectively minimizes the ranging errors and computational burden. Also, the model does not induce a delay in estimating ranging error due to the non-statistical based solution.

Currently, Pedestrian Dead Reckoning (PDR) systems are becoming more attractive in market of indoor positioning. This is mainly due to the development of cheap and light Micro Electro-Mechanical Systems (MEMS) on smartphones and less requirement of additional infrastructures in indoor areas. However, it still faces the problem of drift accumulation and needs the support from external positioning systems. Vision-aided inertial navigation, as one possible solution to that problem, has become very popular in indoor localization with satisfied performance than individual PDR system. In the literature however, previous studies use fixed platform and the visual tracking uses feature-extraction-based methods. This paper instead contributes a distributed implementation of positioning system and uses deep learning for visual tracking. Meanwhile, as both inertial navigation and optical system can only provide relative positioning information, this paper contributes a method to integrate digital map with real geographical coordinates to supply absolute location. This hybrid system has been tested on two common operation systems of smartphones as iOS and Android, based on corresponded data collection apps respectively, in order to test the robustness of method. It also uses two different ways for calibration, by time synchronization of positions and heading calibration based on time steps. According to the results, localization information collected from both operation systems has been significantly improved after integrating with visual tracking data.

The paper deals with indoor navigation using inertial sensors (accelerometers, gyroscopes, etc.) built in a smartphone. The main disadvantage of the use of inertial sensors is the accuracy, which rapidly decreases with the increasing time of the measurement. The reason of the deteriorating accuracy is the presence of errors in inertial measurements, which are accumulated in the integration process. The paper describes the determination of a pedestrian trajectory using step detection method, which is improved with utilization of the adaptive step length estimation algorithm. This algorithm reflects the change of the step length with different types of movement. The proposal of the data processing uses information from floormap, what allows the verification of the pedestrian position and detects the collision of the trajectory with the floormap. The proposed algorithm significantly increases the accuracy of the resulting trajectory. Another extension of the proposed algorithm is the i...

A complete Impulse Radio-Ultra Wide Band (IR-UWB) system is proposed to mitigate the impairments concerning on-body to off-body Wireless Body Area Networks (WBAN). The proposed system maintains the practical communication link between on-body sensors to the fusion center or the monitoring device hold by the medical representative. Also, these sensors are assigned a recently proposed pulse shaping to mitigate the noise existed in the IR-UWB WBAN channel. Furthermore, Virtual Multiple Input Multiple Output (VMIMO) is proposed to perform spatial multiplexing between various sensors transmitted data. Moreover, low complex Low Density Parity Check (LDPC) encoding/decoding algorithms including a new low complex hybrid LDPC decoding algorithm are proposed to reduce the complexity and to enhance the bit error rate of the on-body sensors. Interestingly, low energy consumption at every sensor participating in the on-body to off-body WBAN was achieved by adopting the combination between LDPC decoders and VMIMO technique. According to simulation results, the new system enhanced the BER performance and reduced the complexity of the on-body to off-body IR-UWB communication system effectively.

In this article, an experimental demonstration was developed, to measure the velocity of a fluid by using a sensor based on the spherical parallel mechanism with three degrees-of-freedom. This sensor transforms the kinetic energy of the fluid into potential energy by deforming the parallel mechanism. This deformation is due to the impact of the fluid on a sphere attached to the platform of the parallel mechanism. Through the acquisition of data from an inertial measurement unit in the sphere, an algorithm calculates the velocity and direction of the fluid. The mathematic model and algorithm of the velocity measurement was developed in a previous article. This article built and tested the sensor with the objective of demonstrating the theoretical basis for the sensor. The experiment is based on the underwater movement of the sensor in a linear way along a rail. The velocity that is measured by the linear encoder is equal to the fluid's relative velocity that is measured by the sensor. The measurements taken by the encoder and the sensor were compared in three experiments. The results show that the two measurements were similar, demonstrating that the sensor can accurately measure the velocity of the fluid.

Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other positionestimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch value...

Human activity recognition from sensor readings has proved to be an effective approach in pervasive computing for smart healthcare. Recent approaches in ambient assisted living (AAL) within the home or community setting offers people the prospect of independent care and improved quality of living. However, most of the available AAL systems are limited by several factors including system complexity and computational cost. In this paper, a simple, novel ambient human activity recognition framework using the multivariate Gaussian is proposed. The classification framework augments prior information from passive RFID tags to obtain more detailed activity profiling. The proposed algorithm based on multivariate Gaussian via maximum likelihood estimation is used to learn the features of the human activity model. Twelve sequential and concurrent experimental evaluations are conducted in a mock apartment environment. The sampled activities are predicted using a new dataset of the same activity and high prediction accuracy established. The proposed framework suits well for the single and multi-dwelling environment and offers pervasive sensing environment for elderly, disabled and carers.

—The article proposes a novel approach for direction estimation of a moving pedestrian as perceived in 2D coordinate of field camera. The proposed direction estimation method is intended for pedestrian monitoring in traffic control systems. Apart from traffic control, direction of motion estimation is also very important in accident avoidance system for smart cars, assisted living systems, in occlusion prediction for seamless tracking in visual surveillance etc. The proposed video based direction estimation method exploits the notion of perspective distortion as perceived in monocular vision of 2D camera coordinate. The temporal pattern of change in dimension of pedestrian in a frame sequence is unique for each direction, hence the dimensional change based feature is used to estimate the direction of motion. 8 discrete directions of motion are considered and Hidden Markov Model is used for classification. The experiments are conducted over CASIA Dataset A, CASIA Dataset B, and over a self-acquired dataset: NITR Conscious Walk Dataset. The balanced accuracy of direction estimation for these experiments yields satisfactory results with accuracy indices as 94.58%, 90.87% and 95.83% respectively. The experiment also justifies with suitable test conditions about the characteristic features like robustness towards improper segmentation, partial occlusion, and changing orientation of head or body during walk of a pedestrian. The proposed method can be used as a standalone system or can be integrated with existing frame based direction estimation methods for implementing a pedestrian monitoring system.

The objective of the research presented in this paper is to develop an algorithm for predicting behaviour of complex technical systems in an uncertain working environment. System’s dynamic behaviour modelling and simulation should help to develop new and improve existing architectures of complex technical systems by mapping in both directions, from the structural to the behavioural domain and vice versa. The algorithm for predicting behaviour during system’s architecture development consists of several operations shortly described in this paper. The proposed algorithm was verified on an example of a complex technical system – an air-handling unit.

A major industrial challenge in computer-supported product development today is to establish corporate development across enterprises. Corporative distributed product development and engineering processes require an extensive use of advance information technology. The support to distributed processes and to the management of distributed product data and other engineering information resources is of special importance. This article describes an approach to the integration of the product information and product knowledge management using the proposed information model of product which complies with the STEP (ISO 10303) international standard. The proposed model of product knowledge and data information (PKDIM) is shared between interoperable IEE modules.

This paper proposes a Behaviour Prediction Framework with an objective to help designers tackling the problem of uncertainty emerging from system architecture and the effects of the uncertain operating conditions. The proposed framework combines structural and dynamic system model. The Design Structure Matrix is applied to model structural arrangements and dependencies between the subsystems. The Model Predictive Control is applied to model the system in discrete and continuous dynamic domains. As the result of the proposed framework, stability analysis of subsystems in interaction become possible and feedback on system architecture could be provided. To test validity of the proposed approach, the test case involving climate chamber with heat regeneration is presented.

The objective of the research presented in this paper is to develop an approach modeling and predicting the nonlinear behaviour of complex technical systems in the operating environment, based on a presentation of the system’s dynamic behaviour and recording of such behaviour by using the rules. This approach should help develop new architectures of nonlinear complex systems and improve existing ones, by mapping in both directions, from the structural to the behavioural domain and vice versa. Verification of this approach is presented on the example of a load lifting device.

This paper proposes a Behaviour Prediction Framework with an objective to help designers tackling the problem of uncertainty emerging from system architecture and the effects of the uncertain operating conditions. The proposed framework combines structural and dynamic system model. The Design Structure Matrix is applied to model structural arrangements and dependencies between the subsystems. The Model Predictive Control is applied to model the system in discrete and continuous dynamic domains. As the result of the proposed framework, stability analysis of subsystems in interaction become possible and feedback on system architecture could be provided. To test validity of the proposed approach, the test case involving climate chamber with heat regeneration is presented.

This paper proposes a Behaviour Prediction Framework with an objective to help designers tackling the problem of uncertainty emerging from system architecture and the effects of the uncertain operating conditions. The proposed framework combines structural and dynamic system model. The Design Structure Matrix is applied to model structural arrangements and dependencies between the subsystems. The Model Predictive Control is applied to model the system in discrete and continuous dynamic domains. As the result of the proposed framework, stability analysis of subsystems in interaction become possible and feedback on system architecture could be provided. To test validity of the proposed approach, the test case involving climate chamber with heat regeneration is presented.

This paper proposes a Behaviour Prediction Framework with an objective to help designers tackling the problem of uncertainty emerging from system architecture and the effects of the uncertain operating conditions. The proposed framework combines structural and dynamic system model. The Design Structure Matrix is applied to model structural arrangements and dependencies between the subsystems. The Model Predictive control is applied to model the system in discrete and continuous dynamic domains. As the result of the proposed framework, stability analysis of subsystems in interaction become possible and feedback on system architecture could be provided. To test validity of the proposed approach, the test case involving climate chamber with heat regeneration is presented.

This paper proposes a Behaviour Prediction Framework with an objective to help designers tackling the problem of uncertainty emerging from system architecture and the effects of the uncertain operating conditions. The proposed framework combines structural and dynamic system model. The Design Structure Matrix is applied to model structural arrangements and dependencies between the subsystems. The Model Predictive Control is applied to model the system in discrete and continuous dynamic domains. As the result of the proposed framework, stability analysis of subsystems in interaction become possible and feedback on system architecture could be provided. To test validity of the proposed approach, the test case involving climate chamber with heat regeneration is presented.