Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average.

InPhase: Phase-based Ranging and Localization

Ranging and subsequent localization have become more and more critical in today’s factories and logistics. Tracking goods precisely enables just-in-time manufacturing processes. We present the InPhase system for ranging and localization applications. It employs narrowband 2.4 GHz IEEE 802.15.4 radio transceivers to acquire the radio channel’s phase response. In comparison, most other systems employ time-of-flight schemes with Ultra Wideband transceivers. Our software can be used with existing wireless sensor network hardware, providing ranging and localization for existing devices at no extra cost. The introduced Complex-valued Distance Estimation algorithm evaluates the phase response to compute the distance between two radio devices. We achieve high ranging accuracy and precision with a mean absolute error of 0.149 m and a standard deviation of 0.104 m. We show that our algorithm is resilient against noise and burst errors from the phase-data acquisition. Further, we present a localization algorithm based on a particle filter implementation. It achieves a mean absolute error of 0.95 m in a realistic 3D live tracking scenario.

State of charge estimation for lithium-ion batteries connected in series using two sigma point Kalman filters

This paper proposes a method to estimate state of charge (SoC) for Lithium-ion battery pack (LIB) with 𝑁 series-connected cells. The cell’s model is represented by a second-order equivalent circuit model taking into account the measurement disturbances and the current sensor bias. By using two sigma point Kalman filters (SPKF), the SoC of cells in the pack is calculated by the sum of the pack’s average SoC estimated by the first SPKF and SoC differences estimated by the second SPKF. The advantage of this method is the SoC estimation algorithm performed only two times instead of 𝑁 times in each sampling time interval, so the computational burden is reduced. The test of the proposed SoC estimation algorithm for 7 samsung ICR18650 Lithium-ion battery cells connected in series is implemented in the continuous charge and discharge scenario in one hour time. The estimated SoCs of the cells in the pack are quite accurate, the 3-sigma criterion of estimated SoC error distributions is 0.5%.

Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications

The maintenance of industrial equipment extends its useful life, improves its efficiency, reduces the number of failures, and increases the safety of its use. This study proposes a methodology to develop a predictive maintenance tool based on infrared thermographic measures capable of anticipating failures in industrial equipment. The thermal response of selected equipment in normal operation and in controlled induced anomalous operation was analyzed. The characterization of these situations enabled the development of a machine learning system capable of predicting malfunctions. Different options within the available conventional machine learning techniques were analyzed, assessed, and finally selected for electronic equipment maintenance activities. This study provides advances towards the robust application of machine learning combined with infrared thermography and augmented reality for maintenance applications of industrial equipment. The predictive maintenance system finally selected enables automatic quick hand-held thermal inspections using 3D object detection and a pose estimation algorithm, making predictions with an accuracy of 94% at an inference time of 0.006 s.

Parameter Adaptive Sliding Mode Trajectory Tracking Strategy with Initial Value Identification for the Swing in a Hydraulic Construction Robot

A novel trajectory tracking strategy is developed for a double actuated swing in a hydraulic construction robot. Specifically, a nonlinear hydraulic dynamics model of a double actuated swing is established, and a robust adaptive control strategy is designed to enhance the trajectory tracking performance. When an object is grabbed and unloaded, the inertia of a swing considerably changes, and the performance of the estimation algorithm is generally inadequate. Thus, it is necessary to establish an algorithm to identify the initial value of the moment of inertia of the object. To this end, this paper proposes a novel initial value identification algorithm based on a two-DOF robot gravity force identification method combined with computer vision information. The performance of the identification algorithm is enhanced. Simulations and experiments are performed to verify the effect of the novel control scheme.

Enhanced Attitude and Altitude Estimation for Indoor Autonomous UAVs

In recent years the use of Unmanned Aerial Vehicles (UAVs) has considerably grown in the civil sectors, due to their high flexibility of use. Currently, two important key points are making them more and more successful in the civil field, namely the decrease of production costs and the increase in navigation accuracy. In this paper, we propose a Kalman filtering-based sensor fusion algorithm, using a low cost navigation platform that contains an inertial measurement unit (IMU), five ultrasonic ranging sensors and an optical flow camera. The aim is to improve navigation in indoor or GPS-denied environments. A multi-rate version of the Extended Kalman Filter is considered to deal with the use of heterogeneous sensors with different sampling rates, and the presence of non-linearities in the model. The effectiveness of the proposed sensor platform is evaluated by means of numerical tests on the dynamic flight simulator of a quadrotor. Results show high precision and robustness of the attitude estimation algorithm, with a reduced computational cost, being ready to be implemented on low-cost platforms.

MEMS Accelerometer Noises Analysis Based on Triple Estimation Fractional Order Algorithm

This paper is devoted to identifying parameters of fractional order noises with application to noises obtained from MEMS accelerometer. The analysis and parameters estimation will be based on the Triple Estimation algorithm, which can simultaneously estimate state, fractional order, and parameter estimates. The capability of the Triple Estimation algorithm to fractional noises estimation will be confirmed by the sets of numerical analyses for fractional constant and variable order systems with Gaussian noise input signal. For experimental data analysis, the MEMS sensor SparkFun MPU9250 Inertial Measurement Unit (IMU) was used with data obtained from the accelerometer in x, y and z-axes. The experimental results clearly show the existence of fractional noise in this MEMS’ noise, which can be essential information in the design of filtering algorithms, for example, in inertial navigation.

The usage of power system multi-model forecasting aided state estimation for cyber attack detection

THE PURPOSE. Smart electrical grids involve extensive use of information infrastructure. Such an aggregate cyber-physical system can be subject to cyber attacks. One of the ways to counter cyberattacks is state estimation. State Estimation is used to identify the present power system operating state and eliminating metering errors and corrupted data. In particular, when a real measurement is replaced by a false one by a malefactor or a failure in the functioning of communication channels occurs, it is possible to detect false data and restore them. However, there is a class of cyberattacks, so-called False Data Injection Attack, aimed at distorting the results of the state estimation. The aim of the research was to develop a state estimation algorithm, which is able to work in the presence of cyber-attack with high accuracy.METHODS. The authors propose a Multi-Model Forecasting-Aided State Estimation method based on multi-model discrete tracking parameter estimation by the Kalman filter. The multimodal state estimator consisted of three single state estimators, which produced single estimates using different forecasting models. In this paper only linear forecasting models were considered, such as autoregression model, vector autoregression model and Holt’s exponen tial smoothing. When we obtained the multi-model estimate as the weighted sum of the single-model estimates. Cyberattack detection was implemented through innovative and residual analysis. The analysis of the proposed algorithm performance was carried out by simulation modeling using the example of a IEEE 30-bus system in Matlab.RESULTS. The paper describes an false data injection cyber attack and its specific impact on power system state estimation. A Multi - Model Forecasting-Aided State Estimation algorithm has been developed, which allows detecting cyber attacks and recovering corrupted data. Simulation of the algorithm has been carried out and its efficiency has been proved.CONCLUSION. The results showed the cyber attack detection rate of 100%. The Multi-Model Forecasting-Aided State Estimation is an protective measure against the impact of cyber attacks on power system.