A Novel Anti-Drift Visual Object Tracking Algorithm Based on Sparse Response and Adaptive Spatial-Temporal Context-Aware

Correlation filter (CF) based trackers have gained significant attention in the field of visual single-object tracking, owing to their favorable performance and high efficiency; however, existing trackers still suffer from model drift caused by boundary effects and filter degradation. In visual tracking, long-term occlusion and large appearance variations easily cause model degradation. To remedy these drawbacks, we propose a sparse adaptive spatial-temporal context-aware method that effectively avoids model drift. Specifically, a global context is explicitly incorporated into the correlation filter to mitigate boundary effects. Subsequently, an adaptive temporal regularization constraint is adopted in the filter training stage to avoid model degradation. Meanwhile, a sparse response constraint is introduced to reduce the risk of further model drift. Furthermore, we apply the alternating direction multiplier method (ADMM) to derive a closed-solution of the object function with a low computational cost. In addition, an updating scheme based on the APEC-pool and Peak-pool is proposed to reveal the tracking condition and ensure updates of the target’s appearance model with high-confidence. The Kalam filter is adopted to track the target when the appearance model is persistently unreliable and abnormality occurs. Finally, extensive experimental results on OTB-2013, OTB-2015 and VOT2018 datasets show that our proposed tracker performs favorably against several state-of-the-art trackers.

Data Driven Manufacturing: Challenges and Opportunities

The presentation starts with the history of industrial revolutions, the differences between industry 3.0 and 4.0, the enabling innovation and technologies of industry 4.0. It then explains that smart products, processes, and resources (PPR) that lead to the generation and use of big data. Big data has high implications on both customer-centric activities and operations in the manufacturing sector. Manufacturing operations can be optimized using big data analytics, such as the product or service design, production planning and optimization, material tracking, condition monitoring, quality control, predictive maintenance, and sustainability optimization. Data can revolutionize the industry through data-driven services as add-ons to conventional products. Finally, the presentation presents research and development projects related to data-driven manufacturing, especially for sustainable and collaborative manufacturing.

Slip Buddy App for Weight Management: Randomized Feasibility Trial of a Dietary Lapse Tracking App

Background Although calorie tracking is one of the strongest predictors of weight loss in behavioral weight loss interventions, low rates of adherence are common. Objective This study aims to examine the feasibility and acceptability of using the Slip Buddy app during a 12-week web-based weight loss program. Methods We conducted a randomized pilot trial to evaluate the feasibility and acceptability of using the Slip Buddy app compared with a popular commercial calorie tracking app during a counselor-led, web-based behavioral weight loss intervention. Adults who were overweight or obese were recruited on the web and randomized into a 12-week web-based weight loss intervention that included either the Slip Buddy app or a commercial calorie tracking app. Feasibility outcomes included retention, app use, usability, slips reported, and contextual factors reported at slips. Acceptability outcomes included ratings of how helpful, tedious, taxing, time consuming, and burdensome using the assigned app was. We described weight change from baseline to 12 weeks in both groups as an exploratory outcome. Participants using the Slip Buddy app provided feedback on how to improve it during the postintervention focus groups. Results A total of 75% (48/64) of the participants were female and, on average, 39.8 (SD 11.0) years old with a mean BMI of 34.2 (SD 4.9) kg/m2. Retention was high in both conditions, with 97% (31/32) retained in the Slip Buddy condition and 94% (30/32) retained in the calorie tracking condition. On average, participants used the Slip Buddy app on 53.8% (SD 31.3%) of days, which was not significantly different from those using the calorie tracking app (mean 57.5%, SD 28.4% of days), and participants who recorded slips (30/32, 94%) logged on average 17.9 (SD 14.4) slips in 12 weeks. The most common slips occurred during snack times (220/538, 40.9%). Slips most often occurred at home (297/538, 55.2%), while working (153/538, 28.4%), while socializing (130/538, 24.2%), or during screen time (123/538, 22.9%). The conditions did not differ in participants’ ratings of how their assigned app was tedious, taxing, or time consuming (all values of P>.05), but the calorie tracking condition gave their app higher helpfulness and usability ratings (all values of P<.05). Technical issues were the most common type of negative feedback, whereas simplicity was the most common type of positive feedback. Weight losses of ≥5% of baseline weight were achieved by 31% (10/32) of Slip Buddy participants and 34% (11/32) of calorie tracking participants. Conclusions Self-monitoring of dietary lapses and the contextual factors associated with them may be an alternative for people who do not prefer calorie tracking. Future research should examine patient characteristics associated with adherence to different forms of dietary self-monitoring. Trial Registration ClinicalTrials.gov NCT02615171; https://clinicaltrials.gov/ct2/show/NCT02615171

Performance Evaluation of a Hybrid Photovoltaic/Thermoelectric Generator System under Non-Tracking and Tracking Condition

In this study, the performance of the thermoelectric generator (TEG) modules in converting the excess photovoltaic (PV) temperature to electricity in a hybrid photovoltaic/thermoelectric (PV/TEG) generator system is investigated. A one-dimensional analytical heat transfer model for the hybrid PV/TEG with pin-fin based heat sink is developed in Engineering Equation Solver (EES) and simulated under tracking and non-tracking conditions. The solar irradiance collected by the PV/TEG system under tracking and non-tracking conditions was calculated using the Solar Emulator tool via TracePro software. The effect of varying solar radiation and a varying number of TEGs on PV temperature, PV output, and TEG output is evaluated using solar radiation on March 15, 2020, in the Jalan Taylor’s, Malaysia (3.0626° N, 101.6168° E). Finally, the optimum number of TEG modules required for maximum TEG power output in the hybrid PV/TEG system under tracking and non-tracking conditions is investigated and discussed. The maximum net PTEG is obtained for 336, 339, and 341 TEGs under no-tracking, single-axis, and dual-axis tracking conditions, respectively.

Slip Buddy App for Weight Management: Randomized Feasibility Trial of a Dietary Lapse Tracking App (Preprint)

BACKGROUND Although calorie tracking is one of the strongest predictors of weight loss in behavioral weight loss interventions, low rates of adherence are common. OBJECTIVE This study aims to examine the feasibility and acceptability of using the Slip Buddy app during a 12-week web-based weight loss program. METHODS We conducted a randomized pilot trial to evaluate the feasibility and acceptability of using the Slip Buddy app compared with a popular commercial calorie tracking app during a counselor-led, web-based behavioral weight loss intervention. Adults who were overweight or obese were recruited on the web and randomized into a 12-week web-based weight loss intervention that included either the Slip Buddy app or a commercial calorie tracking app. Feasibility outcomes included retention, app use, usability, slips reported, and contextual factors reported at slips. Acceptability outcomes included ratings of how helpful, tedious, taxing, time consuming, and burdensome using the assigned app was. We described weight change from baseline to 12 weeks in both groups as an exploratory outcome. Participants using the Slip Buddy app provided feedback on how to improve it during the postintervention focus groups. RESULTS A total of 75% (48/64) of the participants were female and, on average, 39.8 (SD 11.0) years old with a mean BMI of 34.2 (SD 4.9) kg/m2. Retention was high in both conditions, with 97% (31/32) retained in the Slip Buddy condition and 94% (30/32) retained in the calorie tracking condition. On average, participants used the Slip Buddy app on 53.8% (SD 31.3%) of days, which was not significantly different from those using the calorie tracking app (mean 57.5%, SD 28.4% of days), and participants who recorded slips (30/32, 94%) logged on average 17.9 (SD 14.4) slips in 12 weeks. The most common slips occurred during snack times (220/538, 40.9%). Slips most often occurred at home (297/538, 55.2%), while working (153/538, 28.4%), while socializing (130/538, 24.2%), or during screen time (123/538, 22.9%). The conditions did not differ in participants’ ratings of how their assigned app was tedious, taxing, or time consuming (all values of P>.05), but the calorie tracking condition gave their app higher helpfulness and usability ratings (all values of P<.05). Technical issues were the most common type of negative feedback, whereas simplicity was the most common type of positive feedback. Weight losses of ≥5% of baseline weight were achieved by 31% (10/32) of Slip Buddy participants and 34% (11/32) of calorie tracking participants. CONCLUSIONS Self-monitoring of dietary lapses and the contextual factors associated with them may be an alternative for people who do not prefer calorie tracking. Future research should examine patient characteristics associated with adherence to different forms of dietary self-monitoring. CLINICALTRIAL Clinicaltrials.gov NCT02615171; https://clinicaltrials.gov/ct2/show/NCT02615171.

Meaningful human control as reason-responsiveness: the case of dual-mode vehicles

In this paper, in line with the general framework of value-sensitive design, we aim to operationalize the general concept of “Meaningful Human Control” (MHC) in order to pave the way for its translation into more specific design requirements. In particular, we focus on the operationalization of the first of the two conditions (Santoni de Sio and Van den Hoven 2018) investigated: the so-called ‘tracking’ condition. Our investigation is led in relation to one specific subcase of automated system: dual-mode driving systems (e.g. Tesla ‘autopilot’). First, we connect and compare meaningful human control with a concept of control very popular in engineering and traffic psychology (Michon 1985), and we explain to what extent tracking resembles and differs from it. This will help clarifying the extent to which the idea of meaningful human control is connected to, but also goes beyond, current notions of control in engineering and psychology. Second, we take the systematic analysis of practical reasoning as traditionally presented in the philosophy of human action (Anscombe, Bratman, Mele) and we adapt it to offer a general framework where different types of reasons and agents are identified according to their relation to an automated system’s behaviour. This framework is meant to help explaining what reasons and what agents (should) play a role in controlling a given system, thereby enabling policy makers to produce usable guidelines and engineers to design systems that properly respond to selected human reasons. In the final part, we discuss a practical example of how our framework could be employed in designing automated driving systems.

Dynamic Characteristics of Rotors on Passive and Active Thrust Fluid-film Bearings with Fixed Pads

Application of fluid-film bearings in rotor machines in many cases could have no alternative due to obvious advantages when compared to roller element bearings. Integration of information technology in mechanical engineering resulting in emergence of a new field of research – mechatronic bearings which allowed tracking condition of the most important parts of a machine and adjusting operational parameters of the system. Application of servo valves to control the flow rate through a fluid-film bearing is the most universal and simple way of rotor’s position control due to relative simplicity of modelling and absence of need to radically change the design of conventional hydrodynamic bearings. In the present paper numerical simulations of passive (conventional as opposed to mechatronic) and active hybrid thrust fluid-film bearings with a central feeding chamber are presented, that are parts of a mechatronic rotor-bearing node. Numerical model of an active thrust bearing is based on solution of equations of hydrodynamics, rotor dynamics and an additional model of a servo valve. Various types of control have been investigated: P, PI and PID control, and the dynamic behaviour of a system has been estimated under various loads, namely static, periodic and impulse. A design of a test rig has been proposed to study passive and active thrust fluid-film bearings aimed at, among other, validation of numerical results of active bearings simulation.

Nonlinear Perfect Tracking Control for a Robot Arm with Uncertainties Using Operator-Based Robust Right Coprime Factorization Approach

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270001/06.jpg"" width=""300"" />Plant uncertainties compensation</div> In this paper, a robust nonlinear perfect tracking control for a robot arm with uncertainties is proposed by using operator-based robust right coprime factorization approach. In general, there exist unknown modelling errors in measuring structural parameters of the robot arm and external disturbances in real situations. In the present control system design, the effect of the modelling errors and disturbances on the system performance is considered to be uncertainties of the robot arm dynamics. Considering the uncertainties, a robust nonlinear perfect tracking control using operator-based robust right coprime factorization is investigated. That is, first, considering the unknown uncertain plant generates limitations in obtaining the so-called universal stability and tracking conditions, the effect of uncertain plant is compensated by designed operator-based feedback control scheme. Second, a new perfect tracking condition is proposed for improving the trajectory of the robot arm. Finally, the effectiveness of the designed system is confirmed by simulation results. </span>

INS-Assisted GNSS Signal Tracking Modeling and Assessment

To improve the tracking performance of GNSS receiver in signal-attenuated environments, phase lock loop (PLL) and delay lock loop (DLL) assisted with Inertial Navigation System (INS) measurements are considered. Combining inertial navigation principles with signal processing, this paper proposes a simplified but efficient mathematical model of INS-assisted second-order tracking loops. Compared with unaided GNSS receiver, the tracking behavior of INS-assisted receiver is quantitatively analyzed, and the kind of INS suitable to guarantee the tracking condition is determined. The results indicate that an INS with 1 deg/h gyro drift is necessary to support PLL, and MEMS inertial sensor with 100 deg/h gyro drift is sufficient to aid DLL to keep favored tracking ability.