Leveraging Automatic High-Level Synthesis Resource Sharing to Maximize Dynamical Voltage Overscaling with Error Control

Approximate Computing has emerged as an alternative way to further reduce the power consumption of integrated circuits (ICs) by trading off errors at the output with simpler, more efficient logic. So far the main approaches in approximate computing have been to simplify the hardware circuit by pruning the circuit until the maximum error threshold is met. One of the critical issues, though, is the training data used to prune the circuit. The output error can significantly exceed the maximum error if the final workload does not match the training data. Thus, most previous work typically assumes that training data matches with the workload data distribution. In this work, we present a method that dynamically overscales the supply voltage based on different workload distribution at runtime. This allows to adaptively select the supply voltage that leads to the largest power savings while ensuring that the error will never exceed the maximum error threshold. This approach also allows restoring of the original error-free circuit if no matching workload distribution is found. The proposed method also leverages the ability of High-Level Synthesis (HLS) to automatically generate circuits with different properties by setting different synthesis constraints to maximize the available timing slack and, hence, maximize the power savings. Experimental results show that our proposed method works very well, saving on average 47.08% of power as compared to the exact output circuit and 20.25% more than a traditional approximation method.

Investigation of the Damping Capacity of CFRP Raft Frames

In this paper, based on the composite laminated plate theory and a strain energy model, the damping capacity of a Carbon Fiber Reinforced Plastics (CFRP) raft frame was studied. According to the finite element analysis (FEA) and damping ratio prediction model, the influences of different layups on the damping capacity of the raft frame and its components (top/bottom plate and I-support) were discussed. Comparing the FEA results with the test results, it can be figured out that the CFRP laminate layup has a great influence on the damping ratio of the raft frame, and the maximum error of the first-order natural frequency and damping ratio of the top/bottom plate were 5.6% and 15.1%, respectively. The maximum error of the first-order natural frequency of the I-support between the FEA result and the test result was 7.5%, suggesting that because of the stress concentration, the error of the damping ratio was relatively large. As for the raft frame, the damping performance was affected by the I-support arrangement and the simulation analysis was in good agreement with the experimental results. This study can provide a useful reference for improving the damping performance of CFRP raft frames.

Risk-Adjusting Mortality in the Nationwide Veterans Affairs Healthcare System

Background The US Veterans Affairs (VA) healthcare system began reporting risk-adjusted mortality for intensive care (ICU) admissions in 2005. However, while the VA’s mortality model has been updated and adapted for risk-adjustment of all inpatient hospitalizations, recent model performance has not been published. We sought to assess the current performance of VA’s 4 standardized mortality models: acute care 30-day mortality (acute care SMR-30); ICU 30-day mortality (ICU SMR-30); acute care in-hospital mortality (acute care SMR); and ICU in-hospital mortality (ICU SMR). Methods Retrospective cohort study with split derivation and validation samples. Standardized mortality models were fit using derivation data, with coefficients applied to the validation sample. Nationwide VA hospitalizations that met model inclusion criteria during fiscal years 2017–2018(derivation) and 2019 (validation) were included. Model performance was evaluated using c-statistics to assess discrimination and comparison of observed versus predicted deaths to assess calibration. Results Among 1,143,351 hospitalizations eligible for the acute care SMR-30 during 2017–2019, in-hospital mortality was 1.8%, and 30-day mortality was 4.3%. C-statistics for the SMR models in validation data were 0.870 (acute care SMR-30); 0.864 (ICU SMR-30); 0.914 (acute care SMR); and 0.887 (ICU SMR). There were 16,036 deaths (4.29% mortality) in the SMR-30 validation cohort versus 17,458 predicted deaths (4.67%), reflecting 0.38% over-prediction. Across deciles of predicted risk, the absolute difference in observed versus predicted percent mortality was a mean of 0.38%, with a maximum error of 1.81% seen in the highest-risk decile. Conclusions and Relevance The VA’s SMR models, which incorporate patient physiology on presentation, are highly predictive and demonstrate good calibration both overall and across risk deciles. The current SMR models perform similarly to the initial ICU SMR model, indicating appropriate adaption and re-calibration.

Evaluation of thermal couple impedance model of power modules for accurate die temperature estimation up to 200 ◦C

This paper presents an experimental evaluation of the thermal couple impedance model of power modules (PMs), in which Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field-Effect-Transistor (MOSFET) dies are implemented. The model considers the thermal cross-coupling effect, representing the temperature rise of a die due to power dissipations by the other dies in the same PM. We propose a characterization method to obtain the thermal couple impedance of the SiC MOSFET-based PMs for model accuracy. Simulation based on the proposed model accurately estimates the measured die temperature of three PMs with different die placements. The maximum error between measured and simulated die temperatures is within 8.1 ◦C in a wide and practical operation range from 70 ◦C to 200 ◦C. The thermal couple impedance model is helpful to design die placements of high power PMs considering the thermal cross-coupling effect.

Macro-strain based deflection and neutral axis position monitoring of reinforced concrete beams during corrosion

This article proposes a new technique of monitoring neutral axis positions and deflection of Reinforced Concrete (RC) beam during corrosion of steel reinforcement using macro-strain measurements of distributed long-gauge sensors. A different group of distributed long-gauge Packaged Carbon Fiber Line (PCFL) sensors with self-compensation and effective packaging system is installed on the compression and tension fibers of the concrete surface and steel reinforcements of RC beam to verify the proposed method experimentally. An accelerated corrosion method utilizing a salt solution and the constant current was used to achieve the required corrosion levels. The estimated deflection measured by the developed method is compared with the results using Linear Variable Displacement Transducer (LVDTs). It has been demonstrated that long-gauge PCFL sensors could provide the same accuracy. The distributed measured strains were utilized to evaluate the deterioration of the structure’s health with the advance of corrosion. Based on corrosion monitoring experimental results, it can be confirmed that using distributed PCFL sensors mounted on steel reinforcements or concrete surface, the locations and progress of the damage with corrosion time can be detected effectively. The maximum error in the estimated deflection from PCFL sensors mounted on the concrete surface compared to the LVDTs before the onset and after 24 h of accelerated corrosion was 0.5% and 2.5%, respectively.

Experimental and Numerical Analysis of Flow Behavior for Reverse Circulation Drill Bit with Inserted Swirl Vanes

A swirling drill bit designed with an integrated vane swirler was developed to improve reverse circulation in down-the-hole hammer drilling. Its entrainment effect and influential factors were investigated by CFD simulation and experimental tests. The numerical results exhibit reasonable agreement with the experimental data, with a maximum error of 13.68%. In addition, the structural parameters of the swirler were shown to have an important effect on the reverse circulation performance of the drill bit, including the helical angle and number of spiral blades, swirler outlet area, and the flushing nozzles. The optimal parameters for the swirling drill bit without flushing nozzles include a helical angle of 60°, four spiral blades, and the area ratio of 2, while it is about 30°, 3, and 3 for the drill bit with flushing nozzles. Moreover, the entrainment ratio of the drill bit without flushing nozzles can be improved by nearly two times compared with one with flushing nozzles under the same conditions.

Integrated Temperature and Position Sensors in a Shape-Memory Driven Soft Actuator for Closed-Loop Control

Soft actuators are a promising option for the advancing fields of human-machine interaction and dexterous robots in complex environments. Shape memory alloy wire actuators can be integrated into fiber rubber composites for highly deformable structures. For autonomous, closed-loop control of such systems, additional integrated sensors are necessary. In this work, a soft actuator is presented that incorporates fiber-based actuators and sensors to monitor both deformation and temperature. The soft actuator showed considerable deformation around two solid body joints, which was then compared to the sensor signals, and their correlation was analyzed. Both, the actuator as well as the sensor materials were processed by braiding and tailored fiber placement before molding with silicone rubber. Finally, the novel fiber-rubber composite material was used to implement closed-loop control of the actuator with a maximum error of 0.5°.

Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.

Research on Co-Estimation Algorithm of SOC and SOH for Lithium-Ion Batteries in Electric Vehicles

The accurate estimation of the state of charge (SOC) and state of health (SOH) is of great significance to energy management and safety in electric vehicles. To achieve a good trade-off between real-time capability and estimation accuracy, a collaborative estimation algorithm for SOC and SOH is presented based on the Thevenin equivalent circuit model, which combines the recursive least squares method with a forgetting factor and the extended Kalman filter. First, the parameter identification accuracy is studied under a dynamic stress test (DST) and the federal urban driving schedule (FUDS) test at different ambient temperatures (0 °C, 25 °C, and 45 °C). Secondly, the FUDS test is used to verify the SOC estimation accuracy. Thirdly, two batteries with different aging degrees are used to validate the proposed SOH estimation algorithm. Subsequently, the accuracy of the SOC estimation algorithm is studied, considering the influence of updating the SOH. The proposed SOC estimation algorithm can achieve good performance at different ambient temperatures (0 °C, 25 °C, and 45 °C), with a maximum error of less than 2.3%. The maximum error for the SOH is less than 4.3% for two aged batteries at 25 °C, and it can be reduced to 1.4% after optimization. Furthermore, calibrating the capacity as the SOH changes can effectively improve the SOC estimation accuracy over the whole battery life.

A New Model for Predicting the Maximum Gas Entrapment Concentration in a Yield Stress Fluid

Summary Gas entrapment is a typical phenomenon in gas-yield stress fluid two-phase flow, and most of the related research focuses on the entrapped condition of the single bubble. However, the amount of entrapped gas, which is more meaningful for engineering, is rarely involved. In this paper, a theoretical model for calculating the maximum gas entrapment concentration (MGEC) is established for the first time. The critical distance between horizontal and vertical entrapped bubbles was determined by the yielded region caused by the buoyancy and the coupled stress field around the multiple bubbles. The MGEC is the ratio of a single bubble volume to its domain volume, which is calculated from the distance between the vertical and the horizontal bubbles. By comparing with the experimental results, the average error of MGEC calculated by this model is 4.42%, and the maximum error is 7.32%. According to the prediction results of the model, an empirical equation that can be conveniently used for predicting MGEC is proposed.