Offset Adjustment Research Articles

Experimental study of DC excitation effect on the performance of moving coil linear compressor

In this paper, a novel Multi-loop Moving Coil Linear Compressor (M-MC-LC) is studied, in which the piston is driven by the Co-core Bilateral Reverse Coil (CBRC) in a reciprocating motion within the magnetic array. The DC excitation technique is introduced to address the phenomenon that the piston is axially offset under the influence of gas force. In order to clarify the effect of DC excitation on the performance of the moving-coil linear compressor, the compressor prototype is developed, and the dynamic performance testing test-bed and the refrigeration performance test-bed are built to qualitatively and quantitatively analyze the relevant performance parameters of the compressor from theoretical and experimental aspects. The results show that when the DC excitation accounts for less than 20% of the total input voltage, the pressure ratio increases with the increase of DC excitation; when the percentage is at 22.9%, a sudden drop in the volumetric efficiency of compressor occurs. Mass flow and COP increases first and then decreases with the increase of DC voltage excitation. The study results provide a guideline for the stable and reliable operation of the system and provide a valuable reference for the linear compressor piston offset adjustment.

Coordinated Control Method for Unequal-Cycle Adjacent Intersections Using Vehicle–Road Collaboration

In areas with significant changes in traffic demand and high vehicle dispersion at adjacent intersections, such as the surrounding roads of large shopping malls and schools, traffic problems are prone to occur. This is due to the unequal signal cycle lengths used at upstream and downstream intersections, which lead to periodic phase offsets as the cycles progress. To address this, we propose a multi-strategy integrated vehicle–road coordinated control method to tackle traffic flow operational issues caused by the offset characteristics of unequal-cycle adjacent intersections. A multi-strategy combined algorithm and control logic is established, which includes downstream intersection coordinated phase green extension, dynamic offset adjustment, and transitional queue speed guidance. The proposed method can substantially minimize the offset from falling into an incompatible threshold, effectively reducing queuing and early arrival of vehicles in the straight-through direction. It enables arriving vehicles to pass through the intersection without or with minimal stopping. Finally, the effectiveness of the method is validated using simulation experiments. A vehicle–road coordinated simulation verification platform was established, and comparative experiments were designed. The results indicate that the multi-strategy combined vehicle–road coordinated control method proposed in this paper, while ensuring the original through capacity for straight movements, can effectively reduce queue lengths, the number of stops, average vehicle delay, and travel time for single-direction straight lanes. This improvement enhances the efficiency of coordinated movements in the unequal–cycle adjacent intersections.

Implementation of hand‐object pose estimation using SSD and YOLOV5 model for object grasping by SCARA robot

AbstractEnforcement of advanced deep learning methods in hand‐object pose estimation is an imperative method for grasping the objects safely during the human–robot collaborative tasks. The position and orientation of a hand‐object from a two‐dimensional image is still a crucial problem under various circumstances like occlusion, critical lighting, and salient region detection and blur images. In this paper, the proposed method uses an enhanced MobileNetV3 with single shot detection (SSD) and YOLOv5 to ensure the improvement in accuracy and without compromising the latency in the detection of hand‐object pose and its orientation. To overcome the limitations of higher computation cost, latency and accuracy, the Network Architecture Search and NetAdapt Algorithm is used in MobileNetV3 that perform the network search for parameter tuning and adaptive learning for multiscale feature extraction and anchor box offset adjustment due to auto‐variance of weight in the level of each layers. The squeeze‐and‐excitation block reduces the computation and latency of the model. Hard‐swish activation function and feature pyramid networks are used to prevent over fitting the data and stabilizing the training. Based on the comparative analysis of MobileNetV3 with its predecessor and YOLOV5 are carried out, the obtained results are 92.8% and 89.7% of precision value, recall value of 93.1% and 90.2%, mAP value of 93.3% and 89.2%, respectively. The proposed methods ensure better grasping for robots by providing the pose estimation and orientation of hand‐objects with tolerance of −1.9 to 2.15 mm along x, −1.55 to 2.21 mm along y, −0.833 to 1.51 mm along z axis and −0.233° to 0.273° along z‐axis.

R-PointNet: Robust 3D Object Recognition Network for Real-World Point Clouds Corruption

Point clouds obtained with 3D scanners in realistic scenes inevitably contain corruption, including noise and outliers. Traditional algorithms for cleaning point cloud corruption require the selection of appropriate parameters based on the characteristics of the scene, data, and algorithm, which means that their performance is highly dependent on the experience and adaptation of the algorithm itself to the application. Three-dimensional object recognition networks for real-world recognition tasks can take the raw point cloud as input and output the recognition results directly. Current 3D object recognition networks generally acquire uniform sampling points by farthest point sampling (FPS) to extract features. However, sampled defective points from FPS lower the recognition accuracy by affecting the aggregated global feature. To deal with this issue, we design a compensation module, named offset-adjustment (OA). It can adaptively adjust the coordinates of sampled defective points based on neighbors and improve local feature extraction to enhance network robustness. Furthermore, we employ the OA module to build an end-to-end network based on PointNet++ framework for robust point cloud recognition, named R-PointNet. Experiments show that R-PointNet reaches state-of-the-art performance by 92.5% of recognition accuracy on ModelNet40, and significantly outperforms previous networks by 3–7.7% on the corruption dataset ModelNet40-C for robustness benchmark.

Single-cycle P Class phasor estimation based on harmonics whitening and off-nominal frequency offset adjustment

Phasor Measurement Units (PMUs) are essential for real-time power system monitoring and control. In this paper, a novel algorithm for protection (i.e., P Class) PMUs is presented. The proposed algorithm consists of four steps: preliminary narrowband disturbance whitening; estimation of the off-nominal fundamental frequency offset through the Interpolated Discrete Time Fourier Transform (IpDFT); data record length adjustment to refine narrowband disturbance whitening over an integer number of fundamental periods; time-domain estimation of the Taylor's series coefficients of the synchrophasor function through a weighted least-squares estimator (after correction of possible static off-nominal deviations). The key novel contribution of the proposed approach is its superior ability to smooth harmonic disturbances over shorter observation intervals than many existing algorithms (which typically require at least two-cycle-long data records to meet P Class accuracy requirements), while ensuring a good tracking capability of transient events with a reasonable execution time. Several simulation and experimental results confirm that, in the case of multi-harmonic distortion, the proposed approach returns more accurate results than both the basic Taylor-series-based Weighted Least-Squares (TWLS) estimator and other whitening-based techniques, while ensuring compliance with P Class specifications even over one-cycle-long data records.

Training and Tuning of Neuro - Fuzzy Control Laws for the Machining of Prosthetics

Adaptive machining is a process of performing real-time parameter updates based on changes in external operating conditions. With the advancements being made in the area of customised implants, this study is conducted to determine the utilisation of Artificial Neural Network (ANN) within a Computer Numerical Control (CNN) manufacturing cell to perform real-time tool offset adjustment in the manufacture of prosthetic knees. This study integrates smart sensor technology in the CNC machining cell, enabling the acquisition of force data for a critical tool used in the machining of a Tibial component. The collected time series data is used to compare performance between a random forest classification algorithm and Bi-directional Long Term Short Memory (LSTM) neural networks. Pre-processing improvements are presented for the random forest algorithm using a time series data conversion step. In the case of the Bi-directional LSTM model, 2D time series data is converted to a 3D array using a novel projection technique. The result of both techniques is then converted to real world tool offset value by implementing a fuzzy logic controller. Results are presented with recommendations to the move from synthetic to real training data for future deployments.

Control and symmetry breaking aspects of a geomagnetic field inversion model.

In this work, we consider the geomagnetic field inversion model proposed by Gissinger et al. [Europhys. Lett. 90(4), 49001 (2010)], where a quadratic term is added for symmetry control purposes. The resulting system is explored in both symmetric and asymmetric modes of operation. In the symmetric case, we report a bursting phenomenon and heterogeneous multistability of six and four different attractors. We show that the model owns an offset adjustment feature. In the asymmetric case, the model develops different phenomena, such as the coexistence of (four and three) asymmetric attractors, asymmetric (periodic and chaotic) bursting oscillation, and transient asymmetric bursting phenomenon. The effect of symmetry breaking is also manifested in the bubbles of bifurcation. It is shown that this system can leave from the multistable state to a monostable state by adjusting the coupling parameter of a linear controller. Moreover, microcontroller-based implementation of the system is considered to check the correctness of the numerical results.

Residential exposure to air pollution and adverse respiratory and allergic outcomes in children and adolescents living in a chipboard industrial area of Northern Italy

BackgroundChipboard production is a source of wood dust, formaldehyde, and combustion-related pollutants such as nitrogen dioxide (NO2) and particulate matter (PM). In this cohort study, we assessed whether exposures to NO2, formaldehyde, PM10, PM2.5, and black carbon were associated with adverse respiratory and allergic outcomes among all 7525 people aged 0–21 years residing in the Viadana district, an area in Northern Italy including the largest chipboard industrial park in the country. MethodsData on hospitalizations, emergency room (ER) admissions, and specialist visits in pneumology, allergology, ophthalmology, and otorhinolaryngology were obtained from the Local Health Unit. Residential air pollution concentrations in 2013 (baseline) were derived using local (Viadana II), national (EPISAT), and continental (ELAPSE) exposure models. Associations were estimated using negative binomial regression models for counts of events occurred during 2013–2017, with follow-up time as an offset term and adjustment for sex, age, nationality, and a census-block socio-economic indicator. ResultsMedian annual exposures to NO2, PM10, and PM2.5 were below the European Union annual air quality standards (40, 40, and 25 μg/m3) but above the World Health Organization 2021 air quality guideline levels (10, 15, and 5 μg/m3). Exposures to NO2 and PM2.5 were significantly associated with higher rates of ER pneumology admissions (13 to 30 % higher rates per interquartile range exposure differences, all p < 0.01). Higher rates of allergology and ophthalmology visits were found for participants exposed to higher pollutants' concentrations. When considering the 4-km buffer around the industries, associations with respiratory hospitalizations became significant, and associations with ER pneumology admissions, allergology and ophthalmology visits became stronger. Formaldehyde was not associated with the outcomes considered. ConclusionUsing administrative indicators of health effects a priori attributable to air pollution, we documented the adverse impact of long-term air pollution exposure in residential areas close to the largest chipboard industries in Italy. These findings, combined with evidence from previous studies, call for an action to improve air quality through preventive measures especially targeting emissions related to the industrial activities.

A DSP-Based EIT System With Adaptive Boundary Voltage Acquisition

Electrical Impedance Tomography (EIT), a valuable technique modality for estimating electrical properties of an object by electrode measurements. During the measurement process, high-frequency excitation signal and the large dynamic range of boundary voltage both cause negative effects on system detection accuracy. Focusing on the induced signal detection, we proposed an adaptive boundary voltage acquisition method, which thoroughly modeled the EIT system by LTSPICE and MATLAB, dynamically adjusted the boundary voltage amplitude gain according to the induced signal voltage range, and changed sampling period for high frequency component of excitation signal. A DSP-based EIT hardware experimental platform under development was constructed, including a current source integrated direct digital synthesis and data acquisition circuit. The data acquisition circuit composed of programmable differential amplifier, low-pass filter and offset adjustment circuit is used for signal acquisition. Comparative experiments are systematically implemented for evaluating the proposed method. Quantitatively, the SNR difference of each measurement channel is reduced from 26.42dB to 6.33dB. The maximum SNR of the system can reach 60.67dB. The maximum image error (IE) is reduced from 28.17% to 15.60%. Accordingly, the proposed adaptive boundary voltage acquisition method is proved to be great beneficial to improve EIT system accuracy.

Translated article] Comparison of the correction of the femoral offset after the use of a stem with modular neck and its monoblock homologue in total primary hip arthroplasty

Introduction and objectiveDual modularity stems were introduced with the theoretical advantage of restoring hip anatomy more precisely through femoral offset and limb length adjustment. Interchangeable necks allow for intraoperative angulation, anteversion and length changes. Our objective is to study whether a better femoral offset correction is achieved with the H MAX-M® prosthesis (Limacorporate, San Daniele, Italy) compared to its monoblock counterpart. Material and methodsA prospective cohort study was conducted by means of consecutive sampling on adult patients undergoing total hip arthroplasty with the diagnosis of coxarthrosis between January 2011 and December 2015. This cohort has two arms, one arm included patients who underwent modular neck arthroplasty and the other included patients who underwent monoblock total hip arthroplasty. Radiographic offset measurement of the operated hip and the contralateral hip was performed, and the difference between both values was calculated. The mean of the measurements obtained for each arm of the cohort were compared with each other. ResultsNo statistically significant differences were observed in the difference in offset between the operated hip and the contralateral hip (p=.323). No statistically significant differences were observed in the correction of the femoral offset, determined as the difference between the operated hip and the contralateral hip (p=.323). Nor were differences observed in the postoperative offset values (p=.097). It should be noted that for both designs, the majority group is the one with restored offset (p=.001).

Comparación de la corrección del offset femoral tras el empleo de un vástago monobloque y un vástago con cuello modular en la artroplastia total de cadera primaria

Introduction and objectiveDual modularity stems were introduced with the theoretical advantage of restoring hip anatomy more precisely through femoral offset and limb length adjustment. Interchangeable necks allow for intraoperative angulation, anteversion and length changes. Our objective is to study whether a better femoral offset correction is achieved with the H MAX-M® prosthesis (Limacorporate, San Daniele, Italy) compared to its monoblock counterpart. Material and methodsA prospective cohort study was conducted by means of consecutive sampling on adult patients undergoing total hip arthroplasty with the diagnosis of coxarthrosis between January 2011 and December 2015. This cohort has two arms, one arm included patients who underwent modular neck arthroplasty and the other included patients who underwent monoblock total hip arthroplasty. Radiographic offset measurement of the operated hip and the contralateral hip was performed, and the difference between both values was calculated. The mean of the measurements obtained for each arm of the cohort were compared with each other. ResultsNo statistically significant differences were observed in the difference in offset between the operated hip and the contralateral hip (P=.323). No statistically significant differences were observed in the correction of the femoral offset, determined as the difference between the operated hip and the contralateral hip (P=.323). Nor were differences observed in the postoperative offset values (P=.097). It should be noted that for both designs, the majority group is the one with restored offset (P=.001).

Correlating Time Series Signals and Event Logs in Embedded Systems.

In many embedded systems, we face the problem of correlating signals characterising device operation (e.g., performance parameters, anomalies) with events describing internal device activities. This leads to the investigation of two types of data: time series, representing signal periodic samples in a background of noise, and sporadic event logs. The correlation process must take into account clock inconsistencies between the data acquisition and monitored devices, which provide time series signals and event logs, respectively. The idea of the presented solution is to classify event logs based on the introduced similarity metric and deriving their distribution in time. The identified event log sequences are matched with time intervals corresponding to specified sample patterns (objects) in the registered signal time series. The matching (correlation) process involves iterative time offset adjustment. The paper presents original algorithms to investigate correlation problems using the object-oriented data models corresponding to two monitoring sources. The effectiveness of this approach has been verified in power consumption analysis using real data collected from the developed Holter device. It is quite universal and can be easily adapted to other device optimisation problems.

A low cost versatile differential preamplifier for electronic engineering

Acquisition of differential signals from low voltage and high impedance sources is difficult when using commonly available laboratory test equipment such as a standard oscilloscope or single ended acquisition system. This paper presents a versatile differential preamplifier for universal signal conditioning. The probe offers a wide input voltage range and can measure signals ranging from a few hundred micro-volts up to 100 V with 1 MHz bandwidth. It also contains a programmable output filter for bandwidth limitation and noise reduction, user-accessible controls for offset adjustment and input coupling mode selection. The probe can be powered through an USB port or by using an internal battery. Input impedance can be made virtually infinite by the JFET input stage, which makes this differential preamplifier usable with a variety of high-impedance sources commonly encountered in the sensors research field and in electronic engineering. Experimental validation shows that the proposed device has better performance levels and interoperability than commercially available instrumentation at a reduced cost, even while using off-the-shelf components.

A digital phase‐based on‐fly offset compensation method for decision feedback equalisers

A low-complexity method to reduce the offset voltage of dynamic comparators employed as samplers in decision feedback equalisers (DFE) is introduced. The authors propose the phase-domain offset reduction technique (PORT), which leverages an all-digital phase estimation of output data for offset compensation, without setting the comparator input to a common-mode voltage (VCM). While traditional techniques might break the data link for offset adjustment, the proposed technique allows calibrating the comparator on-the-fly. Measurements from a 26-dB-loss on-chip emulated channel with chip-scope capability validates PORT through eye-diagrams at sampler input. A prototype was implemented in a TSMC 130 nm 1.2 V process, and experimental results show the possibility of extending PORT to state-of-the-art technology nodes for multi-gigabit operation.

Fast overlap removal for location-related representation elements

In order to resolve the issues with overlapping elements in location-related applications, an element adjustment method is proposed. This adjustment method can be implemented using a non-iterative algorithm which can significantly improve the processing efficiency of overlap removal. The method first sorts out all the elements according to the distance from the preset Starting Point. Repulsive offset adjustment is used for circular elements. It can be validated through theoretical derivation. Finally, the influencing factors of algorithm parameters are analyzed. Suggestions are given to further optimization of the algorithm. Experimental results show that it can effectively remove overlapping. The relative positional relationship among elements can be preserved to the greatest extent. User’s verifications and expert’s evaluation show that it can also achieve high recognition rate between the geographic region and its representation element.

A flexible high input impedance AFE for measurement purposes

Abstract For precision voltage measurements, analog front-ends (AFEs) are often required to have a negligible influence over the potential of the measurement points. Also a high flexibility regarding the adjustment possibilities and the possibility to perform these adjustments remotely are necessary for automatic measurement operations. The paper presents a flexible AFE, meant for measurement purposes, having selectable preset gain coefficients, offset adjustment, dc/ac coupling selection and switchable current source. The AFE can be remotely configurable by means of digital control signals. The paper contains both a theoretical description and the results obtained during the assessment of the AFE. Results acquired while conditioning both small and large input signals are presented. The proposed AFE detains ±15 V dynamic ranges characteristic both to the input and output. It also has an input impedance much higher than 107 Ω. The obtained results show that the AFE can be employed for voltage, current and resistivity measurements.

Revision of Boore (2018) Ground-Motion Predictions for Central and Eastern North America: Path and Offset Adjustments and Extension to 200 m/s≤VS30≤3000 m/s

AbstractThe three sets of ground-motion predictions (GMPs) of Boore (2018; hereafter, B18) are compared with a much larger dataset than was used in deriving the predictions. The B18 GMPs work well for response spectra at periods between ∼0.15 and 4.0 s after an adjustment accounting for a path bias at distances beyond 200 km—this was the maximum distance used to derive the stress parameters on which the simulations in B18 are based. An additional offset adjustment is needed in the B18 predictions for short and long periods. The adjustment at short periods may be because the κ0 of 0.006 s stipulated by the Next Generation Attenuation-East (NGA-East) project to be used in deriving the GMPs is inconsistent with the observations on rock sites. The explanation for the offset adjustment at long periods is not clear, but it could be a combination of limitations of the point-source stochastic model for longer period motions, as well as a decreasing number of observations at longer periods available to constrain the simulations on which the predictions are based.The predictions of B18, developed for very-hard-rock sites (VS30 of 2000 and 3000 m/s), have here been extended down to VS30 values as low as 200 m/s. I find, as have others, that for a given VS30, there is generally less site amplification for central and eastern North America (CENA) than for the active crustal region dataset used for the Boore, Stewart, et al. (2014; hereafter, BSSA14) GMP equations. This might have an impact on conclusions of several previous studies of CENA GMPs that used the site amplifications in BSSA14 in comparing data and predictions.An additional finding is that the κ0 implied by recordings on a subset of stations in the Charlevoix region located on rock (data from these stations were not used in the analysis described earlier) is more consistent with a value near 0.014 s than the 0.006 s value used in B18 and the NGA-East project.

Fully Differential Chopper-Stabilized Multipath Current-Feedback Instrumentation Amplifier with R-2R DAC Offset Adjustment for Resistive Bridge Sensors

A fully differential multipath current-feedback instrumentation amplifier (CFIA) for a resistive bridge sensor readout integrated circuit (IC) is proposed. To reduce the CFIA’s own offset and 1/f noise, a chopper stabilization technique is implemented. To attenuate the output ripple caused by chopper up-modulation, a ripple reduction loop (RRL) is employed. A multipath architecture is implemented to compensate for the notch in the chopping frequency band of the transfer function. To prevent performance degradation resulting from external offset, a 12-bit R-2R digital-to-analog converter (DAC) is employed. The proposed CFIA has an adjustable gain of 16–44 dB with 5-bit programmable resistors. The proposed resistive sensor readout IC is implemented in a 0.18 μm complementary metal-oxide-semiconductor (CMOS) process. The CFIA draws 169 μA currents from a 3.3 V supply. The simulated input-referred noise and noise efficiency factor (NEF) are 28.3 nV/√Hz and 14.2, respectively. The simulated common-mode rejection ratio (CMRR) is 162 dB, and the power supply rejection ratio (PSRR) is 112 dB.

Quasi‐linear SVM classifier with segmented local offsets for imbalanced data classification

Within‐class imbalance problems often occur in imbalanced data classification, which worsen the imbalance distribution problem and increase the learning concept complexity. However, most existing methods for the imbalanced data classification focus on rectifying the between‐class imbalance problem, which is insufficient and inappropriate in many different scenarios. This paper proposes a simple yet effective support vector machine (SVM) classifier with local offset adjustment for imbalance classification problems. First, a geometry‐based partitioning method is modified for imbalanced datasets to divide the input space into multiple linearly separable partitions along the potential separation boundary. Then an F‐score‐based method is applied to obtain local offsets optimized on each local cluster. Finally, by constructing a quasi‐linear kernel based on the partitioning information, a quasi‐linear SVM classifier with local offsets is constructed for the imbalanced datasets. Simulation results on different real‐world datasets show that the proposed method is effective for imbalanced data classifications. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

THE UTILITY OF TBS-ADJUSTED BMD T-SCORE IN THE DISCRIMINATION OF THE MAJOR OSTEOPOROTIC FRACTURES IN THE POSTMENOPAUSAL WOMEN OF THE ROTTERDAM AND THE OSTEOLAUS STUDIES

Introduction Lumbar spine (LS) trabecular bone score (TBS) can be used to modify the output from FRAX to enhance fracture prediction. Leslie et al. (ASBMR 2017) proposed an alternative approach for using TBS in clinical practice based upon a risk-equivalent offset adjustment to the bone mineral density (BMD) T-score. The aim of this study is to test whether the TBS-adjusted BMD T-score (BMDTBS T-score) outperforms BMD T-score in the detection of major osteoporotic fractures (MOF). Methods Our study included 3,018 postmenopausal women (age 46-99 y) from the OsteoLaus (N=1,314) and the Rotterdam (N=1,704) Studies. All women had DXA scans (Hologic Discovery or GE-Lunar Prodigy) of LS and hip (TH). In total, 413 prevalent MOF were self-reported. BMDTBS T-score of femoral neck (FN), TH or LS were calculated using the formulas provided by Leslie et al..Contingency 2 × 2 tables were divided according to the WHO osteoporosis definition (BMD T-score ≤-2.5, or BMDTBS T-score≤-2.5) and according to the presence of prevalent MOF. Sensitivity and specificity of BMD T-score and BMDTBS T-score were then calculated. Additionally, a t-test was performed to study whether non-fractured women classified as osteoporotic by the BMDTBS T-score but not by the BMD T-score, had a higher FRAX probability for MOF. Results Women who fractured were older, had lower TBS, BMD T-score and BMDTBS T-score in all three regions. The results of the analysis on sensitivity and specificity are shown in Table 1. In general, the BMDTBS T-score showed a higher sensitivity than BMD T-score in MOF detection, whereas BMD T-score was more specific. Nevertheless, the increase in sensitivity of the BMDTBS T-score was superior to the decrease in specificity. The non- fractured women classified as osteoporotic by BMDTBS T-scorebut not by BMD T-score, had a significantly higher FRAX probability (all p Conclusion This study illustrates how the adjustment of BMD by TBS contributes to MOF discrimination by increasing the sensitivity. As BMD has been shown to have low sensitivity, this need in the field might be filled by the information provided by TBS. This approach considers both bone parameters and postulates itself as a helpful strategy to improve patient care in regions where intervention guidelines are based solely on the BMD T-score.