Perfect Measurements Research Articles

On IMU preintegration: A nonlinear observer viewpoint and its applications

The inertial measurement unit (IMU) preintegration approach nowadays is widely used in various robotic applications. In this article, we revisit the preintegration theory and propose a novel interpretation to understand it from a nonlinear observer perspective, specifically the parameter estimation-based observer (PEBO). We demonstrate that the preintegration approach can be viewed as a recursive implementation of PEBO in moving horizons, and that the two approaches are equivalent with perfect measurements. We then discuss how these findings can be used to tackle practical challenges in estimation problems. As byproducts, our results lead to a novel hybrid sampled-data observer design and an approach to address statistical optimality for PEBO in the presence of noise.

Robustness of LiDAR-assisted controller towards measurement uncertainty

Many studies have concluded that LiDAR-assisted control can be used to increase annual energy production (AEP) as well as reduce extreme loads and fatigue damage on blades, tower, and main bearing components. However, most studies assume perfect LiDAR measurements and do not consider large measurement uncertainties on LiDAR measurements. In this study we evaluate the potential benefits in terms of fatigue load reduction from using a simple robust feedforward controller. In addition, we evaluate the impact of measurement uncertainties exemplified by a time shift between the expected time the wind hits the rotor and the actual time it hits the rotor as well as a mean wind speed measurement bias. Results shows that fatigue loads can be reduced up to 7% on the tower sections and up to 4% on other main components. However, these results only applies when the LiDAR is assumed to give perfect information about what is hitting the rotor plane. The load reduction degrades with the magnitude of a time shift of the arriving LiDAR wind measurements. A delay up to 2.5 seconds still gives reasonable load reductions. Furthermore, a positive wind speed bias does not affect the performance of the LiDAR-assisted controller, whereas a negative bias significantly deteriorates performance.

Combining Sound and Deep Neural Networks for the Measurement of Jump Height in Sports Science.

Jump height tests are employed to measure lower-limb muscle power of athletic and non-athletic populations. The most popular instruments for this purpose are jump mats and, in recent years, smartphone apps, which compute jump height through the manual annotation of video recordings and recently automatically using the sound produced during the jump to extract the flight time. In a previous work, the afore-mentioned sound systems were presented by the authors in which the take-off and landing events from the audio recordings of jump executions were obtained using classical signal processing. In this work, a more precise, noise-immune, and robust system, capable of working in the most unfavorable environments, is presented. The system uses a deep neural network trained specifically for this purpose. More than 300 jumps were recorded to train and validate the network performance. The ground truth was a jump mat, providing a slightly better accuracy in quiet and medium quiet environments but excellent accuracy in noisy and complicated ones. The developed audio-based system is a trustworthy instrument for measuring jump height accurately in any kind of environment, providing a perfect measurement tool that can be accessed through a mobile phone in the form of an app.

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce delays in large-scale MFD-based urban networks. For MFD-based PCRG, one needs to distinguish between the dynamics of (a) the plant that represents reality and is used as the simulation tool and (b) the model that contains easier-to-measure states than the plant and is used for devising controllers, that is, the model-plant mismatch should be considered. Traditional model-based methods (e.g., model predictive control (MPC)) require an accurate representation of the plant dynamics as the prediction model. However, because of the inherent network uncertainties, such as uncertain dynamics of heterogeneity and demand disturbance, MFD parameters could be time-varying and uncertain. Conversely, existing data-driven methods (e.g., reinforcement learning) do not consider the model-plant mismatch and the limited access to plant-generated data, for example, subregional OD-specific accumulations. Therefore, we develop an iterative adaptive dynamic programming (IADP)-based method to address the limited data source induced by the model-plant mismatch. An actor-critic neural network structure is developed to circumvent the requirement of complete information on plant dynamics. Performance comparisons with other PCRG schemes under various scenarios are carried out. The numerical results indicate that the IADP controller trained with a limited data source can achieve comparable performance with the “benchmark” MPC approach using perfect measurements from the plant. The results also validate the IADP’s robustness against various uncertainties (e.g., demand noise, MFD error, and trip distance heterogeneity) when minimizing the total time spent in the urban network. These results demonstrate the great potential of the proposed scheme in improving the efficiency of multiregion MFD systems. Funding: The work was jointly funded by the National Natural Science Foundation of China under [Grant 72071214] (R. Zhong), the Dit4Tram project from the European Union’s Horizon 2020 Research and Innovation Programme under [Grant 953783] (N. Geroliminis), and the Research Student Attachment Programme of The Hong Kong Polytechnic University (C. Chen). Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0091 .

A Dyson Brownian Motion Model for Weak Measurements in Chaotic Quantum Systems

We consider a toy model for the study of monitored dynamics in many-body quantum systems. We study the stochastic Schrödinger equation resulting from continuous monitoring with a rate Γ of a random Hermitian operator, drawn from the Gaussian unitary ensemble (GUE) at every time t. Due to invariance by unitary transformations, the dynamics of the eigenvalues {λα}α=1n of the density matrix decouples from that of the eigenvectors, and is exactly described by stochastic equations that we derive. We consider two regimes: in the presence of an extra dephasing term, which can be generated by imperfect quantum measurements, the density matrix has a stationary distribution, and we show that in the limit of large size n→∞ it matches with the inverse-Marchenko–Pastur distribution. In the case of perfect measurements, instead, purification eventually occurs and we focus on finite-time dynamics. In this case, remarkably, we find an exact solution for the joint probability distribution of λ’s at each time t and for each size n. Two relevant regimes emerge: at short times tΓ=O(1), the spectrum is in a Coulomb gas regime, with a well-defined continuous spectral distribution in the n→∞ limit. In that case, all moments of the density matrix become self-averaging and it is possible to exactly characterize the entanglement spectrum. In the limit of large times tΓ=O(n), one enters instead a regime in which the eigenvalues are exponentially separated log(λα/λβ)=O(Γt/n), but fluctuations ∼O(Γt/n) play an essential role. We are still able to characterize the asymptotic behaviors of the entanglement entropy in this regime.

Machine Learning the Dark Matter Halo Mass of Milky Way-Like Systems

Despite the Milky Way’s proximity to us, our knowledge of its dark matter halo is fairly limited, and there is still considerable uncertainty in its halo mass. Many past techniques have been limited by assumptions such as the Galaxy being in dynamical equilibrium as well as nearby galaxies being true satellites of the Galaxy, and/or the need to find large samples of Milky Way analogs in simulations.Here, we propose a new technique based on neural networks that obtains high precision (<0.14 dex mass uncertainty with perfect measurements of 30 neighboring galaxies; <0.14 dex including fiducial observational errors) without assuming halo dynamical equilibrium or that neighboring galaxies are all satellites, and which can use information from a wide variety of simulated halos (even those dissimilar to the Milky Way) to improve its performance. This method uses only observable information including satellite orbits, distances to nearby larger halos, and the maximum circular velocity of the largest satellite galaxy. In this paper, we demonstrate a proof-of-concept method on simulated dark matter halos; in future papers in this series, we will apply neural networks to estimate the masses of the Milky Way’s and M31’s dark matter halos, and we will train variations of these networks to estimate other halo properties including concentration, assembly history, and spin axis.

Development of Human Blood Circulation Board Media (PAPEDA) Oriented to Students' Mastery of Science Concepts

This research aims to produce human blood circulation board media (PAPEDA) based on validity, practicality and effectiveness regarding students' mastery of science concepts. This research is development research. The development model used is the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The research subjects were 25 grade 5 students at SDN 14 Cakranegara. The instruments used were validation sheets, teacher assessment sheets, student assessment sheets, and test questions for students' mastery of science concepts. The data analysis technique used is a validity test with the minimum criteria being in the valid category, then a practicality test based on the results of teacher assessment and student assessment with the minimum standards being in the practical type, and an effectiveness test with the criterion that the proportion of students who achieve the classical minimum completeness criteria is at least at 75%. This research shows that PAPEDA learning media meets valid criteria with an average expert assessment score of 84% with perfect measures. The results of field trials show that PAPEDA learning media meets practical and influential standards. The functional aspect assessment was obtained from teacher and student assessments, with average scores of 96% (efficient) and 81% (efficient). The effectiveness aspect is evaluated from the results of students' mastery of science concepts tests. The proportion of students who reach the minimum completeness criteria in the science concept mastery test is 80%, within the effective criteria. Thus, the learning media developed meets valid, practical and influential standards

Research on the Planning and Construction of National Industrial Metrology Center Park

Metrology refers to the activity of achieving unified physical and chemical units and ensuring accurate and reliable measurement values.In the field of metrology, we have always adhered to the principle that “Only accurate measurement can make perfect measurement”, and have clearly defined the important role of metrology in the manufacturing industry, then take “Only to create a fine, can be accurately measured” as the standard, the dialectical unity between the two. Different from the general sense of industrial architecture, the core task of industrial metrology park is “To build houses for precision equipment, to build laboratories for metrologists”, this has put forward many new requirements to the capital construction work from the planning, the design to the construction. With the successful acceptance of the park and put into use, this paper reviews and combs the construction process from the point of view of the pre-planning and design of the park, and puts forward a Shandong model for reference for the construction of similar projects.

Robo-advising: Optimal investment with mismeasured and unstable risk preferences

We develop a robo-advising framework that incorporates interactions with a client who has time-varying risk preferences that can be mismeasured. In the presence of measurement error, a client’s welfare decreases compared to perfect measurements. The worse the quality of the measurements (higher measurement volatility), the worse this is for the client. When measurements are more volatile, interacting frequently can lead to further losses in a client’s welfare. We include a client’s willingness to interact using a budget constraint. A trade-off arises between interacting frequently to obtain up-to-date information with more volatile measurements versus less up-to-date information with more accuracy. We find that integrating client-initiated interactions and using an average of measured risk aversions both lead to more personalised investment advice. Using an alternative investment strategy rather than a time-consistent strategy can lead to improved welfare for the client.

Coherent errors and readout errors in the surface code

We consider the combined effect of readout errors and coherent errors, i.e., deterministic phase rotations, on the surface code. We use a recently developed numerical approach, via a mapping of the physical qubits to Majorana fermions. We show how to use this approach in the presence of readout errors, treated on the phenomenological level: perfect projective measurements with potentially incorrectly recorded outcomes, and multiple repeated measurement rounds. We find a threshold for this combination of errors, with an error rate close to the threshold of the corresponding incoherent error channel (random Pauli-Z and readout errors). The value of the threshold error rate, using the worst case fidelity as the measure of logical errors, is 2.6%. Below the threshold, scaling up the code leads to the rapid loss of coherence in the logical-level errors, but error rates that are greater than those of the corresponding incoherent error channel. We also vary the coherent and readout error rates independently, and find that the surface code is more sensitive to coherent errors than to readout errors. Our work extends the recent results on coherent errors with perfect readout to the experimentally more realistic situation where readout errors also occur.

A state-of-the-art analysis of pharmacological delivery and artificial intelligence techniques for inner ear disease treatment

Over the last several decades, both the academic and therapeutic fields have seen significant progress in the delivery of drugs to the inner ear due to recent delivery methods established for the systemic administration of drugs in inner ear treatment. Novel technologies such as nanoparticles and hydrogels are being investigated, in addition to the traditional treatment methods. Intracochlear devices, which utilize current developments in microsystems technology, are on the horizon of inner ear drug delivery methods and are designed to provide medicine directly into the inner ear. These devices are used for stem cell treatment, RNA interference, and the delivery of neurotrophic factors and steroids during cochlear implantation. An in-depth analysis of artificial neural networks (ANNs) in pharmaceutical research may be found in ANNs for Drug Delivery, Design, and Disposition. This prediction tool has a great deal of promise to assist researchers in more successfully designing, developing, and delivering successful medications because of its capacity to learn and self-correct in a very complicated environment. ANN achieved a high level of accuracy exceeding 0.90, along with a sensitivity of 95% and a specificity of 100%, in accurately distinguishing illness. Additionally, the ANN model provided nearly perfect measures of 0.99%. Nanoparticles exhibit potential as a viable therapeutic approach for bacterial infections that are challenging to manage, such as otitis media. The utilization of ANNs has the potential to enhance the effectiveness of nanoparticle therapy, particularly in the realm of automated identification of otitis media. Polymeric nanoparticles have demonstrated effectiveness in the treatment of prevalent bacterial infections in pediatric patients, suggesting significant potential for forthcoming therapeutic interventions. Finally, this study is based on a research of how inner ear diseases have been treated in the last ten years (2012–2022) using machine learning.

Prior Kinematic Information Fusion for Pedestrian Localization With Toe-Heel-Shank MIMUs

With the development of the micro-electronic mechanical system (MEMS) inertial measurement units (IMU), the foot-mounted pedestrian inertial navigation system (PINS) based on the zero velocity update (ZUPT) is widely used. The systematic errors of MEMS IMU cause low precision of PINS at swing phase. However, the bio-mechanical constraints, which can be used to suppress the divergence of PINS, exist in lower limbs. In this study, three magnetometer and IMUs are fixed on the toe, heel and shank, respectively. The attitude of each segment is estimated by a MAHONY filter. The gait is defined as four successive phases. For each phase, the velocity and position constraints are constructed based on dynamic geometric constraint and Coriolis theory. Based on the perfect measurement method, the kinematic constraints, which are calculated by inertial data and attitude, are added into the observed equations. The noises are considered as additive Gaussian white noise. The linear Kalman filter is set to iteratively estimate the optimal positioning result. Several experiments with different walking velocities and trajectories are performed. The results show that the algorithm which is proposed in this study, greatly suppresses the velocity and positioning error.

A longitudinal study of infant 24-hour sleep: comparisons of sleep diary and accelerometer with different algorithms.

To longitudinally compare sleep/wake identification and sleep parameter estimation from sleep diaries to accelerometers using different algorithms and epoch lengths in infants. Mothers and other caregivers from the Nurture study (southeastern United States, 2013-2018) reported infants' 24-hour sleep in sleep diaries for 4 continuous days, while infants concurrently wore accelerometers on the left ankle at 3, 6, 9, and 12 months of age. We applied the Sadeh, Sadeh Infant, Cole, and Count-scaled algorithm to accelerometer data at 15 and 60 seconds epochs. For sleep/wake identification, we assessed agreement by calculating epoch-by-epoch percent agreement and kappas. We derived sleep parameters from sleep diaries and accelerometers separately and evaluated agreement using Bland-Altman plots. We estimated longitudinal trajectories of sleep parameters using marginal linear and Poisson regressions with generalized estimation equation estimation. Among the 477 infants, 66.2% were black and 49.5% were female. Agreement for sleep/wake identification varied by epoch length and algorithm. Relative to sleep diaries, we observed similar nighttime sleep offset, onset, and total nighttime sleep duration from accelerometers regardless of algorithm and epoch length. However, accelerometers consistently estimated about 1 less nap per day using the 15 seconds epoch, 70 and 50 minutes' shorter nap duration per day using the 15 and 60 seconds epoch, respectively; but accelerometers estimated over 3 times more wake after nighttime sleep onset (WASO) per night. Some consistent sleep parameter trajectories from 3 to 12 months from accelerometers and sleep diaries included fewer naps and WASOs, shorter total daytime sleep, longer total nighttime sleep, and higher nighttime sleep efficiency. Although there is no perfect measure of sleep in infancy, our findings suggest that a combination of accelerometer and diary may be needed to adequately measure infant sleep.

Farm biogas project considering carbon trading indicates promising economic results-a case study.

The application of the farm biogas project is a perfect measure to deal with the increasingly nervous global climate problem and energy crisis and can support the accomplishment of urgently needed carbon peaking and carbon neutrality effectively. But the poor economic benefits hinder its better development. Thus, this paper aims to enhance the economics of farm biogas projects and explore a win-win model for economic and environmental benefits by studying the economic impact of participation in carbon trading on three types of farm biogas projects. First, economic analysis of farm biogas projects based on life cycle cost is carried out using case analysis. Second, the greenhouse gas emission reductions are calculated and benefits from carbon trading are considered. Then, the economic robustness was tested. Specifically, the economics of all three types of farm biogas projects is improved after carbon trading. The ecological farm biogas project has the best economic performance, with a net present value of $551,689.11, internal rate of return of 49%, and payback period of 2.39years. In addition, the emission reductions of 5045.79 t CO2e, 7420.28 t CO2e, and 148.41 t CO2e are very significant. Based on these, suggestions for developing farm biogas projects and introducing biogas projects to carbon trading are put forward for the reference of governments and investors when making investments and reforms. According to the result, participation of farm biogas projects in carbon trading can effectively enhance economic benefits and accelerate the achievement of the greenhouse gas emission reduction target which is of great significance to the urgent goal of global green and low-carbon transformation.

Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability

Nanoparticles (NPs) are a promising alternative to antibiotics for targeting microorganisms, especially in the case of difficult-to-treat bacterial illnesses. Antibacterial coatings for medical equipment, materials for infection prevention and healing, bacterial detection systems for medical diagnostics, and antibacterial immunizations are potential applications of nanotechnology. Infections in the ear, which can result in hearing loss, are extremely difficult to cure. The use of nanoparticles to enhance the efficacy of antimicrobial medicines is a potential option. Various types of inorganic, lipid-based, and polymeric nanoparticles have been produced and shown beneficial for the controlled administration of medication. This article focuses on the use of polymeric nanoparticles to treat frequent bacterial diseases in the human body. Using machine learning models such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), this 28-day study evaluates the efficacy of nanoparticle therapy. An innovative application of advanced CNNs, such as Dense Net, for the automatic detection of middle ear infections is reported. Three thousand oto-endoscopic images (OEIs) were categorized as normal, chronic otitis media (COM), and otitis media with effusion (OME). Comparing middle ear effusions to OEIs, CNN models achieved a classification accuracy of 95%, indicating great promise for the automated identification of middle ear infections. The hybrid CNN-ANN model attained an overall accuracy of more than 0.90 percent, with a sensitivity of 95 percent and a specificity of 100 percent in distinguishing earwax from illness, and provided nearly perfect measures of 0.99 percent. Nanoparticles are a promising treatment for difficult-to-treat bacterial diseases, such as ear infections. The application of machine learning models, such as ANNs and CNNs, can improve the efficacy of nanoparticle therapy, especially for the automated detection of middle ear infections. Polymeric nanoparticles, in particular, have shown efficacy in treating common bacterial infections in children, indicating great promise for future treatments.

Perfect your infection control measures

Perfect your infection control measures

Gradient-based Cooperative Control of quasi-Linear Parameter Varying Vehicles with Noisy Gradients

This paper extends recent results on the exponential performance analysis of gradient based cooperative control dynamics using the framework of exponential integral quadratic constraints. A cooperative source-seeking problem is considered as a specific example where one or more vehicles are embedded in a strongly convex scalar field and are required to converge to a formation located at the minimum of a field. A subset of the agents are assumed to have the knowledge of the gradient of the field evaluated at their respective locations and the interaction graph is assumed to be uncertain. As a first contribution, we extend earlier results on linear systems to non-linear systems by using quasi-linear parameter varying representations. Secondly, we remove the assumption on perfect gradient measurements and consider multiplicative noise in the analysis. Performance-robustness trade off curves are presented to illustrate the use of presented methods for tuning controller gains. The results are demonstrated on a group of non-linear second order vehicles with a velocity-dependent non-linear damping and a local gain-scheduled tracking controller.

Exact Solution and Projection Filters for Open Quantum Systems Subject to Imperfect Measurements

In this letter, we consider an open quantum system undergoing imperfect and indirect measurement. For quantum non-demolition (QND) measurement, we show that the system evolves on an appropriately chosen manifold and we express the exact solution of the quantum filter equation in terms of the solution of a lower dimensional stochastic differential equation. In order to further reduce the dimension of the system under study, we consider the projection on the lower dimensional manifold originally introduced in Gao et al. (2019) for the case of perfect measurements. An error analysis is performed to evaluate the precision of this approximate quantum filter, focusing on the case of QND measurement. Simulations suggest the efficiency of the proposed quantum projection filter, even in presence of a stabilizing feedback control which depends on the projection filter.

Abstract P029: Proteomic age acceleration associated with cancer risk in the Atherosclerosis Risk in Communities (ARIC) study

Abstract Background: Individuals of the same chronological age may have a different pace of physiological dysregulation, so chronological age is not a perfect measure of the aging process. A measure of physiological aging – aging clock – has been proposed, including a novel proteomic aging clock (PAC). The deviation of PAC from chronological age is called proteomic age acceleration. PACs have been linked to age-related diseases, but, to our knowledge, no studies tested their association with cancer risk. We examined the associations of proteomic age acceleration for two PACs – a newly created PAC in ARIC (so called, “new” PAC) and the published 491-protein PAC by Lehallier et al [2020]) with cancer risk in the ARIC study. Methods: ARIC is a prospective cohort of White and Black women and men initiated in 1987, followed for cancer until 2015 (median follow-up=17.89 yrs). At Visit 2 (1990-92), about 5000 plasma proteins were measured in 10834 participants: 3347 who developed cancer during follow-up and 7487 who stayed cancer-free, using an aptamer-based assay SomaScan. Using elastic net regression (alpha=0.5), we constructed a new PAC in the training set of two-thirds randomly selected cancer-free participants. This PAC included 1282 proteins and was internally validated in the remaining one-third cancer-free participants (test set). We also constructed Lehallier’s PAC using weights estimated in ARIC. We calculated proteomic age acceleration as residuals after regressing PAC on chronological age. We used Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for overall cancer risk per 1 standard deviation (SD, ~2.7 yrs for age acceleration for both PACs) increase in age acceleration in participants after excluding the training set. HRs were adjusted for chronological age, sex, race, center, education, BMI, smoking status, alcohol intake, eGFR, diabetes, and aspirin use. We also estimated HRs for the most common cancers (breast, prostate, colorectal, and lung cancer). Results: In the test set, both PACs were correlated with chronological age [Pearson correlation coefficient (r): new PAC=0.82; Lehallier’s PAC=0.76] and with each other [r=0.90]. For both PACs, those with higher age acceleration were more likely to be White, aspirin users, and have diabetes and a lower eGFR. Age acceleration of the new PAC was significantly associated with overall cancer risk [per 1 SD: HR (95% CI)=1.04 (1.00-1.08)] but not Lehallier’s PAC. This association was not modified by sex, race, or smoking status. Age acceleration for both PACs were associated with lung cancer risk [HRs: new PAC=1.23 (1.12-1.36); Lehallier’s PAC=1.20 (1.09-1.32)] and remained among never smokers [HRs=1.40-1.42]. Age acceleration of the new PAC was also associated with colorectal cancer risk [HR=1.15 (1.02-1.30)]. Neither of age accelerations was associated with breast or prostate cancer risk. Conclusion: PAC holds promise as a potential biomarker for cancer risk. Funding: NHLBI, NCI, NPCR Citation Format: Shuo Wang, Anne H. Blaes, Josef Coresh, Corinne E. Joshu, James S. Pankow, Bharat Thyagarajan, Elizabeth A. Platz, Weihua Guan, Anna Prizment. Proteomic age acceleration associated with cancer risk in the Atherosclerosis Risk in Communities (ARIC) study. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P029.

Study on the influence of leisure sports consumption on the economic benefits of sports industry based on Logistic model

In the development of modern economic construction, as one of the positive industries, the sports industry has a positive impact on the social and economic innovation and development. According to the situation of leisure sports consumption in recent years, as the basic component of sports industry, this paper deeply explores the relationship between leisure sports consumption and sports industry economy, and can work out more effective measures for economic construction and development on the basis of solving the problems of leisure sports consumption under the new normal. Therefore, based on the understanding of leisure sports consumption and the economic development trend of the sports industry, this paper combined with the Logistic model mainly discusses the development status of the sports industry in a certain region, and then develops more perfect economic management measures.