Data-driven Evaluation Method Research Articles

Markerless head motion tracking and event-by-event correction in brain PET

Objective. Head motion correction (MC) is an essential process in brain positron emission tomography (PET) imaging. We have used the Polaris Vicra, an optical hardware-based motion tracking (HMT) device, for PET head MC. However, this requires attachment of a marker to the subject’s head. Markerless HMT (MLMT) methods are more convenient for clinical translation than HMT with external markers. In this study, we validated the United Imaging Healthcare motion tracking (UMT) MLMT system using phantom and human point source studies, and tested its effectiveness on eight 18F-FPEB and four 11C-LSN3172176 human studies, with frame-based region of interest (ROI) analysis. We also proposed an evaluation metric, registration quality (RQ), and compared it to a data-driven evaluation method, motion-corrected centroid-of-distribution (MCCOD). Approach. UMT utilized a stereovision camera with infrared structured light to capture the subject’s real-time 3D facial surface. Each point cloud, acquired at up to 30 Hz, was registered to the reference cloud using a rigid-body iterative closest point registration algorithm. Main results. In the phantom point source study, UMT exhibited superior reconstruction results than the Vicra with higher spatial resolution (0.35 ± 0.27 mm) and smaller residual displacements (0.12 ± 0.10 mm). In the human point source study, UMT achieved comparable performance as Vicra on spatial resolution with lower noise. Moreover, UMT achieved comparable ROI values as Vicra for all the human studies, with negligible mean standard uptake value differences, while no MC results showed significant negative bias. The RQ evaluation metric demonstrated the effectiveness of UMT and yielded comparable results to MCCOD. Significance. We performed an initial validation of a commercial MLMT system against the Vicra. Generally, UMT achieved comparable motion-tracking results in all studies and the effectiveness of UMT-based MC was demonstrated.

Data-driven Evaluation Method for Cyber-Physical System Reliability of Integrated Energy System

Background:: In order to achieve the social goal of low carbon and high efficiency, integrated energy system (IES) with deep integration of cyber-physical systems (CPS) are being developed rapidly. The reliability evaluation is an important basis for IES planning and operation. Considering the deep coupling relationship between various systems, energy networks, and cyber networks, and the impact of multiple uncertainties on the integrated energy system, the traditional single index is difficult to quantify the reliability of the system, and the current evaluation methods rely on mechanism analysis and accurate modeling. It is difficult to deal with the coupling relationship between integrated energy systems, energy networks and information networks. So, the quantitative evaluation of the reliability level index of integrated energy systems under the cyber physical system is established, and a data-driven evaluation method is proposed. Methods:: In this article, the energy entropy and information entropy theory are combined to establish the reliability evaluation index of integrated energy system integration of cyber-physical systems, and a data-driven evaluation method based on random matrix theory (RMT) is proposed. Results:: A reliability evaluation model is established through RMT, and the single-ring law is used to analyze the statistical properties of the matrix model to identify the abnormal state of the system. Then the reliability of cyber and physical layers are evaluated based on energy entropy and information entropy, respectively. In addition, sensitivity analyses on the impact of node interruption probability, network topology, access load rate, and other factors are carried out. Conclusion:: The examples of IES are given to verify the feasibility and effectiveness of the proposed method.

Managing the pore system: Regenerating the functional pore spaces of natural soils by soil-health oriented farming systems

Transition towards soil-health sustaining crop production is at the core of climate change mitigation and adaptation efforts in agriculture. Improving soil conditions for resilience to variable and extreme weather conditions are among the main expectations of farmers implementing sustainable management practices. In this study, we evaluate whether the transition from conventional arable towards soil-health oriented farming systems restores soil pore characteristics towards non-arable natural soils and identify factors predominantly shaping different pore domains. Pore size distributions (PSD) at 20 sites with diverse soil types from adjacent fields of (1) standard arable, (2) soil health-oriented pioneer farming and (3) non-arable natural reference soils were measured. Based on parametric PSD descriptors, management-responsive functional pore classes and the role of textural, biochemical and structural drivers in different pore domains were analysed by a novel data-driven evaluation method. Parametric descriptors indicate an evolution from non-disturbed natural to intensively disturbed standard arable soils towards larger structural pores within a less heterogeneous structural domain. These shifts go along with less medium-sized storage pores, which overall was the most management sensitive pore fraction. Our novel data-driven approach confirmed a partial regeneration of natural pore characteristics with soil-health oriented practices. The most responsive pore domain was found in the range of ∼0.3–0.5 µm. The SOC-to-clay ratio was the predominant driver for structural porosity in both pioneer arable and natural reference soils. On the contrary, soil texture exerted the main control on the PSD pattern over the entire range of pore classes for standard farming systems. Overall, our study demonstrated that soil-health oriented pioneer farmers could effectively advance in managing functionally relevant pore domains for climate change resilience by promoting biological agents of structure formation.

Data-Driven Evaluation of Regional Innovation Capability

Regional innovation capability is considered an important driving force for the sustainable and high-quality development of regional economy. Therefore, data-driven evaluation method of regional innovation capability is proposed. First, the article collects regional innovation development statistics; second, min-max standardization method is used for dimensionless data processing, and anti-entropy method is used to calculate index weight; third, a composite system synergy degree model for objective and quantitative evaluation is built; finally, using Anhui Province as an example, the feasibility of the method was verified . The results indicate that the order and synergy degree of the innovation input and innovation output subsystems of Anhui Province have shown an upward trend from 2010 to 2019. However, innovation output has not been synchronized with innovation input. Therefore, suggestions for improving Anhui's regional innovation capability are proposed. This study provides theoretical and methodological support for evaluating and optimizing regional innovation capability.

Real-time anthropometric data-driven evaluation method for complex console layout design

A poor complex console layout can easily cause musculoskeletal diseases in operators, which reduces their comfort and efficiency. Existing evaluation methods rely heavily on the subjective experience of experts and do not fully consider the relevant data of operators. This paper explores an anthropometric data-driven evaluation method for designing complex console layouts that incorporates two artificial intelligence algorithms in tandem. Here an Anthropometric Data Extraction (ADE) algorithm was constructed to identify the operator’s limb angle and other related data in real time. A multi-stage Artificial Neural Network optimized by Genetic Algorithm (GA-ANN) was designed to accurately and quickly establish the non-mapping relationship between indicators. Taking the manned submersible console operation interface as an example, we evaluated and proved the effectiveness of the proposed method. The ADE algorithm achieved an accuracy of 87.7% in joint point recognition. The GA-ANN algorithm had a better fit and convergence speed. The results indicate that the method could help designers better consider the operator’s comfort and perform faster and more accurate design assessments. This could improve the efficiency of design and manufacturing and also provide support for managers in guiding rational posture and task optimization during work.

A Data-Driven Intelligent Prediction Approach for Collision Responses of Honeycomb Reinforced Pipe Pile of the Offshore Platform

The potential collision between the ship and the pipe piles of the jacket structure brings huge risks to the safety of an offshore platform. Due to their high energy-absorbing capacity, honeycomb structures have been widely used as impact protectors in various engineering applications. This paper proposes a data-driven intelligent approach for the prediction of the collision response of honeycomb-reinforced structures under ship collision. In the proposed model, the artificial neural network (ANN) is combined with the dynamic particle swarm optimization (DPSO) algorithm to predict the collision responses of honeycomb reinforced pipe piles, including the maximum collision depth (δmax) and maximum absorption energy (Emax). Furthermore, a data-driven evaluation method, known as grey relational analysis (GRA), is proposed to evaluate the collision responses of the honeycomb-reinforced pipe piles of offshore platforms. Results of the case study demonstrate the accuracy of the DPSO-BP-ANN model, with measured mean-square-error (MSE) of 5.06 × 10−4 and 4.35 × 10−3 and R2 of 0.9906 and 0.9963 for δmax and Emax, respectively. It is shown that the GRA method can provide a comprehensive evaluation of the performance of a honeycomb structure under impact loads. The proposed model provides a robust and efficient assessment tool for the safe design of offshore platforms under ship collisions.

A data-driven evaluation method for low-temperature performance of lithium-ion batteries

Energy storage system plays an important role in smoothing out the electricity supply from renewable energy and improving stability of the power system. At present, most energy storage systems are still battery energy storage systems (BESS). However, the time-varying temperature condition has a significant impact on discharge capacity of lithium-ion batteries. When lithium-ion battery operates in a low temperature environment, the discharge capacity of the battery decreases. Therefore, this paper develops a discharge capacity evaluation method for lithium-ion batteries at low temperature. Firstly, we analyze the battery discharge characteristics. On this basis, battery tests have been conducted and we proposed some health indicators. Finally input the measured data and health indicators into the machine learning model. The applicability and effectiveness of this method are analyzed through numerical results.

Data-driven evaluation of regional agricultural production efficiency for sustainable development

This study aims to improve regional agricultural production efficiency and promote sustainable agricultural development by presenting a data-driven evaluation method for regional agricultural production efficiency. Based on data collection and processing of regional agricultural input-output factors in Anhui Province, China, from 2014 to 2019, a data envelopment analysis Malmquist model is constructed for data modeling. Static analysis of regional agricultural production efficiency and production redundancy is conducted, and the dynamic change of regional agricultural production efficiency is measured. The results show that technical efficiency is the core driving factor for improving regional agricultural production efficiency. The findings indicate significant policy implications for improving agricultural production efficiency from the perspective of regional agricultural high-quality development. This study provides theoretical and methodological support for the sustainable development of regional agriculture.

A data-driven method for user satisfaction evaluation of smart and connected products

As user-designer interactive products, smart, connected products (SCPs) have been highly focused. User satisfaction, being an important indicator to judge the performance of products, might be discussed through online customer reviews in the era of Big Data. Therefore, this paper tries to assess user satisfaction of SCPs with the aid of online reviews and then proposes a novel textual data-driven evaluation method. We first mine core attributes and evaluations by topics extraction and sentiment analysis instead of market survey approaches. Combining frequency weights and position weights, we then introduce an integrated approach to obtain the final importance of attributes. Specifically, a new frequency-oriented formula and a new position score-based formula are defined to calculate frequency and position weights, separately. Entire and partial user satisfaction degrees are subsequently analyzed by probabilistic linguistic-regret theory-multiplicative multi-objective optimization by ratio analysis (PL-RT-MULTIMOORA) and a formula with weighted rejoicing–regret values, respectively. Finally, the designed decision-making method is successfully applied to evaluate user satisfaction of smart speakers. Some useful suggestions are given to customers and manufacturers in the light of entire and partial user satisfaction degrees.

3D Scanning and Model Error Distribution-Based Characterisation of Welding Defects

The inspection of welded structures requires particular attention due to many aspects that define the quality of the product. Deciding on the suitability of welds is a complex process. This work aims to propose a method that can support this qualification. This paper presents a state-of-the-art data-driven evaluation method and its application in the quality assessment of welds. Image processing and CAD modelling software was applied to generate a reference using the Iterative Closest Point algorithm that can be used to generate datasets which represent the model errors. The results demonstrate that the distribution of these variables characterises the typical welding defects. Based on the automated analysis of these distributions, it is possible to reduce the turnaround time of testing, thereby improving the productivity of welding processes.

Data-driven damage tracking and hysteresis evaluation of earthquake-excited structures with test validation

A data-driven damage tracking and hysteresis evaluation method for earthquake-excited structures that adopts the model-reference concept is presented in this paper. A series of target substructures with local interstory hysteresis is first decentralized from the global structure system adopting the Rayleigh damping model. A linear reference model, designed to be identified from nondestructive seismic events, is used to represent the desired healthy behaviors. The tracking error of structural responses with a substructure-level loading reconstruction is subsequently introduced to quantify the potential behavior deviation during a disastrous earthquake. Such a structural behavior deviation, in terms of a normalized form, has been proposed in a novel data-driven damage model; accordingly, a logistic framework has been presented for the quantification of structural damage of interests. Two illustrative examples, including a numerical simulation of a three degree-of-freedom degradation model and an experimental investigation of a large-scale reinforced concrete frame through a shaking table test, have been employed. Comprehensive results and detailed discussion are presented to illustrate the damage tracking and quantification performances of the proposed data-driven evaluation method.

Joint Optimization of Edge Computing Architectures and Radio Access Networks

Virtualized radio access network (vRAN) architectures and multiple-access edge computing (MEC) systems constitute two key solutions for the emerging Tactile Internet applications and the increasing mobile data traffic. Their efficient deployment, however, requires a careful design tailored to the available network resources and user demand. In this paper, we propose a novel modeling approach and a rigorous analytical framework, MEC-vRAN joint design problem (MvRAN), that minimizes vRAN costs and maximizes MEC performance. Our framework selects jointly the base-station function splits, the fronthaul routing paths, and the placement of MEC functions. We follow a data-driven evaluation method, using topologies of three operational networks and experiments with a typical face-recognition MEC service. Our results reveal that MvRAN achieves significant cost savings (up to 2.5 times) compared to non-optimized centralized RAN or decentralized RAN systems, and MEC pushes the vRAN functions to radio units and hence can increase substantially the network cost.

Model-reference damage tracking and evaluation of hysteretic structures with test validation

Abstract A data-driven damage tracking and evaluation method for earthquake-excited hysteretic structures is proposed in this study. The model reference tracking concept is introduced to quantify the shifts of the model status due to the damage developments, and a logistic framework is then designed with the employment on monitoring data from both healthy and damaged conditions of the instrumented structures. The linear behavior modeling of the objective structure is adopted for generating the reference signals and calculating the tracking errors of structural responses. A tracking error-based damage sensitive index in a normalized form is then presented for the simultaneous and combined quantification on the peak, cumulative and residual damage effects within the little knowledge on nonlinear characteristics. Comprehensive results from the numerical simulations of a single degree-of-freedom degradation model and the experimental investigations on several large-scale reinforced concrete columns are studied and discussed for the illustration of the tracking and quantification performances of the proposed data-driven evaluation method.

Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications.