Test-based Method Research Articles

Hybrid Evaluation Method of Bridge Bearing Capacity

This study proposes a new method to assess the bearing capacity of similar bridges while avoiding the disadvantages of costly static loading tests. First, we present a detailed evaluation of the bearing capacity for a repaired pre-stressed concrete continuous-beam bridge following a ship collision. We have developed a finite element model, modified it, and combined with two other methods to evaluate its bearing capacity. The first method proposed is the bridge design code-based method, where the bearing capacity is assessed using specified design parameters. The second is the field test-based method, where the bearing capacity is evaluated using field tests combined with structural appearance observation. Considering the relative merits of these two methods, a new and improved method for bearing capacity evaluation is proposed and implemented by combining the design code, finite element model, and field loading tests. The innovation and contribution of this paper lie in obtaining modal parameters through a convenient dynamic load test to predict the static behaviour of the bridge structure based on the modified finite element model. Based on the dynamic test results, the static behaviour of the bridge, predicted by the modified finite element analysis, and the appearance test data of the bridge structure, the bearing capacity of the bridge structure is evaluated.

HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} scores (average F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

Are adverse events (AEs) during first-line FOLFIRINOX (FFX) more frequent in real-world (RW) patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) who do not proceed to second-line therapy?

630 Background: mPDAC patients (pts) that tolerate 1L FFX may increase their survival with 2L therapy, most often gemcitabine + nab-paclitaxel (GnP). However, some pts discontinue treatment due to the toxic effects of 1L FFX and consequently experience disease progression and reduced survival. Reasons for discontinuation are typically not available in RW sources. To assess whether AEs are more frequent per unit time in pts that stop treatment after 1L, we calculated the RW incidence rates (IRs) of AEs during 1L treatment for two RW cohorts: mPDAC pts treated with 1L FFX and no 2L (FFX-only) vs. pts treated with 1L FFX and 2L GnP (FFX + GnP). Methods: This retrospective observational study utilized the Ontada Clinical Data View: Pancreatic Cancer database. Adult pts diagnosed with mPDAC between November 2018 and November 2021 who initiated treatment with 1L FFX ≤90 days from their metastatic diagnosis were included. Diarrhea, fatigue, nausea and vomiting, and neuropathy were available in the data as ungraded AEs abstracted from clinician notes. Hematologic and hepatic AEs were based on observed lab values and were graded according to the Common Terminology Criteria for AEs v5. Pts were required to have ≥1 year of follow-up unless death occurred first. We evaluated differences in IRs of AEs per pt-month of 1L treatment for statistical significance using the test-based method. Results: 587 pts met study inclusion criteria. 300 pts received FFX-only prior to death or end of follow-up, while 287 received FFX + GnP. For FFX-only vs. FFX + GnP pts, median age was 66 vs. 64 (IQR: 59–71 vs. 57–69), 57% in both groups were male, 70% vs. 69% were white, 55% vs. 62% had an ECOG of 0–1, and 8% vs. 3% had an ECOG of 2. Median 1L duration of therapy (DoT) was 2.1 (IQR: 0.7–5.0) vs. 4.6 months (IQR: 2.1–7.4). Among ungraded AEs, fatigue was more frequent per pt-month of 1L therapy for FFX-only vs. FFX + GnP pts (IR=0.037 vs. 0.027; p=.041). AEs based on lab values were higher per pt-month of 1L therapy for FFX-only vs. FFX + GnP pts for grade 3/4 thrombocytopenia (IR=0.012 vs. 0.006, p=.013), grade 1/2 anemia (IR=0.203 vs. 0.142, p<.001), grade 1/2 and 3/4 hypokalemia (IR=0.099 vs. 0.074, p<.001; IR=0.040 vs. 0.028, p=.010), grade 1/2 elevated ALT (IR=0.138 vs. 0.098, p<.001), grade 1/2 and 3/4 elevated AST (IR=0.150 vs. 0.109, p<.001; IR=0.018 vs. 0.011, p=.021), and grade 1/2 and 3/4 elevated ALP (IR=0.172 vs. 0.123, p<.001; IR=0.034 vs. 0.025, p=.035). IRs of all other AEs were numerically higher for FFX-only pts but differences were not statistically significant. Conclusions: A RW cohort of mPDAC pts receiving FFX-only experienced more AEs per pt-month of 1L and reduced likelihood of proceeding to subsequent lines of therapy compared to pts treated with FFX + GnP. Identifying predictive biomarkers may distinguish FFX non-responders and obviate AEs.

Serum lipidome unravels a diagnostic potential in bile acid diarrhoea

ObjectiveBile acid diarrhoea (BAD) is debilitating yet treatable, but it remains underdiagnosed due to challenging diagnostics. We developed a blood test-based method to guide BAD diagnosis.DesignWe included serum from 50...

Crowd-Sensing Enhanced Parking Patrol Using Sharing Bikes’ Trajectories

Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. The government highly relies on active human efforts to detect illegal parking events. However, such an approach is extremely ineffective to cover a large city since the police have to patrol over the entire city roads. The massive and high-quality sharing bike trajectories from Mobike offer us a unique opportunity to design a ubiquitous illegal parking detection approach, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. The detection result can guide the patrol schedule, i.e., send the patrol policemen to the region with higher illegal parking risks, and further improve the patrol efficiency. Inspired by this idea, three main components are employed in the proposed framework: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trajectory pre-processing</i> , which filters outlier GPS points, performs map-matching, and builds trajectory indexes; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">illegal parking detection</i> , which models the normal trajectories, extracts features from the evaluation trajectories, and utilizes a distribution test-based method to discover the illegal parking events; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">patrol scheduling</i> , which leverages the detection result as reference context, and models the scheduling task as a multi-agent reinforcement learning problem to guide the patrol police. Finally, extensive experiments are presented to validate the effectiveness of illegal parking detection, as well as the improvement of patrol efficiency.

A Simple, Test-Based Method to Control the Overestimation Bias in the Analysis of Potential Prognostic Tumour Markers

The early evaluation of prognostic tumour markers is commonly performed by comparing the survival of two groups of patients identified on the basis of a cut-off value. The corresponding hazard ratio (HR) is usually estimated, representing a measure of the relative risk between patients with marker values above and below the cut-off. A posteriori methods identifying an optimal cut-off are appropriate when the functional form of the relation between the marker distribution and patient survival is unknown, but they are prone to an overestimation bias. In the presence of a small sample size, which is typical of rare diseases, the external validation sets are hardly available and internal cross-validation could be unfeasible. We describe a new method to obtain an unbiased estimate of the HR at an optimal cut-off, exploiting the simple relation between the HR and the associated p-value estimated by a random permutation analysis. We validate the method on both simulated data and set of gene expression profiles from two large, publicly available data sets. Furthermore, a reanalysis of a previously published study, which included 134 Stage 4S neuroblastoma patients, allowed for the identification of E2F1 as a new gene with potential oncogenic activity. This finding was confirmed by an immunofluorescence analysis on an independent cohort.

Yield and Nutrient Uptake of T. Aman Rice as Influenced by Fertilizer Management and Preceding Crop Combinations in Vegetable-Vegetable-Rice Cropping Sequence

In recent years awareness of fertility status of soil and the nutrients requirement for crops according to cropping arrangement has increased. In this context, the study was carried out over 2 years (2018 and 2019) at the Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU) research farm to assess the influence of fertilizer management and preceding crop combinations on yield and yield contributing attributes of subsequent T. Aman rice; to determine the amount of nutrient concentration and taken up by the subsequent T. Aman rice regarding the preceding crops combinations and fertilizer treatments in vegetable–vegetable–rice cropping sequence. Treatments consisted of three prior crop combinations, i.e., before planting T. Aman rice (Potato-Stem Amaranth, Garlic-Stem Amaranth and Cauliflower-Stem Amaranth) including for each, four replications of two fertilizer treatments: commercial organic fertilizer (N based organic compost) and inorganic fertilizer (Soil test based inorganic fertilizer without cow dung). The subsequent T. Aman rice (BRRI dhan75) yields and yield contributing characters were not significantly affected by the interaction effect of preceding crop combinations and fertilizer application. Furthermore, fertilizer treatments had no significant effect on yields and yield components of T. Aman rice (BRRI dhan75) excluding plant height. Preceding crop combination had significant effect on grain panicle-1 of T. Aman rice (BRRI dhan75). At the end of two years, results clearly indicated that yield contributing parameters of BRRI dhan75 performed better under both preceding crop combinations as P2 (Garlic-Stem Amaranth) and P3 (Cauliflower-Stem Amaranth) with F2 (soil test based inorganic fertilizer management). Whereas yield was found greater under both preceding crop combination, P1 (Potato - Stem Amaranth) with F1 (N based organic compost alone) and P2 (Garlic-Stem Amaranth) using F2 (soil test based inorganic fertilizer). The interaction of preceding crop combination and fertilizer application demonstrated significant effect on nutrient concentration (Nitrogen, Phosphorus and Potassium) in grain and straw of T. Aman. The application of inorganic fertilizer using the soil test-based method significantly increased N uptake by grain under both preceding crop combination as P2 (Garlic-Stem Amaranth) and P3 (Cauliflower-Stem Amaranth) compared with a sole application of N based organic compost. Yet, it is too quick to create a decisive remark based on only these very few numbers of preceding crops combinations and fertilizer treatments in vegetable-vegetable-rice cropping sequence. Therefore, a detailed study with a huge number of organic and inorganic fertilizer treatments applied under large number of vegetable-vegetable-rice cropping sequence is obviously needed to reach a precise conclusion.

TBEM: Testing-Based GPU-Memory Consumption Estimation for Deep Learning

Deep Learning (DL) has been successfully implemented and deployed to various software service applications. During the training process of DL, a large amount of GPU computing resources is required, but it is difficult for developers to accurately calculate the GPU resources that the model may consume before running, which brings great inconvenience to the development of DL systems. Especially in today’s cloud-based model training. Therefore, it is very important to estimate the GPU memory resources that the DL model may use in a certain computing framework. Existing work has focused on static analysis methods to assess GPU memory consumption, highly coupled with the framework, and lack of research on low-coupled GPU memory consumption of the framework. In this article, we propose TBEM, which is a test-based method for estimating the memory usage of the DL model. First, TBEM generates enough DL models using an orthogonal array testing strategy and a classical neural network design pattern. Then, TBEM generates DL model tested in a real environment to obtain the real-time GPU memory usage values corresponding to the model. After obtaining the data of different models and corresponding GPU usage values, the data is analyzed by regression.

Group travel behavior in metro system and its relationship with house price

Modeling travel behavior inter-relationship is important to traffic planning and management as well as spread modeling of infectious disease and information. Our ability to understand inter-traveler behavior has remained unsatisfactorily limited due to the lack of empirical travel-together data. Taking advantage of access to a large-scale smart-card dataset from the Beijing metro network, we investigate a KS test-based method to identify group travel and study the spatial–temporal distribution of group travel as well as their interaction with socio-economic attributes. Origin and destination stations, departure time, and travel dates are separately classified into several types according to the passenger flow adopting a tensor factorization technique. The results suggest that temporally passengers tend to travel in groups in the afternoon and on the weekend; spatially areas with moderate house prices are the least source for group demands, while areas with a relatively low house price are the most favorite place for group trips. This novel discovery can help to understand the impact of socio-economic attributes on intra-urban group/passenger flow movement.

Accuracy Improvement of Stator Inductance Identification Method Based on Low-Frequency Current Injection for Three-Level NPC Inverter-Fed IM Drives in Locked-Rotor Standstill Condition

In this paper, a stator inductance identification process is proposed. The process is based on a three-level neutral-point-clamped (NPC) inverter-fed induction motor (IM) drive with a standstill condition. Previously, a low-speed alternating current (AC) injection test for stator inductance identification was proposed to overcome practical problems in conventional identification methods for three-level NPC inverter-based IM drives. However, the low-speed AC injection test-based identification method has some problems if a heavy load or mechanical brake is connected, as these can forcibly bring the rotor to a standstill during parameter identification. Since this low-speed testing-based identification assumes the motor torque is considerably lower in low-speed operations, some inaccuracy is inevitable in this kind of standstill condition. In this paper, the proposed current injection speed generator is based on the previously studied low-speed test-based stator inductance identification method, but the proposed approach gives more accurate estimates under the aforementioned standstill conditions. The proposed method regulates the speed for sinusoidal low-frequency AC injection on the basis of the instantaneous reactive and air-gap active power ratio. This proposed stator inductance identification method is more accurate than conventional fixed low-frequency AC signal injection identification method for three-level NPC inverter-fed IM drive systems with a locked-rotor standstill condition. The proposed method’s accuracy and reliability were verified by simulation and experiment using an 18.5 kW induction motor.

Optimizing the system-wide layout of sewage treatment plants based on enumeration and the orthogonal test.

Optimizing the locations of sewage treatment plants has enormous practical significance. In this study, a large-system mathematical model was developed for optimizing the locations of sewage treatment plants within a system and designing the associated pumping station pipe network. Head loss of pipe segments in the pipe network was the coupling constraint, the economic flow rate of pipe segments was determined by the feasible region constraints of decision variables, and the design variables were the sewage treatment plant locations, the design head of the pumping stations, the pipeline economic life, and the pipe diameter of divided pipe segments. The minimum total annual cost of the sewage treatment plant(s) and the pumping station pipe network was the objective function. A large-system quadratic orthogonal test-based selection method was used with a discrete enumeration comparison and selection method to determine pipeline economic life. A dynamic programming method was used to determine the pipe diameter of the divided pipe segments. By comparing the total annual cost of the sewage treatment plants and the associated pumping station pipe network corresponding to different pipeline economic lifetimes, the optimal solution that generates the minimum total annual cost can be identified. The sewage treatment plant and pumping station pipe network in Taizhou, China, was used as an example to compare and analyze optimization results. The new optimization method would have produced much lower annual cost than that of the existing system. This study provides valuable theoretical references for probing the layout design of urban sewage treatment plants corresponding to different pipeline economic lifetimes.

GLONASS FDMA data for RTK positioning: a five-system analysis

The use of the GLONASS legacy signals for real-time kinematic positioning is considered. Due to the FDMA multiplexing scheme, the conventional CDMA observation model has to be modified to restore the integer estimability of the ambiguities. This modification has a strong impact on positioning capabilities. In particular, the ambiguity resolution performance of this model is clearly weaker than for CDMA systems, so that fast and reliable full ambiguity resolution is usually not feasible for standalone GLONASS, and adding GLONASS data in a multi-GNSS approach can reduce the ambiguity resolution performance of the combined model. Partial ambiguity resolution was demonstrated to be a suitable tool to overcome this weakness (Teunissen in GPS Solut 23(4):100, 2019). We provide an exhaustive formal analysis of the positioning precision and ambiguity resolution capabilities for short, medium, and long baselines in a multi-GNSS environment with GPS, Galileo, BeiDou, QZSS, and GLONASS. Simulations are used to show that with a difference test-based partial ambiguity resolution method, adding GLONASS data improves the positioning performance in all considered cases. Real data from different baselines are used to verify these findings. When using all five available systems, instantaneous centimeter-level positioning is possible on an 88.5 km baseline with the ionosphere weighted model, and on average, only 3.27 epochs are required for a long baseline with the ionosphere float model, thereby enabling near instantaneous solutions.

Comparison of Data Mining Methods for the Signal Detection of Adverse Drug Events with a Hierarchical Structure in Postmarketing Surveillance.

There are several different proposed data mining methods for the postmarketing surveillance of drug safety. Adverse events are often classified into a hierarchical structure. Our objective was to compare the performance of several of these different data mining methods for adverse drug events data with a hierarchical structure. We generated datasets based on the World Health Organization’s Adverse Reaction Terminology (WHO-ART) hierarchical structure. We evaluated different data mining methods for signal detection, including several frequentist methods such as reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC), the likelihood ratio test-based method (LRT), and Bayesian methods such as gamma Poisson shrinker (GPS), Bayesian confidence propagating neural network (BCPNN), the new IC method, and the simplified Bayesian method (sB), as well as the tree-based scan statistic through an extensive simulation study. We also applied the methods to real data on two diabetes drugs, voglibose and acarbose, from the Korea Adverse event reporting system. Only the tree-based scan statistic method maintained the type I error rate at the desired level. Likelihood ratio test-based methods and Bayesian methods tended to be more conservative than other methods in the simulation study and detected fewer signals in the real data example. No method was superior to the others in terms of the statistical power and sensitivity of detecting true signals. It is recommended that those conducting drug‒adverse event surveillance use not just one method, but make a decision based on several methods.

Effectiveness of symmetric rejection for a secure and user convenient multistage biometric system

A multistage biometric verification system uses multiple biometrics and/or multiple biometric verifiers to generate a verification decision. The core of a multistage biometric verification system is reject option which allows a stage not to give a genuine/impostor decision when it is not confident enough. This paper studies the effectiveness of symmetric rejection for multistage biometric verification systems. The symmetric rejection method determines the reject region by symmetrically rejecting equal proportion of genuine and impostor scores. The applicability of a multistage biometric verification system depends on how secure and user convenient it is, which is measured by the performance–cost trade-off. This paper analyzes the performance–cost trade-off of symmetric rejection method by conducting extensive experiments. Experiments are performed on two biometric databases: (1) publicly available NIST database and (2) a keystroke database. In addition, the symmetric rejection method is empirically compared with two existing rejection methods: (1) sequential probability ratio test-based method, which uses score-fusion and (2) Marcialis et al.’s method, which does not use score fusion. Results demonstrate strong effect of symmetric rejection method on creating a secure and user convenient multistage biometric verification system.

A change-point detection and clustering method in the recurrent-event context

Change-point detection in the context of recurrent-event is a valuable analysis tool for the identification of the intensity rate changes. It has been an interesting topic in many fields, such as medical studies, travel safety analysis, etc. If subgroups exist, clustering can be incorporated into the change-point detection to improve the quality of the results. This paper develops a new algorithm named Recurrent-K-means to detect the change-points of the intensity rates and identify clusters of objects with recurrent events. It also proposes a test-based method to perform a heuristic search in determining the number of underlying clusters. In this study, the objects are assumed to fall in several clusters while the objects in the same cluster share identical change-points. The event count for an object is assumed to be a non-homogeneous Poisson process with a piecewise-constant intensity function. The methodology estimates the change-point as well as the intensity rates before and after the change-point for each cluster. The methodology establishes a clustering analysis based on K-means algorithm but enhances the procedure to be model based. The simulation study shows that the methodology performs well in parameter estimation and determination of the number of clusters in different scenarios. The methodology is applied to the UK coal mining disaster data to show its possible role in shaping government regulations and improving coal industry safety.

Seismic microzonation of soil amplification and liquefaction for Padang City

The magnitude 7.6 MW earthquake that occurred on 30 September 2009 in West Sumatera caused significant damages to buildings in the city of Padang related to the phenomenon of amplification and liquefaction. This paper presents the results of the assessment and mapping of amplification and liquefaction, carried out in the coastal area of Padang City. Mapping of soil amplification was carried out in 250 locations using the HVSR microtremor method. Meanwhile, evaluation of the potential for liquefaction was carried out in 95 locations using a cone penetration test-based method. Based on the analysis, Padang City has five seismic susceptibility zonations. Coastal areas, including the sub-districts of Koto Tangah, North Padang, West Padang, and South Padang, are located in high to very high susceptibility to soil amplification and liquefaction. These results are in agreement with the phenomenon of building damage due to amplification and liquefaction during the 2009 earthquake.

A variable-length compatible compression scheme based on tristate signals

The test data volume for the manufacturing test of chips is increasing rapidly. This is due to the fact that the complexity of these chips is increasing rapidly and the transistor count is increasing exponentially. Aimed at solving the problems of large test data volume and long test time in chips test, this paper presents a deterministic test-based compression method that uses tristate signals encoding and compatible test cube merging. Firstly, the partial input reduction is carried out for the original test set. The proportion of “don’t care” bits in the original test set is increased by merging compatible or inversely compatible inputs in the test cubes, so the compatibility among the test cubes is improved. Then, the test set is compressed and encoded by using tristate signal. Each test cube is divided into several data segments, which are encoded by tristate signals and multiple compatibility rules. This scheme can improve the compression ratio of the test set. The experimental results show that the proposed scheme achieves a good compression ratio, without excessive test power and area overhead. The average test compression ratio can reach 82.15%.

Incorporating Statistical Test and Machine Intelligence Into Strain Typing of Staphylococcus haemolyticus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry.

Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing of pathogenic S. haemolyticus is indispensable in modern public health infectious disease control, facilitating the identification of the origin of infections to prevent further infectious outbreak. Rapid identification enables the effective control of pathogenic infections, which is tremendously beneficial to critically ill patients. However, the existing strain typing methods, such as multi-locus sequencing, are of relatively high cost and comparatively time-consuming. A practical method for the rapid strain typing of pathogens, suitable for routine use in clinics and hospitals, is still not available. Matrix-assisted laser desorption ionization-time of flight mass spectrometry combined with machine learning approaches is a promising method to carry out rapid strain typing. In this study, we developed a statistical test-based method to determine the reference spectrum when dealing with alignment of mass spectra datasets, and constructed machine learning-based classifiers for categorizing different strains of S. haemolyticus. The area under the receiver operating characteristic curve and accuracy of multi-class predictions were 0.848 and 0.866, respectively. Additionally, we employed a variety of statistical tests and feature-selection strategies to identify the discriminative peaks that can substantially contribute to strain typing. This study not only incorporates statistical test-based methods to manage the alignment of mass spectra datasets but also provides a practical means to accomplish rapid strain typing of S. haemolyticus.

Combustion variation control strategy with thermal efficiency optimization for lean combustion in spark-ignition engines

A widespread consensus among internal combustion engine researchers is that higher thermal efficiency can be achieved with lean combustion. However, compared with a normal combustion mode (i.e., under a stoichiometric air-fuel ratio), combustion variation under lean operation is much more distinct, creating a bottleneck of thermal efficiency maximization. In this paper, a combustion variation control strategy that considers thermal efficiency optimization is proposed. This proposed strategy consists of two main components. One component focuses on diminishing combustion variation from cylinder to cylinder using a hypothesis test-based method. The other component provides the optimal value by searching for spark timing to maximize thermal efficiency. The effectiveness and performance of the proposed method are experimentally validated on a production spark-ignited gasoline engine test bench. From the experimental results, a notable combustion variation restrain performance with an average of 27% variation reduction is achieved using the proposed method. In addition, the self-optimization performance of spark timing under environmental changes is proven to be effective.

Resonance speed measurement of high-speed spindle using an instruction-domain-based approach

Although the manufacturing and assembling technology of the machine tool has become more and more mature, eccentricity of the spindle is still unavoidable and is an important source of spindle vibration. When the spindle rotation frequency is close or equal to the natural frequency of the spindle structure, spindle vibration will increase sharply, causing resonance (resonance speed). The high-speed spindle is more prone to cause resonance speed due to its wide speed range and large amount of centrifugal force. Machining at the resonance speed not only degrades the surface quality but also accelerates the degradation of the spindle performance. Aiming at this problem, this paper presents a G instruction-domain-based resonance speed measurement method. This method realizes the measurement by performing the spindle acceleration signal analysis in the instruction domain, which overcomes the shortcomings of the hammer impact test-based method (complicated operation), it is suitable for in situ application. The effectiveness of the proposed method was verified by hammering modal test and motor vibration test. The influence of resonance speed on the milling surface quality and the influence of different tools on the resonance speed was explored. Based on the proposed method, a portable spindle resonance speed measurement tool and a CNC system resonance speed avoidance module were developed.