Triggering Probability Research Articles

P-686 Optimal follicle size for triggering modified natural frozen embryo transfers

Abstract Study question What is the optimal follicle size threshold for performing trigger in modified natural cycle frozen embryo transfers (mNC-FET) cycles Summary answer The optimal follicle size threshold for performing trigger is 16 or 17, depending on the clinic’s preference to risk too early or too late triggering What is known already Recent studies reported higher fetal and maternal obstetrical complication rates are observed in artificial cycles compared to natural cycle frozen embryo transfers (NC-FET), making the natural cycle (NC) the preferred method. The modified natural cycle (mNC) provides a partial solution to overcome the disadvantages of the NC, mostly its repeated visits and inability to conveniently schedule the transfer. In a recently published large RCT, pregnancy rates between NC and mNC were similar, but many patients allocated to the mNC group had an LH surge prior to the hCG injection. Study design, size, duration A retrospective study consisting of 5,862 NC-FET was performed between 2018 and 2023 in a large tertiary hospital. Participants/materials, setting, methods Statistical analysis was conducted to determine the optimal follicle size threshold for triggering, for each threshold between 12-20, under the following assumptions: Clinic is closed on Sundays, ultrasound is initially performed on day 8 of the cycle and every 2 days afterwards, trigger is performed if the follicle size is at or above the threshold, unless clinic is closed 2 days after the current test day in which case no trigger was given. Main results and the role of chance The analysis measured 2 main outcomes for each trigger size threshold: The probability of triggering too early, defined as shifting the ovulation by 3 or more days. The probability of spontaneous LH surge to occur before triggering, possibly leading to transfer cancelation due to clinic closure on the designated transfer day. The traditional follicle size-based algorithm performed as follows: [Follicle size threshold]: ] Early trigger probability], [Closed day transfer probability] 12: 71.9%, 0.1% 13: 61.7%, 0.1% 14: 49.0%, 0.2% 15: 33.9%, 0.6% 16: 21.3%, 1.3% 17: 12.9%, 2.8% 18: 7.3%, 4.5% 19: 3.4%, 6.7% 20: 2.2%, 8.6% As can be seen from the results the optimal trigger threshold is either 16 or 17, depending on whether reducing transfers on days the clinic is closed has a higher importance than preventing a very early trigger day. Limitations, reasons for caution This was a retrospective study that performed statistical analysis to determine the probability of achieving each result based on the described logic. A prospective randomized clinical trial is warranted to validate the algorithm’s performance and consider variables that could not be accounted for, such as the patient’s endometrial width. Wider implications of the findings NC-FET protocols require the ability to adapt the transfer to clinics’ requirements. Given a traditional threshold-based methodology for triggering, a threshold of 16-17 is optimal depending on the risk preference of a clinic. Utilizing more advanced artificial intelligence algorithms based on ovulation prediction can significantly reduce both risk types. Trial registration number Not applicable

Neuron Sensitivity Guided Test Case Selection

Deep Neural Networks (DNNs) have been widely deployed in software to address various tasks (e.g., autonomous driving, medical diagnosis). However, they can also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal and repair incorrect behaviors in DNNs, developers often collect rich, unlabeled datasets from the natural world and label them to test DNN models. However, properly labeling a large number of datasets is a highly expensive and time-consuming task. To address the above-mentioned problem, we propose NSS, Neuron Sensitivity Guided Test Case Selection, which can reduce the labeling time by selecting valuable test cases from unlabeled datasets. NSS leverages the information of the internal neuron induced by the test cases to select valuable test cases, which have high confidence in causing the model to behave incorrectly. We evaluated NSS with four widely used datasets and four well-designed DNN models compared to the state-of-the-art (SOTA) baseline methods. The results show that NSS performs well in assessing the probability of failure triggering in test cases and in the improvement capabilities of the model. Specifically, compared to the baseline approaches, NSS achieves a higher fault detection rate (e.g., when selecting 5% of the test cases from the unlabeled dataset in the MNIST&LeNet1 experiment, NSS can obtain an 81.8% fault detection rate, which is a 20% increase compared with SOTA baseline strategies).

Association rule mining of aircraft event causes based on the Apriori algorithm

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events.

A multi-step attack path prediction method for oil & gas intelligence pipeline cyber physics system based on CPNE

Owing to the integration of the 5th Generation Mobile Communication Technology (5 G) and industrial internet construction, Oil & Gas intelligent pipeline network has witnessed accelerated construction and development. The Oil & Gas intelligent pipeline network proposed by China National Petroleum Corporation exhibits the advantages such as high integration, data unification, and intelligent decision-making, which helps to greatly reduce manual management costs, optimize risk decision-making by managers, and avoid risks such as human misuse. However, the highly integrated Cyber-physical system of the Internet and the Internet of Things faces various cyber security threats, and traditional intrusion detection systems exhibit drawbacks in the face of complex cyber-attack behaviors. In response to incomplete multi-step attack path prediction in traditional prediction methods, this paper proposes a multi-step attack path prediction method for Oil & Gas intelligence pipeline cyber physics system (CPS) based on Colored Petri nets and mixed strategy Nash equilibrium (CPNE). Taking into account the importance of assets and the dependency relationships between assets within the cyber-physical system of Oil & Gas intelligent pipeline network, CPNE constructs a node dependency model for the cyber-physical system of Oil & Gas intelligent pipeline network, simulates multi-step attack and defense games, calculates the probability of node failure triggering and transfer using mixed strategy Nash equilibrium, and extracts the risk of multi-step attack paths for different attack strategies. With the filtration and separation unit of a certain natural gas station as an example, this paper establishes a CPNE model to rank the risk of the first multi-step attack single attack source path and predict the risk of subsequent multi-step attack source paths, thus predicting multi-step attack paths. Taking three rounds of attacks as an example, compared to the paths predicted by traditional probability attack graph models, the proposed CPNE model is more advantageous in multi attack source path prediction, with a 2.33-fold increase in the relative coverage rate of path predictions.

Ways to improve the reliability of gas supply systems

Background: The concept of reliability of gas distribution systems implies the possibility of reliable transportation of the required amount of gas to the consumer in compliance with the specified parameters in normal operation for a certain period of time.
 Aim: The peculiarity of gas distribution networks is that they are long-term planning systems, with the growth of cities and the connection of consumers, the radius of their impact increases, expands, rebuilds, worn-out nodes and elements are replaced with new ones. The main goal at this stage of operation is the absence of significant reconstruction measures to improve the reliability of the system.
 Materials and methods: Work has been carried out to analyze the probability of triggering, the failure rate and the probability of failures of gas supply systems for single indicators.
 Results: To ensure the reliability and durability of gas boilers during replacement, the following 8 different measures were proposed.
 Conclusion: The article proposes a closed circuit, the introduction of network redundancy in the system by ringing or duplicating elements and considers the social nature of gas distribution systems.

Landslide susceptibility mapping using GIS Matrix Method and Frequency Ratio, application in the marly context of Moulay Yacoub Region, Morocco

In the recent decades, the growth of population, man-made facilities, infrastructures, and lifelines at the expense of landslides-prone areas has been responsible for an exponential increase in human and economic losses in many parts of the world. In the Moulay Yacoub region, where marly hills dominate, the interaction of the semi-urban and rural socioeconomic development and landslides significantly increases, which urges identifying and prioritizing areas of risk in order to maximize harm reduction and to avoid the disastrous outcomes as is the case of Moulay Yacoub town. This paper aims to develop a landslide susceptibility map in a highly affected sector of the province, where no previous landslide data have been produced, and to find the most predisposing parameters. This goal is attained using two robust methods for landslide susceptibility mapping, the Frequency Ratio and the GIS Matrix Method. Before that, the correlation of 11 predisposing factors was tested. The results show that the anthropogenic factors, particularly the agricultural practices, were highly involved, and the field investigations proved that cereal farming slopes are the most affected. The success rate was about 0.75 (75%) for both models showing good quality results for the two susceptibility maps. Therefore, the two models could be efficiently used, and the new agricultural projects located in landslide-prone areas of the province must include such reliable methods of landslide hazard analysis to minimize the risk, which would put human lives, ecosystems, food production, and infrastructure in threat.

The effect of coolant additives on the vapour explosion behaviour in melt droplet impingement experiments

Vapour explosions are a known hazard with potentially devastating consequences in several industries. They might occur when a hot liquid comes into direct contact with a cold and volatile liquid. Eutectic PbS-Cu2S droplet impingement experiments were performed to investigate the effect of the coolant composition on the vapour explosion behaviour. The composition was varied between water with systematic additions of NaCl, MEG, or a combination of both. The experiments were monitored with a hydrophone and a high-speed camera. Also, the resulting debris was sieved for further analysis. The investigation considered the trigger probability and explosion intensity. The latter was quantified via the size of the explosion, the height of the hydrophone signal, and the debris particle size. NaCl additions were found to increase both the trigger probability and the explosion intensity, whereas MEG additions were found to decrease the trigger probability and explosion intensity. However, combined additions of MEG and NaCl resulted in a decreasing trigger probability, while having an unclear effect on the explosion intensity.

An accurate circuit model of Ge/Si single photon avalanche diode

Ge/Si single-photon avalanche diodes (SPADs) have benefits of low after-pulsing probability, low-cost and compatibility with CMOS electronics compared to InGaAs/InP counterpart, thus presenting great potentials for imaging and optical quantum applications in short-wave infrared (SWIR). In this work, an equivalent circuit model mimicking the behavior of pseudo-planar Ge/Si SPAD is constructed to provide accurate electronic information for the co-design of front-end circuit. The DC properties are modelled by nonlinear resistors, voltage-controlled switches and voltage sources, and the AC characteristics are emulated by junction and stray capacitances. To accurately evaluate the statistical behavior, thermal excitation and trap-assisted tunneling are incorporated in the built SPAD circuit models, where three-level traps are adopted to effectively fit the after-pulsing probability. The key model parameters such as electric field profile, breakdown voltage and avalanche triggering probability, are extracted from 2D device simulation. The developed Ge/Si SPAD circuit model implemented in Verilog-A hardware description language (HDL) with a gated passive quenching/reset architecture is validated by circuit simulation platform. The simulation results are in good agreement with the previously reported measurement data, demonstrating the effectiveness and accuracy of our Ge/Si SPAD circuit model.

Estimation of historical earthquake-induced liquefaction in Fraser River delta using NBCC 2020 GMPEs in deterministic and probabilistic frameworks

Paleo-liquefaction features of sand dykes and sand blows were identified in the 1990s at multiple host sediments in the Fraser River delta in southern British Columbia all younger than 3500 BP. These paleo-liquefaction sites could be linked to Cascadia subduction earthquakes. Empirical magnitude-bound relationships are often used to estimate paleo-earthquake magnitudes. To determine the lower bound magnitude of Cascadia interface earthquakes that could have generated the paleo-liquefaction features, we use ground motion prediction equations for interface earthquakes from the sixth Canadian seismic hazard model of the 2020 National Building Code of Canada. We estimate the minimum M and its peak ground acceleration ( amax) of an interface earthquake required to initiate paleo-liquefaction in the study region. Starting with three full-rupture deterministic scenarios of varying source-to-site distance, we determine the minimum M required from Cascadia subduction zone interface mega-thrust earthquakes to induce liquefaction in the Fraser River delta spans 8.0–8.9 with a corresponding amax range of 0.09–0.13 g. We also perform a back-calculation paleo-liquefaction analysis in a probabilistic framework to incorporate aleatory uncertainties (cone penetration resistance, groundwater table, and amax) and epistemic uncertainties (liquefaction simplified model) via the Monte Carlo simulation. The developed probabilistic methodology is also applicable to a forward liquefaction assessment and other liquefaction sites globally. The median M from this probabilistic paleo-liquefaction for the four investigated sites lies between 8.8 and 9.0. Our probabilistic results also reveal that Cascadia interface earthquakes with M > 8.9 lead to a 31%–57% probability of liquefaction triggering in the Fraser River delta. In addition, we developed deterministic and probabilistic magnitude-bound curves specific to Cascadia interface earthquakes and representative site class E conditions. These curves provide more accurate magnitude estimations for predicting seismic-induced liquefaction from Cascadia interface earthquakes for sites in the Pacific Northwest than empirical bound curves.

The concept of event-size-dependent exhaustion and its application to paraglacial rockslides

Abstract. Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical concept that combines the decrease in disposition with the power-law distribution of rockslide volumes found in several studies. The concept starts from a given initial set of potential events, which are randomly triggered through time at a probability that depends on event size. The developed theoretical framework is applied to paraglacial rockslides in the European Alps, where available data allow for constraining the parameters reasonably well. The results suggest that the probability of triggering increases roughly with the cube root of the volume. For small rockslides up to 1000 m3, an exponential decrease in the frequency with an e-folding time longer than 65 000 years is predicted. In turn, the predicted e-folding time is shorter than 2000 years for volumes of 10 km3, so the occurrence of such huge rockslides is unlikely at the present time. For the largest rockslide possible at the present time, a median volume of 0.5 to 1 km3 is predicted. With a volume of 0.27 km3, the artificially triggered rockslide that hit the Vaiont reservoir in 1963 is thus not extraordinarily large. Concerning its frequency of occurrence, however, it can be considered a 700- to 1200-year event.

Quantifying the probability and uncertainty of multiple-structure rupture for Taiwan

This study identifies structure pairs with the potential for simultaneous rupture in a coseismic period and quantifies their rupture recurrence intervals. To assess the potential for a multiple-structure rupture, we calculated the probability of Coulomb stress triggering between seismogenic structures in Taiwan. We assumed that a multiple-structure rupture would occur if two structures could trigger each other by enhancing the plane with thresholds of a Coulomb stress increase and the distance between the structures and identified various sets of seismogenic structure pairs accordingly. We discussed the uncertainty of multiple-structure pair identification from various thresholds of stress change and structure distances, effective friction coefficient, and rotation of rake angles. To estimate the recurrence intervals for multiple-structure ruptures, we implemented a scaling law and the Gutenberg-Richter law in which the slip rate could be partitioned based on the magnitudes of the individual structure and multiple-structure ruptures. Considering that one structure may be involved in multiple cases of multiple-structure ruptures, we developed new formulas for slip partitioning in a complex fault system. By implementing the range of rupture area and slip rate of each structure, the magnitudes and recurrence intervals of multiple-structure ruptures could be estimated. We discussed the epistemic uncertainties of recurrence interval from deviations of slip rate and rupture area, various empirical formula of rupture parameters. The multiple-structure rupture with a larger characteristic magnitude would be crucial for the safety evaluation of infrastructures.

A Multifrequency Seamless Dual-Link Handover Scheme Based on Beamforming for High-Speed Railway

This paper proposes a multifrequency seamless dual-link handover scheme based on beamforming for the high-speed railway (HSR) communication, i.e., when the train travels to the edge of overlapping area of the adjacent two cells, a gain beam is assigned to the overlapping area by the target base station (BS) using a beamforming technique to cover the entire region to enhance the received signal strength (RSS) and handover opportunity. The switching antenna is allowed to handover multiple times to improve the traditional scheme and reduce the handover failure probability (HFP) greatly. In the process of signal reception, considering the impact of the intercell cochannel interference on the RSS, the proposed scheme uses signal to interference ratio (SIR) instead of the RSS to indicate the received signal quality to optimize handover mode. The handover success probability (HSP) is analyzed to describe the relationship between train location and the probability. We also establish a probabilistic model and corresponding handover algorithm for the proposed scheme to complete the handover operations and theoretically analyze a series of indexes including handover trigger probability (HTP), HFP, communication interruption probability (CIP), and HSP. Theoretical and experimental results show that the proposed scheme can effectively improve the HTP and HSP and greatly reduce the HFP and the CIP.

Detection performance improvement method of single-photon LiDAR by the combination of speckle suppression with adaptive adjustment of aperture diameter and spatial correlation method

The flash array imaging LiDAR with the laser transmitted by the multimode fiber (MMF), will get a low detection performance which is caused by the severe transmitting light field's speckle and high-noise intensity in the daylight condition. In this paper, a scheme of the combination of speckle suppression with adaptive adjustment of aperture diameter (AAAD) and spatial correlation method is proposed to improve the detection performance. In which the speckle suppression by vibrating the MMF with the optimal vibration parameters can effectively suppress the speckle, and the AAAD can help to get the optimal triggering probability, besides, the spatial correlation method can increase the detection number and suppress the speckle in a further step. Firstly, the proposed scheme is built based on the speckle suppression and single-photon triggering probability model. Secondly, the proposed scheme is verified by the theoretical analysis, and it can effectively improve the detection performance under the low SNR condition. Finally, the experimental verification under the high-noise intensity condition is carried out, and the speckle contrast is reduced from 1.247 to 0.057, which is decreased by 95.4%; the target recovery ratio or signal detection probability is increased from 28.8% to 97.7%, which is improved by 239.2%. This work is useful for the effective detection of the laser MMF coupling transmitted single-photon LiDAR under daylight conditions.

Advanced Back-Illuminated Silicon Photomultipliers With Surrounding P+ Trench

This study conducted the TCAD simulation of an advanced back-illuminated silicon photomultipliers (BI-SiPM) device. This BI-SiPM structure with pixels was surrounded by a p+ trench, which conducted the p+ implant region available on the surface toward the back side. The removal of the metal layer from the incident SiPM surface reduced the blocking of incident light and improved the fill factor of single pixels. Therefore, the photon detection efficiency of BI-SiPM was improved to approximately 70% when the bias voltage was 5 V. The TCAD device simulations were performed to study this BI-SiPM structure, and the quenching process based on Mixedmode emulator was investigated to introduce the BI-SiPM pixel structure into a SPICE circuit. The results indicated that the proposed design could improve the uniform distribution of the electric field and avalanche triggering probability on a photon illuminating surface and increase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FF</i> , without affecting the crosstalk between pixels. Moreover, the design consideration of manufacturing process flow for BI-SiPM was proposed.

Research on detection performance improvement of polarization GM-APD LiDAR with adaptive adjustment of aperture diameter and spatial correlation method

The triggering probability of GM-APD (Geiger-mode avalanche photodiode) is affected by the absolute signal intensity and the SNR (Signal-to-Noise Ratio). Through investigation, the polarization detection can effectively enhance the signal’s triggering probability by increasing the signal’s SNR; however, the triggering probability cannot reach the highest value at a certain SNR. In this paper, the polarization detection with the adaptive adjustment of aperture diameter (AAAD) and spatial correlation method was proposed to enhance the detection results, which could help to get the highest triggering probability, and the signal detection performance was obviously improved. Firstly, the theoretical model of the polarization detection with AAAD was built. Secondly, the effects of factors such as noise intensity, range, SNR, etc. on the triggering probabilities of the non-polarization detection, the polarization detection and the polarization detection with AAAD were analyzed; the theoretical analysis showed that the polarization detection with AAAD could effectively increase the triggering probability and the detection probability; besides, by introducing the spatial correlation method, the detection probability could be improved in a further step. Finally, the experimental verifications were carried out in the low SNR surroundings, the results showed that the combination of the polarization detection with AAAD and spatial correlation could enhance the signal detection probability by 80.8% of the initial detection probability, and the target restoration ratio could reach 96.9%. The research in this paper improves the daytime detection performance to a large extent, and it is very useful for the actual application of GM-APD LiDAR.

2-D Photon-Detection-Probability Simulation and a Novel Guard-Ring Design for Small CMOS Single-Photon Avalanche Diodes

CMOS single-photon avalanche diode (SPAD) array is a high-timing-resolution photon-counting image sensor. Pixel size shrinkage for high image resolution is highly desirable but hindered by the lowered photon-detection-probability (PDP) as the edge area becomes significant. We propose and demonstrate a parameter-free method for simulating PDP with the edge effect. Considering the doping profile, electric field, photon generation, and trigger probability distributions in two dimensions, the PDP reduction at the device edge is analyzed in a quantitative way. Besides, a novel guard-ring design for enhancing PDP of small SPAD has been proposed and simulated by our method. A threefold enhancement was obtained with the newly designed guard-ring structure compared with the original one. Our work is useful for developing small-pitch SPAD array for high-resolution imaging applications.

Physics-informed probabilistic models for peak pore pressure and shear strain in layered, liquefiable deposits

In this paper probabilistic models are developed for estimating the peak excess pore pressure ratio ([Formula: see text]) and peak shear strain ([Formula: see text]), which control liquefaction triggering, in layered granular soils under earthquake loading. The models are developed using non-linear regression applied to a database of 167 352 results from one-dimensional, non-linear, effective stress, site response analyses. A pseudo-parametric regression strategy is adopted to account for the theoretical upper bound on [Formula: see text] at a value of 1. The models include the influence of variables that are specific to the individual layer, to the soil profile and to the ground motion, as well as layer-to-layer interaction and the relationship between [Formula: see text] and [Formula: see text]. The variability around model predictions is decomposed, and the relative contributions of layer-, profile- and ground motion-specific parameters are evaluated. Epistemic uncertainty related to soil model selection, calibration and validation is also addressed. The total uncertainty around model predictions ranges from 0·5 to 1·6 in natural log units, with smaller values for scenarios that are of particular engineering interest (e.g. high [Formula: see text]). Finally, the proposed models are used to estimate the probability of liquefaction triggering for case studies from the 2010–2011 Canterbury earthquake sequence. The results are shown to compare favourably with existing deterministic and probabilistic methods in terms of their ability to distinguish between cases where liquefaction was observed in both the Darfield and Christchurch earthquakes and those where no liquefaction was observed in either event.

Disaster propagation in interdependent networks with different link patterns.

Disaster propagation in complex, interdependent, and multilayered networks has attracted considerable research interest in recent years. In this paper, we propose a model that combines two dynamic mechanisms, i.e., the spreading of failure in layer-dependent networks, where each node in a layer depends on one in another layer. We first investigate the robustness of the Erdős-Rényi (ER)-ER, scale-free (sf)-ER, and sf-sf pattern of interdependent networks against cascading failure with different probabilities of triggering, and then use the random link, assortative link, and disassortative link patterns between the networks to analyze the scope of propagation of failure. The numerical results show that with increasing probability of triggering, the number of damaged nodes in both layers increased and the robustness of the scale-free network to random failures decreased due to the interdependence. Regardless of the topological structure, the two layers eventually tended to have similar failure characteristics due to their interdependence. In addition, the different link patterns had a significant effect on enhancing disaster propagation in interdependent networks.

Decentralized LQ-Consistent Event-Triggered Control Over a Shared Contention-Based Network

Consider a network of multiple independent stochastic linear systems where, for each system, a scheduler collocated with the sensors arbitrates data transmissions to a corresponding remote controller through a shared contention-based communication network. While the systems are physically independent, their optimal controller design problems may, in general, become coupled, due to network contention, if the schedulers trigger transmissions based on state-dependent events. In this article, we propose a class of probabilistic admissible schedulers for which the optimal controllers, with respect to local standard linear-quardatic Gaussian (LQG) costs, have the certainty equivalence property and can still be determined decentrally. Then, two subclasses of scheduling policies within this class are introduced; a nonevent-based subclass, so called purely stochastic transmission (PST) policy, and an event-based subclass, both with easily adjustable triggering probabilities at every time step. We then prove that, given a PST policy with given triggering probabilities and an associated closed-loop performance with optimal control law, we can find an event-based scheduler from the proposed subclass and with the same triggering probabilities for which the associated closed-loop performance with optimal control law is strictly superior. Moreover, we show that, for each closed-loop system, the optimal state estimators for both scheduling policies follows a linear iteration. Finally, we provide a method to regulate the triggering probabilities of the schedulers by maximizing a network utility function.

Effectiveness Assessment of the Search-Based Statistical Structural Testing

Search-based statistical structural testing (SBSST) is a promising technique that uses automated search to construct input distributions for statistical structural testing. It has been proved that a simple search algorithm, for example, the hill-climber is able to optimize an input distribution. However, due to the noisy fitness estimation of the minimum triggering probability among all cover elements (Tri-Low-Bound), the existing approach does not show a satisfactory efficiency. Constructing input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation time. Tri-Low-Bound is considered a strong criterion, and it is demonstrated to sustain a high fault-detecting ability. This article tries to answer the following question: if we use a relaxed constraint that significantly reduces the time consumption on search, can the optimized input distribution still be effective in fault-detecting ability? In this article, we propose a type of criterion called fairness-enhanced-sum-of-triggering-probability (p-L1-Max). The criterion utilizes the sum of triggering probabilities as the fitness value and leverages a parameter p to adjust the uniformness of test data generation. We conducted extensive experiments to compare the computation time and the fault-detecting ability between the two criteria. The result shows that the 1.0-L1-Max criterion has the highest efficiency, and it is more practical to use than the Tri-Low-Bound criterion. To measure a criterion’s fault-detecting ability, we introduce a definition of expected faults found in the effective test set size region. To measure the effective test set size region, we present a theoretical analysis of the expected faults found with respect to various test set sizes and use the uniform distribution as a baseline to derive the effective test set size region’s definition.