Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

A Linear Programming-Based Reinforcement Learning Mechanism for Incomplete-Information Games

Dear Editor,Recently, with the development of artificial intelligence, game intelligence decision-making has attracted more and more attention.In particular, incomplete-information games(IIG) have gradually become a new research focus, where players make decisions without sufficient information, such as the opponent's strategies or preferences.

Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.

Influence of Incomplete Soil Plugs on Bearing Capacities of Bucket Foundations in Clay

Due to the uneven seabed and heaving of soil during pumping,incomplete soil plugs may occur during the installation of bucket foundations,and the impacts on the bearing capacities of bucket foundations need to be evaluated.In this paper,the contact ratio(the ratio of the top diameter of the soil plug to the diameter of the bucket)and the soil plug ratio(the ratio of the soil heave height to the skirt height)are defined to describe the shape and size of the incomplete soil plug.Then,finite element models are established to investigate the bearing capacities of bucket foundations with incomplete soil plugs and the influences of the contact ratios,and the soil plug ratios on the bearing capacities are analyzed.The results show that the vertical bearing capacity of bucket foundations in homogeneous soil continuously improves with the increase of the contact ratio.However,in normally consolidated soil,the vertical bearing capacity barely changes when the contact ratio is smaller than 0.75,while the bearing capacity suddenly increases when the contact ratio increases to 1 due to the change of failure mode.The contact ratio hardly affects the horizontal bearing capacity of bucket foundations.Moreover,the moment bearing capacity improves with the increase of the contact ratio for small aspect ratios,but hardly varies with increasing contact ratio for aspect ratios larger than 0.5.Consequently,the reduction coefficient method is proposed based on this analysis to calculate the bearing capacities of bucket foundations considering the influence of incomplete soil plugs.The comparison results show that the proposed reduction coefficient method can be used to evaluate the influences of incomplete soil plug on the bearing capacities of bucket foundations.

Reliability-BasedModel for Incomplete Preventive ReplacementMaintenance of Photovoltaic Power Systems

At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under-repair of equipment.Therefore,a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed.First,a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment,and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle.Then,based on the reliability as a constraint,the average maintenance cost and availability of the equipment are considered,and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs,each maintenance cycle and the replacement cycle of the PV power generation system components.Next,the inverter of a PV power plant is used as a research object.The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold,Through model comparison,when the optimal maintenance strategy is(0.80,4),the average maintenance cost of this paper’s model are decreased by 20.3%and 5.54%and the availability is increased by 0.2395% and 0.0337%,respectively,compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold.Therefore,this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.

Pharmacodynamic Experiment of Dachengqi Decoction and Separated Decoction on Incomplete Intestinal Obstruction in Rats

[Objectives]To investigate the effect and mechanism of Dachengqi Decoction and separated decoction on incomplete intestinal obstruction in rats.[Methods]80 healthy SD rats were selected to establish incomplete intestinal obstruction model by silk ligation.The dosage was 20 mL/kg for 3 d,and the damage index of ileocecal mucosa was analyzed;the morphology of ileocecal mucosa was observed by HE staining;the serum levels of IL-1α,IL-1β,IL-6,IL-18,Ach,NO,ET,IL-1,TNF-αand ultra-micro Na+-K+-ATPase were detected by ELISA.[Results]Compared with the model group,the mucosal damage index of Dachengqi Decoction and each separated decoction group decreased significantly(P<0.05);compared with the normal group and sham operation group,the serum level of IL-1,IL-6,TNF-αand other factors in the model group increased significantly(P<0.05);compared with the model group,the serum IL-1,IL-6 and TNF-αsecretion levels of rats in Dachengqi Decoction group and separated decoction group decreased(P<0.01).[Conclusions]Dachengqi Decoction and each separated decoction can effectively improve intestinal tissue pathological damage in the incomplete intestinal obstruction model rats,and reduce the inflammatory reaction in the rat body.

A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable requirements.However, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency.Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

Disease networks. Uncovering disease-disease relationships through the incomplete interactome

According to the disease module hypothesis,the cellular components associated with a disease segregate in the same neighborhood of the human interactome,the map of biologically relevant molecular interactions.Yet,given the incompleteness of the interactome and the limited knowledge of disease-associated genes,it is not obvious if the available data have sufficient coverage to map out modules associated with each disease.

Disease networks. Uncovering disease-disease relationships through the incomplete interactome

According to the disease module hypothesis,the cellular components associated with a disease segregate in the same neighborhood of the human interactome,the map of biologically relevant molecular interactions.Yet,given the incompleteness of the interactome and the limited knowledge of disease-associated genes,it is not obvious if the available data have sufficient coverage to map out modules associated with each disease.Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases.For example,diseases with overlapping network modules show significant coexpression patterns,symptom similarity,and comorbidity,whereas diseases residing in separated network neighborhoods are phenotypically distinct.These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases,even if they do not share primary disease genes.

Design of PID controller with incomplete derivation based on differential evolution algorithm

To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution （DE） algorithm is presented. The controller is called DE-PID controller. To overcome the disadvantages of the integral performance criteria in the frequency domain such as IAE, ISE, and ITSE, a new performance criterion in the time domain is proposed. The optimization procedures employing the DE algorithm to search the optimal or near optimal PID controller parameters of a control system are demonstrated in detail. Three typical control systems are chosen to test and evaluate the adaptation and robustness of the proposed DE-PID controller. The simulation results show that the proposed approach has superior features of easy implementation, stable convergence characteristic, and good computational efficiency. Compared with the ZN, GA, and ASA, the proposed design method is indeed more efficient and robust in improving the step response of a control system.

Factors associated with incomplete small bowel capsule endoscopy studies

AIM:To identify patient risk factors associated with incomplete small bowel capsule endoscopy(CE) studies.METHODS:Data from all CE procedures performed at St.Paul's Hospital in Vancouver,British Columbia,Canada,between December 2001 and June 2008 were collected and analyzed on a retrospective basis.Data collection for complete and incomplete CE study groups included patient demographics as well as a number of potential risk factors for incomplete CE including indication for the procedure,hospitalization,diabetes mellitus with or without end organ damage,limitations in mobility,renal insufficiency,past history of bowel obstruction,abdominal surgery,abdominal radiation therapy and opiate use.Risk factors were analyzed using a univariable and multivariable logistic regression model.RESULTS:From a total of 535 CE procedures performed,158 were incomplete(29.5%).The univariable analysis showed that CE procedures performed for overt gastrointestinal bleeding(P = 0.002),and for patients with a prior history of abdominal surgery(P = 0.023) or bowel obstruction(P = 0.023) were significantly associated with incomplete CE studies.Patients on opiate medications(P = 0.094) as well as hospitalized patients(P = 0.054) were not statistically significant,but did show a trend towards incomplete CE.The multivariable analysis showed that independent risk factors for an incomplete CE procedure include prior history of bowel obstruction [odds ratios(OR) 2.77,P = 0.02,95% confidence intervals(CI):1.17-6.56] and procedures performed for gastrointestinal bleeding(Occult OR 2.04,P = 0.037,95% CI:1.04-4.02 and Overt OR 2.69,P = 0.002,95% CI:1.44-5.05).Patients with a prior history of abdominal surgery(OR 1.46,P = 0.068,95% CI:0.97-2.19),those taking opiate medications(OR 1.54,P = 0.15,95% CI:0.86-2.76) and hospitalized patients(OR 1.82,P = 0.124,95% CI:0.85-3.93) showed a trend towards statistical significance.CONCLUSION:We have identified a number of risk factors for incomplete CE procedures that can be used to risk-stratify patients and guide interventions to improve completion rates.

Factors associated with incomplete colonoscopy at a Japanese academic hospital

AIM: To evaluate significant risk factors for incomplete colonoscopy at a Japanese academic hospital.

Fault detection and diagnosis for data incomplete industrial systems with new Bayesian network approach

For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.

CONSISTENCY OF MLE OF THE PARAMETER OF EXPONENTIAL LIFETIME DISTRIBUTION FOR RANDOM CENSORING MODEL WITH INCOMPLETE INFORMATION

In this paper, we have discussed a random censoring test with incomplete information, and proved that the maximum likelihood estimator(MLE) of the parameter based on the randomly censored data with incomplete information in the case of the exponential distribution has the strong consistency.

Analysis of Incomplete Data of Accelerated Life Testing with Competing Failure Modes

Data obtained from accelerated life testing （ALT） when there are two or more failure modes, which is commonly referred to as competing failure modes, are often incomplete. The incompleteness is mainly due to censoring, as well as masking which might be the case that the failure time is observed, but its corresponding failure mode is not identified. Because the identification of the failure mode may be expensive, or very difficult to investigate due to lack of appropriate diagnostics. A method is proposed for analyzing incomplete data of constant stress ALT with competing failure modes. It is assumed that failure modes have s-independent latent lifetimes and the log lifetime of each failure mode can be written as a linear function of stress. The parameters of the model are estimated by using the expectation maximum （EM） algorithm with incomplete data. Simulation studies are performed to check＇model validity and investigate the properties of estimates. For further validation, the method is also illustrated by an example, which shows the process of analyze incomplete data from ALT of some insulation system. Because of considering the incompleteness of data in modeling and making use of the EM algorithm in estimating, the method becomes more flexible in ALT analysis.

Multi-criteria decision-making approach with incomplete certain information based on ternary AHP

It is not uncommon in multiple criteria decision-making that the numerical values of alternatives of some criteria are subject to imprecision, uncertainty and indetermination and the information on weights of criteria is incomplete certain. A new multiple criteria decision- making method with incomplete certain information based on ternary AHP is proposed. This improves on Takeda＇s method. In this method, the ternary comparison matrix of the alternatives under each pseudo-criteria is constructed, the eigenvector associated with the maximum eigenvalue of the ternary comparison matrix is attained as to normalize priority vector of the alternatives, then the order of alternatives is obtained by solving two kinds of linear programming problems. Finally, an example is given to show the feasibility and effectiveness of the method.

Finding the solution for incomplete small bowel capsule endoscopy

AIM:To evaluate whether the use of real time viewer(RTV)and administration of domperidone to patients with delayed gastric passage of the capsule could reduce the rate of incomplete examinations(IE)and improve the diagnostic yield of small bowel capsule endoscopy(SBCE).METHODS:Prospective single center interventional study,from June 2012 to February 2013.Capsule location was systematically checked one hour after ingestion using RTV.If it remained in the stomach,the patient received 10 mg domperidone per os and the location of the capsule was rechecked after 30 min.If the capsule remained in the stomach a second dose of10 mg of domperidone was administered orally.After another 30 min the position was rechecked and if the capsule remained in the stomach,it was passed into the duodenum by upper gastrointestinal(GI)endoscopy.The rate of IE and diagnostic yield of SBCE were compared with those of examinations performed before the use of RTV or domperidone in our Department(control group,January 2009-May 2012).RESULTS:Both groups were similar regarding age,sex,indication,inpatient status and surgical history.The control group included 307 patients,with 48(15.6%)IE.The RTV group included 82 patients,with3(3.7%)IE,P=0.003.In the control group,average gastric time was significantly longer in patients with IE than in patients with complete examination of the small bowel(77 min vs 26 min,P=0.003).In the RTV group,the capsule remained in the stomach one hour after ingestion in 14/82 patients(17.0%)vs 48/307(15.6%)in the control group,P=0.736.Domperidone did not significantly affect small bowel transit time(260min vs 297 min,P=0.229).The capsule detected positive findings in 39%of patients in the control group and 49%in the RTV group(P=0.081).CONCLUSION:The use of RTV and selective administration of domperidone to patients with delayed gastric passage of the capsule significantly reduces incomplete examinations,with no effect on small bowel transit time or diagnostic yield.

Design of PID controller with incomplete derivation based on ant system algorithm

A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm ( ASA) . For a given control system with this kind of PID controller, a group of optimal PID controller parameters K p * , T i * , and T d * can be obtained by taking the overshoot, settling time, and steady-state error of the system's unit step response as the performance indexes and by use of our improved ant system algorithm. K p * , T i * , and T d * can be used in real-time control. This kind of controller is called the ASA-PID controller with incomplete derivation. To verify the performance of the ASA-PID controller, three different typical transfer functions were tested, and three existing typical tuning methods of PID controller parameters, including the Ziegler-Nichols method (ZN),the genetic algorithm (GA),and the simulated annealing (SA), were adopted for comparison. The simulation results showed that the ASA-PID controller can be used to control different objects and has better performance compared with the ZN-PID and GA-PID controllers, and comparable performance compared with the SA-PID controller.

Security Defence Policy Selection Method Using the Incomplete Information Game Model

Nowadays, the network defence policy selection using game model of incomplete information ignores the type of the defender, which quantifies cost simply, resulting in unreasonable defence policies selection. Aiming at the problem, we use Bayesian game theory to model the active defence policy selection. We take the types of both the attacker and the defender into consideration. Besides, the traditional quantization method is enhanced. Then, we calculate the equilibrium of static Bayesian game. Based on the analysis of the equilibrium, we select the optimal defence policy through the prediction for attackers' actions. The paper calculates the defence effectiveness of defence policies and provides a defence policies selection algorithm. Ultimately, we present an example to verify the effectiveness of the method and model proposed in the paper.

Method for multiple attribute decision making based on incomplete linguistic judgment matrix

With respect to the multiple attribute decision making problems with linguistic preference relations on alternatives in the form of incomplete linguistic judgment matrix, a method is proposed to analyze the decision problem. The incomplete linguistic judgment matrix is transformed into incomplete fuzzy judgment matrix and an optimization model is developed on the basis of incomplete fuzzy judgment matrix provided by the decision maker and the decision matrix to determine attribute weights by Lagrange multiplier method. Then the overall values of all alternatives are calculated to rank them. A numerical example is given to illustrate the feasibility and practicality of the proposed method.