Interval Weight Research Articles

Rotor Position Estimation of Permanent Magnet Synchronous Motors Using Low-Resolution Sensors

The vibration and noise of permanent magnet synchronous motors are caused by torque ripple. To reduce the torque ripple, it is necessary to know the precise rotor position. Using a high-resolution sensor, it is possible to reduce the harmonics when the controller knows the precise rotor position. However, there are problems using high-resolution sensors including difficult installation due to the narrow space, high cost, and low durability against external shocks. Therefore, the rotor position is estimated using low-resolution sensors. The rotor position is assumed using the average speed and the hall interval weight. However, this value is very difficult to estimate precisely in extremely low speed areas or speed transition periods. Thus, methods to solve this problem are developed using fuzzy logic, which utilizes the speed and delta speed as inputs. It is confirmed that the rotor position error, estimated using fuzzy logic, is less than that estimated using the average of the hall interval.

Logarithmic least squares approaches to deriving interval weights, rectifying inconsistency and estimating missing values for interval multiplicative preference relations

The aim of this paper is to develop logarithmic least squares prioritization and completion methods for interval multiplicative preference relations. A parameterized transformation formula is proposed to convert a normalized interval weight vector into a consistent interval multiplicative preference relation. A logarithmic least squares model is established to derive a normalized interval weight vector from an interval multiplicative preference relation and construct the optimized consistent interval multiplicative preference relation. Subsequently, a logarithmic least squares model is built to rectify inconsistency for a complete interval multiplicative preference relation without consistency, and a logarithmic least squares completion model is developed to estimate missing values for an incomplete interval multiplicative preference relation. Several numerical examples are examined to illustrate the validity and applicability of the proposed methods, and comparisons with other existing methods are also made.

Research on the Comparison of Solar Power Generation with Different Technology Paths

To effectively cope with global climate change and the environment pollution problem, all countries are promoting low-carbon development actively. China is rich in solar energy resources, which has important influence on the sustainable development. Photovoltaic and thermal power generation were compared by constructing the comprehensive comparison model which is based on the interval weight and set pair analysis method. Then, their superiorities can be judged. The results of the study can provide strategic advice for investors in the solar-thermal and photovoltaic power generation to make decisions, which has far-reaching significance for them.

GROUP INTERVAL WEIGHTS BASED ON CONJUNCTION APPROXIMATION OF INDIVIDUAL INTERVAL WEIGHTS

<p>The individual and group decisions in this study are denoted as the normalized interval weights of alternatives as in Interval AHP. It assumes that a decision maker uses crisp values in the interval weights in giving comparisons. The interval weights reflect uncertainty in a decision maker’s mind. Then, the group interval weight is obtained as a conjunction approximation of the individual interval weights. For a consensus, the group interval weight is obtained so as to intersect with all the individual interval weights. In other words, the group interval weight has something in common with each individual interval weight. The group decision depends on how much the decision makers are satisfied or dissatisfied with it. The satisfaction of a decision maker is measured by the ranges of the group interval weights which s/he can support. Similarly, the decision maker’s dissatisfaction is defined by the ranges which are out of his/her decision. It is better to maximize the satisfaction and simultaneously to minimize the dissatisfaction. However, there is a trade-off between these two objectives. In the proposed model, the importance of the satisfaction or dissatisfaction is given. Then, the decision makers find not only the group decision but also their satisfaction and dissatisfaction with it. </p>

Neutrosophic Decision Making Model of School Choice

The purpose of this paper is to present single valued neutrosophic decision making model of school choice. Childhood is a crucial stage in terms of a child's physical, intellectual, emotional and social development i.e. all round development of a child. Mental and physical abilities of children grow at an increasing rate. Children particularly need high quality personal care and learning experiences. Children begin learning from the moment the child takes his/her birth and continues on throughout his/her life. Babies and toddlers need positive early learning experiences for their mental and physical development and this lays the foundation for later school success. So it is necessary to select the best school for the children among all the feasible alternatives by which all needs of the children are fulfilled. A large number of parents have an increasing array of options in choosing the best school for their children among all alternatives. Those options vary from place to place. In this paper, neutrosophic multi-attribute decision- making with interval weight information is used to form a decision-making model for choosing the best school for the children. A numerical example is developed based on expert opinions from english medium schools of Nadia districts, West Bengal, India. The problem is solved to show the effectiveness of the proposed single valued neutrosophic decision making model.

A two-stage linear goal programming approach to eliciting interval weights from additive interval fuzzy preference relations

This article presents a linear goal programming framework to obtain normalized interval weights from interval fuzzy preference relations (IFPRs). A parameterized transformation equation is put forward to convert a normalized interval weight vector into IFPRs with additive consistency. Based on a linearization approximate relation of the transformation equation, a two-stage linear goal programming approach is developed to elicit interval weights and determine an appropriate parameter value from an additive IFPR. The first stage devises a linear goal programming model to generate optimal interval weight vectors by minimizing the absolute deviation between sides of the parameterized linearization approximate relation. The second stage aims to find a benchmark among the optimal solutions derived from the previous stage by minimizing the absolute deviation between the parameter and 1. The obtained benchmark is the closest to the original IFPR and can sufficiently reflect uncertainty of original judgments. A procedure is further proposed for solving group decision making problems with IFPRs. Two numerical examples including a comparative study with existing approaches are provided to illustrate validity and practicality of the proposed model.

Pairwise comparison based interval analysis for group decision aiding with multiple criteria

Interval AHP (Analytic Hierarchy Process) was proposed to obtain interval weights from a given pairwise comparison matrix showing relative importance between criteria. In this paper, Interval AHP is applied to group decision problems. Interval AHP is first revised suitably for comparing alternatives from the viewpoint that the interval weight vector shows the set of agreeable weight vectors for the decision maker. Under individual interval weight vectors obtained from individual pairwise comparison matrices, three approaches to obtaining a consensus interval weight vector are proposed. One is the perfect incorporation approach that obtains consensus interval weight vectors including all individual interval weight vectors. By this approach, we can count out indubitably inferior alternatives. The second is the common ground approach that obtains consensus interval weight vectors included in all individual interval weight vectors. By this approach we can find agreeable group preference between alternatives when all individual opinions are similar. The third is the partial incorporation approach that obtains consensus interval weight vectors intersecting all individual interval weight vectors. By this approach we can find agreeable group preference between alternatives when individual opinions are not similar. The usefulness of the proposed three approaches is demonstrated by simple numerical examples.

Reactivation of Spinal Tuberculosis in a Patient with Rheumatoid Arthritis on Low-dose Methotrexate

42 year old Hispanic woman with rheumatoid arthritis who has been taking methotrexate for 24 weeks was admitted for severe, sharp lumbar back pain for 3 weeks. The pain radiated to both legs, worsened with walking or standing and improved at rest. It was accompanied by low grade fevers, anorexia and generalized fatigue. The patient denied cough or shortness of breath. MRI of the spine showed an L4 pathologic deformity with abnormal marrow infiltration and a ring-enhancing lesion within the left psoas muscle suspicious for abscess. CT-guided biopsy of the psoas showed necrotic debris and neutrophilic infiltrates. PPD test was reported negative. Bacterial and fungal cultures were negative after 5 days. She was discharged with an empiric 30-day regimen of intravenous ceftriaxone and oral linezolid due to high suspicion for a bacterial etiology. Twenty-six days later, the patient returned due to worsening of her back pain, she was unable to ambulate, and had a 15 pound interval weight loss. Previous biopsy cultures were positive for acid-fast bacilli and repeat MRI showed pathologic collapse of L4 with a retropulsive fracture, superimposed phlegmons bilaterally, severe spinal stenosis, and compression of the cauda equina. Three sputum specimens for AFB smears were negative. The patient recalled having had a BCG vaccine and a negative PPD in 1995; she denied exposure to TB or international travel for the past 21 years. The patient was placed on a 6 months regimen of isoniazid, rifampin, pyrazinamide, ethambutol, and pyridoxine. Two months after her presentation, she required an L4 corpectomy with a vertebral replacement cage, an L2-L4 laminectomy, and L3-L5 fusion due severe back pain and spine instability.

Geometric consistency based interval weight elicitation from intuitionistic preference relations using logarithmic least square optimization

This paper introduces the notion of intuitionistic fuzzy geometric indices to capture the central tendency of Atanassov's intuitionistic fuzzy values (A-IFVs). A ratio-based hesitation margin is defined to measure the hesitancy of an A-IFV and used to determine the geometric mean based hesitation margin of an intuitionistic preference relation (IPR). The paper defines geometric consistency of IPRs based on the intuitionistic fuzzy geometric index, and puts forward some properties for geometry consistent IPRs. A parameterized transformation formula is proposed to convert normalization interval weights into geometry consistent IPRs. A logarithmic least square model is developed for constructing the fitted geometry consistent IPR and deriving interval weights from an IPR. Based on the fitted consistent IPR, a method is provided to check the acceptable geometry consistency for IPRs, and an algorithm is further devised to solve multiple criteria decision making problems with IPRs. Two numerical examples and comparisons with existing approaches are provided to illustrate the performance and effectiveness of the proposed models.

Geometric Least Square Models for Deriving[0,1]-Valued Interval Weights from Interval Fuzzy Preference Relations Based on Multiplicative Transitivity

This paper presents a geometric least square framework for deriving[0,1]-valued interval weights from interval fuzzy preference relations. By analyzing the relationship among[0,1]-valued interval weights, multiplicatively consistent interval judgments, and planes, a geometric least square model is developed to derive a normalized[0,1]-valued interval weight vector from an interval fuzzy preference relation. Based on the difference ratio between two interval fuzzy preference relations, a geometric average difference ratio between one interval fuzzy preference relation and the others is defined and employed to determine the relative importance weights for individual interval fuzzy preference relations. A geometric least square based approach is further put forward for solving group decision making problems. An individual decision numerical example and a group decision making problem with the selection of enterprise resource planning software products are furnished to illustrate the effectiveness and applicability of the proposed models.

Changing the culture around cultures.

A 5-day-old female infant presented to a children’s hospital emergency department (ED) with jaundice. She was born at 38 weeks’, 4 days’ gestation with a weight of 3400 g after an unremarkable pregnancy and perinatal course. By parental report, a 36-hour bilirubin level was 9 mg/dL, and she did not receive phototherapy. Her mother’s blood type was B, Rh positive. The baby was exclusively breastfed, nursing every 2 to 4 hours for 20 minutes per breast. Voiding and stooling patterns were appropriate. In the ED, she was afebrile and well appearing with a weight of 3380 g. At 130 hours after birth, her total serum bilirubin (TSB) was 19.4 mg/dL. The ED provider decided to admit the patient for intensive phototherapy. During her observation in the ED, the infant had acute decreases in oxyhemoglobin saturation by pulse oximetry (SpO2) to 90% while asleep, self-resolving within seconds. Therefore, the ED provider initiated a partial sepsis evaluation including blood culture, complete blood count with differential, and C-reactive protein. The complete blood count and C-reactive protein were normal. While an inpatient, she received phototherapy for 6 hours and was discharged from the hospital after an appropriate postphototherapy TSB. At 27 hours of incubation on continuous monitoring, Gram-positive cocci in clusters were detected from the blood culture. The discharging inpatient team referred her back to the ED for further evaluation. The patient had remained afebrile without changes in her activity, breastfeeding, or urine output. An interval weight was appropriate. In the ED, sepsis was presumed given “young age and early growth [of the blood culture].” Therefore, a full evaluation for neonatal sepsis including lumbar puncture was performed. The evaluation was completely reassuring without signs suggestive of infection. She received empirical ampicillin and cefotaxime. Just before inpatient admission, the Verigene BC-GP assay …

Logarithmic least squares prioritization and completion methods for interval fuzzy preference relations based on geometric transitivity

This article introduces the notion of geometric transitivity, which may be used to define consistent interval fuzzy preference relations. Some useful properties are presented for consistent interval fuzzy preference relations. A close investigation reveals that existing methods for determining the analogous property of consistency are not robust to permutations of the pairwise judgments and not able to reflect the hesitancy in the decision-maker’s preference. A parametric transformation formula is put forward to convert a normalized interval weight vector into a consistent interval fuzzy preference relation. By minimizing the squared difference between the logarithm of the ratio of the original judgment and the logarithm of the ratio of the converted consistent one, a logarithmic least squares model is developed to derive interval weights from any interval fuzzy preference relation. Based on geometric transitivity, a logarithmic least squares model is established to rectify inconsistency for complete and inconsistent interval fuzzy preference relations, and a logarithmic least squares completion approach is further developed to estimate missing values for incomplete interval fuzzy preference relations. Numerical examples are furnished to show the validity of the proposed models and compare with other existing methods.

A Modified Learning Algorithm for Interval Perceptrons with Interval Weights

In many applications, it is natural to use interval data to describe various kinds of uncertainties. This paper is concerned with a one-layer interval perceptron with the weights and the outputs being intervals and the inputs being real numbers. In the original learning method for this interval perceptron, an absolute value function is applied for newly learned radii of the interval weights, so as to force the radii to be positive. This approach seems unnatural somehow, and might cause oscillation in the learning procedure as indicated in our numerical experiments. In this paper, a modified learning method is proposed for this one-layer interval perceptron. We do not use the function of the absolute value, and instead, we replace, in the error function, the radius of each interval weight by a quadratic term. This simple trick does not cause any additional computational work for the learning procedure, but it brings about the following three advantages: First, the radii of the intervals of the weights are guaranteed to be positive during the learning procedure without the help of the absolute value function. Secondly, the oscillation mentioned above is eliminated and the convergence of the learning procedure is improved, as indicated by our numerical experiments. Finally, a by-product is that the convergence analysis of the learning procedure is now an easy job, while the analysis for the original learning method is at least difficult, if not impossible, due to the non-smoothness of the absolute value function involved.

Visual Diagnosis: Nasal Congestion and Respiratory Distress in a 7-Day-Old Girl

Visual Diagnosis: Nasal Congestion and Respiratory Distress in a 7-Day-Old Girl

An interval weighed fuzzy c-means clustering by genetically guided alternating optimization

The fuzzy c-means (FCM) algorithm is a widely applied clustering technique, but the implicit assumption that each attribute of the object data has equal importance affects the clustering performance. At present, attribute weighted fuzzy clustering has became a very active area of research, and numerous approaches that develop numerical weights have been combined into fuzzy clustering. In this paper, interval number is introduced for attribute weighting in the weighted fuzzy c-means (WFCM) clustering, and it is illustrated that interval weighting can obtain appropriate weights more easily from the viewpoint of geometric probability. Moreover, a genetic heuristic strategy for attribute weight searching is proposed to guide the alternating optimization (AO) of WFCM, and improved attribute weights in interval-constrained ranges and reasonable data partition can be obtained simultaneously. The experimental results demonstrate that the proposed algorithm is superior in clustering performance. It reveals that the interval weighted clustering can act as an optimization operator on the basis of the traditional numerical weighted clustering, and the effects of interval weight perturbation on clustering performance can be decreased.

Rescue pyloroplasty for refractory delayed gastric emptying following esophagectomy

Delayed gastric emptying (DGE) following esophagectomy is a debilitating complication. Rarely, failure of postoperative endoscopic therapy may necessitate rescue pyloroplasty (rPP). We conducted a retrospective, single-institution review of rPP for post-esophagectomy DGE from 2000 to 2013. Pre- and postoperative symptoms and pharmacologic use were examined. "Successful" rPP was defined as resolution of symptoms and pharmacologic requirements postoperatively. Quality of life (QoL) was evaluated by Short Form-12 and study-specific questionnaires. Thirteen patients underwent open transabdominal rescue Heineke-Mikulicz pyloroplasty. Between esophagectomy and rPP, average interval weight loss was 19 ± 15 lb over 13 months (range, 3-22). Patients underwent 3.4 ± 1.0 preoperative endoscopic balloon dilation (EBD) attempts, with 7 (54%) receiving endoscopic intrapyloric botulinum toxin (IPB) injections. Median follow-up was 12 months (range, 4-23). After rPP, the incidence of nausea, vomiting, bloating, prokinetic use, and total parenteral nutrition/total enteral nutrition dependence decreased (all P < .01). All patients gained weight; 2 developed biliary reflux. Nine of 13 patients were identified as rPP successes; predictors of rPP failure were American Society of Anesthesiologists grade 3 (P = .02), greater number of EBD attempts (P = .02), longer time to rPP (P = .03), and fewer IPB injections (P = .03). QoL assessment revealed general satisfaction with postoperative outcomes and excellent physical and mental functioning. rPP for post-esophagectomy DGE is well-tolerated, results in improvements in symptoms and pharmacologic dependence, and satisfactorily preserves QoL.

The Study of Group Decision Based on Interval AHP

This paper applied the interval AHP to analyze the problems of consistency and weights of interval judgment matrixes in group decision, and applied the models of the least upper approximation and the greatest lower approximation based on interval regression analysis to solute them, and showed the differences between the two models in application by numerical analysis. In order to reduce the uncertainty in ranking weights, the study applied the weight possible value model to obtain the approximate estimate of interval weight, and then combined with the interval weight, which provided more objective and rational decision-making bases for decision maker.

The Safety Assessment of Unpowered Mooring Vessel Based on Unascertained Measure

The safety of the unpowered mooring vessel was influenced by human factors, vessel condition, meteorological factors, hydrological and environmental factors. To ensure the safety of the unpowered mooring vessel during mooring, on the basis of unascertained measure evaluation model, the method of Monte Carlo and interval weight was put forward to assess the safety of the vessel. This method enables policymakers, in the treatment of evaluation, to avoid weighing the key factors from the traditional model. It also provides more valid intervals for policymakers while dealing with uncertain information of safety elements and errors caused by human preference. Through a random value of interval weight using Monte Carlo simulation method, the final average value of the simulation is taken as the final weight. Finally, a 16000t unpowered mooring cargo vessel is taken as an example to have a detailed discussion of the application of this method, and a safety result is obtained according to each key element. This method can provide new evaluation method and references to policymakers concerned.

Interval weight generation approaches for reciprocal relations

In this paper, we propose methods to derive interval weight vectors from reciprocal relations for reflecting the inconsistency when decision makers provide preferences over alternatives (or criteria). Several goal programming models are established to minimize the inconsistency based on multiplicative and additive consistency, respectively. Especially, if we obtain a crisp weight vector from a reciprocal relation, then it is consistent. Then, we extend the proposed methods to incomplete reciprocal relations and interval reciprocal relations and develop the corresponding models to derive interval weight vectors. Several examples are also given to compare the developed methods with the existing ones.

Index Selection Preference and Weighting for Uncertain Network Sentiment Emergency

To more effectively cope with the network public sentiment emergency involved many interval-valued warning indexes, we present a new method of early warning index selection and weight assignment for uncertain network public sentiment emergency decision-making. By using a new interval fuzzy AHP, the interval weight of each early warning index of network public sentiment emergency can be obtained. Then by means of the weighted aggregation values of all the emergency warning indexes, we can rank the severity of every network public sentiment emergency and select the most severe one. Finally, a numerical example is given to illustrate the application of the proposed method of warning index selecting and weighting to uncertain network public sentiment emergency decision. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1898