Hessian-based Similarity Metric for Multimodal Medical Image Registration

One of the fundamental elements of both traditional and certain deep learning medical image registration algorithms is measuring the similarity/dissimilarity between two images. In this work, we propose an analytical solution for measuring similarity between two different medical image modalities based on the Hessian of their intensities. First, assuming a functional dependence between the intensities of two perfectly corresponding patches, we investigate how their Hessians relate to each other. Secondly, we suggest a closed-form expression to quantify the deviation from this relationship, given arbitrary pairs of image patches. We propose a geometrical interpretation of the new similarity metric and an efficient implementation for registration. We demonstrate the robustness of the metric to intensity nonuniformities using synthetic bias fields. By integrating the new metric in an affine registration framework, we evaluate its performance for MRI and ultrasound registration in the context of image-guided neurosurgery using target registration error and computation time

Formation of the Causal Pattern of the Return of Addiction Based on the Components of Perceived Child-rearing Practices, Coping styles and Hidden Propensities in the Recovered Without Return and Reborn

Background: Drug dependence is one of the most important public health problems in societies. The purpose of the research was to develop a model for the return of addiction based on the components of perceived parenting practices, coping styles and hidden propensities in recovered without return and recovered returns.Materials and Methods: The statistical population of this study consisted of all recovered clinics and drug addiction treatment centers in Gilan province (North of Iran) in the 2016-17. After passing the treatment period from the discharge center and obtaining a treatment period certificate, the health card they received at least one year of recovery when they performed this research. The sample group, which consisted of 300 patients aged 18 to 35 years with substance abuse history, were selected through available sampling method in two groups. In this research, in addition to obtaining personal information and obtaining a return status report, 5 tools were used as follows: perceived parenting skills questionnaire, coping skills scale, Adlerian basic scale for interpersonal success of adult version, opinion questionnaire Tempting, and perceived stress questionnaires. Data analyzed with LISREL software.Results: The path and probabilistic relationships between the phenomena were studied. Based on matrix analysis, variance, covariance and correlation matrix, we investigated the possible relationships between the phenomena studied paid. The path analysis was used to determine the model. There was a positive and significant relationship between perceived parenting style, lifestyle, coping styles, tempting beliefs and general stress with returning to addiction.Conclusion: To return of addiction we proposed use of perceived parenting style, lifestyle, coping styles, tempting beliefs and general stress

Line-line fault detection and classification for photovoltaic systems using ensemble learning model based on I-V characteristics

The fault diagnosis of photovoltaic (PV) arrays aims to increase the reliability and service life of PV systems. Line-Line (LL) faults may remain undetected under low mismatch level and high impedance due to low currents of faults, resulting in power losses and fire potential disaster. This paper proposes a novel and intelligent fault diagnosis method based on an ensemble learning model and Current-Voltage (I-V) characteristics to detect and classify LL faults at the DC side of PV systems. For this purpose, first, the key features are extracted via analyzing I-V characteristics under various LL fault events and normal operation. Second, a feature selection algorithm has been applied to select the best features for each learning algorithm in order to reduce the amount of data required for the learning process. Third, an ensemble learning model is developed that combines several learning algorithms based on the probabilistic strategy to achieve superior diagnostic performance. Here, we find an excellent agreement between simulation and experimental results that the proposed method can obtain higher accuracy in detecting and classifying the LL faults, even under low mismatch levels and high fault impedances. In addition, the comparison results demonstrate that the performance of the proposed method is better than individual machine learning algorithms, so that the proposed method precisely detects and classifies LL faults on PV systems under the different conditions with an average accuracy of 99% and 99.5%, respectively

Fault Detection and Classification for Photovoltaic Systems Based on Hierarchical Classification and Machine Learning Technique

Line-Line (LL) and Line-Ground (LG) faults may not be detected by common protection devices in PV arrays due to these faults are not detectable under high fault impedance and low mismatch level. In recent years, many efforts have been devoted to overcome these challenges using intelligent methods. However, these methods could not classify the type of faults and diagnose their severity. This paper proposes a novel and intelligent fault monitoring method to detect and classify LL and LG faults at the DC side of PV systems. For this purpose, the main features of Current-Voltage (I-V) curves under different fault events and normal conditions are extracted. The faults are categorized using the Hierarchical Classification (HC) platform. Later, the LL and LG faults are detected and classified by Machine Learning (ML) methods. The proposed method aims to reduce the amount of dataset which is required for the learning process and also obtain a higher accuracy in detecting and classifying the fault events at low mismatch levels and high fault impedance compared to other fault diagnostic methods. The experimental results verify that the proposed method precisely detects and classifies LL and LG faults on PV systems under the different conditions and severity with the accuracy of 96.66% and 91.66%, respectively

TAHLIL

پژوهش حاضر با هدف بدست آوردن میزان تأثیر مدارس بر نمرات کسب شدۀ دانشآموزان شرکت کننده در آزمونتیمز پی شرفته 2008 و برر سی اعتبار تحلیلهای چند سطحی بر ا ساس نظریه کلا سیک اندازهگیری به روش برآوردبیزی صورت پذیرفت. برای دستیابی به هدف اصلی پژوهش از دادههای مربوط به اجرای آزمون ریاضی تیمز پیشرفتهاست و روند آموزش ریاضیات پیشرفته دانشآموزان سال آخر متوسطه IEA 2008 که خود جزو تازهترین مطالعات)پیشدان شگاهی( را مورد ارزیابی قرار میدهد ا ستفاده شد )مرکز ملی مطالعات تیمز، 1388 (. بر این ا ساس، جامعهآماری و گروه نمونه پژوهش حاضررر، جامعه آماری و گروه نمونه پایپ پیش دانشررگاهی رشررته ریاضرری- فیزیک در1386 به اجرا در آمده اسررت. ح ن نمونه - آزمون ریاضرریات تیمز پیشرررفته اسررت که در سررال تح رریلی 1387دانشآموزان ایرانی در این سن ش برابر 2556 نفر بوده ا ست )گزارش تیمز پی شرفته، 1388 (. نتایج تحلیلها به طورکلی ن شان داد که در مدل صفر 12 در صد، در مدل با کووریت سطح دانش آموز 38 در صد و در مدل با کووریتسطح دانشآموز، مدرسه و معلن حدود 55 درصد از واریانس نمرات پیشرفت تح یلی دانشآموزان ناشی از تفاوتبین مدارس ا ست و این میزان تفاوت زیاد بین مدارس شرکت کننده گویای تفاوت و تبعیض آموز شی بین مدارسزیاد ا ست که این امر عمدتاً نا شی از متغیرهای در سطح مدر سه )منابع مدر سه برای درس ریاضی و سابقه تدریسریاضی معلن( میباش

Hardware realization of a dq based fault detection scheme for 3φ four-leg inverter

The problem of fault effects in power converters has been an important issue in past years and fault detection is a mandatory step in fault-tolerant system design. This paper proposes a digital current control and a fault detection method for current sensors and their hardware implementations for a three phase four-leg inverter. The controller utilizes a predictive cost function to find the optimal switching state to apply to the inverter. The current sensors play an important role in control method. Any wrong information from sensors to controller can affect the whole system performance and cause damages. A fault diagnosis method is proposed to avoid any abnormal operation. This scheme is based on dq-transformation. Variation of d-axis or q-axis can be considered as detection criterion. Fixed-point computations of proposed methods are verified by computer simulations

Autonomous monitoring of line-to-line faults in photovoltaic systems by feature selection and parameter optimization of support vector machine using genetic algorithms

Photovoltaic (PV) monitoring and fault detection are very crucial to enhance the service life and reliability of PV systems. It is difficult to detect and classify the faults at the Direct Current (DC) side of PV arrays by common protection devices, especially Line-to-Line (LL) faults, because such faults are not detectable under high impedance fault and low mismatch conditions. If these faults are not diagnosed, they may significantly reduce the output power of PV systems and even cause fire catastrophe. Recently, many efforts have been devoted to detecting and classifying LL faults. However, these methods could not efficiently detect and classify the LL faults under high impedance and low mismatch. This paper proposes a novel fault diagnostic scheme in accordance with the two main stages. First, the key features are extracted via analyzing Current-Voltage (I-V) characteristics under various LL fault events and normal operation. Second, a genetic algorithm (GA) is used for parameter optimization of the kernel functions used in the Support Vector Machine (SVM) classifier and feature selection in order to obtain higher performance in diagnosing the faults in PV systems. In contrast to previous studies, this method requires only a small dataset for the learning process and it has a higher accuracy in detecting and classifying the LL fault events under high impedance and low mismatch levels. The simulation results verify the validity and effectiveness of the proposed method in detecting and classifying of LL faults in PV arrays even under complex conditions. The proposed method detects and classifies the LL faults under any condition with an average accuracy of 96% and 97.5%, respectively

Brain oximetry is not a good monitor on reducing neurological complications after cardiac surgery

Background Cerebral deoxygenation is related to several adverse systemic consequences. We aimed to assess the effect of noninvasive monitoring of cerebral oxygenation on reducing neurological complications after cardiac surgery. Methods In this randomized clinical trial, subjects were randomized into two groups: intervention group (111 subjects with monitoring of cerebral oximetry) and control group (112 subjects without any monitoring of cerebral oximetry). Monitoring of regional cerebral oxygen saturation (rScO2) was performed in the intervention group without any monitoring of cerebral oxygenation. The rScO2 was not recorded in the control group and no specific treatments were employed. Any neurological complications such as hallucinations, delirium, stroke, and length of stay in ICU after surgery were recorded. A p-value less than 0.05 was used as a cut-off for statistical significance. Results After surgery, in the intervention group one (1/111=0.09%) patient suffered from stroke and one patient had delirium, while in the control group three patients had stroke and three (3/112=2.6%) had delirium. There was no significant difference between the two groups regarding complications (p=0.527). The length of stay in ICU was 3.49 ± 0.96 days in the case group and 3.40 ± 0.80 days in the control group and there was no significant difference in the two groups (p=0.477). Conclusion Monitoring of rScO2 does not seem to be a good monitor for brain oxygenation. Further studies are needed to judge the usefulness of rScO2 for monitoring brain oxygenation

Correction: Salarkaleji, M. Frequency and Polarization-Diversified Linear Sampling Methods for Microwave Tomography and Remote Sensing Using Electromagnetic Metamaterials. <i>Electronics</i> 2017, <i>6</i>, 85

The authors would like to change the affiliation for second author, Mohammadreza Eskandari, as listed in the original version of the article [...