Search results for: Transformer Fault Diagnosis

Authors: Mustafa M. Donma, Orkide Donma

Abstract:

Anthropometric measurements are essential for obesity studies. Waist circumference (WC) is the most frequently used measure and along with hip circumference (HC), it is used in most equations derived for the evaluation of obese individuals. Morbid obesity (MO) is the most severe clinical form of obesity and such individuals may also exhibit some clinical findings leading to metabolic syndrome (MetS). Then, it becomes a requirement to discriminate MO children with MetS (MOMetS+) from MO children without MetS (MOMetS-). Almost all obesity indices can differentiate obese (OB) children from children with normal body mass index (N-BMI). However, not all of them are capable of making this distinction. The aim of this study was to find out the clinical availability of (waist circumference + hip circumference)/2 ((WC+HC)/2) for the differential diagnosis of MOMetS+ and MOMetS- and to compare the possible preponderance of it over some other anthropometric or fat-based obesity indices. 45 MOMetS+ and 45 MOMetS- children were included in the study. Participants have submitted informed consent forms. The study protocol was approved by the Non-interventional Clinical Studies Ethics Committee of Tekirdag Namik Kemal University. Anthropometric measurements were performed. BMI, waist-to-hip circumference (WHR), (WC+HC)/2, trunk-to-leg fat ratio (TLFR), trunk-to-appendicular fat ratio (TAFR), trunk fat+leg fat/2 ((trunk+leg fat)/2), diagnostic obesity notation model assessment index-2 (D2I) and fat mass index (FMI) were calculated for both groups. Study data were analyzed statistically and 0.05 for p value was accepted as the statistical significance degree. Statistically higher BMI, WC, (WC+HC)/2, (trunk+leg fat)/2 values were found in MOMetS+ children than MOMetS- children. No statistically significant difference was detected for WHR, TLFR, TAFR, D2I and FMI between two groups. The lack of difference between the groups in terms of FMI and D2I pointed out the fact that the recently developed fat-based index; (trunk+leg fat)/2 gives much more valuable information during the evaluation of MOMetS+ and MOMetS- children. Upon evaluation of the correlations, (WC+HC)/2 was strongly correlated with D2I and FMI in both MOMetS+ and MOMetS- groups. Neither D2I nor FMI was correlated with W/H. Strong correlations were calculated between (WC+HC)/2 and (trunk+leg fat)/2 in both MOMetS- (r = 0.961; p < 0.001) and MOMetS+ (r = 0.936; p < 0.001) groups. Partial correlations between (WC+HC)/2 and (trunk+leg fat)/2 after controlling the effect of basal metabolic rate were r = 0.726; p < 0.001 in MOMetS- group and r = 0.932; p < 0.001 in MOMetS+ group. The correlation in the latter group was higher than the first group. In conclusion, recently developed anthropometric obesity index (WC+HC)/2 and fat-based obesity index (trunk+leg fat)/2 were of preponderance over the previously introduced classical obesity indices such as WHR, D2I and FMI during the differential diagnosis of MOMetS+ and MOMetS- children.

Keywords: Hip circumference, metabolic syndrome, morbid obesity, waist circumference.

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Authors: Yahia Massinissa, Kalla A, Yahia M, Benbia S

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A lot of recent research have spoken on the relation between the increase of the homocysteinemia and some kinds of cancer . For that, our study was based on the research of a possible relation between the increase of the concentration of this amino-acid in the plasma and the appearance of the disease of the Acute Lymphoblastic Leukaemia in a part of Algerian children with Berber origin in the East of Algeria . The study has done on 47 ill persons with an average age of (09±06 ) years , with whom the disease has diagnosed by blood and marrow examination in the hospital of blood diseases in the CHU of Batna, and on 194 healthy witnesses of the same age. The two groups were benefited by a dosage of the concentration of the homocysteine vitamin B9 ,vitamin B12 , and also of the study of special polymorphisms of indispensable enzymes in the metabolism of this acid , and that by the use of the method ( Light cycler ) Real time PCR , on the following enzymes : MS ( C2756G ), MSR ( A66G ) ,MTHFR1 ( C677T ) and MTHFR2 (A1298C). The obtained results have revealed that the rate of the homozygote muted genotype is the less frequent in the two groups , and that exist at list one genotype of each enzyme in the ill group and in which the percentage exceed with remarkable way the same genotype in the healthy group and we notice specially the muted genotype GG of -the methionine synthetase-and the form TT of the enzyme – methyline tetra hydrofolate reductase – We notice the existence of considerable number of genotypes in the ill group lied with characteristic increase of this Amino-acid ,and that for the reduction of the biologic activity of these enzymes which become inefficient in the transfer of the homocysteine into the methionine and cause the diminution of the biologic activity of these enzymes and with consequence the reduction of the percentage of methylic radicals in the DNA of studied genes and that lead to the increase of the activity and the capacity of transcription , and it-s so probably that this last one is one of the factors of this disease especially if we know that the specific check-up of vitamins is normal and similar in the two groups , which ovoid the hypothesis of the reduction of vitamins . We notice also that the heterozygote genotype is the less in the sick category except the MTHFR2. Wild genotype is more frequent in the witness group except MSR. Even these results are partials; they open a new way in the genetic diagnosis of this malicious disease which allow a precocious diagnosis and the use of an effective and appropriated treatment in the same time.

Keywords: Genetic polymorphism, Acute Lymphoblastic Leukaemia, Biomarkers, Metabolism of homocystein

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Authors: S. H. Iranmanesh, L. Aliabadi, A. Mollajan

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Integrated resilience engineering (IRE) is capable of returning banking systems to the normal state in extensive economic circumstances. In this study, information system of a large bank (with several branches) is assessed and optimized under severe economic conditions. Data envelopment analysis (DEA) models are employed to achieve the objective of this study. Nine IRE factors are considered to be the outputs, and a dummy variable is defined as the input of the DEA models. A standard questionnaire is designed and distributed among executive managers to be considered as the decision-making units (DMUs). Reliability and validity of the questionnaire is examined based on Cronbach's alpha and t-test. The most appropriate DEA model is determined based on average efficiency and normality test. It is shown that the proposed integrated design provides higher efficiency than the conventional RE design. Results of sensitivity and perturbation analysis indicate that self-organization, fault tolerance, and reporting culture respectively compose about 50 percent of total weight.

Keywords: Banking system, data envelopment analysis, DEA, integrated resilience engineering, IRE, performance evaluation, perturbation analysis.

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Authors: Tadatsugu Kitamoto

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Recently, a quality of motors is inspected by human ears. In this paper, I propose two systems using a method of speech recognition for automation of the inspection. The first system is based on a method of linear processing which uses K-means and Nearest Neighbor method, and the second is based on a method of non-linear processing which uses neural networks. I used motor sounds in these systems, and I successfully recognize 86.67% of motor sounds in the linear processing system and 97.78% in the non-linear processing system.

Keywords: Acoustical diagnosis, Neural networks, K-means, Short-time Fourier transformation

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Authors: Juan L. Castro, Maria I. NavarroVictor Lopez, Eduardo M. Eisman, Jose M. Zurita

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This paper describes the Multilingual Virtual Simulated Patient framework. It has been created to train the social skills and testing the knowledge of primary health care medical students. The framework generates conversational agents which perform in serveral languages as virtual simulated patients that help to improve the communication and diagnosis skills of the students complementing their training process.

Keywords: Medical training, conversational agents, patient modeling.

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Authors: Ibtihel Nouira, Asma Ben Abdallah, Ibtissem Kouaja, Mohamed Hèdi Bedoui

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The processing of the electrocardiogram (ECG) signal consists essentially in the detection of the characteristic points of signal which are an important tool in the diagnosis of heart diseases. The most suitable are the detection of R waves. In this paper, we present various mathematical tools used for filtering ECG using digital filtering and Discreet Wavelet Transform (DWT) filtering. In addition, this paper will include two main R peak detection methods by applying a windowing process: The first method is based on calculations derived, the second is a time-frequency method based on Dyadic Wavelet Transform DyWT.

Keywords: Derived calculation methods, Electrocardiogram, R peaks, Wavelet Transform.

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Authors: Faisal AlShammari, Abdulmajid Addali

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Monitoring the conditions of rotating machinery, such as bearings, is important in order to improve the stability of work. Acoustic Emission (AE) and vibration analysis are some of the most accomplished techniques used for this purpose. Acoustic emission has the ability to detect the initial phase of component degradation. Moreover, it has been observed that vibration analysis is not as successful at low rotational speeds (below 100 rpm). This because the energy generated within this speed region is not detectable using conventional vibration. From this perspective, this paper has presented a brief review of using acoustic emission techniques for monitoring bearing conditions.

Keywords: Condition monitoring, stress wave analysis, low-speed bearings, bearing defect diagnosis.

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Authors: WooYoung Jung, HoYoung Son

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This study presented to reduce earthquake damage and emergency rehabilitation of critical structures such as schools, hightech factories, and hospitals due to strong ground motions associated with climate changes. Regarding recent trend, a strong earthquake causes serious damage to critical structures and then the critical structure might be influenced by sequence aftershocks (or tsunami) due to fault plane adjustments. Therefore, in order to improve seismic performance of critical structures, retrofitted or strengthening study of the structures under aftershocks sequence after emergency rehabilitation of the structures subjected to strong earthquakes is widely carried out. Consequently, this study used composite material for emergency rehabilitation of the structure rather than concrete and steel materials because of high strength and stiffness, lightweight, rapid manufacturing, and dynamic performance. Also, this study was to develop or improve the seismic performance or seismic retrofit of critical structures subjected to strong ground motions and earthquake aftershocks, by utilizing GFRP-Corrugated Infill Panels (GCIP).

Keywords: Composite material, GFRP, Infill Panel, Aftershock, Seismic Retrofitting.

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Authors: Mustafa M. Donma, Orkide Donma

Abstract:

Pediatric overweight and obesity need attention because they may cause morbid obesity, which may develop metabolic syndrome (MetS). Criteria used for the definition of adult MetS cannot be applied for pediatric MetS. Dynamic physiological changes that occur during childhood and adolescence require the evaluation of each parameter based upon age intervals. The aim of this study is to investigate the distribution of blood pressure (BP) values within diverse pediatric age intervals and the possible use and clinical utility of a recently introduced Diagnostic Obesity Notation Model Assessment Tension (DONMA tense) Index derived from systolic BP (SBP) and diastolic BP (DBP) [SBP+DBP/200]. Such a formula may enable a more integrative picture for the assessment of pediatric obesity and MetS due to the use of both SBP and DBP. 554 children, whose ages were between 6-16 years participated in the study; the study population was divided into two groups based upon their ages. The first group comprises 280 cases aged 6-10 years (72-120 months), while those aged 10-16 years (121-192 months) constituted the second group. The values of SBP, DBP and the formula (SBP+DBP/200) covering both were evaluated. Each group was divided into seven subgroups with varying degrees of obesity and MetS criteria. Two clinical definitions of MetS have been described. These groups were MetS3 (children with three major components), and MetS2 (children with two major components). The other groups were morbid obese (MO), obese (OB), overweight (OW), normal (N) and underweight (UW). The children were included into the groups according to the age- and sex-based body mass index (BMI) percentile values tabulated by WHO. Data were evaluated by SPSS version 16 with p < 0.05 as the statistical significance degree. Tension index was evaluated in the groups above and below 10 years of age. This index differed significantly between N and MetS as well as OW and MetS groups (p = 0.001) above 120 months. However, below 120 months, significant differences existed between MetS3 and MetS2 (p = 0.003) as well as MetS3 and MO (p = 0.001). In comparison with the SBP and DBP values, tension index values have enabled more clear-cut separation between the groups. It has been detected that the tension index was capable of discriminating MetS3 from MetS2 in the group, which was composed of children aged 6-10 years. This was not possible in the older group of children. This index was more informative for the first group. This study also confirmed that 130 mm Hg and 85 mm Hg cut-off points for SBP and DBP, respectively, are too high for serving as MetS criteria in children because the mean value for tension index was calculated as 1.00 among MetS children. This finding has shown that much lower cut-off points must be set for SBP and DBP for the diagnosis of pediatric MetS, especially for children under-10 years of age. This index may be recommended to discriminate MO, MetS2 and MetS3 among the 6-10 years of age group, whose MetS diagnosis is problematic.

Keywords: Blood pressure, children, index, metabolic syndrome, obesity.

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Authors: Hyun-Woo Cho

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Detection of incipient abnormal events is important to improve safety and reliability of machine operations and reduce losses caused by failures. Improper set-ups or aligning of parts often leads to severe problems in many machines. The construction of prediction models for predicting faulty conditions is quite essential in making decisions on when to perform machine maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of machine measurement data. The calibration model is used to predict two faulty conditions from historical reference data. This approach utilizes genetic algorithms (GA) based variable selection, and we evaluate the predictive performance of several prediction methods using real data. The results shows that the calibration model based on supervised probabilistic principal component analysis (SPPCA) yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: Prediction, operation monitoring, on-line data, nonlinear statistical methods, empirical model.

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Authors: S. Panda, N. P. Patidar, R. Singh

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Power system stability enhancement by simultaneous tuning of a Power System Stabilizer (PSS) and a Static Var Compensator (SVC)-based controller is thoroughly investigated in this paper. The coordination among the proposed damping stabilizers and the SVC internal voltage regulators has also been taken into consideration. The design problem is formulated as an optimization problem with a time-domain simulation-based objective function and Real-Coded Genetic Algorithm (RCGA) is employed to search for optimal controller parameters. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. The nonlinear simulation results are presented to show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and disturbances. Further, the proposed design approach is found to be robust and improves stability effectively even under small disturbance and unbalanced fault conditions.

Keywords: Real-Coded Genetic Algorithm (RCGA), Static Var Compensator (SVC), Power System Stabilizer (PSS), Low Frequency Oscillations, Power System Stability.

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Authors: Sidhartha Panda, N. P. Padhy, R.N.Patel

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In this paper, genetic algorithm (GA) opmization technique is applied to design Flexible AC Transmission System (FACTS)-based damping controllers. Two types of controller structures, namely a proportional-integral (PI) and a lead-lag (LL) are considered. The design problem of the proposed controllers is formulated as an optimization problem and GA is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. The proposed controllers are tested on a weakly connected power system subjected to different disturbances. The non-linear simulation results are presented to show the effectiveness of the proposed controller and their ability to provide efficient damping of low frequency oscillations. It is also observed that the proposed SSSC-based controllers improve greatly the voltage profile of the system under severe disturbances. Further, the dynamic performances of both the PI and LL structured FACTS-controller are analyzed at different loading conditions and under various disturbance condition as well as under unbalanced fault conditions..

Keywords: Genetic algorithm, proportional-integral controller, lead-lag controller, power system stability, FACTS.

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Authors: Patience Muchini, Electdom Matandiroya, Emmanuel Mashonjowa

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A ground-end manifestation of space weather phenomena is known as geomagnetically induced currents (GICs). GICs flow along the electric power transmission cables connecting the transformers and between the grounding points of power transformers during significant geomagnetic storms. Zimbabwe has no study that notes if grid failures have been caused by GICs. Research and monitoring are needed to investigate this possible relationship purpose of this paper is to characterize GICs with a power grid network. This paper analyses data collected, which are geomagnetic data, which include the Kp index, Disturbance storm time (DST) index, and the G-Scale from geomagnetic storms and also analyses power grid data, which includes reactive power, relay tripping, and alarms from high voltage substations and then correlates the data. This research analysis was first theoretically analyzed by studying geomagnetic parameters and then experimented upon. To correlate, MATLAB was used as the basic software to analyze the data. Latitudes of the substations were also brought into scrutiny to note if they were an impact due to the location as low latitudes areas like most parts of Zimbabwe, there are less severe geomagnetic variations. Based on theoretical and graphical analysis, it has been proven that there is a slight relationship between power system failures and GICs. Further analyses can be done by implementing measuring instruments to measure any currents in the grounding of high-voltage transformers when geomagnetic storms occur. Mitigation measures can then be developed to minimize the susceptibility of the power network to GICs.

Keywords: Adverse space weather, DST index, geomagnetically induced currents, Kp index, reactive power.

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Authors: Abdel-Badeeh M. Salem

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Case based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computeraided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing intelligent medical diagnoses systems. Successful applications in cancer and heart diseases developed by Medical Informatics Research Group at Ain Shams University are also discussed.

Keywords: Medical Informatics, Computer-Aided MedicalDiagnoses, AI in Medicine, Case-Based Reasoning.

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Authors: Brahim Hamid, Bertrand Le Saec, Mohamed Mosbah

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The vertex connectivity of a graph is the smallest number of vertices whose deletion separates the graph or makes it trivial. This work is devoted to the problem of vertex connectivity test of graphs in a distributed environment based on a general and a constructive approach. The contribution of this paper is threefold. First, using a preconstructed spanning tree of the considered graph, we present a protocol to test whether a given graph is 2-connected using only local knowledge. Second, we present an encoding of this protocol using graph relabeling systems. The last contribution is the implementation of this protocol in the message passing model. For a given graph G, where M is the number of its edges, N the number of its nodes and Δ is its degree, our algorithms need the following requirements: The first one uses O(Δ×N2) steps and O(Δ×logΔ) bits per node. The second one uses O(Δ×N2) messages, O(N2) time and O(Δ × logΔ) bits per node. Furthermore, the studied network is semi-anonymous: Only the root of the pre-constructed spanning tree needs to be identified.

Keywords: Distributed computing, fault-tolerance, graph relabeling systems, local computations, local knowledge, message passing system, networks, vertex connectivity.

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Authors: G. Kishor Babu, B. Madhu Kiran

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This paper presented an intend scheme of Modular-Multilevel-Converter (MMC) levels for move towering the practical conciliation flanked by the precision and divisional competence. The whole process is standard by a Thevenin-equivalent 133-level MMC model. Firstly the computation scheme of the fundamental limit imitation time step is offered to faithfully represent each voltage level of waveforms. Secondly the earlier industrial Improved Analytic Hierarchy Process (IAHP) is adopted to integrate the relative errors of all the input electrical factors interested in one complete virtual fault on each converter level. Thirdly the stable AC and DC ephemeral condition in virtual faults effects of all the forms stabilize and curve integral stand on the standard form. Finally the optimal MMC level will be obtained by the drown curves and it will give individual weights allowing for the precision and efficiency. And the competence and potency of the scheme are validated by model on MATLAB Simulink.

Keywords: Modular multilevel converter, improved analytic hierarchy process, ac and dc transient, high voltage direct current, voltage sourced converter.

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Authors: Ersin Kaya, Bulent Oran, Ahmet Arslan

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In this study, fuzzy rule-based classifier is used for the diagnosis of congenital heart disease. Congenital heart diseases are defined as structural or functional heart disease. Medical data sets were obtained from Pediatric Cardiology Department at Selcuk University, from years 2000 to 2003. Firstly, fuzzy rules were generated by using medical data. Then the weights of fuzzy rules were calculated. Two different reasoning methods as “weighted vote method" and “singles winner method" were used in this study. The results of fuzzy classifiers were compared.

Keywords: Congenital heart disease, Fuzzy rule-basedclassifiers, Classification

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Authors: D. R. Patil, U. Gudaru

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Most electrical distribution systems are incurring large losses as the loads are wide spread, inadequate reactive power compensation facilities and their improper control. A typical static VAR compensator consists of capacitor bank in binary sequential steps operated in conjunction with a thyristor controlled reactor of the smallest step size. This SVC facilitates stepless control of reactive power closely matching with load requirements so as to maintain power factor nearer to unity. This type of SVC-s requiring a appropriately controlled TCR. This paper deals with an air cored reactor suitable for distribution transformer of 3phase, 50Hz, Dy11, 11KV/433V, 125 KVA capacity. Air cored reactors are designed, built, tested and operated in conjunction with capacitor bank in five binary sequential steps. It is established how the delta connected TCR minimizes the harmonic components and the operating range for various electrical quantities as a function of firing angle is investigated. In particular firing angle v/s line & phase currents, D.C. components, THD-s, active and reactive powers, odd and even triplen harmonics, dominant characteristic harmonics are all investigated and range of firing angle is fixed for satisfactory operation. The harmonic spectra for phase and line quantities at specified firing angles are given. In case the TCR is operated within the bound specified in this paper established through simulation studies are yielding the best possible operating condition particularly free from all dominant harmonics.

Keywords: Binary Sequential switched capacitor bank, TCR, Nontriplen harmonics, step less Q control, Active and Reactivepower, Simulink

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as physiological vital signs, images and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful to influence the judgement of clinical sentiment in ICU clinical notes. This paper presents two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of a clinical transformer model that can reliably predict clinical sentiment for notes of COVID patients in ICU. We train the model on clinical notes for COVID-19 patients, ones not previously seen by Bio_ClinicalBERT or Bio_Discharge_Summary_BERT. The model which was based on Bio_ClinicalBERT achieves higher predictive accuracy than the one based on Bio_Discharge_Summary_BERT (Acc 93.33%, AUC 0.98, and Precision 0.96). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and Precision 0.92).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation.

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Authors: Archana Mukherjee, Aruna Korde, Sudipta Chakraborty, H. D. Sarma, Grace Samuel, M. R. A. Pillai

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Radiolabeled cyclic RGD peptides targeting integrin αvβ3 are reported as promising agents for the early diagnosis of metastatic tumors. With an aim to improve tumor uptake and retention of the peptide, cyclic RGD peptide dimer E[c (RGDfK)] 2 (E = Glutamic acid, f = phenyl alanine, K = lysine) coupled to the bifunctional chelator DOTA was custom synthesized and radiolabelled with 68Ga. Radiolabelling of cyclic RGD peptide dimer with 68Ga was carried out using HEPES buffer and biological evaluation of the complex was done in nude mice bearing HT29 tumors.

Keywords: 68Ga peptides, Angiogenesis imaging, Cyclic RGD peptides, PET Imaging.

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Authors: Ren-Junn Hwang, Chung-Chien Lu, Jain-Shing Wu

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Cloud outsource storage is one of important services in cloud computing. Cloud users upload data to cloud servers to reduce the cost of managing data and maintaining hardware and software. To ensure data confidentiality, users can encrypt their files before uploading them to a cloud system. However, retrieving the target file from the encrypted files exactly is difficult for cloud server. This study proposes a protocol for performing multikeyword searches for encrypted cloud data by applying k-nearest neighbor technology. The protocol ranks the relevance scores of encrypted files and keywords, and prevents cloud servers from learning search keywords submitted by a cloud user. To reduce the costs of file transfer communication, the cloud server returns encrypted files in order of relevance. Moreover, when a cloud user inputs an incorrect keyword and the number of wrong alphabet does not exceed a given threshold; the user still can retrieve the target files from cloud server. In addition, the proposed scheme satisfies security requirements for outsourced data storage.

Keywords: Fault-tolerance search, multi-keywords search, outsource storage, ranked search, searchable encryption.

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Authors: Sheema Shuja Khattak, Gule Saman, Imran Khan, Abdus Salam

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Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi and Tsallis entropies. This work has novelty based on the detection of skin lesion caused by the bite of a parasite called Sand Fly causing the disease is called Cutaneous Leishmaniasis.

Keywords: Shannon, Maximum entropy, Renyi, Tsallis entropy.

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Authors: M. Das Gupta, S. Banerjee

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Content Based Image Retrieval (CBIR) coupled with Case Based Reasoning (CBR) is a paradigm that is becoming increasingly popular in the diagnosis and therapy planning of medical ailments utilizing the digital content of medical images. This paper presents a survey of some of the promising approaches used in the detection of abnormalities in retina images as well in mammographic screening and detection of regions of interest in MRI scans of the brain. We also describe our proposed algorithm to detect hard exudates in fundus images of the retina of Diabetic Retinopathy patients.

Keywords: Case based reasoning, Exudates, Retina image, Similarity based retrieval.

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Authors: Gopi Kandaswamy, P. Balamuralidhar

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Fully autonomous small Unmanned Aerial Vehicles (UAVs) are increasingly being used in many commercial applications. Although a lot of research has been done to develop safe, reliable and durable UAVs, accidents due to electronic and structural failures are not uncommon and pose a huge safety risk to the UAV operators and the public. Hence there is a strong need for an automated health monitoring system for UAVs with a view to minimizing mission failures thereby increasing safety. This paper describes our approach to monitoring the electronic and structural components in a small UAV without the need for additional sensors to do the monitoring. Our system monitors data from four sources; sensors, navigation algorithms, control inputs from the operator and flight controller outputs. It then does statistical analysis on the data and applies a rule based engine to detect failures. This information can then be fed back into the UAV and a decision to continue or abort the mission can be taken automatically by the UAV and independent of the operator. Our system has been verified using data obtained from real flights over the past year from UAVs of various sizes that have been designed and deployed by us for various applications.

Keywords: Fault detection, health monitoring, unmanned aerial vehicles, vibration analysis.

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Authors: Yao-Tien Wang, Hsiang-Fu Yu, Dung Chen Chiou

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In wireless and mobile communications, this progress provides opportunities for introducing new standards and improving existing services. Supporting multimedia traffic with wireless networks quality of service (QoS). In this paper, a grey-fuzzy controller for radio resource management (GF-RRM) is presented to maximize the number of the served calls and QoS provision in wireless networks. In a wireless network, the call arrival rate, the call duration and the communication overhead between the base stations and the control center are vague and uncertain. In this paper, we develop a method to predict the cell load and to solve the RRM problem based on the GF-RRM, and support the present facility has been built on the application-level of the wireless networks. The GF-RRM exhibits the better adaptability, fault-tolerant capability and performance than other algorithms. Through simulations, we evaluate the blocking rate, update overhead, and channel acquisition delay time of the proposed method. The results demonstrate our algorithm has the lower blocking rate, less updated overhead, and shorter channel acquisition delay.

Keywords: radio resource management, grey prediction, fuzzylogic control, wireless networks, quality of service.

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Authors: Alshalaa A. Shleeg, Issmail M. Ellabib

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Breast cancer is a major health burden worldwide being a major cause of death amongst women. In this paper, Fuzzy Inference Systems (FIS) are developed for the evaluation of breast cancer risk using Mamdani-type and Sugeno-type models. The paper outlines the basic difference between Mamdani-type FIS and Sugeno-type FIS. The results demonstrated the performance comparison of the two systems and the advantages of using Sugeno- type over Mamdani-type.

Keywords: Breast cancer diagnosis, Fuzzy Inference System (FIS), Fuzzy Logic, fuzzy intelligent technique.

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Authors: Wei Zhang, Hong Yu, Xian-ping Zhao, Da-da Wang, Fei Xue

Abstract:

For lack of the visualization of the ultrasonic detection method of partial discharge (PD), the ultrasonic detection technology combined with the X-ray visual detection method (UXV) is proposed. The method can conduct qualitative analysis accurately and conduct reliable positioning diagnosis to the internal insulation defects of GIS, and while it could make up the blindness of the X-ray visual detection method and improve the detection rate. In this paper, an experimental model of GIS is used as the trial platform, a variety of insulation defects are set inside the GIS cavity. With the proposed method, the ultrasonic method is used to conduct the preliminary detection, and then the X-ray visual detection is used to locate and diagnose precisely. Therefore, the proposed UXV technology is feasible and practical.

Keywords: GIS, ultrasonic, visual detection, X-ray

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Authors: Youngji Yoo, Cheong-Sool Park, Jun Seok Kim, Young-Hak Lee, Sung-Shick Kim, Jun-Geol Baek

Abstract:

In the semiconductor manufacturing process, large amounts of data are collected from various sensors of multiple facilities. The collected data from sensors have several different characteristics due to variables such as types of products, former processes and recipes. In general, Statistical Quality Control (SQC) methods assume the normality of the data to detect out-of-control states of processes. Although the collected data have different characteristics, using the data as inputs of SQC will increase variations of data, require wide control limits, and decrease performance to detect outof- control. Therefore, it is necessary to separate similar data groups from mixed data for more accurate process control. In the paper, we propose a regression tree using split algorithm based on Pearson distribution to handle non-normal distribution in parametric method. The regression tree finds similar properties of data from different variables. The experiments using real semiconductor manufacturing process data show improved performance in fault detecting ability.

Keywords: Semiconductor, non-normal mixed process data, clustering, Statistical Quality Control (SQC), regression tree, Pearson distribution system.

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Authors: Kefaya Qaddoum

Abstract:

The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: Neural network, conformal prediction, cancer classification, regression.

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Authors: O. Kadri, M. D. Mouss, L.H. Mouss, F. Merah

Abstract:

This paper proposes a novel hybrid algorithm for feature selection based on a binary ant colony and SVM. The final subset selection is attained through the elimination of the features that produce noise or, are strictly correlated with other already selected features. Our algorithm can improve classification accuracy with a small and appropriate feature subset. Proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through a real Rotary Cement kiln dataset. The results show that our algorithm outperforms existing algorithms.

Keywords: Binary Ant Colony algorithm, Support VectorMachine, feature selection, classification.

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