Accuracy Of Output Data Research Articles

Assessment of Technical Means Quality Indicators for Smart Transport Systems

The purpose of this article is to show that it is necessary to assess the quality indicators for the technical means of smart transport systems. The quality of technical means, including smart transport systems, is associated with the selection of rational information processing technologies. Data in information systems is processed in different sections. The authors suggest an assessment technology for the operational quality of technical means of automated transport information systems. They identify the key quality indicators for the operation of technical means of information systems. The authors demonstrate that the operation of technical means can be characterized by the system task time, output data accuracy, and system implementability. They present the calculation of technical means quality indicators for smart transport systems

The use of cartographic control points in the harmonization and revision of MRDBs

The main purpose of the research is to achieve fully automated approach for supplying multi-resolution databases with linear objects in each scale. Moreover, the proposed solutions maintain the repeatability and accuracy of output data wherever possible according to the input scale. These properties are achieved by keeping the minimal object dimensions as well as the appropriate data pre-processing, based on the classification of source points. The classification distinguishes three classes of points: constant (unchangeable), temporary, and inherited. These classes build a structure of cartographic control points. Based on these solutions, the authors proposed an algorithm for linear object simplification based on minimal object dimensions and cartographic control points. It was also confirmed that the simplification between constant points does not cause geometry discrepancies in relation to the global simplification of the whole line.

NGS and blood group systems: State of the art and perspectives

Molecular analysis, or genotyping, of genes involved in the expression of blood group antigens has been a standard strategy used in immunohaematology laboratories routinely. For the past ten years, next-generation sequencing (NGS), or second-generation sequencing, has become the reference method in genetics. Extensive study of distinct targets, large genomic regions, and even whole genome is henceforth possible by this approach at minimal cost. Blood group genotyping has thus taken advantage of this technological advent. A few preliminary studies have open the way to NGS in this field by studying one or several genes, in a wide range of samples (donors and patients) by using several different platforms. These works have helped in the identification of both the benefits and limitations of the technology. Other recently published studies have benefited from these preliminary data to improve the methodology, specificity and accuracy of output data. In parallel novel strategies, i.e. third-generation sequencing, which can sequence long DNA regions at the single-molecule level, have emerged and shown promise for the potential resolution of complex rearrangements involving genes of the Rh and MNS blood group systems respectively. As technological and methodological hurdles have been overcome, these approaches may be used in a clinical situation in a near future.

Mold Growth Risk Analysis on the Surfaces of an Organic Building Structure Based on <i>In Situ</i> Monitoring

This article deals with the assessment of methodologies for water vapor condensation determination and mold growth risk analysis on the surfaces of an organic building structure based on in-situ monitoring, carried out on the structural elements of renovated roof truss above the greenhouse “Květná zahrada” in Kroměříž. The garden including the building is signed on the UNESCO Word Heritage List for its historical and architectonical value. The monitoring was focused on hygrothermal behavior of a particular attic area during winter term 2014 - 2015. The risks of water vapor condensation and mold growth were then analyzed on the basis of data obtained by the monitoring process. These also include measurement and output data accuracy is included as well. The Results were used as recommendation for a control system of forced ventilation in the monitored attic area.

Computer Aided Instrument to be Used as an Automatic Design Component

The paper presents an original method to enhance the productivity of the design activity based on a dimensioning stage which integrates information from various sources: tabular data, diagrams, experimental data and others. The integration of such data in an analytic way is based on anoriginal data processorwhich interpolates data and outputs diagrams, and outputs information regarding the laws of variation expressed as comma separated values data files, as well as automatically generated source code libraries in C++, Java and MATHLAB/GNU Octave. This automatically generated code is optimized in terms of execution time and it was verified in several applications. The data processor allows the user to visually verify the output data accuracy. The paper also presents some of the applications of the data processor: automatic design, method to enhance the accuracy of computer based calculus for both analytic and numerical methods, conversion of the discontinuous experimental data into laws of variation and experimental data processing in applied elasticity. There are several directions offuture research, such as: a more general mathematical approach, development of a more general computer based instrument with increased flexibility and accuracy. Also, a complex software for applied elasticity calculi based on the results of the data processor is under development. Implementation of these applications in commercial CAD/CAE applications is another direction of research.

Platform to Build the Knowledge Base by Combining Sensor Data and Context Data

Sensor data is structured and generally lacks of meaning by itself, but life-logging data (time, location, etc.) out of sensor data can be utilized to create lots of meaningful information combined with social data from social networks like Facebook and Twitter. There have been many platforms to produce meaningful information and support human behavior and context-awareness through integrating diverse mobile, social, and sensing input streams. The problem is that these platforms do not guarantee the performance in terms of the processing time and even let the accuracy of output data be addressed by new studies in each area where the platform is applied. Thus, this study proposes an improved platform which builds a knowledge base for context awareness by applying distributed and parallel computing approach considering the characteristics of sensor data that is collected and processed in real-time, and compares the proposed platform with existing platforms in terms of performance. The experiment shows the proposed platform is an advanced platform in terms of processing time. We reduce the processing time by 40% compared with existing platform. The proposed platform also guarantees the accuracy compared with existing platform.

Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

ObjectiveThe purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models.Materials and MethodsFive hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n = 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05).ResultsThe AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer.ConclusionThe performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.