Complex Computational Procedures Research Articles

Assessing the Possibility of Implementing Distributed Diagnostic Systems for Hydrogen Fuel Cells Using Local and Global Telecommunication Networks

The development of effective methods and means for diagnosing hydrogen fuel cells during their operation is an urgent task, since their implementation will improve the energy characteristics and stability of operation by detecting incorrect operating modes and malfunctions at the early stages of development. This problem can be solved by using various approaches based on the analysis of impedance, relaxation or fluctuation-noise characteristics. All of these methods require the implementation of rather complex computational procedures and the accumulation of fairly large amount of statistical data, which makes it difficult to implement diagnostic devices on inexpensive embedded microprocessor tools. To overcome this drawback, this work proposes the use of distributed diagnostic systems with high-performance server devices connected to local modules via global or local telecommunication networks. Such an implementation will significantly simplify the requirements for local diagnostic modules and reduce their cost, but this raises the question of the efficiency of making diagnostic decisions by such systems.The work carried out, is a study of delays in making diagnostic and control decisions when using remote server devices for signal processing. It has been shown that the use of such systems makes it possible to achieve delays of less than one second even when using intercontinental communication lines. It should be noted that in applications critical to time delays, the distance between the client and server systems should be reduced. The advantages of the proposed implementation of a diagnostic system for electrochemical energy sources are the possibility of simplifying local measurement systems installed on individual sources, the possibility of accelerating signal processing through the use of more productive computing tools and the accumulation of statistical data.

A simple but actionable metric for assessing inequity in resident greenspace exposure

An increasing number of studies aim to improve and perfect the evaluation system for assessing greenspace exposure, yet it may also become more difficult to apply the evaluation index to landscape planning. Here we propose a simple but actionable index system – Greenspace Exposure Inequity index (GEII), for assessing the inequity of residents' greenspace exposure. GEII includes quantity-based availability, distance-based accessibility, and inequity-based Gini index for assessing the difference in greenspace exposure pattern. Then we selected Shanghai as a case to test the feasibility of GEII, analyzing the spatiotemporal evolution of greenspace exposure patterns, and further demonstrating the operability of the index. (1) Availability inequity for 2012, 2015, 2018, and 2021 was 0.603, 0.512, 0.514, and 0.489. The Gini index was between 0.4 and 0.6, and presented a downward trend. (2) Accessibility inequity for 2012, 2015, 2018, and 2021 was 0.372, 0.368, 0.364, and 0.344. It can be clearly seen that it has changed less over ten years, but overall equality has been rising. (3) Using GEII to calculate the inequity of Shanghai, the Gini index for 2012, 2015, 2018, and 2021 was 0.392, 0.378, 0.373, and 0.357. The inequity of greenspace exposure assessed by GEII is gradually decreasing similarly, which illustrates the positive impact of urban greening policies. The GEII has three highlights: serviceability, human-oriented, and expandability. GEII abandons the complex computational evaluation procedures of numerous indicators and bridges the gap between theoretical research on inequity and practical planning, so GEII is of great value for alleviating the uneven exposure of residents' greenspace and scientifically optimizing landscape planning.

Determining the Main Characteristics of Motor Lubricants Using Color Indices and Prediction Models

This paper proposes tools for predicting the main characteristics of motor lubricating oils that can be used to assess their quality. A comparative analysis of four measuring devices, operating on optical principle, was performed to predict the main characteristics of motor lubricating oils. It has been found that the combined use of seven color indices leads to increased accuracy in predicting the value characteristics of motor oils. The two main components are needed to reduce the data volume of the vector containing these features, which leads to a reduction in the volume of data used. It was found that according to colorimeter data, regression models have higher predictive properties than those obtained with a video camera and a digital camera. The proposed research tools can be used with single-board microcomputer systems, which do not require complex computational procedures. The use of the proposed in this article techniques and tools in practice would reduce the effect of the measuring instrument experience.

A Novel Method for Spectrometry Based on Imaging Systems

γ-ray spectrometry is a well-known technique in environmental radioactivity measurements where easily handled systems are needed. Scintillators coupled to a photomultiplier tube (PMT), are typically favoured over solid-state detectors as mobile spectrometers. Replacing PMT with position sensitive devices represents an innovative solution that provides the evaluation of the interaction point of the incident radiation. The knowledge of spectrometry as a function of the depth of interaction (DoI) assures a better understanding of the spectrum and a more reliable identification of the source. In this paper, the efficiency of a simple DoI estimator has been studied using a CRY018 monolithic crystal coupled to a multi-anode photomultiplier tube. The DoI estimator has been evaluated studying charge distributions and the dependency of spectrometric properties on the DoI has been qualitatively analyzed. The estimator has shown to be highly sensitive to the DoI, enabling a better understanding of the internal interaction processes of light and an efficient rejection of the background component on the spectra. The novelty of this work lies in the application of the DoI selection in spectrometry made available by the use of MAPMT. The proposed method is practical since it does not require complicated hardware solutions or complex computational procedures.

DYNAMIC HANDWRITTEN SIGNATURE IDENTIFICATION USING SPIKING NEURAL NETWORK

The article proposes a method for dynamic signature identification based on a spiking neural network. Three dynamic signature parameters l(t), xy(t), p(t) are used, which are invariant to the signature slope angle, and after their normalization, also to the signature spatial and temporal scales. These dynamic parameters are fed to the spiking neural network for recognition simultaneously in the form of time series without preliminary transformation into a vector of static features, which, on the one hand, simplifies the method due to the absence of complex computational transformation procedures, and on the other hand, prevents the loss of useful information, and therefore increases the accuracy and reliability of signature identification and recognition (especially when recognizing forged signatures that are highly correlated with the genuine). The spiking neural network used has a simple training procedure, and not all neurons of the network are trained, but only the output ones. If it is necessary to add new signatures, it is not necessary to retrain the entire network as a whole, but it is enough to add several output neurons and learn only their connections. In the results of experimental studies of the software implementation of the proposed system, it’s EER = 3.9% was found when identifying skilled forgeries and EER = 0.17% when identifying random forgeries.

ВЕРИФІКАЦІЯ ТА РОЗПІЗНАВАННЯ ПІДПИСУ ЯК БАГАТОПАРАМЕТРИЧНОГО ПРОЦЕСУ НА ОСНОВІ СПАЙКІНГОВОЇ НЕЙРОННОЇ МЕРЕЖІ

The article reviews the known methods of dynamic signature verification, which are summarized in the classification table. A method of dynamic signature verification based on a spiking neural network is proposed. Three dynamic parameters of the signature l(t), Dα(t), Z(t)  are chosen, which are invariant to the angle of inclination of the signature, and after their normalization - also to the spatial and temporal scales of the signature. These dynamic signature parameters are simultaneously fed to the spiking neural network for recognition in the form of time series without prior conversion into a vector of static features. This, on the one hand, simplifies the method due to the absence of complex computational conversion procedures and, on the other hand, prevents the loss of useful information and therefore increases the accuracy and reliability of signature verification and recognition (especially for forgery signatures that are highly correlated with genuine ones). The used neural network has a simple learning procedure, and not all neurons of the network are trained, but only the output neurons. If you need to add new signatures, you do not need to retrain the entire network, but just add a few output neurons and learn only their connections.

Cooperating Edge Cloud-Based Hybrid Online Learning for Accelerated Energy Data Stream Processing in Load Forecasting

The data analysis platform used in smart grid is important to provide more accurate data validation and advanced power services. Recently, the researches based on deep neural network have been increasing in data analytic platforms to address various problems using artificial intelligence. The main problem to analyze multiple meter data based on deep learning is that the data distribution is varying according to both different client and time flow. Some studies, such as continual learning, are effective in dynamically fluctuating data distributions, but require additional complex computational procedures that make it difficult to construct an online learning system for processing data streams. In this paper, we proposed a hybrid deep learning scheduling algorithm to improve accuracy and accelerate learning performance in a multiple smart meter source environment, of which biased data feature varies dynamically. We use a simple analysis method, cosine similarity, to reduce computation complexity. By analyzing the frequency distribution of cosine similarity, a model recognizes that biased data feature of power consumption patterns. The skewed data distribution is reduced by using the zero skewness property of of an uniform distribution. The diversity of memory buffer was increased by update strategy which maximizes variance of pattern. When scheduling an online and offline gradient in different computational complexity, the proposed model reduces processing time by selectively calculating gradient considering the degree of data feature transition. To verify the performance of the proposed algorithm, we conducted three experiments with AMI stream data on the proposed method and the existing method of online learning. The experimental results demonstrate that our method can achieve reasonable performance in terms of trade-off between accuracy and processing time.

Development of algorithm of computer-information control by nonlinear dynamic technology

The computer-information technology of the production of soda ash in the ammoniac way consists of basic and auxiliary computer-information technologies with complex technological processes and devices with closed cycles on material flows. The feature of these technologies is to change the characteristics in time due to unforeseen violations of the technological regime due to the presence of aggressive and abrasive media that crystallize. In addition, the main technologies of computer-information technology for the production of soda ash in the ammoniac way relate to nonlinear dynamic technologies. One of the main technologies is the computer-information technology of gas regeneration from solutions of the technology of production of soda ash in an ammoniac way in order to saturate the purified brine to obtain the ammonized brine of a given quality. The presence of closed cycles of ammonia and carbon dioxide significantly complicates the management of this technology. One of the problems arising in the development of computer-information management by nonlinear dynamic technologies is the development of a rather simple and convenient way to calculate algorithms for managing such technologies. This is primarily due to the fact that solving the problem of computer-information management by nonlinear dynamic technologies in the traditional formulation and commonly used methods leads to complex computational procedures that are difficult to manage in real time. Therefore, certain interests are management algorithms that simplify the process of controlling nonlinear dynamic technologies without losing its quality. The development of algorithms for such real technologies is performed using passive experiments. The algorithm of computer-information management of non-linear dynamic technology of gas regeneration from solutions of the technology of production of calcined soda by the ammoniac method was developed in order to saturate the purified brine with the obtaining of the ammonized brine of a given quality, which essentially differs by the simplicity of implementation and high speed, has similar properties to the properties of optimal algorithms, and has the ability, with constant correction of models, to adapt to the properties of nonlinear dynamic ones technologies. In addition, the algorithm developed reduces the cost of the original product, greatly simplifies and improves the process of computer-information management with nonlinear dynamic technologies with the help of modern multifunctional, high-performance, multichannel, high-speed and reliable microprocessor controllers with special programs.

Optimal Coherent Processing Interval Selection for Aerial Maneuvering Target Imaging Using Tracking Information

When a target is in complex three-dimensional (3-D) motions, inverse synthetic aperture radar (ISAR) images cannot be obtained with any motion compensation algorithms. In this case, selecting optimal coherent processing interval (CPI) (or frame) of the fixed rotational axis is the only way to obtain focused ISAR images. In this article, we focus on selecting optimal CPI for ISAR imaging of maneuvering targets, and a novel method using tracking information is proposed. Without complex computational procedures, the proposed method provides robust performance. The method includes three steps. First, target’s orientation (roll, pitch, and yaw) is estimated by using an extended Kalman filter. Second, the least-square method is adopted to find the best fitted lines of roll, pitch, and yaw angles in different time intervals, and the linearity degrees of the three rotation angles are measured over the imaging intervals. Third, the thresholds of the linearity degrees are set, according to which, optimal CPIs can be selected. The performance of proposed method is demonstrated using both simulated data and measured data. In both cases, optimal CPIs can be selected and well-focused ISAR images can be achieved.

Statistical Methods in Medical Research

Since John Snow first conducted a modern epidemiological study in 1854 during a cholera epidemic in London, statistics has been associated with medical research. After Austin Bradford Hill published a series of articles on the use of statistical methodology in medical research in 1937, statistical considerations and computational tools have been paramount in conductingmedical research [1]. For the past century, statistics has played an important role in the advancement of medical research and medical research has stimulated rapid development of statistical methods. For example, the development of modern survival analysis-an important branch of statistics has aimed to solve problems encountered in clinical trials and large-scale epidemiological studies. In this era of evidence-based medicine, the development of novel statistical methods will continue to be crucial in medical research. With the expansion of computer capacity and advancement of computational techniques, it is inevitable that modern statistical methods will likely incorporate, to a greater degree, complex computational procedures. This issue focuses on statistical methods in medical research. Several novel methods aiming on solving different medical research questions are introduced. Some unique approaches of statistical analysis are also present. Hanagal and Sharma contribute two papers. The first one deals with a bivariate survival model. They examine a parameter estimation issue when the samples are taken from a bivariate log-logistic distribution with shared gamma frailty. They propose to use a Bayesian approach along with theMarkov ChainMonte Carlo computational technique for implementation. The computer simulation is conducted for performance evaluation. Two well-known datasets, one about acute leukemia and the other about kidney infection are applied as examples. The second paper contributed by Hanagal and Sharma examines the shared inverse Gaussian frailty model with the bivariate exponential baseline hazard. They first derive the likelihood of the joint survival function. In their Bayesian approach, the parameters of the baseline hazard are assumed to follow a gamma distribution while the coefficients of the regression relationship are assumed to follow an independent normal distribution. The dependence of two components of the survival function is tested. Three information criteria are used for model comparisons. The proposed method is applied to analyze diabetic retinopathy data. The paper by Chang, Lyer, Bullitt and Wang provides a method to find determinants of the brain arterial system. They represent the brain arterial system as a binary tree and apply the mixed logistic regression model to find significant covariates. The authors also demonstrate model selection methods for both fixed and random effects. A case study is presented using the method. This paper provides a rigorous approach for analyzing the binary branching structure data. It is potentially applicable to other tree structure data. Chakraborty proposes two probabilistic models to estimate male-to-female HIV-1 transmission rate in one sexual contact. One model is applicable when the transmitter cell counts are known and the other model is applicable when the receptor cell counts are known. By first uniformizing each transmitter (or receptor) cell count and assuming as a beta distribution, this paper algebraically derives the transition probability by imposing some boundary conditions based on scientific phenomena related to HIV infection. The paper by Yeh, Jiang, Garrard, Lei and Gajewski proposes to use a zero-truncated Poisson model to analyze human cancer tissues transplanted to mice when the positive counts of affected ducts is subject to right censoring. A Bayesian approach choosing a Gamma distribution as the prior is adopted. After implementing through complex computational procedures, this paper obtains the estimates of the coefficients and demonstrates model fitting through

Comparison between the method of Fuzzy Control and Discriminate Function in the classification of some wells Nineveh

In this study, the construction of a Fuzzy Inference Control to determine the validity of the quality of ground water wells of some of Nineveh province, through a series of observations that were obtained from the areas under study. To illustrate the importance of the model it was compared with the Discriminate Function that classify the individual to the right community. The model , has proved a model of control Fuzzy high efficiency in the determination of the validity of each well is compared to a model Discriminate Function was the only classification of the wells under study where there it is valid or invalid to drink, as well as easy construction of the computational procedures required to build a Fuzzy Inference model compared to complex computational procedures required by the Discriminate Function. The configured model can be used in future to distinguish the quality of any groundwater wells based on qualitative characteristics of the waters. Keyword: Fuzzy Control, Discriminate Function, groundwater.

Diagnosing skills of statistical hypothesis testing using the Rule Space Method

This study illustrated the use of the Rule Space Method to diagnose students’ proficiencies in, skills and knowledge of statistical hypothesis testing. Participants included 96 undergraduate and, graduate students, of whom 94 were classified into one or more of the knowledge states identified by, the rule space analysis. Analysis at the level of proficiency groups showed that the critical difference, between low and medium proficiency groups was the understanding of statistical concepts and, knowledge while the critical skill discriminating the medium proficiency group from the high, proficiency group was to mange complex computational procedures. In addition, attribute profiles of, two students showed how students with the same total score can possess different strengths and, weaknesses.

MR diffusion tensor imaging

Magnetic Resonance Imaging is arguably the most versatile and complete imaging modality in Medicine nowadays. Its performance and techniques are totally dependent on complex computational procedures relying on powerful computers. Diffusion Imaging is one of the newest and most exciting of the advanced MRI techniques, providing structural and microscopic functional information about the mobility of water in the intra and extra cellular spaces and the shape of cellular spaces where water is present. The most dramatic of these techniques is the one called White Matter Fiber Tracking, which allows us to see the structure of the axon fibers within the brain.

Reversing the Error-Correction Scheme for a Fault-Tolerant Indexing

Summary form only given. The article presents an innovative approach to approximate matching of multi-attribute objects based on reversing the conventional scheme of error-correction coding. The approximate matching problem primarily arises in information retrieval systems, which can store fuzzily described items and operate with nebulous searching criteria. To establish an approximate equivalence relation on a set of multi-attribute objects it has been suggested to apply a decoding procedure to binary vectors corresponding to these objects and to use the obtained message words as hash codes. With this hashing technique it is possible to construct fault-tolerant indices allowing certain mismatches of binary vectors in terms of Hamming metrics. The simplest practical realization of this technique is based on the so-called perfect Golay code which maps 23-bit vectors into 12-bit message words. In this case, two different 23-bit vectors at a Hamming distance of 2 would have some common 12-bit indices. This provides an organization of a direct retrieval of a neighborhood of 23 bit-vectors with up to two mismatches from a given key. The proposed technique employs a reasonable redundancy and can trade utilization of extra memory for the speed and range of searching. Besides a direct application to information retrieval, the developed technique is also beneficial for complex computational procedures incorporating near-matching operations. A typical procedure of this kind is recovering of closed matches from vector-quantization tables.

The use of neural networks in predicting turning forces

The purpose of this research is to develop a predictive turning-force model based on neural networks. In the first stage of the research, a cutting-force model based on orthogonal machining theory is studied. Turning forces can be estimated from this model using complex computational procedures when a knowledge of the flow stress and thermal properties of the work material and the cutting conditions is given. In the second stage of the research, a feed-forward neural network is trained by the cutting-force model. After the training process is finished, the neural network becomes a knowledge-based turning-force system. Good correlation between the neural prediction and experimental verification of the turning forces is shown.