Search results for: stock movement prediction.

Authors: Hamza Nejib, Okba Taouali

Abstract:

This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: KLMS, online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS.

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Authors: M. F. V. Montezuma, E. P. Deus, M. C. Carvalho

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Steel bridges are normally subjected to random loads with different traffic frequencies. They are structures with dynamic behavior and are subject to fatigue failure process, where the nucleation of a crack, growth and failure can occur. After locating and determining the size of an existing fault, it is important to predict the crack propagation and the convenient time for repair. Therefore, fracture mechanics and fatigue concepts are essential to the right approach to the problem. To study the fatigue crack growth, a computational code was developed by using the root mean square (RMS) and the cycle-by-cycle models. One observes the variable amplitude loading influence on the life structural prediction. Different loads histories and initial crack length were considered as input variables. Thus, it was evaluated the dispersion of results of the expected structural life choosing different initial parameters.

Keywords: Fatigue crack propagation, life prediction, variable loadings, steel bridges.

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Authors: Mihai Lungu, Romulus Lungu, Lucian Grigorie

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The paper presents a new system for the automat control of the aircrafts- flight in lateral plane using the cinematic model and the dynamic inversion. Starting from the equations of the aircrafts- lateral movement, the authors use two axes systems and obtained a control law that cancels the lateral deviation of the flying objects from the runway line. This system makes the aircrafts- direction angle to follow the direction angle of the runway line. Simulations in Matlab/Simulink have been done for different aircraft-s initial points and direction angles. The inconvenience of this system is the long duration of the “transient regime". That is why this system can be used independently, but the results are not very good; thus, it can be a part (subsystem) of other systems. The main system that cancels the lateral deviation from the runway line is based on dynamic inversion and uses, as subsystem, the control system for the lateral movement using the cinematic model. Using complex Matlab/Simulink models, the authors obtained the time evolution of the direction angle and the time evolution of the aircraft lateral deviation with respect to the runway line, for different values of the initial direction angle and for different wind types. The system has a very good behavior for all initial direction angles and wind types.

Keywords: Direction angle, Dynamic inversion, Lateraldeviation, Lateral movement

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Authors: Suelane Garcia Fontes, Silvio Luiz Stanzani, Pedro L. Pizzigatti Corrła Ronaldo G. Morato

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Environmental changes and major natural disasters are most prevalent in the world due to the damage that humanity has caused to nature and these damages directly affect the lives of animals. Thus, the study of animal behavior and their interactions with the environment can provide knowledge that guides researchers and public agencies in preservation and conservation actions. Exploratory analysis of animal movement can determine the patterns of animal behavior and with technological advances the ability of animals to be tracked and, consequently, behavioral studies have been expanded. There is a lot of research on animal movement and behavior, but we note that a proposal that combines resources and allows for exploratory analysis of animal movement and provide statistical measures on individual animal behavior and its interaction with the environment is missing. The contribution of this paper is to present the framework AniMoveMineR, a unified solution that aggregates trajectory analysis and data mining techniques to explore animal movement data and provide a first step in responding questions about the animal individual behavior and their interactions with other animals over time and space. We evaluated the framework through the use of monitored jaguar data in the city of Miranda Pantanal, Brazil, in order to verify if the use of AniMoveMineR allows to identify the interaction level between these jaguars. The results were positive and provided indications about the individual behavior of jaguars and about which jaguars have the highest or lowest correlation.

Keywords: Data mining, data science, trajectory, animal behavior.

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Authors: Tamara Grujic, Mirjana Bonkovic

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Measurements and quantitative analysis of kinematic parameters of human hand movements have an important role in different areas such as hand function rehabilitation, modeling of multi-digits robotic hands, and the development of machine-man interfaces. In this paper the assessment and evaluation of the reachto- grasp movement by using computerized and robot-assisted method is described. Experiment involved the measurements of hand positions of seven healthy subjects during grasping three objects of different shapes and sizes. Results showed that three dominant phases of reach-to-grasp movements could be clearly identified.

Keywords: Human hand, kinematics, reach-to-grasp movement.

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Authors: E. Ardil, Erdem Uçar, Parvinder S. Sandhu

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As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA-s public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software.

Keywords: Software Metrics, Fuzzy, Neuro-Fuzzy, SoftwareFaults, Accuracy, MAE, RMSE.

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Authors: Constantin Pehoiu

Abstract:

School physical education, through its objectives and contents, efficiently valorizes the pupils- abilities, developing them, especially the coordinative skill component, which is the basis of movement learning, of the development of the daily motility and also of the special, refined motility required by the practice of certain sports. Medium school age offers the nervous and motor substratum needed for the acquisition of complex motor habits, a substratum that is essential for the coordinative skill. Individuals differ as to the level at which this function is performed, the extent to which this function turns an individual into a person that is adapted and adaptable to complex and various situations. Spatio-temporal orientation, together with movement combination and coupling, and with kinesthetic, balance, motor reaction, movement transformation and rhythm differentiation form the coordinative skills. From our viewpoint, these are characteristic features with high levels of manifestation in a complex psychomotor act - valorizing the quality of one-s talent - as well as indices pertaining to one-s psychomotor intelligence and creativity.

Keywords: development, lesson, spatio-temporal orientation, physical education.

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Authors: Ali Aqa Mohammad Jawad

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Taking a relational approach, this paper analyzes the causal mechanisms associated with successful mobilization and rapid demobilization of the Enlightening Movement in post-2001 Afghanistan. The movement emerged after the state-owned Da Afghan Bereshna Sherkat (DABS) decided to divert the route for the Turkmenistan-Uzbekistan-Tajikistan-Afghanistan-Pakistan (TUTAP) electricity project. The grid was initially planned to go through the Hazara-inhabited province of Bamiyan, according to Afghanistan’s Power Sector Master Plan. The reroute served as an aide-mémoire of historical subordination to other ethno-religious groups for the Hazara community. It was also perceived as deprivation from post-2001 development projects, financed by international aid. This torched the accumulated grievances, which then gave birth to the Enlightening Movement. The movement had a successful mobilization. However, it demobilized after losing much of its mobilizing capabilities through an amalgamation of external and internal relational factors. The successful mobilization yet rapid demobilization constitutes the puzzle of this paper. From the theoretical perspective, this paper is significant as it establishes the applicability of contentious politics theory to protest mobilizations that occurred in Afghanistan, a context-specific, characterized by ethnic politics. Both primary and secondary data are utilized to address the puzzle. As for the primary resources, media coverage, interviews, reports, public media statements of the movement, involved in contentious performances, and data from Social Networking Services (SNS) are used. The covered period is from 2001-2018. As for the secondary resources, published academic articles and books are used to give a historical account of contentious politics. For data analysis, a qualitative comparative historical method is utilized to uncover the causal mechanisms associated with successful mobilization and rapid demobilization of the Movement. In this pursuit, both mobilization and demobilization are considered as larger political processes that could be decomposed to constituent mechanisms. Enlightening Movement’s framing and campaigns are first studied to uncover the associated mechanisms. Then, to avoid introducing some ad hoc mechanisms, the recurrence of mechanisms is checked against another case. Mechanisms qualify as robust if they are “recurrent” in different episodes of contention. Checking the recurrence of causal mechanisms is vital as past contentious events tend to reinforce future events. The findings of this paper suggest that the public sphere in Afghanistan is drastically different from Western democracies known as the birthplace of social movements. In Western democracies, when institutional politics did not respond, movement organizers occupied the public sphere, undermining the legitimacy of the government. In Afghanistan, the public sphere is ethicized. Considering the inter- and intra-relational dynamics of ethnic groups in Afghanistan, the movement reduced to an erosive inter- and intra-ethnic conflict. This undermined the cohesiveness of the movement, which then kicked-off its demobilization process.

Keywords: Enlightening movement, contentious politics, mobilization, demobilization.

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Authors: Chang S. Woo, Hyun S. Park, Wan D. Kim

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Fatigue life prediction and evaluation are the key technologies to assure the safety and reliability of automotive rubber components. The objective of this study is to develop the fatigue analysis process for vulcanized rubber components, which is applicable to predict fatigue life at initial product design step. Fatigue life prediction methodology of vulcanized natural rubber was proposed by incorporating the finite element analysis and fatigue damage parameter of maximum strain appearing at the critical location determined from fatigue test. In order to develop an appropriate fatigue damage parameter of the rubber material, a series of displacement controlled fatigue test was conducted using threedimensional dumbbell specimen with different levels of mean displacement. It was shown that the maximum strain was a proper damage parameter, taking the mean displacement effects into account. Nonlinear finite element analyses of three-dimensional dumbbell specimens were performed based on a hyper-elastic material model determined from the uni-axial tension, equi-biaxial tension and planar test. Fatigue analysis procedure employed in this study could be used approximately for the fatigue design.

Keywords: Rubber, Material test, Finite element analysis, Strain, Fatigue test, Fatigue life prediction.

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Authors: Chia-Ta Tsai, Wen-Lin Huang, Shinn-Jang Ho, Li-Sun Shu, Shinn-Ying Ho

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Prediction of bacterial virulent protein sequences can give assistance to identification and characterization of novel virulence-associated factors and discover drug/vaccine targets against proteins indispensable to pathogenicity. Gene Ontology (GO) annotation which describes functions of genes and gene products as a controlled vocabulary of terms has been shown effectively for a variety of tasks such as gene expression study, GO annotation prediction, protein subcellular localization, etc. In this study, we propose a sequence-based method Virulent-GO by mining informative GO terms as features for predicting bacterial virulent proteins. Each protein in the datasets used by the existing method VirulentPred is annotated by using BLAST to obtain its homologies with known accession numbers for retrieving GO terms. After investigating various popular classifiers using the same five-fold cross-validation scheme, Virulent-GO using the single kind of GO term features with an accuracy of 82.5% is slightly better than VirulentPred with 81.8% using five kinds of sequence-based features. For the evaluation of independent test, Virulent-GO also yields better results (82.0%) than VirulentPred (80.7%). When evaluating single kind of feature with SVM, the GO term feature performs much well, compared with each of the five kinds of features.

Keywords: Bacterial virulence factors, GO terms, prediction, protein sequence.

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Authors: Chuan-Ching Sue, Shi-Zhou Chen, Ting-Yu Huang

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Dynamic bandwidth allocation in EPONs can be generally separated into inter-ONU scheduling and intra-ONU scheduling. In our previous work, the active intra-ONU scheduling (AS) utilizes multiple queue reports (QRs) in each report message to cooperate with the inter-ONU scheduling and makes the granted bandwidth fully utilized without leaving unused slot remainder (USR). This scheme successfully solves the USR problem originating from the inseparability of Ethernet frame. However, without proper setting of threshold value in AS, the number of QRs constrained by the IEEE 802.3ah standard is not enough, especially in the unbalanced traffic environment. This limitation may be solved by enlarging the threshold value. The large threshold implies the large gap between the adjacent QRs, thus resulting in the large difference between the best granted bandwidth and the real granted bandwidth. In this paper, we integrate AS with a cooperative prediction mechanism and distribute multiple QRs to reduce the penalty brought by the prediction error. Furthermore, to improve the QoS and save the usage of queue reports, the highest priority (EF) traffic which comes during the waiting time is granted automatically by OLT and is not considered in the requested bandwidth of ONU. The simulation results show that the proposed scheme has better performance metrics in terms of bandwidth utilization and average delay for different classes of packets.

Keywords: EPON, Inter-ONU and Intra-ONU scheduling, Prediction, Unused slot remainder

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Authors: Sofien Chtourou, Mohamed Chtourou, Omar Hammami

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Embedded systems need to respect stringent real time constraints. Various hardware components included in such systems such as cache memories exhibit variability and therefore affect execution time. Indeed, a cache memory access from an embedded microprocessor might result in a cache hit where the data is available or a cache miss and the data need to be fetched with an additional delay from an external memory. It is therefore highly desirable to predict future memory accesses during execution in order to appropriately prefetch data without incurring delays. In this paper, we evaluate the potential of several artificial neural networks for the prediction of instruction memory addresses. Neural network have the potential to tackle the nonlinear behavior observed in memory accesses during program execution and their demonstrated numerous hardware implementation emphasize this choice over traditional forecasting techniques for their inclusion in embedded systems. However, embedded applications execute millions of instructions and therefore millions of addresses to be predicted. This very challenging problem of neural network based prediction of large time series is approached in this paper by evaluating various neural network architectures based on the recurrent neural network paradigm with pre-processing based on the Self Organizing Map (SOM) classification technique.

Keywords: Address, data set, memory, prediction, recurrentneural network.

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Authors: Mahdi Yaghobi, Mohammad Haeri

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The current methods of predictive controllers are utilized for those processes in which the rate of output variations is not high. For such processes, therefore, stability can be achieved by implementing the constrained predictive controller or applying infinite prediction horizon. When the rate of the output growth is high (e.g. for unstable nonminimum phase process) the stabilization seems to be problematic. In order to avoid this, it is suggested to change the method in the way that: first, the prediction error growth should be decreased at the early stage of the prediction horizon, and second, the rate of the error variation should be penalized. The growth of the error is decreased through adjusting its weighting coefficients in the cost function. Reduction in the error variation is possible by adding the first order derivate of the error into the cost function. By studying different examples it is shown that using these two remedies together, the closed-loop stability of unstable nonminimum phase process can be achieved.

Keywords: GPC, Stability, Varying Weighting Coefficients.

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Authors: Yu Zhao, Min Zhang, Yuanqian Wang, Qiuyu Yu

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The prediction of aesthetics of apparel is helpful for the development of a new type of apparel. This study is to build the quantitative relationship between the aesthetics and its design parameters. In particular, women’s pants have been preliminarily studied. This aforementioned relationship has been carried out by statistical analysis. The contributions of this study include the development of a more personalized apparel design mechanism and the provision of some empirical knowledge for the development of other products in the aspect of aesthetics.

Keywords: Aesthetics, crease line, cropped straight leg pants, knee width.

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Authors: Sagir M. Yusuf, Chris Baber

Abstract:

In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Lèvy flight, situation awareness, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence.

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Authors: V. G. Rifert, V. V. Gorin, V. V. Sereda, V. V. Treputnev

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The analysis of heat transfer design methods in condensing inside plain tubes under existing influence of shear stress is presented in this paper. The existing discrepancy in more than 30-50% between rating heat transfer coefficients and experimental data has been noted. The analysis of existing theoretical and semi-empirical methods of heat transfer prediction is given. The influence of a precise definition concerning boundaries of phase flow (it is especially important in condensing inside horizontal tubes), shear stress (friction coefficient) and heat flux on design of heat transfer is shown. The substantiation of boundary conditions of the values of parameters, influencing accuracy of rated relationships, is given. More correct relationships for heat transfer prediction, which showed good convergence with experiments made by different authors, are substantiated in this work.

Keywords: Film condensation, heat transfer, plain tube, shear stress.

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Authors: Chih-Hsiang Chang, Pei-Shan Kao

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This paper discusses the reaction of investors in the Taiwan stock market to the most probable unknown earnings-related information and the most probable known earnings-related information. As compared with the previous literature regarding the effect of an official announcement of earnings forecast revision, this paper further analyzes investors’ cognitive bias toward the unknown and known earnings-related information, and the role of media during the investors' reactions to the foresaid information shocks. The empirical results show that both the unknown and known earnings-related information provides useful information content for a stock market. In addition, cognitive bias and disposition effect are the behavioral pitfalls that commonly occur in the process of the investors' reactions to the earnings-related information. Finally, media coverage has a remarkable influence upon the investors' trading decisions.

Keywords: Cognitive bias, role of media, disposition effect, earnings-related information, behavioral pitfall.

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Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

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Sea level rise threatens to increase the impact of future  storms and hurricanes on coastal communities. Accurate sea level  change prediction and supplement is an important task in determining  constructions and human activities in coastal and oceanic areas. In  this study, support vector machines (SVM) is proposed to predict  daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal  parameter values of kernel function are determined using a genetic  algorithm. The SVM results are compared with the field data and  with back propagation (BP). Among the models, the SVM is superior  to BPNN and has better generalization performance.

 

Keywords: Tides, Prediction, Support Vector Machines, Genetic Algorithm, Back-Propagation Neural Network, Risk, Hazards.

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Authors: Kamaldeep Kaur, Arvinder Kaur, Ruchika Malhotra

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The aim of this paper is to rank the impact of Object Oriented(OO) metrics in fault prediction modeling using Artificial Neural Networks(ANNs). Past studies on empirical validation of object oriented metrics as fault predictors using ANNs have focused on the predictive quality of neural networks versus standard statistical techniques. In this empirical study we turn our attention to the capability of ANNs in ranking the impact of these explanatory metrics on fault proneness. In ANNs data analysis approach, there is no clear method of ranking the impact of individual metrics. Five ANN based techniques are studied which rank object oriented metrics in predicting fault proneness of classes. These techniques are i) overall connection weights method ii) Garson-s method iii) The partial derivatives methods iv) The Input Perturb method v) the classical stepwise methods. We develop and evaluate different prediction models based on the ranking of the metrics by the individual techniques. The models based on overall connection weights and partial derivatives methods have been found to be most accurate.

Keywords: Artificial Neural Networks (ANNS), Backpropagation, Fault Prediction Modeling.

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Authors: Yagya Sharma, Basanta K. Rana, Arup K. Das

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Rotary entrainment is a phenomenon in which the interface of two immiscible fluids are subjected to external flux by means of rotation. Present work reports the experimental study on rotary motion of a horizontal cylinder between the interface of air and water to observe the penetration of gas inside the liquid. Experiments have been performed to establish entrainment of air mass in water alongside the cylindrical surface. The movement of tracer and seeded particles has been tracked to calculate the speed and path of the entrained air inside water. Simplified particle image velocimetry technique has been used to trace the movement of particles/tracers at the moment they are injected inside the entrainment zone and suspended beads have been used to replicate the particle movement with respect to time in order to determine the flow dynamics of the fluid along the cylinder. Present paper establishes a thorough experimental analysis of the rotary entrainment phenomenon between air and water keeping in interest the extent to which we can intermix the two and also to study its entrainment trajectories.

Keywords: Entrainment, gas-liquid flow, particle image velocimetry, stratified layer mixing.

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Authors: T.A. Adegbola, IEA Aghachi, E.R. Sadiku

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To investigates the effect of fiberglass clamping process improvement on drape simulation prediction. This has great effect on the mould and the fiber during manufacturing process. This also, improves the fiber strain, the quality of the fiber orientation in the area of folding and wrinkles formation during the press-forming process. Drape simulation software tool was used to digitalize the process, noting the formation problems on the contour sensitive part. This was compared with the real life clamping processes using single and double frame set-ups to observe the effects. Also, restrains are introduced by using clips, and the G-clamps with predetermine revolution to; restrain the fabric deformation during the forming process.The incorporation of clamping and fabric restrain deformation improved on the prediction of the simulation tool. Therefore, for effective forming process, incorporation of clamping process into the drape simulation process will assist in the development of fiberglass application in manufacturing process.

Keywords: clamping, fiberglass, drape simulation, pressforming.

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Authors: Sulata Maharana, B Biswas, Adity Ganguly, Ashok Kumar

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In this paper, the requirement for Coke quality prediction, its role in Blast furnaces, and the model output is explained. By applying method of Artificial Neural Networking (ANN) using back propagation (BP) algorithm, prediction model has been developed to predict CSR. Important blast furnace functions such as permeability, heat exchanging, melting, and reducing capacity are mostly connected to coke quality. Coke quality is further dependent upon coal characterization and coke making process parameters. The ANN model developed is a useful tool for process experts to adjust the control parameters in case of coke quality deviations. The model also makes it possible to predict CSR for new coal blends which are yet to be used in Coke Plant. Input data to the model was structured into 3 modules, for tenure of past 2 years and the incremental models thus developed assists in identifying the group causing the deviation of CSR.

Keywords: Artificial Neural Networks, backpropagation, CokeStrength after Reaction, Multilayer Perceptron.

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Authors: Ivo Jánský, Milan Rippel

Abstract:

The paper evaluates several hundred one-day-ahead VaR forecasting models in the time period between the years 2004 and 2009 on data from six world stock indices - DJI, GSPC, IXIC, FTSE, GDAXI and N225. The models model mean using the ARMA processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are estimated on the data from the in-sample period and their forecasting accuracy is evaluated on the out-of-sample data, which are more volatile. The main aim of the paper is to test whether a model estimated on data with lower volatility can be used in periods with higher volatility. The evaluation is based on the conditional coverage test and is performed on each stock index separately. The primary result of the paper is that the volatility is best modelled using a GARCH process and that an ARMA process pattern cannot be found in analyzed time series.

Keywords: VaR, risk analysis, conditional volatility, garch, egarch, tarch, moving average process, autoregressive process

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Authors: Revanth Sai Kosaraju, Michael Ramscar, Melody Dye

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Accounts of language acquisition differ significantly in their treatment of the role of prediction in language learning. In particular, nativist accounts posit that probabilistic learning about words and word sequences has little to do with how children come to use language. The accuracy of this claim was examined by testing whether distributional probabilities and frequency contributed to how well 3-4 year olds repeat simple word chunks. Corresponding chunks were the same length, expressed similar content, and were all grammatically acceptable, yet the results of the study showed marked differences in performance when overall distributional frequency varied. It was found that a distributional model of language predicted the empirical findings better than a number of other models, replicating earlier findings and showing that children attend to distributional probabilities in an adult corpus. This suggested that language is more prediction-and-error based, rather than on abstract rules which nativist camps suggest.

Keywords: Abstractness, child psychology, language acquisition, prediction and error.

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Authors: Prasit Wonglersak, Prakarnkiat Youngkong, Ittipon Cheowanish

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This paper aims to improve a fine lapping process of hard disk drive (HDD) lapping machines by removing materials from each slider together with controlling the strip height (SH) variation to minimum value. The standard deviation is the key parameter to evaluate the strip height variation, hence it is minimized. In this paper, a design of experiment (DOE) with factorial analysis by twoway analysis of variance (ANOVA) is adopted to obtain a statistically information. The statistics results reveal that initial stripe height patterns affect the final SH variation. Therefore, initial SH classification using a radial basis function neural network is implemented to achieve the proportional gain prediction.

Keywords: Stripe height variation, Two-way analysis ofvariance (ANOVA), Radial basis function neural network, Proportional gain prediction.

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Authors: E. Muzenda

Abstract:

Group contribution methods such as the UNIFAC are of major interest to researchers and engineers involved synthesis, feasibility studies, design and optimization of separation processes as well as other applications of industrial use. Reliable knowledge of the phase equilibrium behavior is crucial for the prediction of the fate of the chemical in the environment and other applications. The objective of this study was to predict the solubility of selected volatile organic compounds (VOCs) in glycol polymers and biodiesel. Measurements can be expensive and time consuming, hence the need for thermodynamic models. The results obtained in this study for the infinite dilution activity coefficients compare very well those published in literature obtained through measurements. It is suggested that in preliminary design or feasibility studies of absorption systems for the abatement of volatile organic compounds, prediction procedures should be implemented while accurate fluid phase equilibrium data should be obtained from experiment.

Keywords: Volatile organic compounds, Prediction, Phaseequilibrium, Environmental, Infinite dilution.

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Authors: J. Menčík, B. Culek, Jr., L. Beran, J. Mareš

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Large metal and concrete structures suffer by various kinds of deterioration, and accurate prediction of the remaining life is important. This paper informs about two methods for its assessment. One method, suitable for steel bridges and other constructions exposed to fatigue, monitors the loads and damage accumulation using information systems for the operation and the finite element model of the construction. In addition to the operation load, the dead weight of the construction and thermal stresses can be included into the model. The second method is suitable for concrete bridges and other structures, which suffer by carbonatation and other degradation processes, driven by diffusion. The diffusion constant, important for the prediction of future development, can be determined from the depth-profile of pH, obtained by pH measurement at various depths. Comparison with measurements on real objects illustrates the suitability of both methods.

Keywords: Bridges, carbonatation, concrete, diagnostics, fatigue, life prediction, monitoring, railway, simulation, structures.

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Authors: Yong Zhao, Jian He, Cheng Zhang

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Cardiovascular disease resulting from hypertension poses a significant threat to human health, and early detection of hypertension can potentially save numerous lives. Traditional methods for detecting hypertension require specialized equipment and are often incapable of capturing continuous blood pressure fluctuations. To address this issue, this study starts by analyzing the principle of heart rate variability (HRV) and introduces the utilization of sliding window and power spectral density (PSD) techniques to analyze both temporal and frequency domain features of HRV. Subsequently, a hypertension prediction network that relies on HRV is proposed, combining Resnet, attention mechanisms, and a multi-layer perceptron. The network leverages a modified ResNet18 to extract frequency domain features, while employing an attention mechanism to integrate temporal domain features, thus enabling auxiliary hypertension prediction through the multi-layer perceptron. The proposed network is trained and tested using the publicly available SHAREE dataset from PhysioNet. The results demonstrate that the network achieves a high prediction accuracy of 92.06% for hypertension, surpassing traditional models such as K Near Neighbor (KNN), Bayes, Logistic regression, and traditional Convolutional Neural Network (CNN).

Keywords: Feature extraction, heart rate variability, hypertension, residual networks.

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Authors: P. Bhramara, V. D. Rao, K. V. Sharma , T. K. K. Reddy

Abstract:

In designing of condensers, the prediction of pressure drop is as important as the prediction of heat transfer coefficient. Modeling of two phase flow, particularly liquid – vapor flow under diabatic conditions inside a horizontal tube using CFD analysis is difficult with the available two phase models in FLUENT due to continuously changing flow patterns. In the present analysis, CFD analysis of two phase flow of refrigerants inside a horizontal tube of inner diameter, 0.0085 m and 1.2 m length is carried out using homogeneous model under adiabatic conditions. The refrigerants considered are R22, R134a and R407C. The analysis is performed at different saturation temperatures and at different flow rates to evaluate the local frictional pressure drop. Using Homogeneous model, average properties are obtained for each of the refrigerants that is considered as single phase pseudo fluid. The so obtained pressure drop data is compared with the separated flow models available in literature.

Keywords: Adiabatic conditions, CFD analysis, Homogeneousmodel and Liquid – Vapor flow.

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Authors: Bushra Suhale Al-Busoda, Laith Kadim Al-Anbarry

Abstract:

The behavior of model piles embedded in a very high expansive soil was investigated, a specially manufactured saturation-drying tank was used to apply three cycles of wetting and drying to the expansive soil surrounding the model straight shaft and under reamed piles, the relative movement of the piles with respect to the soil surface was recorded with time, also the exerted uplift pressure of the piles due to soil swelling was recorded. The behavior of unloaded straight shaft and under reamed piles was investigated. Two design charts were presented for straight shaft and under reamed piles one for the required pile depth for zero upward movement due to soil swelling, while the other for the required pile depth to exert zero uplift pressure when the soil swells. Under reamed piles showed a decrease in upward movement of 20% to 30%, and an uplift pressure decrease of 10% to 30%.

Keywords: Expansive Soil, Piles, under reamed, wetting drying cycles.

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