Diagnostic Signals Research Articles

Machine learning application to predict the electron temperature on the J-TEXT tokamak

The reliability of diagnostic systems in tokamak plasma is of great significance for physics researches or fusion reactor. When some diagnostics fail to detect information about the plasma status, such as electron temperature, they can also be obtained by another method: fitted by other diagnostic signals through machine learning. The paper herein is based on a machine learning method to predict electron temperature, in case the diagnostic systems fail to detect plasma temperature. The fully-connected neural network, utilizing back propagation with two hidden layers, is utilized to estimate plasma electron temperature approximately on the J-TEXT. The input parameters consist of soft x-ray emission intensity, electron density, plasma current, loop voltage, and toroidal magnetic field, while the targets are signals of electron temperature from electron cyclotron emission and x-ray imaging crystal spectrometer. Therefore, the temperature profile is reconstructed by other diagnostic signals, and the average errors are within 5%. In addition, generalized regression neural network can also achieve this function to estimate the temperature profile with similar accuracy. Predicting electron temperature by neural network reveals that machine learning can be used as backup means for plasma information so as to enhance the reliability of diagnostics.

Detection of Noise Signal in the Additive Mixture Based on the Second-Order Cumulant

In order to determine the technical condition of energetic objects with the objective of ensuring their operational reliability, durability and safety, systems of noise diagnostics, which are based on the analysis of acoustic diagnostic signals. A promising area of noise diagnostics are cumulant methods, based on cumulant analysis, which involves the use of cumulants and cumulant coefficients. In known literature no characteristics of detection of a signal within an interference-containing additive mixture with the use of a second-order cumulant (variance) can be found. That is why the objective of the paper is to study the use of cumulant method on the basis of point estimations of variance for a sample of momentary values for detection of an acoustic signal against the background of noise interference. The research was carried out by way of modeling the additive mixture of signal and interference using the MATLAB® software package. Interference is a model of a noise acoustic signal, which accompanies the operation of properly functional equipment. Signal is a model of an acoustic signal which is created with the occurrence of a malfunction. Signal and interference are independent random variables, so the property of additivity of cumulants was used – the variance of a mixture equals the sum of variances of signal and interference. The decision about the presence of a signal was made on the basis of testing two statistical hypotheses. The null hypothesis – the signal is absent, variance equals to the variance of the interference. The first hypothesis – the signal is present, variance equals to the variance of the mixture. Additional parameters: probability of a Type I error 0,01, probability of correct determination 0,99. The relative error of estimation determined the minimal sample size. These values allowed for the calculation of the threshold value, upon the exceeding of which by the variance estimation, the decision on the presence of signal is made. For each sample, assessments of variance were made. Experimental probability of correct determination is calculated as a total number of decisions taken regarding the presence of a signal, divided by the number of realizations, and corresponds to the value of the specified probability of correct determination. Its relative error was calculated in order to control the validity of the results. Also, kernel density estimation of the probability of the variance assessment for the case of a signal with normal distribution. As shown by the graphs, the assessments have a distribution that is close to normal. The conducted study proves that a variance -based cumulant method allows to detect a signal against the background of noise interference. The necessary sample size, which shows the number of the necessary momentary values, is given in the paper. That is to say that with the help of the frequency of an analogue digital converter the needed duration of the recording of a real for assessment of its variance can be obtained, and the decision on the presence or absence of a signal is to be made on the basis of the specified threshold values. The results of the study can be added to the known sample sizes and threshold values for the coefficients of asymmetry and excess with different distributions. Application of the described method requires additional testing on real acoustic signals and has the areas of use in systems of noise diagnostics.

Absolute Configuration Sensing of Chiral Aryl- and Aryloxy-Propionic Acids by Biphenyl Chiroptical Probes

The absolute configuration of chiral 2-aryl and 2-aryloxy propionic acids, which are among the most common chiral environmental pollutants, has been readily and reliably established by either electronic circular dichroism spectroscopy or optical rotation measurements employing suitably designed 4,4′-disubstituted biphenyl probes. In fact, the 4,4′-biphenyl substitution gives rise to a red shift of the diagnostic electronic circular dichroism signal of the biphenyl A band employed for the configuration assignment, removing its overlap with other interfering dichroic bands and allowing its clear sign identification. The largest A band red shift, and thus the most reliable results, are obtained by employing as a probe the 4,4′-dinitro substituted biphenylazepine 3c. The method was applied to the absolute configuration assignment of 2-arylpropionic acids ibuprofen (1a), naproxen (1b), ketoprofen (1c) and flurbiprofen (1d), as well as to the 2-aryloxypropionic acids 2-phenoxypropionic acid (2a) and 2-naphthoxypropionic acid (2b). This approach, allowing us to reveal the sample’s absolute configuration by simple optical rotation measurements, is potentially applicable to online analyses of both the enantiomeric composition and absolute configuration of these chiral pollutants.

Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study.

BackgroundDiagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors. Once errors are identified, sophisticated data analytics and machine learning techniques can be applied to existing electronic health record (EHR) data sets to shed light on potential risk factors influencing diagnostic decision making.ObjectiveThis study aims to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques.MethodsThis study plans to use trigger algorithms within EHR data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on whether they meet certain criteria. Samples from both data sets will be validated using medical record reviews, upon which we expect to find a higher number of diagnostic safety events in the trigger-positive subset. Machine learning will be used to evaluate relationships between certain patient factors, provider-care team factors, and system-level risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts.ResultsThis federally funded study was approved by the institutional review board of 2 academic medical centers with affiliated community hospitals. Trigger queries are being developed at both organizations, and sample cohorts will be labeled using the triggers. Machine learning techniques such as association rule mining, chi-square automated interaction detection, and classification and regression trees will be used to discover important variables that could be incorporated within future clinical decision support systems to help identify and reduce risks that contribute to diagnostic errors.ConclusionsThe use of large EHR data sets and machine learning to investigate risk factors (related to the patient, provider-care team, and system-level) in the diagnostic process may help create future mechanisms for monitoring diagnostic safety.International Registered Report Identifier (IRRID)DERR1-10.2196/24642

(Automated) Synthesis of Well-defined Staphylococcus Aureus Wall Teichoic Acid Fragments.

Wall teichoic acids (WTAs) are important components of the cell wall of the opportunistic Gram‐positive bacterium Staphylococcus aureus. WTAs are composed of repeating ribitol phosphate (RboP) residues that are decorated with d‐alanine and N‐acetyl‐d‐glucosamine (GlcNAc) modifications, in a seemingly random manner. These WTA‐modifications play an important role in shaping the interactions of WTA with the host immune system. Due to the structural heterogeneity of WTAs, it is impossible to isolate pure and well‐defined WTA molecules from bacterial sources. Therefore, here synthetic chemistry to assemble a broad library of WTA‐fragments, incorporating all possible glycosylation modifications (α‐GlcNAc at the RboP C4; β‐GlcNAc at the RboP C4; β‐GlcNAc at the RboP C3) described for S. aureus WTAs, is reported. DNA‐type chemistry, employing ribitol phosphoramidite building blocks, protected with a dimethoxy trityl group, was used to efficiently generate a library of WTA‐hexamers. Automated solid phase syntheses were used to assemble a WTA‐dodecamer and glycosylated WTA‐hexamer. The synthetic fragments have been fully characterized and diagnostic signals were identified to discriminate the different glycosylation patterns. The different glycosylated WTA‐fragments were used to probe binding of monoclonal antibodies using WTA‐functionalized magnetic beads, revealing the binding specificity of these WTA‐specific antibodies and the importance of the specific location of the GlcNAc modifications on the WTA‐chains.

Screening of COVID-19 using Cough Audio Frequencies

Clinicians routinely use biomedical and audio signals (e.g. sighs, breathing, pulse, digestion, sounds of vibration) as markers to diagnose diseases or to evaluate the progression of diseases. Until recently, these signals were normally obtained during scheduled visits by manual auscultation. With the advancement of technologies, digital methods are used to collect the body sounds for cardiovascular or respiratory testing (e.g. digital stethoscopes to predict the progression of diseases. A few early studies showed promising results for the detection of COVID-19 using voice and diagnostic signals. In the proposed model, an effective analysis is performed through the collection of large, multi-group, airborne acoustic sound data to perform the screening of COVID-19. The technique uses cough and breathing patterns to show the distinctive features of COVID-19 and it is reported that the cough patterns of COVID-19 are identifiable from asthma cough patterns. Using machine learning algorithms, an efficient classification model is developed for the screening of COVID-19.The area below the curve (AUC) of our proposed model exceeds 80%. The present study also explores the analysis of air patterns that can be recorded using the breathing styles of the infected persons to enhance the efficiency of the proposed screening techniques.

Numerical Analysis of reactor coolant pump performance under reverse conditions

Since no anti-reverse device is equipped in the system of the third generation AP 1000 of nuclear power plant, the reactor coolant pump (RCP) might operate in the reverse condition. However, it is hard to carry out the experiments of RCP in reverse condition due to its high temperature and special thrust bearing (their thrust bearing may not afford the reverse condition either). Hence, CFX with unsteady RANS turbulence model is employed to thoroughly study the characteristics of the reactor coolant pump in reverse pump mode as well as the normal pump condition. Their different flow patterns between the normal case and the reverse case are compared. It is found that a beat frequency captured in the impeller in the reverse case is suggested to be the diagnostic signal of the generation of reverse condition.

Degradation Diagnostics in Graphite-NMC Cells Under Fast SEI Formation

The formation process is the last step in the lithium ion battery manufacturing process but plays an outsize role in determining cell costs [1], [2]. A typical formation protocol consists of multiple low-rate charge-discharge cycles which can take many days to complete. During formation, the solid-electrolyte interface (SEI) is created through irreversible lithium reduction reactions with the electrolyte at the negative electrode surface. Formation protocols are difficult to optimize in part because their impact to long term cell lifetime and safety remain under debate. For example, while some authors have reported that the voltage window and charging rate during formation could impact cycle life[3], [4], others have found that formation protocols as fast as 14 hours[5] or ones causing lithium plating[6] have little to no impact on cycle life. The current evidence suggests that there remain opportunities to optimize the formation protocol for a given cell chemistry and design. However, finding an optimal formation protocol will require a more detailed understanding of the long-term impact on cell degradation and with consideration for cell safety.In this study, we design a test sequence and identify diagnostic signals to elucidate the impact of a fast formation protocol[5] on the long-term cycle life behavior of NMC/graphite cells. The study consists of 40 identical pouch cells built in-house at the University of Michigan Battery Lab. Half of the cells were subjected to the fast formation protocol and the remaining cells used a baseline formation protocol. Each set of cells were further split into two groups to be cycled at room temperature and inside a temperature-controlled oven at 45°C. The aging test consists of 1C charge-discharge cycles between 3.0V and 4.2V. Periodic reference performance tests were inserted as part of the aging test to provide additional diagnostic signals, including cell internal resistances as a function of state of charge and low-rate voltage curves for differential capacity analysis. Cells were stopped after discharge capacities were measured to be below 50%. At the end of the test, the degree of cell swelling was measured.The results showed that the cells with fast formation had a longer cycle life compared to control, but the same cells also showed higher cell-to-cell variability towards the end of life. Nearly all cells with fast formation showed moderate to severe swelling when cycled at 45°C, in contrast to the cells with baseline formation which showed little to no volume change. Taken together, these results suggest that it is possible for a fast formation protocol to yield similar or better cycle life but still potentially compromise the safety of the cell through increased gas generation rates. We also monitored the cycle life of several cells that showed a non-monotonic current behavior during formation attributable to a reversible lithium plating process. The cycle life and resistance growth of these cells fell within the expected range of values after considering cell-to-cell variation, suggesting that the presence of transient lithium plating during formation does not have a long-term impact to cell lifetime. Finally, in exploring the diagnostic signals, we identify two beginning-of-life signals that showed positive correlation with the maximum cycle count and cell resistance measured at the end-of-life: (1) the cell resistance measured at low states of charge, and (2) the voltage decay during a rest step inserted at the end of the last formation charge cycle. This last finding revitalizes the possibility of designing cell lifetime predictors using simple metrics measured at the beginning of life.[1] D. L. Wood, J. Li, and C. Daniel, J. Power Sources, vol. 275, pp. 234–242, 2015[2] K. Kuhlmann, S. Wolf, C. Pieper, G. Xu, and J. Ahmad, Bost. Consult. Gr., pp. 1–22, 2018[3] T. S. Pathan, M. Rashid, M. Walker, W. D. Widanage, and E. Kendrick, J. Phys. Energy, vol. 1, no. 4, p. 044003, 2019[4] C. Mao et al., J. Power Sources, vol. 402, no. July, pp. 107–115, 2018[5] D. L. Wood, J. Li, and S. J. An, Joule, vol. 3, no. 12, pp. 2884–2888, 2019[6] M. Ryan et al., J. Power Sources, vol. 484, pp. 0–7, 2021 Figure 1

Using identity calls to detect structure in acoustic datasets

Abstract Acoustic analyses can be powerful tools for illuminating structure within and between populations, especially for cryptic or difficult to access taxa. Acoustic repertoires are often compared using aggregate similarity measures across all calls of a particular type, but specific group identity calls may more clearly delineate structure in some taxa. We present a new method—the identity call method—that estimates the number of acoustically distinct subdivisions in a set of repertoires and identifies call types that characterize those subdivisions. The method uses contaminated mixture models to identify call types, assigning each call a probability of belonging to each type. Repertoires are hierarchically clustered based on similarities in call type usage, producing a dendrogram with ‘identity clades’ of repertoires and the ‘identity calls’ that best characterize each clade. We validated this approach using acoustic data from sperm whales, grey‐breasted wood‐wrens and Australian field crickets, and ran a suite of tests to assess parameter sensitivity. For all taxa, the method detected diagnostic signals (identity calls) and structure (identity clades; sperm whale subpopulations, wren subspecies and cricket species) that were consistent with past research. Some datasets were more sensitive to parameter variation than others, which may reflect real uncertainty or biological variability in the taxa examined. We recommend that users perform comparative analyses of different parameter combinations to determine which portions of the dendrogram warrant careful versus confident interpretation. The presence of group‐characteristic identity calls does not necessarily mean animals perceive them as such. Fine‐scale experiments like playbacks are a key next step to understand call perception and function. This method can help inform such studies by identifying calls that may be salient to animals and are good candidates for investigation or playback stimuli. For cryptic or difficult to access taxa with group‐specific calls, the identity call method can aid managers in quantifying behavioural diversity and/or identifying putative structure within and between populations, given that acoustic data can be inexpensive and minimally invasive to collect. ​

Analysis of the Possibility of Plastic Deformation Characterisation in X2CrNi18-9 Steel Using Measurements of Electromagnetic Parameters.

An analysis was conducted on the possibility of making an assessment of the degree of plastic deformation ε in X2CrNi18-9 steel by measuring three electromagnetic diagnostic signals: the Barkhausen noise features, the impedance components in in-series LCR circuits, and the residual magnetic field components. The impact of ε on a series of different extracted features of diagnostic signals was investigated. The occurrence of two regions of sensitivity was found for all the features of the analysed signals. The two regions were separated by the following critical deformation value: ε ~ 10% for the components of the residual magnetic field and ε ~ 15% for the normalised components of impedance. As for the Barkhausen noise signal, the values were as follows: ε ~ 20% for the mean value, ε ~ 20% for the peak value of the signal envelope, and ε ~ 5% for the total number of the signal events. Metallographic tests were performed, which revealed essential changes in the microstructure of the tested material for the established critical values. The martensite transformation occurring during the plastic deformation process of X2CrNi18-9 austenitic steel process generated a magnetic phase. This magnetic phase was strong enough to relate the strain state to the values of diagnostic signals. The changes in the material electromagnetic properties due to martensitic transformation (γ → α’) began much earlier than indicated by the metallographic testing results.

Synthesis of Functionalized Diethyl(pyrrolidin-2-yl)phosphonate and Diethyl(5-oxopyrrolidin-2-yl)phosphonate.

Short and efficient syntheses of functionalized (pyrrolidin-2-yl)phosphonate and (5-oxopyrrolidin-2-yl)phosphonate have been developed. The synthetic strategy involved the diastereospecific 1,3-dipolar cycloaddition of N-benzyl-C-(diethoxyphosphoryl)nitrone to cis-1,4-dihydroxybut-2-ene and dimethyl maleate, respectively. O,O-Diethyl 3-carbamoyl-4-hydroxy(5-oxopyrrolidin-2-yl)phosphonate was obtained from O,O-diethyl 2-benzyl-4,5-dimethoxycarbonyl(isoxazolidin-3-yl)phosphonate by hydrogenation and subsequent treatment with ammonia, whereas transformation of O,O-diethyl 2-benzyl-4,5-dihydroxymethyl(isoxazolidin-3-yl)phosphonate into O,O-diethyl 3-aminomethyl-4-hydroxy(pyrrolidin-2-yl)phosphonate was accomplished by mesylation followed by hydrogenolysis to undergo intramolecular cyclization and the introduction of amino group via ammonolysis. Stereochemistry of the isoxazolidine cycloadducts, as well as the final functionalized (pyrrolidin-2-yl)- and (5-oxopyrrolidin-2-yl)phosphonates were established based on conformational analyses using vicinal H–H, H–P, and C–P couplings and supported by the observed diagnostic NOESY correlation signals.

Recent Developments in Ion-Sensitive Systems for Pharmaceutical Applications.

Stimuli-responsive carriers of pharmaceutical agents have been extensively researched in recent decades due to the possibility of distinctively precise targeted drug delivery. One of the potentially beneficial strategies is based on the response of the medical device to changes in the ionic environment. Fluctuations in ionic strength and ionic composition associated with pathological processes may provide triggers sufficient to induce an advantageous carrier response. This review is focused on recent developments and novel strategies in the design of ion-responsive drug delivery systems. A variety of structures i.e., polymeric matrices, lipid carriers, nucleoside constructs, and metal-organic frameworks, were included in the scope of the summary. Recently proposed strategies aim to induce different pharmaceutically beneficial effects: localized drug release in the desired manner, mucoadhesive properties, increased residence time, or diagnostic signal emission. The current state of development of ion-sensitive drug delivery systems enabled the marketing of some responsive topical formulations. Concurrently, ongoing research is focused on more selective and complex systems for different administration routes. The potential benefits in therapeutic efficacy and safety associated with the employment of multi-responsive systems will prospectively result in further research and applicable solutions.

Application for Analysis of the Multiple Coherence Function in Diagnostic Signal Separation Processes

Application for Analysis of the Multiple Coherence Function in Diagnostic Signal Separation Processes

Neural Networks in the Diagnostics Process of Low-Power Solar Plant Devices

The article presents the problems of diagnostics of low-power solar power plants with the use of the three-valued (3VL) state assessment {2, 1, 0}. The 3VL diagnostics is developed on the basis of two-valued diagnostics (2VL), and it is elaborated on. In the (3VL) diagnostics, the range of changes in the values of the signals from the 2VL logic was accepted for the serviceability condition: state {12VL}. This range of signal value changes for logic (3VL) was divided into two signal value change sub-ranges, which were assigned two status values in the logic (3VL): {23VL}—serviceability condition and {13VL}—incomplete serviceability condition. The state of failure for both logics applied of the valence of states is interpreted equally for the same changes in the values of diagnostic signals, the possible changes of which exceed the ranges of their permissible changes. The DIAG 2 intelligent system based on an artificial neural network was used in diagnostic tests. For this purpose, the article presents the structure, algorithm and rules of inference used in the DIAG intelligent diagnostic system. The diagnostic method used in the DIAG 2 system utilizes the method known from the literature to compare diagnostic signal vectors with the reference signal vectors assigned. The result of this vector analysis is the metric developed of the difference vector. The problem of signal analysis and comparison is carried out in the input cells of the neural network. In the output cells of the neural network, in turn, the classification of the states of the object’s elements is realized. Depending on the condition of the individual elements that make up the object, the method is able to indicate whether the elements are in working order, out of order or require quick repair/replacement.

Synthesis and Characterization of 16π Antiaromatic 2,7-Dihydrodiazapyrenes: Antiaromatic Polycyclic Hydrocarbons with Embedded Nitrogen.

We describe the two-electron reduction of N,N'-dimethyl-2,7-diazapyrenium dications (MDAP2+ ), which afforded the corresponding reduced form (MDAP0 ) as a highly electron-rich 16π antiaromatic system. A single-crystal X-ray diffraction analysis of MDAP0 revealed a distorted quinoidal structure with high bond-length alternation. The 1 H NMR spectrum of MDAP0 exhibited a diagnostic proton signal (4.6 ppm) that is distinctly upfield shifted compared to that of aromatic diazapyrene (8.3 ppm). Theoretical calculations supported the existence of a paratropic ring current. These results indicate that MDAP0 exhibits antiaromatic character derived from its peripheral 16π-electron conjugation.

Synthesis and Characterization of 16π Antiaromatic 2,7‐Dihydrodiazapyrenes: Antiaromatic Polycyclic Hydrocarbons with Embedded Nitrogen

AbstractWe describe the two‐electron reduction of N,N′‐dimethyl‐2,7‐diazapyrenium dications (MDAP2+), which afforded the corresponding reduced form (MDAP0) as a highly electron‐rich 16π antiaromatic system. A single‐crystal X‐ray diffraction analysis of MDAP0 revealed a distorted quinoidal structure with high bond‐length alternation. The 1H NMR spectrum of MDAP0 exhibited a diagnostic proton signal (4.6 ppm) that is distinctly upfield shifted compared to that of aromatic diazapyrene (8.3 ppm). Theoretical calculations supported the existence of a paratropic ring current. These results indicate that MDAP0 exhibits antiaromatic character derived from its peripheral 16π‐electron conjugation.

Effects of Oversaturated Cathode Humidity Conditions on the Performance Degradation of PEMFCs and Diagnostic Signals of Warburg Impedance under Low Humidity Conditions

A proton exchange membrane fuel cell (PEMFC) inevitably experiences performance degradation during long-term operation under various corrosive conditions. In particular, cell operation under cathode flooding conditions results in severe cell degradation through sequential processes that induce the degradation of each component in the membrane electrode assembly (MEA). Furthermore, an excessive amount of water remaining at the cathode can diffuse into the anode side (back diffusion of water), and repeated instances can cause anode flooding (leading to fuel starvation). Herein, we focus on the collateral anode degradation of a stationary PEMFC operating under cathode flooding conditions with an oversaturated cathode in terms of relative humidity (RH, 150% RH). We suggest low-humidity electrochemical impedance spectroscopy (EIS-RH30) as a fault identification method to detect the degradation of the MEA, including the anode, before severe cell deterioration. Specifically, the EIS-RH30 analysis enables the detection of the malfunctioning anode reaction by limiting the hydrogen oxidation reaction (HOR) and proton conductivity, through which the phase difference between transporting protons from the anode to the cathode can be reflected as the horizontal aspect of the Warburg impedance. A generalized finite-length Warburg is introduced to understand the variation in the Warburg impedance spectra, and its numerical analysis can act as a diagnosable signal to monitor the sequential degradation of an MEA. A feasibility study of EIS-RH30 on actual degradation behavior that occurs under cathode flooding conditions was performed along with a microstructural investigation inside the MEA.

Atypical package design and product category prestige

AbstractInnovation in the design of products has become an important competitive tool of success in the market for many firms (see the virtual issue on Design Innovation in the Journal of Product Innovation Management [2020]). A few firms have had success in the market by adopting innovative designs. But not all innovative designs give success to firms. This research focuses on package design as an important marketing tool of innovation, influencing consumer responses to products. When designing packaging, firms typically follow category‐based visual codes, but they can adopt an innovative design that deviates from the codes, producing atypical package designs. This research shows that the effectiveness of atypical package design depends on product category prestige. Consumers respond to signals about product quality, such as package design, when they believe the signals are credible. When a product category is prestigious, consumers perceive package design as a diagnostic signal. Consumers are more likely to infer high product performance from an atypical package design and choose that product. We compare three dimensions of product design (aesthetics, functionality, and symbolism) to reveal that functionality is the reason why consumers prefer an atypical package design in a prestigious category. A series of studies show that the preference for atypical package design becomes stronger when consumers are choosing products in a prestigious category than in a standard category. The preference for atypical package design is attenuated when other information related to performance is provided, even when the aesthetics and symbolism of atypical package design do not change.

Address identification method of diagnostic signals in automated control systems of technological processes and productions

Address identification method of diagnostic signals in automated control systems of technological processes and productions

Experiment data-driven modeling of tokamak discharge in EAST

A neural network model of tokamak discharge is developed based on the experimental dataset of a superconducting long-pulse tokamak (EAST) campaign 2016–2018. The purpose is to reproduce the response of diagnostic signals to actuator signals without introducing additional physical models. In the present work, the discharge curves of electron density n e, stored energy W mhd, and loop voltage V loop were reproduced from a series of actuator signals. For n e and W mhd, the average similarity between the modeling results and the experimental data achieve 89% and 97%, respectively. The promising results demonstrate that the data-driven methodology provides an alternative to the physical-driven methodology for tokamak discharge modeling. The method presented in the manuscript has the potential of being used for validating the tokamak’s experimental proposals, which could advance and optimize experimental planning and validation.