Frequency Vector Research Articles

Efficiency of the slime mold algorithm for damage detection of large‐scale structures

SummaryOptimization‐based methods are increasingly being implemented for structural damage detection problems through the minimization of the objective functions based on vibration data. The adopted optimization algorithm and objective function play an important role in the accurate detection and quantification of damages. Meanwhile, the challenge of long computational time is another aspect of structural damage identification problems, especially upon addressing large‐scale structures. In this paper, recently developed optimization techniques called slime mold algorithm (SMA) and marine predators algorithm (MPA) are applied to damage assessment of large‐scale structures for the first time. The performance of these algorithms is compared with those obtained using other well‐known optimization techniques such as ant lion optimizer (ALO), whale optimization algorithm (WOA), and grasshopper optimization algorithm (GOA). Furthermore, the sensitivity of three objective functions based on modal assurance criterion (MAC), modified total modal assurance criterion (MTMAC), and natural frequency vector assurance criterion (NFVAC) are examined. Two numerical studies, including the 53‐bar planar truss and the Guangzhou New TV tower, and a full‐scale three‐story frame as an experimental investigation are conducted to present a statistical comparison. The overall results show that the combination of SMA and objective function based on MTMAC provides an accurate tool for damage identification. However, improved SMA (ISMA) has been introduced to enhance the capability of standard SMA for damage detection, and especially finite element model updating in the experimental example. Five benchmark functions are also used to evaluate the global optimization capacity of ISMA and SMA. The results show that the ISMA has many benefits in terms of tackling global optimization problems. MPA‐MTMAC can provide promising results compared with ALO‐MTMAC, WOA‐MTMAC, and GOA‐MTMAC. However, excessive computation time is a big drawback for MPA. The overall results confirm the perfection of the objective function based on MTMAC compared with two others based on MAC and NFVAC.

Tailored acoustic metamaterials. Part I. Thin- and thick-walled Helmholtz resonator arrays.

We present a novel multipole formulation for computing the band structures of two-dimensional arrays of cylindrical Helmholtz resonators. This formulation is derived by combining existing multipole methods for arrays of ideal cylinders with the method of matched asymptotic expansions. We construct asymptotically close representations for the dispersion equations of the first band surface, correcting and extending an established lowest-order (isotropic) result in the literature for thin-walled resonator arrays. The descriptions we obtain for the first band are accurate over a relatively broad frequency and Bloch vector range and not simply in the long-wavelength and low-frequency regime, as is the case in many classical treatments. Crucially, we are able to capture features of the first band, such as low-frequency anisotropy, over a broad range of filling fractions, wall thicknesses and aperture angles. In addition to describing the first band we use our formulation to compute the first band gap for both thin- and thick-walled resonators, and find that thicker resonator walls correspond to both a narrowing of the first band gap and an increase in the central band gap frequency.

Natural Language Processing of Electronic Patient Records to Predict Psychiatric Inpatients at Risk of Early Readmission to Hospital Using Predictive Models Derived Through Machine Learning

AimsPsychiatric readmissions cause a burden on the healthcare system, incur a monetary cost and cause additional distress to acutely unwell patients. This project explores the use of the free-text of electronic patient records to predict inpatients in psychiatric hospitals at risk of readmission using predictive models generated by machine learning.MethodsFree-text was extracted from the electronic patient records of patients admitted to hospitals in Birmingham and Solihull Mental Health Foundation Trust (BSMHFT) during the five years 2015–2019 inclusive. The anonymised records were obtained via the CRIS (Clinical Record Interactive Search) database. A total of 17208 records were extracted.The free-text entered by clinicians during an admission was extracted and processed using techniques of natural language processing to generate input vectors suitable to be used with machine learning algorithms. tf-idf (term frequency-inverse document frequency) vectors were used.A selection of algorithms were used to train predictive models. Two-thirds of the records were used as training data with the remainder as test data. Baseline model performance was assessed and then best-performing candidates underwent hyperparameter optimisation using five-fold cross-validation to improve performance. Bayesian optimisation was used to automate hyperparameter tuning during cross-validation. Hyperparameters were optimised on the log loss function. As the dataset was imbalanced with negative instances outnumbering positive instances to a significant degree, various techniques such as random undersampling of negative instances in the training data were used to deal with class imbalance throughout this process. Following cross-validation, the best-performing models underwent performance analysis.Models were used to make predictions on the test data. Performance was assessed using F1-measures, precision-recall curves and the average precision metric (equivalent to area under the precision-recall curve). These metrics were chosen due to their suitability in assessing models trained on imbalanced datasets.ResultsThe best F1 score obtained was 0.233 using a Random Forest model trained using unigram tf-idf vectors of 500 token dimension.The best average precision obtained was 0.157 using a Support Vector Machine trained using unigram tf-idf vectors of 2000 token dimension.Both the above results required the use of random oversampling of positive instances to improve performance on the imbalanced dataset.ConclusionThe performance indicates that the models generated are unlikely to have significant practical utility. Nevertheless, this exploratory project has produced a processed dataset with knowledge about its characteristics. This could be used for the further development of models using more complex techniques such as language modelling using neural networks.

Thermal Fluctuations and Electromagnetic Noise Spectra in Quantum Statistical Mechanics

We derive the thermal noise spectrum of the longitudinal and transverse electric field operator of a given wave vector starting from the quantum-statistical definitions and relate it to the frequency and wave vector dependent complex conductivity in a homogeneous, isotropic system of electromagnetic interacting charged particles in the frame of the non-relativistic QED. No additional assumptions except the validity of linear response are used in the proof. The Nyquist formula for vanishing frequency, as well as the noise spectral density of Callen-Welton follow as byproduct. Furthermore we discuss also the noise of the photon occupation numbers.

Oil price, green innovation and institutional pressure: A China's perspective

Oil is of dominated role in global energy market, and its price volatility would bring significant economic and environmental consequents. Given China is the largest oil importer, this paper applies the mixed frequency vector autoregression (MF-VAR) to investigate the causal relationship among oil price (OP), green innovation (GI), and institutional pressure (IP). The empirical results from MF-VAR model show that quarterly OP would stimulate GI with time-varying, which is not found in low frequency vector autoregression (LF-VAR). The new evidence mainly due to the mix frequency technique, which allows heterogeneous impacts exist between high and low frequency variables. In addition, higher IP is also discovered to promote GI. The major contribution is that a theoretical framework is initially constructed, and oil price is proven to as important booster for China's green innovation. Therefore, some policies such as innovation subsidy and green credit need to be employed to reduce the impacts from oil price fluctuations and main sustainable GI capability. Meanwhile, IP mainly from environmental protection requires government to launch stricter environmental regulations and laws, and establish reward and punishment mechanism to promote GI.

A new averaging-extrapolation method for quasi-periodic frequency refinement

In a recent work, we presented an averaging-extrapolation approach for the numerical computation of frequencies and amplitudes of a discrete-time quasi-periodic signal. This approach assumes analyticity of the signal and a Diophantine frequency vector. Given an approximation to one of the frequencies and a sufficiently large number of iterates of the signal, the main outcome of the method is a refined approximation to the frequency. The crucial aspect of this method consists in building an appropriate complex signal which, by the geometrical implications of its construction, is referred to as an unfolded signal. The projection of the unfolded signal on the unit circle defines a quasi-periodic signal of the circle whose rotation frequency is the target frequency. This allows to refine the target frequency by adapting a previously developed method for computing, with great accuracy, Diophantine rotation numbers of analytic circle maps. Both the unfolding and refinement processes require computing appropriate weighted averages of the iterates and performing Richardson’s extrapolation. In the present work, we reformulate this averaging-extrapolation approach to frequency refinement. This leads to a simpler method that completely avoids the unfolding process but that is capable of producing accurate values for frequencies and amplitudes at a reasonable computational cost, which is mostly independent of the number of basic frequencies of the signal.

An approach of method‐level bug localization

Bug localization is an important field in software engineering research. The traditional bug localization approaches based on information retrieval separate words through lexical analysis. In this way, the comments of the source code are ignored or treated as plain text, which will lose some semantic information. In this paper, MBL_SHL, an automatic Method-level Bug Localization approach, which utilises code Summarization, Historical fixed bugs and code Length, is presented. Based on the code summarization technology, this approach first supplements the comment for uncommented code, and then calculates the Word2vec vector and Term Frequency–Inverse Document Frequency vector for the bug report, methods and comments, respectively. After that the authors calculate separately the similarity between the bug report and each method, the bug report and each comment. The code length information and historical fix information are also considered as a weight and a part of the score, respectively, to calculate the final score of each method. Finally, the scores are sorted to determine the list of methods that may need to be modified when fixing the software bugs. We built a method-granular bug localization dataset, which contains five open-source projects. The experimental results show that the proposed approach significantly outperforms the existing approaches on the method level.

Abstract B010: Ecological epistasis can drastically alter evolutionary trajectories on genotypic fitness landscapes, maintaining, masking or mimicking genetic selection in evolutionary simulations

Abstract The evolution of drug resistance is broadly understood in terms of the evolution and survival of the genotype that is "fittest" under treatment. There are many computational evolutionary models built upon the assumption that under treatment, cancer cells with the fittest genotype persist and ultimately prevail. Many of these models lack sufficient complexity to predict tumor evolution in the clinic. In particular, the standard Wright-Fisher approach taken from population genetics neither considers nor accurately predicts the heterogeneity that we see in patients and cell-cell interactions are not included. To combat this we develop a model of evolution that incorporates average cell-cell interactions by adding game theoretic interactions to a Wright-Fisher model with mutation and use this to explore and model cases of interest. In our agent-based model we start with N wildtype cells and represent the genotype of each cell with a binary string of n alleles. We represent mutations to the genotype as transitions from 0 to 1 and vice-versa in an allele and each unique genotype is a cell type. Each cell in the non-spatial, well-mixed agent-based model can be selected to reproduce with probability p proportional to the fitness of its genotype and each daughter cell can undergo mutation with probability μ. At each new time step we modify the fitness of each cell type, using the multiplication of the payoff matrix with the cell type frequency vector to reflect the average fitness benefits and costs of cell-cell interactions. We vary the payoff matrices for up to 4 alleles (16 possible genotypes) and show how the addition of these ecological game interactions between different cell populations can modify evolutionary trajectories. We find, for example, that with certain strengths of game interactions, the evolution on an initially selective genotypic landscape can mimic neutral landscapes and vice versa. We term this addition of ecological game interactions to genotypic landscapes "ecological epistasis". We derive mathematical expressions for the equilibrium population distribution in the single allele (two genotype) with arbitrary games case. We also derive expressions for the payoff matrices required to mimic selective games and to make selective landscapes neutral. Furthermore we derive an expression for the curvature of the resultant population distribution, producing a "game signature” with which to probe temporal evolutionary data for the presence of games. This model emphasizes that the emergence of a dominant cell-type can be caused by both ecological and genotypic factors and conversely that the genotypic landscape does not need to be neutral for heterogeneity to persist. Due to the multiclonal and heterogeneous nature of tumors and their microenvironment, these results are of key importance in considering treatment approaches and interpreting existing patient data. We highlight how game interactions may explain heterogeneous patient outcomes and contradicting results on the fitness of cancer genotypes in the literature. Citation Format: Steph J. Owen, Michael Hinczewski, Jacob G. Scott. Ecological epistasis can drastically alter evolutionary trajectories on genotypic fitness landscapes, maintaining, masking or mimicking genetic selection in evolutionary simulations [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B010.

Influence of the Earth’s atmosphere rotation on the spectrum of acoustic-gravity waves

It was shown in a recent paper by Cheremnykh et al. (Advances in Space Research, 2022) that taking into account the rotation of the Earth's atmosphere leads to the appearance of a new region of evanescent waves with a continuous frequency spectrum on the diagnostic diagram of acoustic-gravity waves. The region is located below the lower limit of gravity waves, which is equal $2\Omega$ at all wavelengths, where $\Omega$ is the angular frequency of the atmosphere rotation. This result was obtained for high-latitude regions of the atmosphere, in which we can limit ourselves to considering only the vertical component of the Earth's rotation frequency. In the proposed paper we show that taking into account both components of the vector of the atmosphere rotation frequency $\overrightarrow{\Omega}$ - horizontal, $\Omega\cos\varphi$ , and vertical, $\Omega\sin\varphi$, where $\varphi$ is the Earth`s latitude, the dominant role in the acoustic-gravity waves propagation is played by the vertical component. It is shown that the horizontal component leads to an insignificant modification on the diagnostic diagram of the boundaries of the areas of acoustic and gravity waves, which can be neglected. Also shown that the strongest vertical component of the frequency affects the lower limit of gravity waves, which depends at all wavelengths on the latitude of the observation site and is equal to $2\Omega\sin\varphi$.

Design of Reconfigurable Spin-Wave Nanochannels Based on Strain-Mediated Multiferroic Heterostructures and Logic Device Applications

A method based on the magnetoelectric effect present in multiferroic heterostructures was used to design a reconfigurable waveguide (WG) channel for spin wave (SW) transmission. The multiferroic heterostructures consisted of a piezoelectric (PZT) layer and an adjacent ferromagnetic (CoFeB) layer within the WG region. By applying voltage to the PZT layer, a piezostrain would be generated and transferred to the ferromagnetic layer, leading to a magnetoelastic field in the latter, which could modulate SW dispersion, including propagation frequency and SW vector. Two applications based on these piezostrain-modulated SW dispersion parameters were explored. A piezostrain-modulated, multiple-shaped WG channel was first proposed to allow specific-frequency SW propagation via control of the applied strain. Then, piezostrain was used to perform a phase shift modulation in the multiferroic SW channel. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\pi }$ </tex-math></inline-formula> -phase shift in the propagation of SW was obtained by customizing the length of strain operation for a given frequency. A phase shifter was designed to build an interferometer to fulfill the destruct and construct logic functions based on strain-controlled SWs. These simulation results highlight the considerable potential of strain engineering to control logical and computational reconfigurable SW devices.

Infant Mortality and Inflation in China: Based on the Mixed Frequency VAR Analyses.

Reducing neonatal mortality is an important goal in the Sustainable Development Goals (SDGs), and with the outbreak of the new crown epidemic and severe global inflation, it is extremely important to explore the relationship between inflation and infant mortality. This paper investigates the causal relationship between inflation and infant mortality using a mixed frequency vector autoregressive model (MF-VAR) without any filtering procedure, along with impulse response analysis and forecast misspecification variance decomposition, and compares it with a low frequency vector autoregressive model (LF-VAR). We find that there is a causal relationship between inflation and infant mortality, specifically, that is inflation increases infant mortality. Moreover, the contribution of CPI to IMR is greater in the forecast error variance decomposition in the MF-VAR model compared to the LF-VAR model, indicating that CPI has stronger explanatory power for IMR in mixed-frequency data. The results of the study have important implications for China and other developing countries in reducing infant mortality and achieving the Sustainable Development Goals (SDGs). Policymakers should focus on inflation as a macroeconomic variable that reduces the potential negative impact of inflation on infant mortality. The results of the analysis further emphasize the importance of price stability in the context of global inflation caused by the outbreak of the coronavirus pandemic outbreak.

Reconciled Estimates of Monthly GDP in the United States

In the United States, income and expenditure-side estimates of gross domestic product (GDP) (GDP and GDP) measure “true” GDP with error and are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled quarterly estimates of true GDP. In this paper, we extend these methods to provide reconciled historical true GDP estimates at a monthly frequency. We do this using a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDP, GDP, unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error perspective (i.e., the two GDP proxies are assumed to equal true GDP plus measurement error). Without further restrictions, our model is unidentified. We consider a range of restrictions that allow for point and set identification of true GDP and show that they lead to informative monthly GDP estimates. We illustrate how these new monthly data contribute to our historical understanding of business cycles and we provide a real-time application nowcasting monthly GDP over the pandemic recession.

Fuzzy-based estimation of reference flux, reference torque and sector rotation for performance improvement of DTC-IM drive

In this study, the fuzzy-based reference flux estimator (RFE), reference torque estimator (RTE) and sector rotation strategy called fuzzy logic estimator are proposed to direct torque control of induction motor (DTC-IM) drive for performance improvement. The basic DTC-IM drive with conventional RFE, RTE and sector division causes large torque ripple, variable switching frequency and uneven voltage vector contribution in stator flux. The torque and speed responses of the proposed system are investigated with load variations. The simulation results of the proposed DTC-IM drive are compared with the basic DTC-IM drive. The assessment of the proposed system shows improved performance. A hardware is developed using Xilinx Spartan-6XC6SLX45-Field Programmable Gate Array (FPGA) Kit for experimental verification of the results. Moreover, sinusoidal pulse width modulation and space vector pulse width modulation techniques are applied to reduce the torque ripples. The performance of the drive is investigated for various speed ranges. The comparison of the simulated and experimental results proves that the proposed fuzzy-based DTC-IM drive provides better performance than the basic DTC-IM drive.

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals.

In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided.

Exploring the topic evolution of Dunhuang murals through image classification

Dunhuang is a unique art treasure and a world heritage site. In order to organise and manage Dunhuang cultural heritage resources, this article studies the classification of Dunhuang murals in different dynasties, and explores the topic distribution characteristics and evolution rules of them. First, image features are extracted through scale-invariant feature transform (SIFT) and Canny and scale-invariant feature transform (CSIFT), a visual dictionary is generated through the k-means clustering algorithm, and the term frequency–inverse document frequency (TF-IDF) vector is calculated and combined with the colour feature vector extracted via hue, saturation and value (HSV). Second, Dunhuang mural images are collected and the support vector machine (SVM) classifier is built. Finally, the knowledge graph-based topic maps are constructed, and graph theory is introduced to analyse the topic distribution and evolution of Dunhuang murals in different dynasties. The results show that the Dunhuang murals of different dynasties can be effectively classified through the bag of words, HSV and support vector machine (BOW_HSV_SVM) based on their visual features. Through topic maps, the topic distribution characteristics and evolution rules of Dunhuang murals with the dynasties are revealed.

Lyapunov unstable elliptic equilibria

A new diffusion mechanism from the neighborhood of elliptic equilibria for Hamiltonian flows in three or more degrees of freedom is introduced. We thus obtain explicit real entire Hamiltonians on R 2 d \mathbb {R}^{2d} , d ≥ 4 d\geq 4 , that have a Lyapunov unstable elliptic equilibrium with an arbitrary chosen frequency vector whose coordinates are not all of the same sign. For non-resonant frequency vectors, our examples all have divergent Birkhoff normal form at the equilibrium. On R 4 \mathbb {R}^4 , we give explicit examples of real entire Hamiltonians having an equilibrium with an arbitrary chosen non-resonant frequency vector and a divergent Birkhoff normal form.

Vector Control and Modeling of Modular Multilevel High Voltage Converter for Rail Vehicle Traction

Rail transit plays an important role in the social and economic life of China and even all countries in the world, especially some populous countries or regions. The traction drive system of rail vehicle provides three-phase AC with adjustable voltage and frequency for the traction motor, controls the speed and torque of the traction motor, and then controls the operation of the vehicle. The modular multilevel converter has the advantages of low harmonic, good power quality of output waveform, high reliability, no input filtering and power compensation, and is suitable in the field of frequency conversion. In this work, the open-loop scalar control and vector closed-loop control of modular multi-level high-voltage inverter were adopted. It was found that driven by modular multi-level variable frequency vector control system, asynchronous motor not only has less harmonic content of voltage and current waveform, but also its speed regulation characteristics have been improved.

EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool -for Autism diagnosis- compared to multi-scale entropy approach.

Autism spectrum disorder (ASD) is usually characterised by altered social skills, repetitive behaviours, and difficulties in verbal/nonverbal communication. It has been reported that electroencephalograms (EEGs) in ASD are characterised by atypical complexity. The most commonly applied method in studies of ASD EEG complexity is multiscale entropy (MSE), where the sample entropy is evaluated across several scales. However, the accuracy of MSE-based classifications between ASD and neurotypical EEG activities is poor owing to several shortcomings in scale extraction and length, the overlap between amplitude and frequency information, and sensitivity to frequency. The present study proposes a novel, nonlinear, non-stationary, adaptive, data-driven, and accurate method for the classification of ASD and neurotypical groups based on EEG complexity and entropy without the shortcomings of MSE. The proposed method is as follows: (a) each ASD and neurotypical EEG (122 subjects × 64 channels) is decomposed using empirical mode decomposition (EMD) to obtain the intrinsic components (intrinsic mode functions). (b) The extracted components are normalised through the direct quadrature procedure. (c) The Hilbert transforms of the components are computed. (d) The analytic counterparts of components (and normalised components) are found. (e) The instantaneous frequency function of each analytic normalised component is calculated. (f) The instantaneous amplitude function of each analytic component is calculated. (g) The Shannon entropy values of the instantaneous frequency and amplitude vectors are computed. (h) The entropy values are classified using a neural network (NN). (i) The achieved accuracy is compared to that obtained with MSE-based classification. (j) The consistency of the results of entropy 3D mapping with clinical data is assessed. The results demonstrate that the proposed method outperforms MSE (accuracy: 66.4%), with an accuracy of 93.5%. Moreover, the entropy 3D mapping results are more consistent with the available clinical data regarding brain topography in ASD. This study presents a more robust alternative to MSE, which can be used for accurate classification of ASD/neurotypical as well as for the examination of EEG entropy across brain zones in ASD.

Author Identification with Machine Learning Algorithms

Author identification is one of the application areas of text mining. It deals with the automatic prediction of the potential author of an electronic text among predefined author candidates by using author specific writing styles. In this study, we conducted an experiment for the identification of the author of a Turkish language text by using classical machine learning methods including Support Vector Machines (SVM), Gaussian Naive Bayes (GaussianNB), Multi Layer Perceptron (MLP), Logistic Regression (LR), Stochastic Gradient Descent (SGD) and ensemble learning methods including Extremely Randomized Trees (ExtraTrees), and eXtreme Gradient Boosting (XGBoost). The proposed method was applied on three different sizes of author groups including 10, 15 and 20 authors obtained from a new dataset of newspaper articles. Term frequency-inverse document frequency (TF-IDF) vectors were created by using 1-gram and 2-gram word tokens. Our results show that the most successful method is the SGD with a classification performance accuracy of 0.976% by using word unigrams and most successful method is the LR with a classification performance accuracy of 0.935% by using word bigrams.

Relevance of context of brand mentions in fiction to the contemporary brand values and target audience (the case of Nestlé in K. Chukovsky’s memoirs about A. Akhmatova)

In the theory of brand management, the influence of brand mentions in fiction on the perception of a brand by the target audience remains unexplored. One position is based on the fact that any brand mentions are valuable, and the bigger the audience coverage of a literary source, the greater its value. Another approach is based on evaluation of the quality of audience of a literary work. The analysis of the contemporary readership of works created in the past centuries is a difficult task. This paper proposes an approach to analyze the value of brand mentions in literature from the perspective of quantitative semantics. The methodology includes the transformation of an array of words from a literary fragment and a text reflecting brand values, translated from the official website into vectors of frequencies of semantic groups of Roget’s Thesaurus. As a standard for comparison, the verbalization of the values of the target group was used. All three vectors were compared with each other in pairs according to the cosine proximity formula. The analysis was carried out on the example of the mention of one of Nestlé’s products in memoirs about Anna Akhmatova by K. Chukovsky. The standard target group was the contemporary Russian youth in the age of 14–23 years old. The study demonstrated that the values of the group are relatively distant from the context of mention. The semantic context of the literary fragment is different from Nestlé’s values and is further away from the values of the young people as compared to the postulated official brand values. This methodology can be useful for automatic or semi-automatic analysis of brand mentions in works of literature. The proposed approach can be developed and adapted for a deep context-dependent analysis of brand mentions in the media, social networks and transcripts of oral speech.