Signal Correction Research Articles

Fast determination of lipid and protein content in green coffee beans from different origins using NIR spectroscopy and chemometrics

The chemical compounds including lipid and protein in green coffee beans are important indicators of the final quality of the coffee products, which are usually determined by time-consuming and destructive chemical methods. Therefore, a fast and reliable method was attempted to exploit by near-infrared (NIR) spectroscopy combined with partial least squares (PLS) regression for the determination of lipid and protein in green coffee beans from different origins. Orthogonal signal correction (OSC) and several traditional spectral pretreatment methods were compared during the PLS regression model building process. Important variables selection was further achieved based on the regression coefficients (β). The results showed that the 1st and 2nd derivative reduced the model quality, while OSC, MSC, and SNV pretreatment enhanced the model quality. The quality of PLS models was significantly improved after important variable selection. Especially, OSC-PLS models were the most robust for protein and lipid prediction with the best performance indicators (R2p>0.982, RPD > 7.55, RMSEcv <0.101, RMSEP < 0.106). The excellent performance showed that the NIR technique together with PLS regression could be applied as a substitute way to determine the protein and lipid content in green coffee beans.

A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar

Pets have been indispensable members for many families in modern life, especially significant for the elderly and the blind. However, they may cause false alarm when misused as signal source in non-contact monitoring of the vital signs using ultra-wideband (UWB) radar. Distinguishing between humans and pets can help ensure the correct signal source. Nevertheless, existing solutions are few or only utilize a single feature, which can hinder robustness and accuracy because of individual differences. In this study, we proposed a robust multi-feature based method to solve the problem. First, 19 discriminative features were extracted to reflect differences in aspects of energy, frequency, wavelet entropy, and correlation coefficient. Second, the features were ranked by recursive feature elimination algorithm and the top eight were then selected to build an optimal support vector machine (SVM) model. The area under the receiver operating characteristic curve (AUC) of the optimal SVM model reached 0.9620. The false and missing alarms for identifying humans were 0.0962 and 0.0600, respectively. Finally, comparison with the state-of-the-art method that only employed one feature validated the advance and accuracy of the proposed method. The method is envisioned to facilitate the UWB radar applications in non-contact and continuous vital signs monitoring.

Longitudinal Study Comparing Orthogonal Signal Correction Algorithms Coupled with Partial Least-Squares for Quantitative Near-Infrared Spectroscopy

Several orthogonal signal correction (OSC) methods coupled with partial least-squares (PLS) were applied to near-infrared spectra in a longitudinal study for the prediction of glucose, urea, and triacetin in liquid samples. The OSC methods included Wold’s OSC, Fearn’s OSC, orthogonal projection to latent structures (OPLS), and direct orthogonal signal correction (DOSC). These preprocessing methods are designed to simplify the spectra and remove information that is orthogonal to the analyte. Samples consisted of various concentrations of glucose, urea, and triacetin in the range from 1 to 19 mM. Calibration and prediction data were collected over 1.7 years to test how well each of the OSC methods helped to maintain analyte prediction performance over time. When comparing the OSC methods, it was found that all of the OSC methods improved the predictions in most data sets throughout the study but that DOSC clearly outperformed the others. On the basis of 18 prediction sets collected across 617 days after the collection of the calibration data, the average percent improvement in the standard error of prediction (SEP) when applying DOSC was 4.6%, 83.1%, and 43.0%, respectively, for glucose, urea, and triacetin. These comparisons were made relative to the SEP values obtained with unprocessed spectra.

A gradient descend method‐based none‐line‐of‐sight‐induced code delay searching in global navigation satellite system

In urban areas with narrow streets and tall buildings, none-line-of-sight (NLOS) or multipath (MP) signals severely attenuate the positioning accuracy of the global navigation satellite system (GNSS). vector tracking loop (VT) has been employed in NLOS/MP signal detection and correction; its feasibility and superior performance have been assessed and demonstrated in previous research studies. In VT, the navigation solutions are fed back to calculate the signal tacking parameters and complete the carrier and code tracking. The NLOS/MP reception induces code delay to the code tracking. The generated local signal replicas are mixed with the received signal; the code and carrier discriminators’ outputs are employed as the measurements to estimate the navigation solutions in the VT navigation filter. In order to extract the code delay induced by the NLOS or MP, multiple correlators (MCs) are usually utilised to search for the code delay. However, more correlators will bring much heavier computation load and a real position is necessary for calculating the signal tracking parameters. The strap-down inertial navigation system (SINS) can generate short-term accurate navigation solutions. In this work, a gradient descend (GD) method is designed for extracting the NLOS/MP-induced delay in the correlators’ measurements level. The VT/SINS ultra-tightly coupled navigation system (VT-UTC) is developed to obtain mutual aiding between these individual systems. In VT-UTC, the corrected SINS navigation solutions are employed for calculating the signal tracking parameters, and reliable navigation solutions from the SINS can help to extract the code delays induced by the NLOS. Two field tests with global positioning system L1 signal are carried out for assessing the performance of the GD method in the VT-UTC framework. Comparisons between the MC and GD method show that the correlators’ computation reduces a lot since much fewer correlators are calculated in the code delay searching. Also, reliable navigation solutions from the SINS could help to calculate the signal tracking parameter while in-view GNSS satellites are limited.

Correcting replicate variation in spectroscopic data by machine learning and model-based pre-processing

In this study we present a pre-processing and an augmentation approach based on Extended Multiplicative Signal Correction (EMSC) for removing and modelling replicate variation in spectroscopic data. The EMSC replicate correction method estimates replicate variation from replicate samples and integrates the estimated variation into the EMSC model. In the field of deep learning, augmentation is a frequently applied approach to deal with variability in images. In this study, we suggest augmentation of vibrational spectroscopic data with replicate variation. Replicate correction and replicate augmentation can be considered as two inverse procedures which are compared in our study. Three data sets of Fourier Transform Infrared spectra of yeasts, filamentous fungi and bacteria were used in this study to compare classification performance on genus and species level by (1) Random Forest using replicate corrected spectra vs. (2) Deep Learning using augmented spectra. Both technical and biological replicate correction/augmentation were considered.

Diacylglycerol Kinase alpha in X Linked Lymphoproliferative Disease Type 1.

Diacylglycerol kinases are intracellular enzymes that control the balance between the secondary messengers diacylglycerol and phosphatidic acid. DGKα and DGKζ are the prominent isoforms that restrain the intensity of T cell receptor signalling by metabolizing PLCγ generated diacylglycerol. Thus, their activity must be tightly controlled to grant cellular homeostasis and refine immune responses. DGKα is specifically inhibited by strong T cell activating signals to allow for full diacylglycerol signalling which mediates T cell response. In X-linked lymphoproliferative disease 1, deficiency of the adaptor protein SAP results in altered T cell receptor signalling, due in part to persistent DGKα activity. This activity constrains diacylglycerol levels, attenuating downstream pathways such as PKCθ and Ras/MAPK and decreasing T cell restimulation induced cell death. This is a form of apoptosis triggered by prolonged T cell activation that is indeed defective in CD8+ cells of X-linked lymphoproliferative disease type 1 patients. Accordingly, inhibition or downregulation of DGKα activity restores in vitro a correct diacylglycerol dependent signal transduction, cytokines production and restimulation induced apoptosis. In animal disease models, DGKα inhibitors limit CD8+ expansion and immune-mediated tissue damage, suggesting the possibility of using inhibitors of diacylglycerol kinase as a new therapeutic approach.

Improved agreement between N2 and SF6 multiple-breath washout in healthy infants and toddlers with improved EXHALYZER D sensor performance.

Recent studies indicate limited utility of nitrogen multiple-breath washout (N2MBW) in infancy and advocate for using sulfur hexafluoride (SF6) MBW in this age-group. Modern N2MBW systems, such as EXHALYZER D (ECO MEDICS AG, Duernten, Switzerland), use O2 and CO2 sensors to calculate N2 concentrations (in principle, N2% = 100 - CO2% - O2%). High O2 and CO2 concentrations have now been shown to significantly suppress signal output from the other sensor, raising apparent N2 concentrations. We examined whether improved EXHALYZER D N2 signal, accomplished after thorough examination of this CO2 and O2 interaction on gas sensors and its correction, leads to better agreement between N2MBW and SF6MBW in healthy infants and toddlers. Within the same session, 52 healthy children aged 1-36 mo [mean = 1.30 (SD = 0.72) yr] completed SF6MBW and N2MBW recordings (EXHALYZER D, SPIROWARE version 3.2.1) during supine quiet sleep. SF6 and N2 SPIROWARE files were reanalyzed offline with in-house software using identical algorithms as in SPIROWARE with or without application of the new correction factors for N2MBW provided by ECO MEDICS AG. Applying the improved N2 signal significantly reduced mean [95% confidence interval (CI)] differences between N2MBW and SF6MBW recorded functional residual capacity (FRC) and lung clearance index (LCI): for FRC, from 26.1 (21.0, 31.2) mL, P < 0.0001, to 1.18 (-2.3, 4.5) mL, P = 0.5, and for LCI, from 1.86 (1.68, 2.02), P < 0.001, to 0.44 (0.33, 0.55), P < 0.001. Correction of N2 signal for CO2 and O2 interactions on gas sensors resulted in markedly closer agreement between N2MBW and SF6MBW outcomes in healthy infants and toddlers.NEW & NOTEWORTHY Modern nitrogen multiple-breath washout (N2MBW) systems such as EXHALYZER D use O2 and CO2 sensors to calculate N2 concentrations. New corrections for interactions between high O2 and CO2 concentrations on the gas sensors now provide accurate N2 signals. The correct N2 signal led to much improved agreement between N2MBW and sulfur hexafluoride (SF6) MBW functional residual capacity (FRC) and lung clearance index (LCI) in 52 sleeping healthy infants and toddlers, suggesting a role for N2MBW in this age-group.

Novel pseudo random key &amp; cosine transformed chaotic maps based satellite image encryption

Image encryption converts the images into unrecognizable forms that seems like a white noise. Digital Chaos has also emerged as one of the important technique to design secure and efficient image encryption schemes. The chaos theory possesses various desirable properties required for the encrypting images like initial state sensitivity, unpredictability and behavioural complexity. However, image encryption schemes suffer from vulnerabilities like differential attack, statistical, known/chosen plaintext attack, and brute force attack. This paper proposes a novel approach to generate a pseudorandom key. This pseudo random key is combined with Logistic map (LM), cosine transformed Logistic map (CTLM) and cosine transformed Logistic-Sine Map (CTLSM), one by one, to implement three secure and efficient methods for satellite image encryption. The scheme uses the 384-bit of share key to perform encryption during the process. The proposed approaches are tested for parameters such as Entropy, Correlation Coefficient (CC), Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), Avalanche effect, Bit Correct Ratio (BCR) and Peak Signal to Noise Ratio (PSNR). The work also analyses the various cryptanalytic attacks on the proposed chaos and novel Pseudo random key combinations. The results show that the proposed pseudo random key and CTLSM combination outperforms the other two combinations, and is more efficient in resisting all type of attacks as well.

A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-Based Manipulation Tasks.

While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behavior. Hence, building detailed computational models for the human brain is one of the reasonable ways to attain this. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepancies from the reference values. While most studies consider providing the teaching signal as error in joint-space, few studies consider the error in task-space and even fewer consider the spiking nature of the cerebellum on the cellular-level. In this study, a detailed cellular-level forward cerebellar model is developed, including modeling of Golgi and Basket cells which are usually neglected in previous studies. To preserve the biological features of the cerebellum in the developed model, a hyperparameter optimization method tunes the network accordingly. The efficiency and biological plausibility of the proposed cerebellar-based controller is then demonstrated under different robotic manipulation tasks reproducing motor behavior observed in human reaching experiments.

Simultaneous detection of glucose, triglycerides, and total cholesterol in whole blood by Fourier-Transform Raman spectroscopy

In this paper, a reagent-free simultaneous and direct detection method of three analytes in human blood based on Fourier-transform Raman (FT-Raman) spectroscopy with 1064 nm laser radiation was proposed for the first time. A total of 161 human blood samples were characterized by FT-Raman spectroscopy under the excitation laser source of 1064 nm. In order to achieve a robust regression model, the Nonlinear Iterative Partial Least Squares (NIPALS) with orthogonal signal correction (OSC) algorithm and sample set partition based on a joint x-y distance (SPXY) is used to establish multivariate calibration models. The root means square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), correlation coefficients (R2) and ratio of performance to deviation (RPD) were 0.34255 mg/dL, 0.3662 mg/dL, 0.99982 and 56.3524 for glucose, 0.33656 mg/dL, 0.75736 mg/dL, 0.99967 and 34.9169 for total cholesterol (TC), and 0.29956 mg/dL, 0.27469 mg/dL, 0.99998 and 173.5098 for triglycerides (TG), respectively. The analysis results showed that the proposed method could be able to accurately predict the concentration of glucose, TC and TG in blood. This method can instantaneous multi-component detection on whole blood.

Performance of PLS, GA-PLS and OSC-GA-PLS Models in QSAR Study of Pyrazolone Derivatives Inhibitory Activity, and Image Processing for New Compounds Designing

Nowadays, Pyrazolone and its derivatives have gained a lot of attention due to their biological and medicinal applications. These compounds have antimicrobial, antifungal and anticancer properties. Therefore, using simple methods to prepare these compounds is important. Pyrazolone is one of the inhibitors of kinase domain containing receptor KDR or VEGFR-2. In this study, Quantitative Structure-Activity Relationship (QSAR) analysis was used to predict the inhibitory activity of new pyrazolone derivatives. Also, Bi-dimensional images were used to calculate pixels for QSAR modeling. Furthermore, the partial least squares (PLS) was used to establish a relationship between IC50-dependent variables and independent variables, i.e., pixels or hidden variables. In addition, Genetic Algorithm (GA) was used in PLS method (GA-PLS) to select the descriptors. In this method, the variables which selected to form the calibration model had negligible errors with acceptable characteristics. Pre-processing methods such as Orthogonal Signal Correction (OSC) were used to provide a suitable input for modeling as well as to improve the results of the GA (OSC-GA-PLS). Furthermore, Root Mean Squared Error of Prediction (RMSEP) was used to assess the performance of the models to predict the pIC50 of the studied compounds, the value of which was obtained equal to 0.30, 0.22, and 0.19 for PLS, GA-PLS and OSC-GA-PLS models, respectively. Finally, the proposed QSAR model was developed with the OSC-GA-PLS method to predict the inhibitory activity of the new compounds.

Lyn Kinase Activity Is Required for Akt Mediated Erythroleukemia Cell Differentiation

Erythroleukemia (M6 subtype of Acute Myeloid Leukaemia) is uncommon but has a poor prognosis, with reports of successful differentiation therapy using erythropoietin (Epo). Signaling through the Epo-receptor, which involves JAK2 and Lyn tyrosine kinases, controls red blood cell progenitor development. We have highlighted the importance of Lyn for regulating downstream Akt, and feed-back inhibitory signaling of the Epo-receptor through analysis of Lyn-/-, Lynup/up (hyperactive Lyn) and Cbp-/- (Csk-binding protein, a negative regulator of Lyn) erythroid cells. However, the importance of maintaining Lyn activity as opposed to Lyn protein for erythroid cell development and signaling, has not been delineated. To address this, we generated LynKD/KD mice (expressing a kinase dead K275M mutant Lyn), and analysed their erythroid compartment and signaling in immortalized erythroid progenitors. We show that LynKD/KD mice display splenic extramedullary erythropoiesis and have evidence of elevate bone marrow erythropoiesis, similar to Lyn-/- mice but with a less severe phenotype. Immortalized erythroid progenitors from LynKD/KD mice show impaired Epo-induced differentiation and a greater dependence on Epo for viability, but unaltered proliferation, compared to wild-type cells. Epo-induced signaling of LynKD/KD cells showed enhanced pJAK2/pSTAT5, reduced pAkt/pGAB2, and substantially reduced ALAS-e levels, compared to wild-type cells. Importantly, elevating Akt signaling in LynKD/KD cells by addition of phosphatase inhibitors (okadaic acid or Calyculin A), or via expression of active Akt, restored their differentiation capacity (and ALAS-e levels) and reduced their dependence on Epo for viability. We have unveiled that Lyn kinase activity, and not just its expression, is required for correct signaling of Akt to GATA-1 to maintain ALAS-e expression in erythroid cells, enabling hemoglobin production and viability during differentiation (Fig-1).

A class of new nonparametric circular‐grid charts for signal classification

AbstractIn this paper, some efficient monitoring and post‐signal follow‐up approaches are studied and compared for joint surveillance of location and scale of a process using the notions of circular‐grid (CG) schemes. Precisely, three variants of CG Cucconi schemes are introduced and compared with three variants of percentile modified Lepage (PML) schemes. One of the PML schemes is equivalent to the traditional CG Lepage scheme, while another may be viewed as the Lepage type statistic using Gastwrith score, which is also a powerful tool for process surveillance. Overall, one of the proposed CG Cucconi schemes is most effective in identifying a class of signals, whether it is a location shift or scale shift or a shift in both parameters. It also indicates the direction of the shifts in either or both the parameters. Detecting a downward scale shift is the most challenging task in joint monitoring, and to this end, a new bias‐corrected CG Lepage scheme is introduced. We compare the competing schemes in terms of correct signal classification probabilities. We illustrate the use of the proposed schemes in monitoring the trip‐duration data in cab services. Some concluding remarks and future research problems are offered.

Development of an Algorithm for Clustering Cardiac ECG Signals with Post-Correction for Long-Term ECG Monitoring

Introduction. The most common method for diagnosing cardiovascular diseases is the method of ECG monitoring. In order to facilitate the analysis of the obtained monitorograms, special software solutions for automated ECG processing are required. One possible approach is the use of algorithms for automated ECG processing. Such algorithms perform clustering of cardiac signals by dividing the ECG into complexes of similar cardiac signals. The most representative complexes obtained by statistical averaging are subject to further analysis.Aim. Development of an algorithm for automated ECG processing, which performs clustering of cardiac signals by dividing the ECG into complexes of similar cardiac signals.Materials and methods. Experimental testing of the developed software was carried out using patient records provided by the Pavlov First State Medical University of St Petersburg. The software module was implemented in the MatLab environment.Results. An algorithm for clustering cardiac signals with post-correction for the tasks of long-term ECG monitoring and a software module on its basis were proposed.Conclusion. The presence of a small number of reference cardiac signal complexes, obtained through ECG processing using the proposed algorithm, allows physicians to optimize the process of ECG analysis. The as- obtained information serves as a basis for assessing dynamic changes in the shape and other parameters of cardiac signals for both a particular patient and groups of patients. The paper considers the effect of synchronization errors of clustered cardiac signals on the shape of the averaged cardiac complex. The classical solution to the deconvolution problem leads to significant errors in finding an estimate of the true form of a cardiac signal complex. On the basis of analytical calculations, expressions were obtained for the correction of clustered cardiac signals. Such correction was shown to reduce clusterization errors associated with desynchronization, which creates a basis for investigating the fine structure of ECG signals.

Investigating the effect of trigger delay on cardiac 31P MRS signals

The heart’s geometry and its metabolic activity vary over the cardiac cycle. The effect of these fluctuations on phosphorus (31P) magnetic resonance spectroscopy (MRS) data quality and metabolite ratios was investigated. 12 healthy volunteers were measured using a 7 T MR scanner and a cardiac 31P-1H loop coil. 31P chemical shift imaging data were acquired untriggered and at four different times during the cardiac cycle using acoustic triggering. Signals of adenosine-triphosphate (ATP), phosphocreatine (PCr), inorganic phosphate (Pi) and 2,3-diphosphoglycerate (2,3-DPG) and their fit quality as Cramér-Rao lower bounds (CRLB) were quantified including corrections for contamination by 31P signals from blood, flip angle, saturation and total acquisition time. The myocardial filling factor was estimated from cine short axis views. The corrected signals of PCr and gamma-ATP were higher during end-systole and lower during diastasis than in untriggered acquisitions (P<0.05). Signal intensities of untriggered scans were between those with triggering to end-systole and diastasis. Fit quality of PCr and gamma-ATP peaks was best during end-systole when blood contamination of ATP and Pi signals was lowest. While metabolite ratios and pH remained stable over the cardiac cycle, signal amplitudes correlated strongly with myocardial voxel filling. Triggering of cardiac 31P MRS acquisitions improves signal amplitudes and fit quality if the trigger delay is set to end-systole. We conclude that triggering to end-systole is superior to triggering to diastasis.

The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging

Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.

Key taste components in two wild edible Boletus mushrooms using widely targeted metabolomics

Compounds from wild edible mushrooms has been reported to exert biological activities and contribute to the different flavors of mushrooms in our diet. Wild edible Boletus mushrooms are popular in Southwest China. In this study, we performed ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) based on widely targeted metabolomics analysis to identify key components. A total of 194 metabolites (113 upregulated, 81 downregulated) divided into 11 groups (49 metabolites in group lipids, 34 in amino acids and derivatives, 30 in organic acids, 18 in phenolic acids, 16 in nucleotides and derivatives, 13 in alkaloids, 6 in flavonoids, 3 in lignans and coumarins, 3 in tannins, 2 in terpenoids, and 20 in others) were found among Boletus bainiugan compared with B. subsplendidus. Through clustering analysis, principal component analysis (PCA), and orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA), different metabolites from fruiting bodies were clearly identified. Significant differences were observed in the metabolites between Boletus bainiugan and B. subsplendidus. These metabolites are involved in important biological functions. Our results provide new insights into important metabolites and theoretical basis for the taste difference of two wild edible Boletus mushrooms.

Reliability analysis method for redundancy systems based on GO methodology and the universal generation function

In this study, Goal-Oriented (GO) methodology, as an important technique for reliability analysis, is used in multi-state redundancy systems. However, traditional GO operation is usually not sufficiently effective for such multi-state redundancy systems because of the complex computational problem. To solve this problem, an efficient UGF-based GO operation that combines the advantages of GO methodology and the universal generation function (UGF) is proposed. The algorithm can avoid the complex calculation process caused by the algorithm-based GO operation of traditional probability formulas, and can simplify the correction of the shared signal by regarding it as the system’s conditional probability. In addition, the further correction problem in which some dependent shared signals with a common failure are caused by other shared signals is also considered. Some application examples are presented to illustrate the proposed UGF-based GO methodology, as well as its effectiveness and practicability.

INFORMATION SUPPORT FOR PROCESS OBSERVING FLUX LINKAGEOF THE ROTOR ASYNCHRONOUS MOTOR

The article examines the quality indicators of information support of the process of observing the flux linkageof the rotor of an induction motor in vector control systems under the conditions of the action of parametric disturbances.This line of research is in-tended to provide a solution to the problem of building a high-quality information control system for an asynchronous electric drive, which eliminates the high sensitivity to deviations inthe parameters of the control object during its operation.First of all, this refers to temperature changes in the active resistances of the stator and rotor. The obtaining information about the rotor flux linkage is as-signed to the observer, which consists of four circuits.In each of the four circuits of the observation system model, two correction signals of thenon-agreementof the stator currents of the asynchronous machine and the observer are introduced through the weighting coefficients. The full vector of correcting feedbacksof the observation system contains in this case eight coefficients. The application of the principles of modal control to determine their numerical values leads to unwieldy equations. The problem is solved more elegant using the direct Lyapunov's method, which makes it possible to determine the possible structure of an observation in-formation system that is stable under parametric disturbances. The numerical values of a part of the coefficients of correcting feed-backs are obtained by the way ofthe analysis of the quadratic form of the total time-respect derivative of the Lyapunov'sfunction of the synthesized information system. To determine the optimal ranges of variation of the remaining coefficients, numerical methods were used to calculate the roots ofthe characteristic equation of a closed-circuit observation system. Thus, the idea of the studyis to use the largest number of correcting feedbacks in the structure of the observation information system and to select their values using the direct Lyapunov's method and numerical methods to ensure its low sensitivity to deviations of the parameters of an asynchronous machine.

IBeacon 신호 보정을 통한 스마트폰 기반 실내 측위

본 논문에서는 iBeacon과 스마트폰, 특히 iPhone 기반의 보행자 추측 항법 (Pedestrian Dead Reckoning; PDR)을 이용하는 지능형 하이브리드 실내 측위 시스템을 제안한다. iPhone은 향상된 PDR을 사용하여 초기 위치를 찾는 동시에 iBeacon 정보를 통합하여 위치를 보정한다. 전형적인 대학교 건물 내에서 진행한 실험을 통해 제안 기법이 기존 기법보다 정확도 높은 측위 성능을 갖는 것을 확인하였다.