Transformation Parameters Research Articles

A color image contrast enhancement method based on improved PSO.

Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle's flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.

A generalized weighted total least squares-based, iterative solution to the estimation of 3D similarity transformation parameters

The essence of point cloud registration is to estimate the seven similarity transformation parameters of the Bursa-Wolf model which describes the relative position of the two neighboring LiDAR stations. So far, Gauss–Markov (GM) model has been widely used in point cloud registration, however, only the random errors contained in one LiDAR station are considered, which is seriously inconsistent with the reality. To simultaneously take the random errors contained in both of the two neighboring LiDAR stations into account in registration, the EIV model is constructed based on the introduction of unit quaternions to describe the spatial rotation in 3D similarity transformation, and an iterative solution to the estimation of the transformation parameters is systematically discussed. Detailed derivation of the formulas for the estimation of the seven transformation parameters are displayed step by step. Finally, three experiments are designed to verify the correctness and effectiveness of the solution.

Effective Electrical Properties and Fault Diagnosis of Insulating Oil Using the 2D Cell Method and NSGA-II Genetic Algorithm.

In this paper, an experimental analysis of the quality of electrical insulating oils is performed using a combination of dielectric loss and capacitance measurement tests. The transformer oil corresponds to a fresh oil sample. The paper follows the ASTM D 924-15 standard (standard test method for dissipation factor and relative permittivity of electrical insulating liquids). Effective electrical parameters, including the tan δ of the oil, were obtained in this non-destructive test. Subsequently, a numerical method is proposed to accurately determine the effective electrical resistivity, σ, and effective electrical permittivity, ε, of an insulating mineral oil from the data obtained in the experimental analysis. These two parameters are not obtained in the ASTM standard. We used the cell method and the multi-objective non-dominated sorting in genetic algorithm II (NSGA-II) for this purpose. In this paper, a new numerical tool to accurately obtain the effective electrical parameters of transformer insulating oils is therefore provided for fault detection and diagnosis. The results show improved accuracy compared to the existing analytical equations. In addition, as the experimental data are collected in a high-voltage domain, wireless sensors are used to measure, transmit, and monitor the electrical and thermal quantities.

Methodological Research on Image Registration Based on Human Brain Tissue In Vivo

As one of the critical steps in brain imaging analysis and processing, brain image registration plays a significant role. In this paper, we proposed a technique of human brain image registration based on tissue morphology in vivo to address the problems of previous image registration. First, different feature points were extracted and combined, including those at the boundary of different brain tissues and those of the maximum or minimum from the original image. Second, feature points were screened through eliminating their wrong matching pairs between moving image and reference image. Finally, the remaining matching pairs of feature points were used to generate the model parameters of spatial transformation, with which the brain image registration can be finished by combining interpolation techniques. Results showed that compared with the Surf, Demons, and Sift algorithms, the proposed method can perform better not only for four quantitative indicators (mean square differences, normalized cross correlation, normalized mutual information and mutual information) but also in spatial location, size, appearance contour, and registration details. The findings may suggest that the proposed method will be of great value for brain image reconstruction, fusion, and statistical comparison analysis.

Development Of A Cloud-Based Condition Monitoring Scheme For Distribution Transformer Protection

Distribution transformers are a necessity to ensure a reliable power supply to consumers and their inability to function properly or even breakdown should be avoided due to the high cost of replacing them. Distribution transformers are large in numbers and randomly distributed in cities and there is a need to accurately monitor their daily/hourly performance. To achieve this, real-time monitoring of the transformer’s health status is proposed rather than the use of the traditional approach involving physical inspection and testing which is slow, tedious and time-consuming. This paper presents a cloud-based monitoring scheme applied to a prototype distribution transformer. A 10kVA, 0.415 kV prototype distribution transformer has been acquired and connected to three residences for data acquisition. A data acquisition system has been developed to monitor and record the parameters of the prototype transformer for 14 days. The parameters, monitored in real-time include load current, phase voltage, transformer oil level, ambient temperature and oil temperature. The acquired real-time data of the transformer is validated with the standard measuring instrument. An algorithm was developed to transmit and log the data to ThinkSpeak cloud server via node MCU (ESP 8266). Results obtained in this study, which can be visualized via the graphical user interface of ThinkSpeak, indicate that the proposed scheme can acquire vital data from the distribution transformers and transmit the information to the monitoring centre.

A CVT Parameter Identification Method Based on Improved Grey Wolf Optimizer

Aiming at the problem that harmonic simulation model parameters of capacitive voltage transformer (CVT) are difficult to determine, an identification method of equivalent circuit parameters of CVT is proposed. By studying the frequency response, a nonlinear equation about the equivalent circuit parameters of CVT is established. By analyzing the characteristics of the equation and combining with the actual use, an improved grey wolf optimizer is applied to achieve the accurate identification of the key parameters of CVT. On the basis of grey wolf optimizer, the algorithm introduced a good point set and comprehensive dimension learning strategy to prevent the algorithm from falling into local optimum, and modified the convergence factor according to the actual needs to improve the convergence accuracy. Finally, three numerical examples are simulated by MATLAB/Simulink software, which proves the feasibility and superiority of the proposed method in CVT application.

An application of pulsar timing in dynamic reference frame transformation of solar system

AbstractThe establishment of pulsar space–time reference frame is of great significance for X‐ray pulsar deep space navigation. To demonstrate that the pulsar parameters are accurate enough to be used as a spatial reference, the transformation parameters, that is, α, β, and γ, of different reference frames determined based on pulsar timing are shown and compared with that of the heliocentric coordinates of Earth‐Moon barycenter. The JPL ephemerides such as DE421, DE200 and INPOP series, are mainly selected for conversion parameter calculation. For the best‐determined parameter γ, the discrepancy between the two different data is 0.38 mas/1.33σ. The timing positions of pulsars clearly show the frame‐tie of different ephemerides, indicating that MSPs have the potential to be a candidate for constructing time–space standard. It is also found that the rotation angles change linearly with time, and PSR J0437 − 4715 plays a critical role in the fitting of α. By employing a few high‐quality MSPs, the transformation precision of the right ascension is evaluated to be on the order of sub‐milliarcsecond, which is an order of magnitude higher than that of the declination.

Direct leaf-peeling method for areca protoplasts: a simple and efficient system for protoplast isolation and transformation in areca palm (Areca catechu)

BackgroundAreca palm (Areca catechu) is a woody perennial plant of both economical and medicinal importance grown in tropical and subtropical climates. Yet, the molecular biology study of areca palm is extremely impeded by its unavailability of a transformation method. An efficient protoplast isolation and transformation system could be highly desirable to overcome this barrier.ResultsHere, we described a simple and efficient method for protoplast isolation and transformation from the perennial plant areca palm. A high yield of protoplasts (2.5 × 107 protoplasts per gram of fresh leaf tissues) was obtained from the fresh light green leaflet from the newly-emerged leaf digested overnight in the enzyme solution [2% (w/v) cellulase R10, 0.5% (w/v) macerozyme R10, 0.7 M mannitol, 10 mM CaCl2, 20 mM KCl, 20 mM MES and 0.1% (w/v) BSA, pH 5.7] by the direct leaf-peeling method. The isolated areca protoplasts maintain viability of 86.6% and have been successfully transformed with a green fluorescent protein (GFP)-tagged plasmid (pGreen0029-GFP, 6.0 kb) via the polyethylene glycol (PEG)-mediated transformation. Moreover, the mannitol concentration (optimal: 0.7 M) was determined as a key factor affecting areca protoplast isolation. We also demonstrated that the optimal density of areca protoplast for efficient transformation was at 1.0–1.5 × 106 cells/ml. With the optimization of transformation parameters, we have achieved a relatively high transformation efficiency of nearly 50%.ConclusionWe have established the first efficient protocol for the high-yield isolation and transformation of areca palm protoplasts. This method shall be applied in various biological studies of areca palm, such as gene function analysis, genome editing, protein trafficking and localization and protein–protein interaction. In addition, the protoplast system offers a great genetic transformation approach for the woody perennial plant-areca palm. Moreover, the established platform may be applied in protoplast isolation and transformation for other important species in the palm family, including oil palm and coconut.

Analysis and Design of a CLC/N Compensated CC-Type WPT System with Compact and Low-Cost Receiver

Wireless power transfer (WPT) has been extensively studied by technicians for its advantages of safety, convenience and aesthetics. The load-independent constant current (CC) output is the focus of WPT research and has been initially applied in various fields, such as light-emitting diodes (LEDs) driving, CC charging of electric vehicles (EVs), etc. However, the existing CC-type WPT system has problems in that the output current is constrained by the loosely coupled transformer (LCT) parameters, the receiver is bulky, and the development cost is high. Therefore, this manuscript proposes a new CLC/None (CLC/N) compensated WPT system with a CC output function that eliminates the receiver-side compensation components, ensures the compactness of the receiver, and saves on production costs. The conditions for satisfying the CC output and zero-phase-angle (ZPA) operation of the proposed system are first discussed. Then, the detailed parameter design method is provided, and the characteristic that the output current is unconstrained by the LCT parameters is illustrated. In addition, the implementation of zero-voltage switching (ZVS) operation of the proposed system and the sensitivity of the changes of compensation components to the output current are analyzed in detail. Furthermore, to demonstrate the superiority of the proposed system, several other typical CC-type WPT systems are introduced for comparison. Finally, a confirmatory experimental prototype with an output current of 2 A is fabricated, and the experimental results are consistent with the theoretical analysis.

Predictive value of hemogram parameters in malignant transformation of the endometrium in patients with different risk factors.

To investigate whether the pretreatment hemogram parameters and their ratios can be used in predicting the endometrial transformation in patients with abnormal uterine bleeding. Records of all patients who underwent an endometrial histopathological evaluation between 2011 and 2021 were investigated. Hemogram, neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) were analyzed. Chi square and Mann Whitney U tests were used for analysis. P<0.05 was considered statistically significant. 427 patients were included, of whom 117 were presented with endometrial hyperplasia without atypia (27.4%; mean age, 42±9.7; Group II), 70 with atypia (16.3%; mean age, 53.4±9; Group III), 102 with early endometrial cancer (EC) (23.8%; mean age, 63±7.8; Group IV) and 38 with advanced disease (8.8%; mean age, 63.3±10.5; Group V). Patients without pathology constituted the control group (23.4%; mean age, 42.2±9.5; Group I). Risk factors for atypia and carcinoma were determined as age, postmenopausal state, obesity, diabetes, and increased estrogen exposure (each, p<0.05). There was no significant difference in NLR and PLR (p>0.05). However, hemoglobin and hematocrit levels were higher in Groups IV and V (13.9 vs 13.1 mg/dL, and 39.1 vs 38.8%, respectively; p<0.01). Platelet value was significantly higher in Groups III to V (282x109/L, 283x109/L and 295x109/L; p<0.05, p<0.05 and p<0.01, respectively). Our findings support the impact of inflammation on malign transformation from normal endometrial mucosa to atypia and carcinoma. NLR and PLR values showed no statistical difference. Instead, thrombocytosis may have a predictive role in EC.

Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks

Objective. To develop a novel patient-specific cardio-respiratory motion prediction approach for X-ray angiography time series based on a simple long short-term memory (LSTM) model. Approach. The cardio-respiratory motion behavior in an X-ray image sequence was represented as a sequence of 2D affine transformation matrices, which provide the displacement information of contrasted moving objects (arteries and medical devices) in a sequence. The displacement information includes translation, rotation, shearing, and scaling in 2D. A many-to-many LSTM model was developed to predict 2D transformation parameters in matrix form for future frames based on previously generated images. The method was developed with 64 simulated phantom datasets (pediatric and adult patients) using a realistic cardio-respiratory motion simulator (XCAT) and was validated using 10 different patient X-ray angiography sequences. Main results. Using this method we achieved less than 1 mm prediction error for complex cardio-respiratory motion prediction. The following mean prediction error values were recorded over all the simulated sequences: 0.39 mm (for both motions), 0.33 mm (for only cardiac motion), and 0.47 mm (for only respiratory motion). The mean prediction error for the patient dataset was 0.58 mm. Significance. This study paves the road for a patient-specific cardio-respiratory motion prediction model, which might improve navigation guidance during cardiac interventions.

High-precision deformation analysis of yingxian wooden pagoda based on UAV image and terrestrial LiDAR point cloud

The monitoring of wooden pagodas is a very important task in the restoration of wooden pagodas. Traditionally, this labor has always been carried out by surveying personnel, who manually check all parts of the pagoda, which not only consumes huge manpower, but also suffers from low efficiency and measurement errors. This article evaluates the feasibility of combining portable 3D light detection and ranging (LiDAR) scanning and unmanned aerial vehicle (UAV) photogrammetry to perform these inspection tasks easily and accurately. The wooden pagoda's exterior picture and inside point cloud are acquired using a UAV and a LiDAR scanner, respectively. We propose a feature−based global alignment method to register the site point cloud. The error equation of the column of observed values is utilized as the beginning value of the feature constraint for global leveling. The beam method leveling model solves the spatial transformation parameters and the unknown point leveling values. Then, the Structure from Motion (SfM) algorithm of computer vision is used to realize the fusion of the dense point cloud of the exterior of the wooden pagoda generated from multiple non−measured images by global optimization and the LiDAR point cloud of the interior of the wooden pagoda to obtain the complete point cloud of the wooden pagoda, which makes the deformation monitoring of the pagoda more detailed and comprehensive. After experimental verification, the overall registration accuracy of the Yingxian wooden pagoda reaches 0.006 m. Compared with the scanning point cloud data in 2018, the model is more accurate and complete. By analyzing and comparing the data of the second floor of the wooden pagoda, we knew that the inclination of a second bright layer and a second dark layer is still developing steadily. Overall, the western outer trough inclines thoughtfully, and the column frame slopes from southwest to northeast. Some internal columns showed a negative offset in 2020, and the deformation analysis of a single column was realized by comparing it with the standard column model. The main contribution of this method lies in the effective integration of UAV images and point cloud data to provide accurate data sources for good modeling. This research will provide theoretical and methodological support for the digital protection of architectural heritage and GIS data modeling. The analysis results can provide a scientific basis for the restoration scheme design.

РАЗРАБОТКА МЕТОДИКИ РАСЧЕТА ПАРАМЕТРИЧЕСКИХ ТРАНСФОРМАТОРОВ

Relevance During the operation of semiconductor converters and electrical installations (for example: ignition units, converters of ozonizers and gasdischarge lamps, energy facilities where corona discharge periodically occurs, etc.), an increased level of electromagnetic interference often occurs. Under these conditions, increased requirements for electromagnetic compatibility and galvanic isolation are imposed on power sources and information transmission devices. Similar questions sometimes arise when installing information equipment in the field, where it is not possible to connect the ground. Satisfaction of these requirements is possible when using parametric transformers (PT). At the same time, the operation of the PT is characterized by special requirements for their design and the choice of the main parameters of its elements due to the electromagnetic mode associated with the need to saturate the core and due to the occurrence of significant reactive power circulating in the PT along with active power. Due to the special field of application of PTs, as well as the complexity of the electromagnetic processes occurring in them, at present there are no generalized and sufficiently accurate methods for calculating PTs for practice. The well-known approximate methods for designing PTs affect only certain issues of choosing their parameters. Aim of research This article proposes the creation of a methodology for calculating the PT, which at the same time allows the design of a device not only to ensure electromagnetic compatibility, but also with the aim of further possibility of transition to optimization of the parameters obtained as a result of the calculation of the PT, as well as with the aim of further developing generalized methods for designing parametric electrical machines, which are also of particular interest. Research objects Parametric transformers, orthogonal magnetic systems. Research methods When developing a methodology for calculating parametric transformers, traditional research methods based on the classical theory of electrical and magnetic circuits were used. Results As a result of the work carried out, it was possible to create a methodology for calculating parametric transformers, which can be used in the design of special secondary power sources and other power conversion devices. The calculation algorithm and methods for calculating the main parameters of transformers of this type are determined.

Study of the geometry influence of the support points in coordonates transformation: application from WGS84 to NS59 datum

The use of a transformation method for the passage from one geodetic system to another requires the use of some common points as support points, these points are used in the determination of the transformation parameters. Generally, the choice of the support points is effected manually by choosing the best distribution of these points in the transformation area. In the present study, we present a methodology of selection of these points where an algorithm takes into account the computation of the transformation parameters with all combinations between the common points and the best result will be adopted. An application of this methodology are carried out in North-East of Algeria to determine the best set of the 09 transformation parameters between the WGS84 system and the National North Sahara system using 10 common points (05 support and 05 control). This methodology is efficient in the case where the common points are near one another.

Geomagnetically induced current uncertainty quantification based on the earth conductivity models constructed by the Kriging method

• Proposed and verified the superiority of Kriging method in constructing earth conductivity model. • Illustrate the necessity of uncertainty analysis for GIC. • Obtained the sensitivity of GIC input parameters(transformer parameters, transmission line parameters and ground resistance parameters) in 1000kV UHV Northern China power grid. With the expansion of power grid, the hazard of geomagnetically induced current (GIC) has attracted more attention. Due to the small number of Magnetotelluric detection points, the earth conductivity model based on limited data cannot be adapted to the GIC calculation of large-scale power grids. In addition, it is necessary to consider the effect of simultaneous changes in multiple input variables on GIC calculation results. Therefore, this paper presents a method for modeling the conductivity of the earth which is used in the case of less data, and uses the model to quantify the uncertainty. Based on the existing Magnetotelluric data in Northern China, the conductivity model within the power grid region is established using the Kriging method. The uncertainty quantification model of the GIC is constructed based on the Polynomial Chaos Expansions(PCE) method. The uncertainty quantification analysis is carried out for the 1000 kV ultra-high voltage Northern China Power Grid. The statistical information of the GIC and its sensitivity to each input parameter are obtained.

Integration of 3D environment models generated from the sections of the image sequence based on the consistency of the estimated camera trajectories

This paper describes a method that integrates Three-Dimensional (3D) environment models reconstructed from image sequences to reduce the computation time of 3D environment modeling that estimates camera poses and simultaneously reconstructs a 3D environment model from images based on photogrammetry. However, 3D environment modeling is time-consuming when using many images because it finds correspondence points between them by feature matching. Therefore, we assume that the computation time is reduced by reconstructing 3D environment models from image sequences divided from an image sequence because the number of images used in 3D environment modeling becomes less. However, it is difficult to integrate the 3D environment models because the scale between them may not be the same, and overlapping regions between 3D environment models are small for integrating the models. In this paper, we propose a method that integrates 3D environment models based on camera trajectories corresponding to overlapped images between image sequences used in 3D environment modeling. To integrate them, transformation parameters are calculated from poses of camera trajectories between 3D environment models. After that, the transformed camera trajectory is aligned using coarse and fine registration. Consequently, compared with 3D environment modeling that processes an image sequence in batch, the proposed method could reduce the computation time and reconstruct a comparable integrated model.

Device Features Based on Linear Transformation With Parallel Training Data for Replay Speech Detection

Replay speech poses a growing threat to speaker verification systems, thus the detection of replay speech becomes increasingly important. A critical factor differentiating replay speech and genuine speech is the representation of device information. Replay speech carries physical device information that originates from recording device, playback device, and environmental noise. In this work, a device-related linear transformation strategy is proposed to disentangle non-device information from replay speech. First, we conduct factor analysis by introducing a common vector for both replay utterance and the corresponding genuine speech utterance on parallel training data; then, we derive an expectation maximization formula to obtain the parameters of the device-related linear transformation; subsequently, three device feature extraction methods are developed based on the device-related linear transformation. The developed device features are evaluated on ASVspoof 2017 version 2.0 and ASVspoof 2021 physical access corpora. The experimental results demonstrate that our proposed linear transformation strategy is effective for replay spoofing detection, and the resultant device features outperform many typical features. Moreover, our spoofing detection systems display superior performance over several competitive state-of-the-art systems.

Real-Time Monocular Depth Estimation Merging Vision Transformers on Edge Devices for AIoT

Depth estimation is requisite to build 3D perceiving capability of artificial intelligence of things (AIoT). Real-time inference with extremely low computing resource consumption is critical on edge devices. However, most single-view depth estimation networks focus on the improvement of accuracy running on high-end GPUs, which go opposite to the real-time requirement on edge devices. To address this issue, this article proposed a novel encoder-decoder network to realize real-time monocular depth estimation on edge devices. The proposed network merges semantic information at global field via an efficient transformer-based module to provide more details of the object for depth assignment. The transformer-based module is integrated in the lowest level resolution of an encoder-decoder architecture to largely reduce the parameters of the Vision Transformer (ViT). In particular, we proposed a novel patch convolutional layer for low-latency feature extraction in the encoder and a SConv5 layer for effective depth assignment in the decoder. The proposed network achieves outstanding balance between accuracy and speed on the NYU Depth v2 dataset. A low RMSE of 0.554 and a fast speed of 58.98 FPS on NVIDIA Jetson Nano device with TensorRT optimization are obtained on NYU Depth v2, outperforming most state-of-the-art real-time results.

Вплив роботи трансформаторів напруги підстанцій 35/10 кВ на похибки системи обліку електроспоживання

The work is devoted to the issue of the optimal functioning of electricity consumption accounting systems in the conditions of market relations and the related problem of reliable and rational use of the operating modes of voltage transformers. The possibility of organizing the electricity consumption accounting system in the absence of voltage transformers on the high side of the substation was analyzed and the possible under-accounting of electricity in this case was calculated. The influence of the parameters of power transformers on the operation of the commercial electricity accounting system when current and voltage transformers are installed at different voltage levels is studied. It was determined that the loading of the power transformer and the switching scheme of its windings affects the error value of the measuring channels in the automated systems of control and accounting of electricity. If you use the classic T-shaped transformer replacement scheme, you can get the equation for its full power. After making some mathematical transformations, we get a formula that calculates the underaccounting of electrical energy. As we can see, the shortfall is proportional to the voltage drop in the transformer, which depends on the load power. According to the constructed dependences of electric energy underaccounting on the load for several types of power transformers (ТМН-4000/35, TМН-6300/35, TДН-10000/35), it was found that the underaccounting of electricity increases in proportion to the load. For reactive power, it is about an order of magnitude more than for active power. Thus, the formation of the accounting system at substations according to the scheme (Fig. 1) is unacceptable, due to the underaccounting of active and reactive electricity. For the optimal functioning of the accounting systems at the "upper" level, it is necessary to install a voltage transformer on the high side of the substation.

Research on Transient Low-Frequency Oscillation of Electrified Railway Vehicle-Grid Coupling System

At the moment of electric locomotive passing the neutral-section, the voltage and current of traction network may have low-frequency oscillation (LFO), which will seriously affect the safe operation of the railway. Aiming at eliminating LFO, a modeling method of vehicle-grid coupling system based on flux-current theory is presented in this paper. In the model of electric locomotive, the transient current control strategy and magnetic saturation of on-board transformer are considered, and the equivalent model of excitation inductance of on-board transformer is established based on the flux-current theory. Using this model, based on the Middlebrook impedance ratio criterion, the stability region of vehicle-grid coupling system is analyzed under the influence of average saturation of on-board transformer and control parameters of electric locomotive. Simulation results and analysis are established to evaluate and verify the proposed modeling method of LFO. This work has the benefits of defining the opening and closing phase angle region of system stability as well as providing guidance to adaptively modify the control parameters to suppress the LFO.