Introduction: Usually osseointegration takes between three to six months after implant placement but patients are interested to have early loading. There are no definitive criteria for measuring bone mineral density (BMD), insertion torque (IT) (final torque force) and resonance frequency analysis (RFA) (primary implant stability) to determine exact loading time based on the relationship between the above-mentioned parameters. The aim of this study was to determine the relationship between IT, RFA and BMD in screw-type implants.Materials and Methods: This clinical trial was conducted on 18 patients who were candidates for ITI implant placement. Written consent was taken and jaw bone density was determined via a digital radiography technique before surgery. After implant placement, RFA and IT were measured. Fifty-five ITI implants of the total 62 implants placed were evaluated; the implants were 12 mm long with a diameter of 4.1 mm. Data was analyzed with Pearson’s test using SPSS.15 software (a=0.05).Results: There was a significant relationship between IT, RFA and BMD. Pearson’s test showed a correlation coefficient of 0.872 to 0.789 between the three parameters, indicating a strong relationship between them. The mean bone density was 1.468±0.042 g/cm2; the mean RFA was 66.01±2.2 ISQ and the mean IT was 34.62±3.33 N/cm2.Conclusion: Based on the results of the present study there is a significant relationship between, IT, RFA and BMD (p value=0.001).

This paper presents a novel approach to detect and estimate cracks in Timoshenko Beams using frequencies and frequency response functions and extreme learning machine. For this purpose, the extreme learning machine used the three first natural frequencies and frequency response functions of beam structure as input which may be noisy or noise free and crack states in beam as output. This data is acquired by the analysis of cracked beams applying the finite element method. To demonstrate the potential of the proposed vibration analysis over existing ones, a validation study has been done. The performance of the presented method has been verified through two numerical examples, namely, a cantilever beam and simply supported beam containing single or multi cracks. Results indicate that the proposed method works well in prediction and estimation of crack and obtained results are accurate. Also, the results show that the presented method is sensitive to the location and severity of crack in spite of the noisy modal data.

Frequency response diagrams of a system include detailed and recognizable ‎information about the structural and parameter effects of the transfer function model ‎of the system. The information are qualitatively and quantitatively obtainable from ‎simultaneous consideration of amplitude ratio and phase information. In this paper, ‎some rules and relationships are presented for making use of frequency response ‎information in order to identify the structure of rational and irrational models in a ‎heuristic manner. Estimation of the values of the parameters are also accomplished in ‎a graphical trial and error procedure. As an example, the heuristic method was applied ‎for identifying the simplified model of a rotary cement kiln from the frequency ‎response information of the analytically derived model of the system. The analytical ‎model of the kiln, used in this paper, was obtained elsewere in a detailed procedure ‎with some assumptions and by using initial and boundary conditions. Results obtained ‎from identification of the simplified model for rotary cement kiln not only reveal the ‎application of the heuristic method for model identification, but also demonstrate the ‎appearance of unstable poles and zeros in the model of the system‏.‏

In this paper, a new algorithm for system identification based on frequency response is presented. In this method, given a set of magnitudes and phases of the system transfer function in a set of discrete frequencies, a system of linear equations is derived which has a unique and exact solution for the coefficients of the transfer function provided that the data is noise-free and the degrees of the numerator and denominator are selected correctly. If the data is corrupted with (bounded) noise, then the answer is no longer unique and an acceptable transfer function is one that has a frequency response with a noise bound that covers the noisy data. To find one of these acceptable results, a new performance index is defined as "the least squares distance in the coefficient space". By minimizing this index, an initial transfer function is obtained which passes optimally through the noisy data. Then, using the so-called dynamic programming technique, the noise is reduced in such a way that at each step the resulting transfer function is pushed toward one of the acceptable noisefree systems. An illustrative example shows the effectiveness of the proposed algorithm.

Power management in microgrids is a major challenge due to its low total inertia and capacity. The lower the microgrid generation capacity is, the higher the share of each generation unit in total power will be, and the higher the frequency deviation in less time will be when an outage occurs. So, preventive actions can be more reasonable and affordable than corrective actions for microgrid power and frequency control. In this regard, a new primary frequency response-constrained unit commitment model is presented here to prevent excessive frequency deviations by more commitment of higher inertia power plants and more contribution of renewable energy resources or energy storage systems’ fast inertia response. To have a mixed-integer linear programming model, the primary frequency response constraints are linearized. The model is solved by the combination of two commercial solvers named MOSEK and YALMIP in the MATLAB 2018 environment. The proposed model is examined on a real isolated microgrid (an island). The results show that by activating the primary frequency support of distributed energy resources, the power can be managed with lower costs because there will be less need to start up fast (and expensive) gas turbine generation units. In addition, although comparing the model with others shows the more expensive management procedure, better frequency stability is obtained in contingencies.

The development of damage detection techniques for offshore jacket structures is vital for preventing catastrophic events. This paper applies a frequency response based method for the purpose of structural health monitoring. In this approach, the concept of a minimum rank perturbation theory is used. The feasibility of using a finite number of sensors and its effect on damage detection capabilities is investigated. In addition, the performance of the proposed method is evaluated in the case of multiple damages. The aforementioned points are illustrated using the numerical study of a two dimensional jacket platform.

The electrical properties of rock types or channel structures have been the object of intense studies for many years. The main aim of the present work is to show the frequency response of electrical properties of some Granite samples concerning chemical, minor and major composition. This may be used as a fingerprint for the characterization of some Granite rocks. Electrical measurements on Granite samples (surface, Aswan, Egypt) were measured (100 Hz up to 107 Hz). Differences in samples due to changes in texture lead to different changes in electrical properties. The changes at conductivity and dielectric constant are reflections from the texture within grains. Conductivity and dielectric constant values extend from ~10-8 to 10-4 (S/m) and 24 to 3. 5, respectively. With frequency increase, conductivity increases, and dielectric constant decreases (10 kHz), and then it settles down. Differences in electrical properties between samples are small as a direct result of the homogeneity and texture of most samples. There is a high dispersion of dielectric constant for relatively low-frequency values (<10 kHz) and no dispersion at relatively high frequencies. In conclusion, the dielectric constant decreases due to the shrinkage of energy levels for electrons and the increase of hopping particles between different particles.

The observed signal at the seismic stations is under the influence of the recording system. One of the basic reasons is the recording system transfer function or frequency response. Meanwhile knowledge of one of these items is inevitable for the processing and interpretation of the digital seismograms. On the other hand one should never forget that the recorded ground motion is considerably different from the generated signal by seismic sources. Studying the frequency response will help seismologist to separate the path and instrument effects in order to approach the real source signals. So in this paper we have studied frequency response of the Tehran Telemetric seismographic network in displacement, velocity and acceleration domains.