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This study optimized the ultrasound image of carotid artery stenosis using Taguchi dynamic analysis and an indigenous water phantom. Eighteen combinations of seven essential factors of the ultrasound scan facility were organized according to Taguchi’s L18 orthogonal array. The seven factors were assigned as follows: (1) angle of probe; (2) signal gain; (3) resolution vs. speed; (4) dynamic range; (5) XRES; (6) zoom; (7) time gain compensation. An indigenous water phantom was customized to satisfy the quantified need in Taguchi’s analysis. Unlike the conventional dynamic Taguchi analysis, an innovative quantified index, the figure of merit (FOM), was proposed to integrate four specific quality characteristics, namely (i) average difference between the practical scan and theoretically preset area (78.5, 50.2 and 12.6 mm2) of stenosis, (ii) standard deviation of the average, (iii) practical scan’s sensitivity β to various stenosis diameters (10, 8, and 4 mm), and (iv) correlation coefficient r2 of the linear regressed sensitivity curve. The highest value (FOM = 0.413) was furnished by the optimal combination of factors on 18 groups under study, yielding high r2 and low β or standard deviation values and the best quality of ultrasound images for the further clinical diagnosis. The comparison between FOM and the conventional signal-to-noise (S/N) ratio in Taguchi’s analysis revealed that FOM compiled more quality characteristics that were superior by nature to fulfill the practical need in clinical diagnosis. The alternative choice in ultrasound scan optimization can be based on stenosis diameter variation from a different perspective to be explored in the follow-up study.

The thyroid biokinetic model of radioactive I-131 was re-evaluated using a refined nine-compartmental model and applied to twelve thyroid cancer patients. In contrast to the simplified four-compartmental model regulated by the ICRP-56 report, the revised model included nine compartments specified in the ICRP-128 report, namely, oral, stomach, body fluid, thyroid, whole body, liver, kidney, bladder, and remainder (i.e., the whole body minus kidney and bladder). A self-developed program run in MATLAB was designed to solve the nine first-order simultaneous linear differential equations. The model was realized in standard and simplified versions. The latter neglected two feedback paths (body fluid to oral, i31, and kidney to the whole body, i87) to reduce computations. Accordingly, the biological half-lives for the major compartments (thyroid and body fluid + whole body) were 36.00 ± 15.01, 15.04 ± 5.63, 34.33 ± 15.42, and 14.83 ± 5.91 of standard and simplified version. The correlations between theoretical and empirical data for each patient were quantified by the dimensionless AT (agreement) index and, the ATtot index integrated each individual AT of a specific organ of one patient. Since small AT values indicated a closer correlation, the obtained range of ATtot (0.048 ± 0.019) proved the standard model’s reliability and high accuracy, while the simplified one yielded slightly higher ATtot (0.058 ± 0.023). The detailed outcomes among various compartments of twelve patients were calculated and compared with other researchers’ work. The correlation results on radioactive I-131 evolution in thyroid cancer patients’ bodies are instrumental in viewpoint of radioactive protection of patients and radiological personnel.

This study tried to propose an innovated idea of solidifying the resolution of gamma camera in routine quality control and recommended a quantified index as minimum detectable difference (MDD) of gamma camera SPECT images using the Taguchi analysis and an indigenous V-shaped slit gauge. The gauge was customized to fulfill the quantitative requirement of the Taguchi analysis. The MDD among slit gauge of derived SPECT image was calculated from two overlapped peak profiles collected from a tangent slice of the V-shaped slit gauge with two nearby peaks. In particular, MDD was evaluated as minimum distance between two peak centers through the Student’s t-test with a constant, 1.96, which indicates that two peak centers separated distant enough to create a 95% confidence level of separation. Eighteen combinations of six gamma camera scanned factors were organized according to Taguchi analysis. Accordingly, (A) collimator, (B) detector to targe distance, (C) total counts, (D) acquired energy width, (E) Matrix size, and (F) zoom of collected ROI with each of two or three levels were organized into 18 groups to collect the slit gauge images according to Taguchi L18 orthogonal array. Then, three well-trained radiologists were ranked the scanned gauge images to derive the fish-bone-plot of signal-to-noise ratio (S/N, dB) and correlated ANOVA. Furthermore, the quantified MDD was proposed to verify the optimal suggestion of gamma camera scanned protocol, and obtained the MDD as 8.4, 7.9, and 7.1 mm for the second group of original L18 preset, conventional, and the optimal preset, respectively. Thus, the optimal preset of gamma camera was achieved in this study. The MDD proved to be a successful index in quantifying the imaging resolution of a gamma camera.

Smart spring support is a kind of active damping device based on piezoelectric material. It can effectively suppress the vibration of a shaft system in an overly critical state by changing the stiffness and damping of the support. The support parameters have a significant impact on the vibration of the system. By studying the influence of the smart spring support parameters on the vibration characteristics of the transmission shaft system, the support parameters can be configured more reasonably so that the vibration of the transmission system can be reduced as much as possible. Based on the finite element method, this paper studies the influence of the stiffness, damping and mass of the smart spring support on the vibration characteristics of the three-support shafting. Firstly, the smart spring shafting test bed is built, and the vibration reduction performance test of the smart spring is carried out to verify the damping effect of the smart spring. Then, the shafting dynamic model is established by the finite element method, and the inherent characteristics of the system are analyzed. Finally, the influence of the stiffness, damping, mass and other parameters of the smart spring support on the dynamic response of the system is studied. The results show that increasing the stiffness of the smart spring support can effectively reduce the vibration amplitude of the system. The damping of the smart spring support has no obvious effect on the vibration of the shafting. The smaller the mass of the smart spring support, the more favorable the system is.

Smart Spring is a kind of active vibration control device based on piezoelectric material, which can effectively suppress the vibration of the shaft system in an over-critical state, and the selection of control strategy has great influence on the vibration reduction effect of the Smart Spring. In this paper, the authors investigate the control of the over-critical vibration of the transmission shaft system with Smart Spring, based on the ADAMS and MATLAB joint simulation method. Firstly, the joint simulation model of three-support shafting with Smart Spring is established, and the over-critical speed simulation analysis of the three-support shafting under the fixed control force of the Smart Spring is carried out. The simulation results show that the maximum vibration reduction rate is 71.6%. The accuracy of the joint simulation model is verified by the experiment of the three-support shafting subcritical vibration control. On this basis, a function control force vibration control strategy with time-varying control force is proposed. By analyzing the axis orbit of the shafting, the optimal fixed control force at different speeds is obtained, the control force function is determined by polynomial fitting, and the shafting critical crossing simulation under the function control force is carried out. The simulation results show that the displacement response of the shafting under the function control force is less than that under the fixed control force in the whole speed range.