Small Error Research Articles

Stretchable spring‐sheathed yarn sensor for 3D dynamic body reconstruction assisted by transfer learning

AbstractA wearable sensing system that can reconstruct dynamic 3D human body models for virtual cloth fitting is highly important in the era of information and metaverse. However, few research has been conducted regarding conformal sensors for accurately measuring the human body circumferences for dynamic 3D human body reshaping. Here, we develop a stretchable spring‐sheathed yarn sensor (SSYS) as a smart ruler, for precisely measuring the circumference of human bodies and long‐term tracking the movement for the dynamic 3D body reconstruction. The SSYS has a robust property, high resilience, high stability (>18 000), and ultrafast response (12 ms) to external deformation. It is also washable, wearable, tailorable, and durable for long‐time wearing. Moreover, geometric, and mechanical behaviors of the SSYS are systematically investigated both theoretically and experimentally. In addition, a transfer learning algorithm that bridges the discrepancy of real and virtual sensing performance is developed, enabling a small body circumference measurement error of 1.79%, noticeably lower than that of traditional learning algorithm. Furtherly, 3D human bodies that are numerically consistent with the actual bodies are reconstructed. The 3D dynamic human body reconstruction based on the wearing sensing system and transfer learning algorithm enables excellent virtual fitting and shirt customization in a smart and highly efficient manner. This wearable sensing technology shows great potential in human‐computer interaction, intelligent fitting, specialized protection, sports activities, and human physiological health tracking.image

NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks

We introduce NeuralVDB, which improves on an existing industry standard for efficient storage of sparse volumetric data, denoted VDB [Museth 2013 ], by leveraging recent advancements in machine learning. Our novel hybrid data structure can reduce the memory footprints of VDB volumes by orders of magnitude, while maintaining its flexibility and only incurring small (user-controlled) compression errors. Specifically, NeuralVDB replaces the lower nodes of a shallow and wide VDB tree structure with multiple hierarchical neural networks that separately encode topology and value information by means of neural classifiers and regressors respectively. This approach is proven to maximize the compression ratio while maintaining the spatial adaptivity offered by the higher-level VDB data structure. For sparse signed distance fields and density volumes, we have observed compression ratios on the order of 10× to more than 100× from already compressed VDB inputs, with little to no visual artifacts. Furthermore, NeuralVDB is shown to offer more effective compression performance compared to other neural representations such as Neural Geometric Level of Detail [Takikawa et al. 2021 ], Variable Bitrate Neural Fields [Takikawa et al. 2022a ], and Instant Neural Graphics Primitives [Müller et al. 2022 ]. Finally, we demonstrate how warm-starting from previous frames can accelerate training, i.e., compression, of animated volumes as well as improve temporal coherency of model inference, i.e., decompression.

An evaluation of root resorption associated with the use of photobiomodulation during orthodontic treatment with clear aligners: a retrospective cohort pilot study.

To evaluate the change in tooth root volume using cone-beam computed tomography (CBCT) in a group of patients treated concurrently with clear aligners and an adjunctive photobiomodulation (PBM) device. This retrospective cohort pilot study included the records of 32 consecutively treated clear aligner patients (23 female, 9 male) from the private practice of one orthodontist. The PBM group (n = 16) used the device once per day for 5 minutes per arch and was compared with a matched control group (n = 16). A semiautomated segmentation technique was used to obtain tooth volume of anterior teeth from CBCT imaging prior to (T0) and during or immediately following (T1) orthodontic treatment with clear aligners. The change in root volume between time points was assessed. There was no statistically significant difference between the pre- and posttreatment root volumes of maxillary and mandibular anterior teeth, regardless of which intervention group the patient belonged to (P > .05). There was also no difference in the mean percentage change in root volume between clear aligner patients in this study who were treated with the PBM device compared with a matched control group (P > .05). Clear aligner patients in this study who changed their aligners every 3 to 5 days and used adjunctive photobiomodulation therapy did not experience clinically relevant orthodontically induced external root resorption. Due to the small sample size and measurement error in the root segmentation process, the results should be interpreted with caution.

Analisis Pembebanan Statis Komponen Crossbar Clinker Cooler dengan Variasi Beban Horizontal terhadap Lubang Pasak

A clinker cooler is a very important piece of equipment in the cement industry because it functions to cool the slag or clinker that comes out of the kiln. In the cement industry, the processed clinker is first heated in a rotary kiln at °C and then lowered to temperature again. Next, the crossbar hammer crusher driven by the drive plate crushes the clinker. Clinker is transferred from the cooler inlet to the cooler outlet by this device. The performance of the main clinker cooler due to small errors can cause the kiln to stop working or disrupt other operations, such as in the mile finish area. In certain cases, cement production was disrupted due to a break in the crossbar component in the clinker cooler using the tested software Ansys Workbench 2020. It is hoped that the data from this simulation can be used as a benchmark between the results of the simulation, it can be used as a benchmark between the results of the best modified design for increasing crossbar durability through statistical loading tests with the FEA method.

Vibration IOT Control Algorithm for Steel Structures of Concrete Buildings with Elastic Boundary Constraints

Abstract Because of the influence of steel structure’s elastic properties in concrete buildings, the traditional vibration Internet of Things (IOT) control algorithm of steel structures has the problem of low control efficiency. Based on the research subject of concrete buildings, a corresponding steel structure model is established, and the corresponding vibration control standard is determined according to the characteristics of the model. In this model, the boundary elastic constraints of the steel structure are set according to its structural characteristics, and the vibration characteristics of the steel structure are analyzed under the constraints. The external vibration signal is simulated, and the response of the building’s steel structure under different signal excitations is observed. According to the response characteristics of the steel structure, the vibration controller is designed and installed, the real-time vibration signal is input into the controller, and finally the vibration control of steel structure of concrete building is realized. By comparison with the traditional vibration IOT control algorithm, it is concluded that the vibration control method designed by combining vibration frequency and vibration amplitude has smaller error and shorter time cost, that is, the designed vibration IOT control algorithm has more advantages in control efficiency.

Research on the 3D indoor positioning technology based on feature light source

Nowadays, positioning technologies mainly consist of three methods: positioning based on satellites, positioning based on RF and positioning based on self-contained sensors. However, due to the complex propagation characteristics of wireless signals in indoor environments, the existing positioning technologies widely used in outdoor environments cannot meet the requirements of indoor applications. Compared to outdoor environments, indoor environments are more complex, and wireless signals are affected by various factors, making it difficult for existing indoor positioning systems to meet the requirements of high accuracy and low cost. Thus, a three-dimensional indoor positioning technology based on a characteristic light source and a spherical lighting device is proposed in this paper to solve the problems. The technology measures the angle of the optical signal through a spherical lighting device, which can receive light in all directions and obtain height information compared with flat mode. Pseudo-source interference caused by the wave nature of light will not be introduced with the use of the particle nature of light. Array layout and optimization algorithms can further improve positioning accuracy. A simulation experiment in Matlab is conducted. The experimental results and the analysis of accuracy confirmed that the positioning accuracy can be controlled within a small error. Three-dimensional indoor positioning technology can achieve high accuracy at a low cost.

Investigation on the dynamic characteristics of anchor rods considering size effect and pull-out loads based on IIR-FDM

When the surrounding rock of tunnel anchor bars has boundaries, the signals obtained from non-destructive testing (NDT) of anchor rods will be simultaneously affected by size effects and pull-out loads, leading to potential misinterpretation of the test results. In order to accurately assess the anchorage quality of anchor rods under this interference, NDT and numerical simulations of anchor rods subjected to different anchorage quality were conducted under the influence of size effects and pull-out loads. A signal filtering method, which combines the infinite impulse response-finite difference method, was proposed for identify the impact of size effects. The study analyzed the variations in wave velocity and fundamental frequency of anchor rods with anchorage defects and intact anchor rods under the influence of size effects and pull-out loads. The results indicate that the IIR-FDM method, in comparison to the traditional wavelet method, provides better agreement between the calculated wave velocities and existing findings. With the increase of size effect, the wave velocity and amplitude ratio of the anchor rods decrease, and the fundamental frequency increases first and then decreases. As the working load on the anchor rods increases, the wave velocity initially decreases, then rises, while the fundamental frequency initially increases and then experiences a slight decrease. The presence of anchorage defect results in an increase in the wave velocity and amplitude ratio of the anchor rods, while causing a decrease in the fundamental frequency. This effect of defects on wave velocity and fundamental frequency is more pronounced at lower loads. The wave velocity after IIR-FDM processing has a small error with the simulated wave velocity, indicating the reliability of the processing method.

SimMACT, a Software Demonstrator to Improve Maximum Actuation Joint Torques Simulation for Ergonomics Assessment.

The maximum actuation joint torques that operators can perform at the workplace are essential parameters for biomechanical risk assessment. However, workstation designers generally only have at their disposal the imprecise and sparse estimates of these quantities provided with digital manikin digital human model (DHM) software. For instance, such tools consider only static postures and ignore important specificities of the human musculoskeletal system such as interjoints couplings. To alleviate the weaknesses of existing approaches implemented in digital human modeling tools relying on torque databases, this paper describes a methodology based on a class of polytopes called zonotopes and musculoskeletal simulation to assess maximum actuation torques. It has two main advantages, the ability to estimate maximum joint torques for any posture and taking into account musculoskeletal specificities unlike existing digital human modeling tools. As a case study, it also compares simulated maximum actuation torques to those recorded during an experiment described in the literature, focusing on an isometric task of the upper limb. This simulation has led to similar or smaller errors than DHM software tools. Hence, this methodology may help in interpreting interjoint couplings, choosing appropriate mathematical models or design experimental protocols. It may also be implemented in DHM software to provide designers with more comprehensive and more reliable data.

Accurate Electronic and Optical Properties of Organic Doublet Radicals Using Machine Learned Range-Separated Functionals.

Luminescent organic semiconducting doublet-spin radicals are unique and emergent optical materials because their fluorescent quantum yields (Φfl) are not compromised by the spin-flipping intersystem crossing (ISC) into a dark high-spin state. The multiconfigurational nature of these radicals challenges their electronic structure calculations in the framework of single-reference density functional theory (DFT) and introduces room for method improvement. In the present study, we extended our earlier development of ML-ωPBE [J. Phys. Chem. Lett., 2021, 12, 9516-9524], a range-separated hybrid (RSH) exchange-correlation (XC) functional constructed using the stacked ensemble machine learning (SEML) algorithm, from closed-shell organic semiconducting molecules to doublet-spin organic semiconducting radicals. We assessed its performance for a new test set of 64 doublet-spin radicals from five categories while placing all previously compiled 3926 closed-shell molecules in the new training set. Interestingly, ML-ωPBE agrees with the nonempirical OT-ωPBE functional regarding the prediction of the molecule-dependent range-separation parameter (ω), with a small mean absolute error (MAE) of 0.0197 a0-1, but saves the computational cost by 2.46 orders of magnitude. This result demonstrates an outstanding domain adaptation capacity of ML-ωPBE for diverse organic semiconducting species. To further assess the predictive power of ML-ωPBE in experimental observables, we also applied it to evaluate absorption and fluorescence energies (Eabs and Efl) using linear-response time-dependent DFT (TDDFT), and we compared its behavior with nine popular XC functionals. For most radicals, ML-ωPBE reproduces experimental measurements of Eabs and Efl with small MAEs of 0.299 and 0.254 eV, only marginally different from those of OT-ωPBE. Our work illustrates a successful extension of the SEML framework from closed-shell molecules to doublet-spin radicals and will open the venue for calculating optical properties for organic semiconductors using single-reference TDDFT.

Development of a Position Control System for Wheeled Humanoid Robot Movement Using the Swerve Drive Method Based on Fuzzy Logic Type-2

A humanoid robot is capable of mimicking human movements, which poses a challenge for researchers. This has led some to utilise wheels to facilitate its motion. However, achieving smooth and accurate movements at desired positions remains a challenge, necessitating the development of an optimal control system and movement method. In this study, solutions to address these challenges include the use of type-2 fuzzy logic controller (FLC) and the swerve drive method. During the steering rotation movement testing, type-1 FLC exhibits the fastest response time of 0.8 seconds, but oscillations occur, reaching up to 117 degrees to achieve the set point of 90 degrees. Additionally, type-1 FLC cannot reach the set point of -90 degrees. On the contrary, type-2 FLC aligns successfully with both set points of 90 and -90 degrees. In coordinate movement testing, type-1 FLC still shows an error between 1 cm and 2 cm compared to type-2 FLC, particularly with 3 and 5 members, which are equal to the given set point. The results of the tests indicate that type-2 FLC is reliable, showing a small steady-state error, stability, and no overshoot, despite its longer response time and processing duration compared to type-1 FLC.

The pair correlation function of multi-dimensional low-discrepancy sequences with small stochastic error terms

In any dimension d≥2, there is no known example of a low-discrepancy sequence which possesses Poisssonian pair correlations. This is in some sense rather surprising, because low-discrepancy sequences always have β-Poissonian pair correlations for all 0<β<1d and are therefore arbitrarily close to having Poissonian pair correlations (which corresponds to the case β=1d). In this paper, we further elaborate on the closeness of the two notions. We show that d-dimensional Kronecker sequences for badly approximable vectors α→ with an arbitrary small uniformly distributed stochastic error term generically have β=1d-Poissonian pair correlations.

A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM

Due to the nonlinearity, fluctuation, and intermittency of wind speed, its accurate prediction is essential for improving efficiency in wind power operation systems. In this regard, a hybrid model that combines the rime optimization algorithm (RIME), variational mode decomposition (VMD), multi-headed self-attention (MSA) mechanism and long short-term memory (LSTM) is proposed for wind speed prediction. First, the number of modes and VMD penalty parameter are optimized with RIME, the optimized parameters are brought into the VMD to decompose the raw wind speeds, and a Lagrange multiplier and quadratic penalty function are introduced to obtain the input series. Then, a LSTM short-term wind speed prediction model is constructed based on the MSA mechanism and solved for the hidden states and weights of each layer of attention in the model. Finally, a ReLU activation function is used to activate the hidden states of the LSTM model, and a weighted sum vector is used as the final sequence representation, which is inputted to the output layer for specific prediction to obtain the short-term wind speed prediction results. To verify the effectiveness of the proposed model, wind speed data from four wind farms in Ningxia, China, and two sets of wind speed data from an M2 tower in the USA are selected, and 19 models are built to compare the performance of the proposed model. The results show that the proposed model outperforms other models on all datasets in terms of all five performance metrics, with smaller errors and higher prediction accuracy.

Errors Analysis in Using Simple Past Tense in Nursing Students’ Writing Assignments

Nursing students learn General English (GE) to prepare them to study English for Nursing as their core subject. The primary goal of GE is to prepare them to be able to talk about daily topics and grammar points including simple past tense which was taught implicitly. In fact, teaching grammar is assumed to be trivial lately as teachers shift to more into English teaching trends. Therefore, it is important to go to the root and find ways to help students with their grammar struggle. This study seeks to find the nursing students’ error in simple past tense through scrutinizing their writing and thus can evaluate the teaching grammar method. The methodology was descriptive quantitative where 50 students’ writing assignments were the data. The errors were identified in the table and were classified based on the error types based on Duly’s surface strategy taxonomy. The errors were then analysed quantitatively to find percentages of the errors. The excerpts from the students’ writing were included in the result to support the analysis. The findings indicate that mis-formation were the biggest errors with 57.59%, mis-ordering with 20.98%, addition with 12.95%, and omission with the smallest error with 8.48%. The analysis indicates that the interlingual and intralingual transfer contribute to these errors made. Thus, there needs to be a reflection on the grammar teaching done in nursing students now. Teachers should focus on helping the students with their grammar difficulties and flexibly switch the teaching implicitly and explicitly to suit the students need.

Inter-comparison study of wind measurement between the three-lidar-based virtual tower and four lidars using VAD techniques

ABSTRACT The accurate three-dimensional wind field obtained from a Doppler lidar not only helps to comprehend the refined structure of complex airflow but also provides important and valuable solutions for many fields. However, the underlying homogeneity assumption of the typical wind retrieval methods, such as Doppler Beam Swinging (DBS) and Velocity Azimuth Display (VAD) based on a single-lidar, will introduce the measurement uncertainty in complex terrain. In this paper, a new design of a wind measurement campaign involving seven lidars was carried out, which contained the three-lidar-based Virtual Tower (VT) using a time-space synchronization technique and four single-lidars with different elevation angles. This study investigates the performance of VT and VAD measurements under various conditions and evaluates the sensitivity of wind measurement uncertainty of VAD to the horizontal spatial- and probe volume-average effects associated with elevation angles of the laser beam. The inter-comparison results between VT and four VADs show consistent trends with small relative errors under neutral atmospheric conditions with weak wind velocity. Under convective or high Turbulence Intensity (TI) conditions, the relative errors between VT and VAD become larger and more fluctuant. Moreover, it is found that the measurement uncertainty of VAD increases at a given elevation angle with the increasing measurement heights, which is caused by the horizontal homogeneity associated with the conical scanning area. Additionally, the simulated and measured results of four VADs indicate that a larger elevation angle corresponds to a lower measurement uncertainty for a given height.

Calculation of probabilistic harmonic power flow based on improved three-point estimation method and maximum entropy as distributed generators access to distribution network

Uncertainty and volatility are the features of distributed generation (DG), which can impact greatly on the operational status of distribution network. Thus, an improved three-point estimation method and maximum entropy theory (ITPEM&ME) for calculating the probabilistic harmonic power flow in distribution network is proposed in this study. Firstly, the probabilistic model is constructed according to the uncertainty of input variables and the improved three-point estimate method is utilized to convert a probabilistic problem into a deterministic problem. Secondly, in order to obtain the distribution of the random harmonics in distribution network, the maximum entropy theory is adopted to reconstruct the statistical characteristics after the deterministic calculation is realized for each harmonic voltages. The case of an IEEE-33 system under various conditions is conducted. The results by the different algorithms indicate that the proposed method ITPEM&ME has a smaller error than the traditional method, as well as the fitting effect of the probability density function (PDF) is much closer to the practical engineering. It is feasible and accurate in dealing with probabilistic harmonic power flow problems.

Dynamic sample rate acquisition system based on time-to-digital converter technology

In this paper, we propose a novel acquisition system, which can flexibly adjust the sampling rate according to different sampling requirements. First, we propose a sampling model and conduct feasibility verification experiments on it. The experimental results show that the sampling principle has excellent sampling function. Subsequently, we successfully implemented the system. The system combines the differential comparator of Field-Programmable Gate Array (FPGA) with the characteristics of Time-to-digital converter (TDC), which makes full use of the hardware resources of FPGA to realize efficient digital sampling and reconstruction of unknown signals. Among them, the TDC we designed has a time error of less than 100 ps at sampling rates lower than 50 MSps the sampled signal is transmitted via Ethernet, and the measured on-chip data link throughput rate reaches 600 Mbps the sampled information is decoded, fitted and reconstructed in the host computer. In addition, we have thoroughly studied the factors affecting the sampling quality in this acquisition method and summarized the sampling laws after acquiring multiple signals. To ensure the minimum distortion rate, we choose the threshold signal amplitude as 0–5 times of the highest amplitude of the sampled signal, and select the frequency as 10–30 times. Regarding the system resource consumption, the LUT utilization rate is 12.22%, the Slice utilization rate is 23.37%, and the overall design power consumption is 2.094 W. After a comprehensive evaluation of the performance and comparison with the literature (Kalisz et al 1997 IEEE Trans. Instrum. Meas. 46 51–55), it can be concluded that the system has the advantages of small error, high accuracy, and dynamic sampling function. Summarizing this paper, the system shows good engineering practicability and feasibility in the measurement field.

Retrieval of Surface Energy Fluxes Considering Vegetation Changes and Aerosol Effects

The exchange of moisture and energy between the land and the atmosphere plays a crucial role in terrestrial hydrological cycle and climate change. However, existing studies on the retrieval of surface water and heat flux tend to overlook the dynamic changes in surface vegetation and atmospheric aerosols, which directly affect surface energy and indirectly alter various meteorological factors, including cloud, precipitation, and temperature. In this study, we assess the machine-learning retrieval method for surface fluxes that takes into account vegetation changes and aerosol effects, using FLUXNET observations and remote sensing data to retrieve latent heat flux (LE) and sensible heat flux (H). We constructed four sets of deep neural network models: (a) The first set considers only meteorological factors, (b) the second set considers meteorological factors and aerosols, (c) the third set considers meteorological factors and vegetation changes, and (d) the fourth set comprehensively considers meteorological factors, aerosols, and vegetation changes. All model performances were evaluated using statistical indicators. ERA5 reanalysis and remote sensing data were used to drive the models and retrieve daily H and LE. The retrieved results were validated against ground observation sites that were not involved in model training or the FLUXCOM product. The results show that the model that considers meteorological factors, aerosols, and vegetation changes has the smallest errors and highest correlation for retrieving H and LE (RH = 0.85, RMSEH = 24.88; RLE = 0.88, RMSELE = 22.25). The ability of the four models varies under different vegetation types. In terms of seasons, the models that consider meteorological factors and vegetation changes, as well as those that comprehensively consider meteorological factors, aerosols, and vegetation changes, perform well in retrieving the surface fluxes. As for spatial distribution, when atmospheric aerosols are present in the region, the model that considers both meteorological factors and aerosols retrieves higher values of H compared to the model that considers only meteorological factors, while the LE values are relatively lower. The model that considers meteorological factors and vegetation changes, as well as the model that comprehensively considers meteorological factors, aerosols, and vegetation changes, retrieves lower values in most regions. Through the validation of independent observation sites and FLUXCOM products, we found that the model, considering meteorological factors, aerosols, and vegetation changes, was generally more accurate in the retrieval of surface fluxes. This study contributes to improving the retrieval and future prediction accuracy of surface fluxes in a changing environment.

A new test of gravity – II. Application of marked correlation functions to luminous red galaxy samples

ABSTRACT We apply the marked correlation function test proposed by Armijo et al. (Paper I) to samples of luminous red galaxies (LRGs) from the final data release of the Sloan Digital Sky Survey (SDSS) III. The test assigns a density-dependent mark to galaxies in the estimation of the projected marked correlation function. Two gravity models are compared: general relativity (GR) and $f(R)$ gravity. We build mock catalogues which, by construction, reproduce the measured galaxy number density and two-point correlation function of the LRG samples, using the halo occupation distribution model (HOD). A range of HOD models give acceptable fits to the observational constraints, and this uncertainty is fed through to the error in the predicted marked correlation functions. The uncertainty from the HOD modelling is comparable to the sample variance for the SDSS-III LRG samples. Our analysis shows that current galaxy catalogues are too small for the test to distinguish a popular $f(R)$ model from GR. However, upcoming surveys with a better measured galaxy number density and smaller errors on the two-point correlation function, or a better understanding of galaxy formation, may allow our method to distinguish between viable gravity models.

Formulas for Calculating Deformations and Natural Frequency of Free Vibrations of a Hexagonal Tower

Statement of the problem. A scheme of a six-sided prismatic statically determinate spatial girder is proposed. The task is set by induction to derive formulas for the dependence of the deflection of the structure and the lower limit of the main frequency of natural vibrations on the number of panels along the height of the prism. Materials and methods. The forces in the rods along with the reactions of the supports are found in an analytical form by means of the method of cutting nodes in the Maple symbolic mathematics system. One of the nodes at the base of the truss has a spherical support, one has a cylindrical support, the remaining four supports are racks. The top deflection is determined by the Maxwell-Mohr formula. From the analysis of the sequences of coefficients in the formulas for individual structures with a different number of panels, their common members are determined, which are included in the desired calculation formula. The Dunkerley method is used to estimate the first frequency of free oscillations. Results. For various types of loads, formulas for the dependence of girderf deflections on the number of panels are obtained. The coefficients in the solution are polynomial in the number of panels. The derived analytical dependence of the first frequency on the number of panels in comparison with the numerical solution has a small error, which decreases with increasing number of panels. Conclusions. A design of an axisymmetric statically determinate tower-type girder has been developed, which allows analytical solutions to the problem of deflection and the problem of the first natural frequency for an arbitrary number of panels. The resulting formulas can be used to assess the accuracy of numerical solutions and for preliminary calculations of models of structures of this type.

Secondary Piggyback Intraocular Lens for Correcting Refractive Surprise Post Silicone Oil Removal

Introduction : Pseudophakic refractive error can be caused by IOL malposition and inaccurate biometric examination. Treatment consists of IOL exchange, refractive laser correction, and secondary piggyback IOL. Secondary Piggyback IOL consist of customized IOL implantation on ciliary sulcus.&#x0D; Case Illustration : A Male, 62 years old, complains of blurry right eye. He underwent retinal surgery with silicone oil tamponade surgery 5 years ago and underwent silicone oil evacuation, phacoemulsification, and intraocular lens implantation one year later. The visual acuity of the right eye was 1/60 with BCVA of 0.2. He was diagnosed with Pseudophakia of both eyes and Hypermetropia Surprise of the right eye. He was planned to undergo piggyback intraocular lens implantation in the right eye (Figure 1). The visual acuity one week after operation of the patient's right eye was 0.32 (Figure 2).&#x0D; Discussion : Biometric examination in eye with silicon oil tamponade have longer axial length, causing pseudophakic refractive errors. Treatments consist of IOL replacement, refractive laser correction, and piggyback IOL implantation. IOL replacement has a high risk and requires quite high operator skills. Refractive laser correction is performed in small refractive errors. Piggyback IOL implantationis easier, safer, have more accurate IOL power, and reversible.&#x0D; Conclusion : Power of the intraocular lens is influenced by axial length, keratometry reading, lens thickness, and other parameters. Axial length measurement in patients with silicone oil tamponade requires greater accuracy to prevent pseudophakic refractive errors. IOL Piggyback implantation procedure is an alternative in person that can’t undergo laser correction or IOL exchange.