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Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

This research serves to advance the development of engineering computational fluid dynamics (CFD) computing efficiency for the analysis of pressurized water reactor (PWR) core using rod-type fuel assemblies with mixing vanes (one kind of typical PWR core). In this research, a CFD scheme based on the reconstruction of the initial fine flow field (RIF CFD scheme) is proposed and analyzed. The RIF scheme is based on the quantitative regulation of flow velocities in the rod-type PWR core and the principle that the CFD computing efficiency can be improved greatly by a perfect initialization. In this paper, it is discovered that the RIF scheme can significantly improve the computing efficiency of the CFD computation for the rod-type PWR core. Furthermore, the RIF scheme also can reduce the computing resources needed for effective data storage of the large fluid domain in a rod-type PWR core. Moreover, a flow-ranking RIF CFD scheme is also designed based on the ranking of the flow rate, which enhances the utilization of the flow field with a closed flow rate to reconstruct the fine flow field. The flow-ranking RIF CFD scheme also proved to be very effective in improving the CFD efficiency for the rod-type PWR core.

In this paper, the coupling of fine-mesh computational fluid dynamics (CFD) thermal-hydraulics (TH) code and neutronics code is achieved using the Ansys Fluent User Defined Function (UDF) for code development, including parallel meshing mapping, data computation, and data transfer. Also, some CFD schemes are designed for mesh mapping and data transfer to guarantee physical conservation in the coupling computation. Because there is no rigorous research that gives robust guidance on the various CFD schemes that must be obtained before the fine-mesh coupling computation, this work presents a quantitative analysis of the CFD meshing and mapping schemes to improve the accuracy of the value and location of key physical prediction. Furthermore, the effect of the sub-pin scale coupling computation is also studied. It is observed that even the pin-resolved coupling computation can also create a large deviation in the maximum value and spatial locations, which also proves the significance of the research on mesh mapping and data transfer for CFD code in a coupling computation.

Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside o...

Chen (2018) proposed a scalable spectral clustering algorithm for cosine similarity to handle the task of clustering large data sets. It runs extremely fast, with a linear complexity in the size of the data, and achieves state of the art accuracy. This paper conducts perturbation analysis of the algorithm to understand the effect of discarding a perturbation term in an eigendecomposition step. Our results show that the accuracy of the approximation by the scalable algorithm depends on the connectivity of the clusters, their separation and sizes, and is especially accurate for large data sets.

In a microgrid with multiple types of power sources, the distribution and capacity configuration of power sources will have a greater impact on the economics of the microgrid. Aiming at the grid-connected hybrid AC/DC microgrid of wind, solar, and storage, the power flow model with the goal of reducing the economic cost of the microgrid operation was established. Taking the self-balancing rate and converter loss of the microgrid as the evaluation indicators of the microgrid, a suitable operation control strategy is proposed. At the same time, using the actual data of a certain place, the genetic algorithm is used to optimize the solution, and the optimal scheme of its capacity configuration is given, which verifies the effectiveness of the control strategy proposed in this paper. The results show that increasing the battery capacity and coordinated configuration of wind turbines and photovoltaics can make the microgrid have better operating economy.

The conventional deterministic safety margin lacks the capability for real safety margin estimation in nuclear power plants (NPPs). To improve the accuracy of safety margin analysis, the risk-informed safety margin characterization (RISMC) approach has been proposed to capture the real safety margin in NPPs. However, existing literature lacks detailed application of the RISMC approach to improving the efficiency of safety margin analysis. To solve this problem, an advanced probability safety margin (APSM) analysis approach is proposed to improve safety margin analysis efficiency. The proposed APSM combines, the best estimate plus uncertainty (BEPU) analysis approach and the probability safety assessment (PSA) approach to screen key sequences. To reduce the number of calculation samples of Monte Carlo approach, the small sample estimation method is used to obtain the conditional exceedance probability (CEP). The proposed approach is subsequently used to estimate the reactor core damage frequency (CDF) of a 900 MW pressurized water reactor (PWR) under a small break lack of coolant accident, and for two reactor power levels. Evaluation results show that the proposed APSM analysis approach efficiently and accurately quantifies the core damage frequency under different reactor statuses. Moreover, the total number of calculation sequences is reduced from 64 to 8 and the total number of iterations is reduced from 8,320 to 520. In addition, the proposed APSM approach is compared with the PSA-only approach under 105% power condition. The comparison result shows that, the CDF obtained by APSM is 41% lower than that of the PSA. Moreover, for the APSM, the core damage frequency increased by 152.98% with core power uprates at 5%.

Without efficiency, the CFD simulation of a reactor will not be affordable. However, numerous narrow flow channels with thousands of complex spacer grid exist in a typical pressurized water reactor (PWR) core. The huge burden in both computational resources and time is a key challenge. To realize a reduction of the computational burden, research on flow characteristics affected by RANS models and typical structures of PWR have been done. Based on the flow characteristics, a CFD scheme was designed by a hybrid application of multiple RANS models (MRANS). Different from the traditional turbulence scheme which just use one single RANS model, MRANS keeps the isotropic RANS models used in the major domains, and the anisotropic RANS modes used in the small local domains. After some validations, it was found that MRANS can successfully keep the accuracy and improve the computing efficiency by nearly two times. This superiority in efficiency could play an important role in the CFD simulation for a whole PWR.

Single-scale Gaussian-kernel support vector machines (SVM) have achieved competitive accuracy in many practical tasks; however, a fundamental limitation of the underlying model is its use of a single bandwidth parameter which essentially assumes that the training and test data has a uniform scale everywhere. In cases of data with multiple scales, only one parameter may be unable to fully capture the heterogeneous scales present in the data. In this paper, we present two efficient approaches to constructing multiscale Gaussian kernels for SVM classification by following the multiple-kernel learning research by Gonen and Alpaydin (J Mach Learn Res 12: 2211–2268, 2011) and a self-tuning spectral clustering procedure introduced by Zelnik-Manor and Perona (Advances in Neural Information Processing Systems 17:1601–1608, 2004) in the unsupervised setting, respectively. The resulting kernels adapt to the different scales of the data and are directly computable from the training data, thus avoiding expensive hyperparameter tuning tasks. Numerical experiments demonstrate that our multiscale kernels lead to superior accuracy and fast speed.

In order to improve the reliability of The Control Rod Drive Mechanism (CRDM), a reliability trade-off method was proposed, which aims at the total cost-reliability. Firstly, the Goal-Oriented (GO) model of the CRDM was established. Then, the total cost-reliability composed of Research and Development (R&D) production cost and operation and maintenance cost was used as the objective function of the model. Finally, based on GO methodology and Genetic Algorithm (GA), the reliability trade-off optimization allocation results of the CRDM were obtained, which proves the feasibility of the proposed method.

A step-by-step fault diagnosis method based on Empirical Mode Decomposition (EMD) combined with Random Forest algorithm was proposed for actual requirements of rolling bearing vibration fault diagnosis. Firstly, the preliminary fault monitoring was carried out, and a Linear Support Vector Machine model was established by extracting the Permutation Entropy of vibration signals as characteristic parameters to judge whether the bearing was faulty or not. Then, the fault location identification and the fault degree determination were carried out, and high-dimensional characteristic parameters in time domain, frequency domain and time-frequency domain are respectively extracted as inputs of the Random Forest algorithm. Finally, through the step-by-step diagnostic test of rolling bearing vibration data, the results show that each step of diagnosis can achieve 100% diagnostic accuracy and appropriate training time, which proves that EMD and Random Forest have good effect on step-by-step fault diagnosis of rolling bearing.

In order to solve the problem of non-stationary vibration signals of rolling bearings and large sample size, a small sample fault diagnosis method based on wavelet packet theory and Random Forest algorithm was proposed in this paper. Firstly, the signal was preprocessed, then three-layer wavelet packet decomposition was carried out, and the decomposition coefficients are extracted as feature vectors, which were used to train BP Neural Network, Support Vector Machine (SVM) and Random Forest model. Through the diagnosis of multi-fault states of rolling bearings, the diagnostic accuracy rates of the three groups of classifiers are respectively 25%, 93.75% and 100%, which proves that Random Forest has better diagnosis effect and robustness for rolling bearings with small samples.

A The combat command and control system is the nerve center to carry out joint operations. It is an important part of military operations in the information age and plays the role of "amplification multiplier" in the enemy operations. In this paper, a combat command network system was taken as the research object, and the reliability modeling and analysis of the combat command network were conducted based on dynamic Bayes-GO method. Firstly, the mapping rules for transforming common operators, type 18A and type 20 operators in GO method into dynamic Bayesian networks are given. Then, the task of the brigade command vehicle 2 commanding the combat vehicle 5 to strike the target in a combat command network was modeled and analyzed. Finally, the dynamic availability curve of the system within t = 300h and the availability corresponding to the system at each moment are calculated, which can enable the combat commander to clearly judge the reliability of the system in successfully hitting the target at each moment, thus the other combat equipment can be better dispatched for cooperative operations.

The command and control network is the center of information collection and command release in modern information warfare, and its reliability will directly affect the outcome of the war. In this paper, the equipment and environmental factors in the command and control network are comprehensively considered, and Bayes-GO method is used to analyze the two-terminal reliability of the artillery command vehicle to command the howitzer to strike the target. According to its command and control path, the GO model is established, and the GO graph is transformed into the corresponding Bayesian network on the basis of the mapping rules between models. In accordance with the forward and backward inference calculation rules of the Bayesian network, the quantitative analysis results of the two-terminal reliability of the command and control network can be obtained, and the weak links of the system are diagnosed.

A fault diagnosis method based on wavelet packet (WP) reconstruction of energy entropy, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) is proposed for non-stationary vibration signals of rolling bearings. Firstly, the vibration signal is preprocessed, followed by 3-layer wavelet packet decomposition, and the energy entropy percentage of the reconstruction coefficient is extracted as the feature vector. Then, the 8-dimensional fault feature vector is reduced to a 2-dimensional feature vector by principal component analysis (PCA). Finally, the 2-dimensional feature vector is taken as the input sample of PSO-LSSVM. In order to diagnose the three fault states of the inner ring, the ball and the outer ring of the rolling bearing, four LSSVM classifiers are established. After the simulation analysis of the bearing vibration data, the diagnostic accuracy rate of the LSSVM multi-classifier group was 100%, which proves the feasibility and effectivity of the method.

The requirements, principles and steps of building reliability index system of the nuclear power system are proposed in this paper. First, due to the particularity of the nuclear power system, its reliability index system must meet the requirements of any different person, different levels, different factors and general engineering. Then, the principles of integrity, coordination, necessity, consistency, operability, industry characteristics and standardability should be met when establishing the reliability index system of the nuclear power system. Finally, the construction of the reliability index system of nuclear power system should follow the correct steps. The Control Rod Drive Mechanism (CRDM) is taken as an example to construct the reliability index system in this paper, which lays a foundation for the allocation and prediction of reliability indicators.

A fault diagnosis method based on principal component analysis (PCA) and support vector machine (SVM) model is proposed to solve the problem of high dimension and large sample size of wind turbine fault data. Firstly, The PCA is used to extract low-dimensional fault features from high-dimensional fault data to eliminate the correlation between features. Then, the grid search algorithm (GSA) is used to optimize the loss parameters and kernel function parameters of the SVM model. Secondly, low-dimensional fault features are used as input training classifiers for SVM. Finally, fault diagnosis is carried out through feature classification. Simulation results have shown that the diagnostic accuracy could reach 100% when Polynomial kernel function and two-dimensional principal component analysis were used, indicating that this method can quickly and effectively diagnose various faults.

We briefly review recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals. For detecting chemicals of known spectrum, we extend the technique of using a single subspace for modeling the background to a “mixture of subspaces” model to tackle more complicated background. Furthermore, we use partial least squares regression on a resampled training set to boost performance. For the detection of unknown chemicals, we view the problem as an anomaly detection problem and use novel estimators with low-sampled complexity for intrinsically low-dimensional data in high dimensions that enable us to model the “normal” spectra and detect anomalies. We apply these algorithms to benchmark datasets made available by the Automated Target Detection program cofunded by NSF, DTRA, and NGA, and compare, when applicable, to current state-of-the-art algorithms, with favorable results.

Prediction and analysis of coolant flow and mixing problems in the reactor pressure vessel (RPV) are some of the most critical studies to ensure system safety and balance of nuclear power plant. The flow mixing problem in the reactor primary system with complicated interior structure is a large domain three-dimensional problem. In this paper, we investigated the performance of utilizing multiple RANS models (MRANS) coupling method in the computational fluid dynamics (CFD) code ANSYS FLUENT to study flow mixing. Based on the domain decomposition technique, the 2-equation and 7-equation RANS models were coupled and applied to simulate the flow mixing phenomenon in the Rossendorf Coolant Mixing Model (ROCOM) test facility. First, a CFD model of ROCOM was developed, and the explicit domain decomposition coupling simulation was achieved with a user-defined Python control program. To ensure the best CFD model was utilized, three different turbulence models and varying resolution grids were used for turbulence model feasibility analysis and grid sensitivity testing. Then the applicability of MRANS coupling method was evaluated by comparing the results from the MRANS simulations with those obtained from flow mixing experiments. In addition, single and MRANS model simulation results were compared. Results show good agreements between the MRANS coupling simulations and the experimental data. Compared with overall simulations by single RANS models, the MRANS coupling method also have superior performance in accuracy and efficiency for the CFD simulation study of mixing phenomena in the reactor primary coolant system.

We present the implementation details of a scalable spectral clustering algorithm with cosine similarity (ICPR 2018, Beijing, China), which are based on simple, efficient matrix operations. The sensitivity of its parameters is also discussed.

Through the investigation of the research status of commonly used system reliability modeling and analysis methods, this paper mainly gives a new definition and rules for more stringent system reliability common signals. At the same time, the precise algorithm for the common signals of the existing GO method is improved. Furthermore, a reliability modeling and analysis method for nuclear power systems with common signals based on GO method is established. Finally, the lifting mechanism inside the nuclear island was selected as a system analysis case with common signals, and the reliability modeling and analysis are performed using the method proposed in this paper. The results of the analysis were compared with the results of quantitative analysis of the Monte Carlo method, which verified the correctness and applicability of the proposed method.

We propose a unified scalable computing framework for three versions of spectral clustering - Normalized Cut (Shi and Malik, 2000), the Ng-Jordan-Weiss (NJW) algorithm (2001), and Diffusion Maps (Coifman and Lafon, 2006), in the setting of cosine similarity. We assume that the input data is either sparse (e.g., as a document-term frequency matrix) or of only a few hundred dimensions (e.g., for small images or data obtained through PCA). We show that in such cases, spectral clustering can be implemented solely based on efficient operations on the data matrix such as elementwise manipulation, matrix-vector multiplication and low-rank SVD, thus entirely avoiding the weight matrix. Our algorithm is simple to implement, fast to run, accurate and robust to outliers. We demonstrate its superior performance through extensive experiments which compare our scalable algorithm with the plain implementation on several benchmark data sets.

A new reliability analysis method for pyrotechnic system by considering typical characteristics based on GO Method is proposed in this paper. First, common reliability analysis methods are introduced and compared for pyrotechnic system. Then, taken the cockpit glass throwing system as an example, its reliability quantitative analysis and qualitative analysis are conducted based on GO Method. Finally, the results of the reliability analysis of the GO Method are compared with the results of the Fault Tree Analysis (FTA) qualitative analysis and the Monte Carlo Simulation (MCS) quantitative analysis. The comparison results show that the reliability analysis results of the GO method are completely consistent with the results of the FTA qualitative analysis and similar to the Monte Carlo simulation quantitative analysis, and illustrate the advantages, reasonability, and feasibility of the GO Method. In general, this study not only widens the application of GO Method, but also provides an effective approach for reliability analysis of pyrotechnic system by taking into consideration the typical characteristics.

Spectral clustering has received a lot of attention due to its ability to separate nonconvex, non-intersecting manifolds, but its high computational complexity has significantly limited its applicability. Motivated by the document-term co-clustering framework by Dhillon (2001), we propose a landmark-based scalable spectral clustering approach in which we first use the selected landmark set and the given data to form a bipartite graph and then run a diffusion process on it to obtain a family of diffusion coordinates for clustering. We show that our proposed algorithm can be implemented based on very efficient operations on the affinity matrix between the given data and selected landmarks, thus capable of handling large data. Finally, we demonstrate the excellent performance of our method by comparing with the state-of-the-art scalable algorithms on several benchmark data sets.

We extend our recent work on scalable spectral clustering with cosine similarity (ICPR’18) to other kinds of similarity functions, in particular, the Gaussian RBF. In the previous work, we showed that for sparse or low-dimensional data, spectral clustering with the cosine similarity can be implemented directly through efficient operations on the data matrix such as elementwise manipulation, matrix-vector multiplication and low-rank SVD, thus completely avoiding the weight matrix. For other similarity functions, we present an embedding-based approach that uses a small set of landmark points to convert the given data into sparse feature vectors and then applies the scalable computing framework for the cosine similarity. Our algorithm is simple to implement, has clear interpretations, and naturally incorporates an outliers removal procedure. Preliminary results show that our proposed algorithm yields higher accuracy than existing scalable algorithms while running fast.

Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as super-resolution of images, the sensing matrix has a non-random structure with highly correlated columns. Here we present a compressive sensing type recovery algorithm, called Partial Inversion (PartInv), that overcomes the correlations among the columns. We provide theoretical justification as well as empirical comparisons., arXiv admin note: substantial text overlap with arXiv:1302.3918

We extend recent work that models the background in hyperspectral images by a single distribution (Gaussian or subspace) to use a mixture of such distributions. This seems to better capture the complexity of the background, which often consists of heterogeneous regions (e.g., sky, mountain and ground). We derive mixture versions of the previous estimators and apply them to benchmark data sets for detecting chemical plumes of known chemicals in hyperspectral images and movies. Our experiments show that the mixture background models consistently outperform their counterparts with a single distribution.

Adaptive dictionary learning has become a hot-topic research field during the past decade. Though several algorithms have been proposed and achieved impressive results, they are all computationally intensive due to the lack of structure in their output dictionaries. In this paper we build upon our previous work and take a geometric approach to develop better, more efficient algorithms that can learn adaptive structured dictionaries. While inheriting many of the advantages in the previous construction, the new algorithm better utilizes the geometry of data and effectively removes translational invariances from the data, thus able to produce smaller, more robust dictionaries. We demonstrate the performance of the new algorithm on two data sets, and conclude the paper by a discussion of future work.

Large data sets arise in a wide variety of applications and are often modeled as samples from a probability distribution in high-dimensional space. It is sometimes assumed that the support of such probability distribution is well approximated by a set of low intrinsic dimension, perhaps even a low-dimensional smooth manifold. Samples are often corrupted by high-dimensional noise. We are interested in developing tools for studying the geometry of such high-dimensional data sets. In particular, we present here a multiscale transform that maps high-dimensional data as above to a set of multiscale coefficients that are compressible/sparse under suitable assumptions on the data. We think of this as a geometric counterpart to multi-resolution analysis in wavelet theory: whereas wavelets map a signal (typically low dimensional, such as a one-dimensional time series or a two-dimensional image) to a set of multiscale coefficients, the geometric wavelets discussed here map points in a high-dimensional point cloud to a multiscale set of coefficients. The geometric multi-resolution analysis (GMRA) we construct depends on the support of the probability distribution, and in this sense it fits with the paradigm of dictionary learning or data-adaptive representations, albeit the type of representation we construct is in fact mildly nonlinear, as opposed to standard linear representations. Finally, we apply the transform to a set of synthetic and real-world data sets.

Modeling data by multiple low-dimensional planes is an important problem in many applications such as computer vision and pattern recognition. In the most general setting where only coordinates of the data are given, the problem asks to determine the optimal model parameters (i.e., number of planes and their dimensions), estimate the model planes, and cluster the data accordingly. Though many algorithms have been proposed, most of them need to assume prior knowledge of the model parameters and thus address only the last two components of the problem. In this paper we propose an efficient algorithm based on multiscale SVD analysis and spectral methods to tackle the problem in full generality. We also demonstrate its state-of-the-art performance on both synthetic and real data.

We discuss recent work based on multiscale geometric analyis for the study of large data sets that lie in high-dimensional spaces but have low-dimensional structure. We present three applications: the first one to the estimation of intrinsic dimension of sampled manifolds, the second one to the construction of multiscale dictionaries, called Geometric Wavelets, for the analysis of point clouds, and the third one to the inference of point clouds modeled as unions of multiple planes of varying dimensions.

Geometric Wavelets is a new multi-scale data representation technique which is useful for a variety of applications such as data compression, interpretation and anomaly detection. We have developed an interactive visualization with multiple linked views to help users quickly explore data sets and understand this novel construction. Currently the interface is being used by applied mathematicians to view results and gain new insights, speeding methods development.

Data sets are often modeled as point clouds in ℝD, for D large. It is often assumed that the data has some interesting low-dimensional structure, for example that of a d-dimensional manifold M, with d much smaller than D. When M is simply a linear subspace, one may exploit this assumption for encoding efficiently the data by projecting onto a dictionary of d vectors in ℝD (for example found by SVD), at a cost (d + n)D for n data points. When M is nonlinear, there are no “explicit” constructions of dictionaries that achieve a similar efficiency: typically one uses either random dictionaries, or dictionaries obtained by black-box optimization. In this paper we construct data-dependent multiscale dictionaries that aim at efficient encoding and manipulating of the data. Their construction is fast, and so are the algorithms to map data points to dictionary coefficients and vice versa. In addition, data points are guaranteed to have a sparse representation in terms of the dictionary. We think of dictionaries as the analogue of wavelets, but for approximating point clouds rather than functions.

Multi-manifold modeling is increasingly used in segmentation and data representation tasks in computer vision and related fields. While the general problem, modeling data by mixtures of manifolds, is very challenging, several approaches exist for modeling data by mixtures of affine subspaces (which is often referred to as hybrid linear modeling). We translate some important instances of multi-manifold modeling to hybrid linear modeling in embedded spaces, without explicitly performing the embedding but applying the kernel trick. The resulting algorithm, Kernel Spectral Curvature Clustering, uses kernels at two levels - both as an implicit embedding method to linearize nonflat manifolds and as a principled method to convert a multiway affinity problem into a spectral clustering one. We demonstrate the effectiveness of the method by comparing it with other state-of-the-art methods on both synthetic data and a real-world problem of segmenting multiple motions from two perspective camera views., accepted to 2009 ICCV Workshop on Dynamical Vision

We apply the Spectral Curvature Clustering (SCC) algorithm to a benchmark database of 155 motion sequences, and show that it outperforms all other state-of-the-art methods. The average misclassification rate by SCC is 1.41% for sequences having two motions and 4.85% for three motions., Comment: Accepted to 2009 ICCV Workshop on Dynamical Vision