3D tomographic analysis of equatorial plasma bubble using GNSS-TEC data from Indonesian GNSS Network

Equatorial Plasma Bubbles(EPBs)are ionospheric irregularities that take place near the magnetic equator.EPBs most commonly occur after sunset during the equinox months,although they can also be observed during other seasons.The phenomenon significantly disrupts radio wave signals essential to communication and navigation systems.The national network of Global Navigation Satellite System(GNSS)receivers in Indonesia(>30°longitudinal range)provides an opportunity for detailed EPB studies.To explore this,we conducted preliminary 3D tomography of total electron content(TEC)data captured by GNSS receivers following a geomagnetic storm on December 3,2023,when at least four EPB clusters occurred in the Southeast Asian sector.TEC and extracted TEC depletion with a 120-minute running average were then used as inputs for a 3D tomography program.Their 2D spatial distribution consistently captured the four EPB clusters over time.These tomography results were validated through a classical checkerboard test and comparisons with other ionospheric data sources,such as the Global Ionospheric Map(GIM)and International Reference Ionosphere(IRI)profile.Validation of the results demonstrates the capability of the Indonesian GNSS network to measure peak ionospheric density.These findings highlight the potential for future three-dimensional research of plasma bubbles in low-latitude regions using existing GNSS networks,with extensive longitudinal coverage.

Segmentation of retinal fluid based on deep learning:application of three-dimensional fully convolutional neural networks in optical coherence tomography images

AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography(OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional(3D) fully convolutional network for segmentation in the retinal OCT images.RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%. CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.

Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography

BACKGROUND The accurate classification of focal liver lesions(FLLs)is essential to properly guide treatment options and predict prognosis.Dynamic contrast-enhanced computed tomography(DCE-CT)is still the cornerstone in the exact classification of FLLs due to its noninvasive nature,high scanning speed,and high-density resolution.Since their recent development,convolutional neural network-based deep learning techniques has been recognized to have high potential for image recognition tasks.AIM To develop and evaluate an automated multiphase convolutional dense network(MP-CDN)to classify FLLs on multiphase CT.METHODS A total of 517 FLLs scanned on a 320-detector CT scanner using a four-phase DCECT imaging protocol(including precontrast phase,arterial phase,portal venous phase,and delayed phase)from 2012 to 2017 were retrospectively enrolled.FLLs were classified into four categories:Category A,hepatocellular carcinoma(HCC);category B,liver metastases;category C,benign non-inflammatory FLLs including hemangiomas,focal nodular hyperplasias and adenomas;and category D,hepatic abscesses.Each category was split into a training set and test set in an approximate 8:2 ratio.An MP-CDN classifier with a sequential input of the fourphase CT images was developed to automatically classify FLLs.The classification performance of the model was evaluated on the test set;the accuracy and specificity were calculated from the confusion matrix,and the area under the receiver operating characteristic curve(AUC)was calculated from the SoftMax probability outputted from the last layer of the MP-CDN.RESULTS A total of 410 FLLs were used for training and 107 FLLs were used for testing.The mean classification accuracy of the test set was 81.3%(87/107).The accuracy/specificity of distinguishing each category from the others were 0.916/0.964,0.925/0.905,0.860/0.918,and 0.925/0.963 for HCC,metastases,benign non-inflammatory FLLs,and abscesses on the test set,respectively.The AUC(95%confidence interval)for differentiating each category from the others was 0.92(0.837-0.992),0.99(0.967-1.00),0.88(0.795-0.955)and 0.96(0.914-0.996)for HCC,metastases,benign non-inflammatory FLLs,and abscesses on the test set,respectively.CONCLUSION MP-CDN accurately classified FLLs detected on four-phase CT as HCC,metastases,benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs.

Quantification of 3D macropore networks in forest soils in Touzhai valley(Yunnan,China)using X-ray computed tomography and image analysis

The three dimensional （3D） geometry of soil macropores largely controls preferential flow, which is a significant infiltrating mechanism for rainfall in forest soils and affects slope stability. However, detailed studies on the 3D geometry of macropore networks in forest soils are rare. The intense rainfall-triggered potentially unstable slopes were threatening the villages at the downstream of Touzhai valley （Yunnan, China）. We visualized and quantified the 3D macropore networks in undisturbed soil columns （Histosols） taken from a forest hillslope in Touzhai valley, and compared them with those in agricultural soils （corn and soybean in USA; barley, fodder beet and red fescue in Denmark） and grassland soils in USA. We took two large undisturbed soil columns （250 mm^25o mmxsoo mm）, and scanned the soil columns at in-situ soil water content conditions using X-ray computed tomography at a voxel resolution of 0.945 × 0.945 × 1.500o mm^3. After reconstruction and visualization, we quantified the characteristics of macropore networks. In the studied forest soils, the main types of maeropores were root channels, inter-aggregate voids, maeropores without knowing origin, root-soil interfaee and stone-soil interface. While maeropore networks tend to be more eomplex, larger, deeper and longer. The forest soils have high maeroporosity, total maeropore wall area density, node density, and large maeropore volume, hydraulie radius, mean maeropore length, angle, and low tortuosity. The findings suggest that maeropore networks in the forest soils have high inter- connectivity, vertical continuity, linearity and less vertically oriented.

When linear inversion fails:Neural-network optimization for sparse-ray travel-time tomography of a volcanic edifice

In this study,we present an artificial neural network(ANN)-based approach for travel-time tomography of a volcanic edifice under sparse-ray coverage.We employ ray tracing to simulate the propagation of seismic waves through the heterogeneous medium of a volcanic edifice,and an inverse modeling algorithm that uses an ANN to estimate the velocity structure from the“observed”travel-time data.The performance of the approach is evaluated through a 2-dimensional numerical study that simulates i)an active source seismic experiment with a few(explosive)sources placed on one side of the edifice and a dense line of receivers placed on the other side,and ii)earthquakes located inside the edifice with receivers placed on both sides of the edifice.The results are compared with those obtained from conventional damped linear inversion.The average Root Mean Square Error(RMSE)between the input and output models is approximately 0.03 km/s for the ANN inversions,whereas it is about 0.4 km/s for the linear inversions,demonstrating that the ANN-based approach outperforms the classical approach,particularly in situations with sparse ray coverage.Our study emphasizes the advantages of employing a relatively simple ANN architecture in conjunction with second-order optimizers to minimize the loss function.Compared to using first-order optimizers,our ANN architecture shows a~25%reduction in RMSE.The ANN-based approach is computationally efficient.We observed that even though the ANN is trained based on completely random velocity models,it is still capable of resolving previously unseen anomalous structures within the edifice with about 5%anomalous discrepancies,making it a potentially valuable tool for the detection of low velocity anomalies related to magmatic intrusions or mush.

Classification of Benign and Malignancy in Lung Cancer Using Capsule Networks with Dynamic Routing Algorithm on Computed Tomography Images

There is a widespread agreement that lung cancer is one of the deadliest types of cancer,affecting both women and men.As a result,detecting lung cancer at an early stage is crucial to create an accurate treatment plan and forecasting the reaction of the patient to the adopted treatment.For this reason,the development of convolutional neural networks(CNNs)for the task of lung cancer classification has recently seen a trend in attention.CNNs have great potential,but they need a lot of training data and struggle with input alterations.To address these limitations of CNNs,a novel machine-learning architecture of capsule networks has been presented,and it has the potential to completely transform the areas of deep learning.Capsule networks,which are the focus of this work,are interesting because they can withstand rotation and affine translation with relatively little training data.This research optimizes the performance of CapsNets by designing a new architecture that allows them to perform better on the challenge of lung cancer classification.The findings demonstrate that the proposed capsule network method outperforms CNNs on the lung cancer classification challenge.CapsNet with a single convolution layer and 32 features(CN-1-32),CapsNet with a single convolution layer and 64 features(CN-1-64),and CapsNet with a double convolution layer and 64 features(CN-2-64)are the three capsulel networks developed in this research for lung cancer classification.Lung nodules,both benign and malignant,are classified using these networks using CT images.The LIDC-IDRI database was utilized to assess the performance of those networks.Based on the testing results,CN-2-64 network performed better out of the three networks tested,with a specificity of 98.37%,sensitivity of 97.47%and an accuracy of 97.92%.

Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography

To minimize radiation risk,dose reduction is important in the diagnostic and therapeutic applications of computed tomography(CT).However,image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance.Deep learning approaches with convolutional neural networks(CNNs)have been proposed for natural image denoising;however,these approaches might introduce image blurring or loss of original gradients.The aim of this study was to compare the dose-dependent properties of a CNN-based denoising method for low-dose CT with those of other noise-reduction methods on unique CT noise-simulation images.To simulate a low-dose CT image,a Poisson noise distribution was introduced to normal-dose images while convoluting the CT unit-specific modulation transfer function.An abdominal CT of 100 images obtained from a public database was adopted,and simulated dose-reduction images were created from the original dose at equal 10-step dose-reduction intervals with a final dose of 1/100.These images were denoised using the denoising network structure of CNN(DnCNN)as the general CNN model and for transfer learning.To evaluate the image quality,image similarities determined by the structural similarity index(SSIM)and peak signal-to-noise ratio(PSNR)were calculated for the denoised images.Significantly better denoising,in terms of SSIM and PSNR,was achieved by the DnCNN than by other image denoising methods,especially at the ultra-low-dose levels used to generate the 10%and 5%dose-equivalent images.Moreover,the developed CNN model can eliminate noise and maintain image sharpness at these dose levels and improve SSIM by approximately 10%from that of the original method.In contrast,under small dose-reduction conditions,this model also led to excessive smoothing of the images.In quantitative evaluations,the CNN denoising method improved the low-dose CT and prevented over-smoothing by tailoring the CNN model.

Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis

BACKGROUND Efforts should be made to develop a deep-learning diagnosis system to distinguish pancreatic cancer from benign tissue due to the high morbidity of pancreatic cancer.AIM To identify pancreatic cancer in computed tomography(CT)images automatically by constructing a convolutional neural network(CNN)classifier.METHODS A CNN model was constructed using a dataset of 3494 CT images obtained from 222 patients with pathologically confirmed pancreatic cancer and 3751 CT images from 190 patients with normal pancreas from June 2017 to June 2018.We established three datasets from these images according to the image phases,evaluated the approach in terms of binary classification(i.e.,cancer or not)and ternary classification(i.e.,no cancer,cancer at tail/body,cancer at head/neck of the pancreas)using 10-fold cross validation,and measured the effectiveness of the RESULTS The overall diagnostic accuracy of the trained binary classifier was 95.47%,95.76%,95.15%on the plain scan,arterial phase,and venous phase,respectively.The sensitivity was 91.58%,94.08%,92.28%on three phases,with no significant differences(χ2=0.914,P=0.633).Considering that the plain phase had same sensitivity,easier access,and lower radiation compared with arterial phase and venous phase,it is more sufficient for the binary classifier.Its accuracy on plain scans was 95.47%,sensitivity was 91.58%,and specificity was 98.27%.The CNN and board-certified gastroenterologists achieved higher accuracies than trainees on plain scan diagnosis(χ2=21.534,P<0.001;χ2=9.524,P<0.05;respectively).However,the difference between CNN and gastroenterologists was not significant(χ2=0.759,P=0.384).In the trained ternary classifier,the overall diagnostic accuracy of the ternary classifier CNN was 82.06%,79.06%,and 78.80%on plain phase,arterial phase,and venous phase,respectively.The sensitivity scores for detecting cancers in the tail were 52.51%,41.10%and,36.03%,while sensitivity for cancers in the head was 46.21%,85.24%and 72.87%on three phases,respectively.Difference in sensitivity for cancers in the head among the three phases was significant(χ2=16.651,P<0.001),with arterial phase having the highest sensitivity.CONCLUSION We proposed a deep learning-based pancreatic cancer classifier trained on medium-sized datasets of CT images.It was suitable for screening purposes in pancreatic cancer detection.

An ECT System Based on Improved RBF Network and Adaptive Wavelet Image Enhancement for Solid/Gas Two-phase Flow

Electrical capacitance tomography(ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance.To solve the nonlinear and ill-posed inverse problem:image reconstruction of ECT system,this paper proposed a new image reconstruction method based on improved radial basis function(RBF) neural network combined with adaptive wavelet image enhancement.Firstly,an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured.Then,for better image quality,adaptive wavelet image enhancement technique was emphatically analyzed and studied,which belongs to a space-frequency analysis method and is suitable for image feature-enhanced.Through multi-level wavelet decomposition,edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band;noise distribution in the space-frequency domain can be estimated based on statistical characteristics;after that a self-adaptive edge enhancement gain can be constructed.Finally,the image is reconstructed with adjusting wavelet coefficients.In this paper,a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction algorithm.Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement,and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal(PC)/air].

Effect of Data Augmentation of Renal Lesion Image by Nine-layer Convolutional Neural Network in Kidney CT

Artificial Intelligence(AI)becomes one hotspot in the field of the medical images analysis and provides rather promising solution.Although some research has been explored in smart diagnosis for the common diseases of urinary system,some problems remain unsolved completely A nine-layer Convolutional Neural Network(CNN)is proposed in this paper to classify the renal Computed Tomography(CT)images.Four group of comparative experiments prove the structure of this CNN is optimal and can achieve good performance with average accuracy about 92.07±1.67%.Although our renal CT data is not very large,we do augment the training data by affine,translating,rotating and scaling geometric transformation and gamma,noise transformation in color space.Experimental results validate the Data Augmentation(DA)on training data can improve the performance of our proposed CNN compared to without DA with the average accuracy about 0.85%.This proposed algorithm gives a promising solution to help clinical doctors automatically recognize the abnormal images faster than manual judgment and more accurately than previous methods.

NEAR-INFRARED OPTICAL TOMOGRAPHY IMAGE RECONSTRUCTION APPROACH BASED ON TWO-LAYERED BP NEURAL NETWORK

An image-reconstruction approach for optical tomography is presented,in which a two-layered BP neural network is used to distinguish the tumor location.The inverse problem is solved as optimization problem by Femlab software and Levenberg–Marquardt algorithm.The concept of the average optical coefficient is proposed in this paper,which is helpful to understand the distribution of the scattering photon from tumor.The reconstructive¯µ
s by the trained network is reasonable for showing the changes of photon number transporting inside tumor tissue.It realized the fast reconstruction of tissue optical properties and provided optical OT with a new method.

A Neural Network for Weighted Least－Squares Criteria of Traveltime Tomography

TH network has been applied successfully to linear optimum and some quadratic optimum problems.This paper discusses how to determine the gain function of the net amplifiers and choose appropriate parameters to solve weighted least-squares problems.To test the performance of the net,it is used for seismic traveltime tomography in computer simulation. Two media of different contrast are taken in simulation.The experimental results show that if the parameters are determined appropriately, the performance of the network is good and the results are close to the ideal ones.

A Multiresolution Reconstructive Algorithm Based on Network Theory for Electrical Capacitance Tomography

Electrical capacitance tomography technique reconstructs dielectric constant distribution in an object by measuring the capacitances between the eletrode pairs which are mounted around this object. Because of the limitation of measurement condition, the measured data are imcomplet. This paper describes a multiresolution reconstructive algorithm which is based on network theory for electrical capacitance tomography technique. The dielectric constant distribution of flow of two components in a pipeline is reconstructed. The algorithm is as follows: Firstly, construct a rough, first level system model, and assume the dielectric constant distribution of the region to be reconstructed. After iteration, the dielectic constant of each unit can be reconstructed. Secondly, construct a finer, second level the system model and determine the initial dielectric constant of each unit in the region to be reconstructed according to related information between two levels. After iteration, the image of the pipeline's cross section can be reconstructed. The results of simulated experiments about different kinds of medium distributions show that this algorithm is effective and can converge.

Locating Impedance Change in Electrical Impedance Tomography Based on Multilevel BP Neural Network

Electrical impedance tomography(EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object's periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using the back projection method.

Estimation of thermal conductivity of cemented sands using thermal network models

Effective thermal conductivity of soils can be enhanced to achieve higher efficiencies in the operation of shallow geothermal systems.Soil cementation is a ground improvement technique that can increase the interparticle contact area,leading to a high effective thermal conductivity.However,cementation may occur at different locations in the soil matrix,i.e.interparticle contacts,evenly or unevenly around particles,in the pore space or a combination of these.The topology of cementation at the particle scale and its influence on soil response have not been studied in detail to date.Additionally,soils are made of particles with different shapes,but the impact of particle shape on the cementation and the resulting change of effective thermal conductivity require further research.In this work,three kinds of sands with different particle shapes were selected and cementation was formed either evenly around the particles,or along the direction parallel or perpendicular to that of heat transfer.The effective thermal conductivity of each sample was computed using a thermal conductance network model.Results show that dry sand with more irregular particle shape and cemented along the heat transfer direction will lead to a more efficient thermal enhancement of the soil,i.e.a comparatively higher soil effective thermal conductivity.

Photon-counting computed tomography thermometry via material decomposition and machine learning

Thermal ablation procedures,such as high intensity focused ultrasound and radiofrequency ablation,are often used to eliminate tumors by minimally invasively heating a focal region.For this task,real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings.Current computed tomography(CT)thermometry is based on energy-integrated CT,tissue-specific experimental data,and linear relationships between attenuation and temperature.In this paper,we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest.In our feasibility study,distilled water,50 mmol/L CaCl2,and 600 mmol/L CaCl2 are chosen as the base materials.Their attenuations are measured in four discrete energy bins at various temperatures.The neural network trained on the experimental data achieves a mean absolute error of 3.97°C and 1.80°C on 300 mmol/L CaCl2 and a milk-based protein shake respectively.These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dis-similar to our base materials.

Multi-Scale Network for Thoracic Organs Segmentation

Medical Imaging Segmentation is an essential technique for modern medical applications.It is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical intervention.Although significant successes have been achieved in the segmentation of medical images,DL(deep learning)approaches.Manual delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT images.Till now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to others.To segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL model.We have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of OARs.Proposed methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background 0.99891.The results showed that our proposed framework can be segmented organs accurately.

Tuberculosis Detection from Computed Tomography with Convolutional Neural Networks

Convolutional neural network (CNN), a class of deep neural networks (most commonly used in visual image analysis), has become one of the most influential innovations in the field of computer vision. In our research, we built a system which allows the computer to extract the feature and recognize the image of human lungs and to automatically conclude the health level of the lungs based on database. Here, we built a CNN model to train the datasets. After the training, the system could do certain preliminary analysis already. In addition, we used the fixed coordinate to reduce the noise and combined the Canny algorithm and the Mask algorithm to further improve the accuracy of the system. The final accuracy turned out to be 87.0%, which is convincing. Our system can contribute a lot to the efficiency and accuracy of doctors’ analysis of the patients’ health level. In the future, we will do more improvement to reduce noise and increase accuracy.

Development of Segmentation and Classification Algorithms for Computed Tomography Images of Human Kidney Stone

Computed tomography(CT)scan diagnostics procedures adopt the use of image infbnnation retrieval system with the help of radiographer's expertise.However,this technique is prone to errors,Significant height of accuracy is required in healthcare decision support,as 20%of CT scans are associated with error.The application of artificial intelligence(Al)can improve performance level,mitigate human error,and enhance clinical decision support in the context of time and accuracy.The study introduced machine learning algorithm to analyze stream of anonymous CT scans of kidney.The research adopted deep learning approach for segmentation and classification of kidney stone(renal calculi)images in Python(with Keras and TensorFlow)environment.A control volume of data along with 336 kidney stone images were used to train the deep learning network with 10 testing images.The training images were divided into two sets(folders)as follows;one was labeled as STONE(containing 167 images)and the other as NO-STONE(containing 169 images);10 iterations were performed for model training.The network layers were structured as input layer in the following with 2-D convolutional neural network machine learning(CNN-ML),ReLU activation,Maxpooling,and fully connected(dense)layer including the sigmoid activation layer.The training adopted a batch size of 8 with 10%validation.The output result,upon testing the model,has an accuracy of 90%,sensitivity value of 80%and effectiveness of 89%.The segmentation and classification algorithm model could be embedded in future CT diagnostic procedure to enhance medical decision support and accuracy.

Windows NT站点的安全性研究

1 引言 1999年底，Find/SVP公司对美国、欧洲和亚洲的808位高级信息管理员进行了一次调查，调查他们最主要关心的问题是什么。结果表明，认为网络安全是他们最主要关心的问题的人占被调研人数的77％；其次是资源利用率和不同平台的集成。事实上，自从计算机网络产生以来，网络安全一直是人们关注的焦点之一。早在1984年，ISO/TC97/SC16/WG1就起草了一份OSI安全体系结构建议草案，于1989年正式颁布为国际标准ISO 7498-2。该标准描述了OSI安全体系结构应用范围和领域、安全服务和机构等问题。基于该安全体系结构，人们研究并提出了许多网络安全措施或策略。但是，提出OSI安全体系结构的目的是为了保障OSI安全而提供的一个一致性的安全方法。为确保具体的网络站点的安全性，必须建立相应的安全性模型，并根据模型采取具体有效的措施和策略，特别是切实可行的技术措施。