APPLICATION OF ARTIFICIAL NEURAL NETWORK IN COMPLEX SYSTEMS OF REGIONAL SUSTAINABLE DEVELOPMENT

Meeting the challenge of sustainable development requires substantial advances in understanding the interaction of natural and human systems. The dynamics of regional sustainable development could be addressed in the context of complex system thinking. Three features of complex systems are that they are uncertain, non-linear and self-organizing. Modeling regional development requires a consideration of these features. This paper discusses the feasibility of using the artificial neural networt(ANN) to establish an adjustment prediction model for the complex systems of sustainable development (CSSD). Shanghai Municipality was selected as the research area to set up the model, from which reliable prediction data were produced in order to help regional development planning. A new approach, which could help to manage regional sustainable development, is then explored.

Preoperative prediction of hepatocellular carcinoma microvascular invasion based on magnetic resonance imaging feature extraction artificial neural network

BACKGROUND Hepatocellular carcinoma(HCC)recurrence is highly correlated with increased mortality.Microvascular invasion(MVI)is indicative of aggressive tumor biology in HCC.AIM To construct an artificial neural network(ANN)capable of accurately predicting MVI presence in HCC using magnetic resonance imaging.METHODS This study included 255 patients with HCC with tumors<3 cm.Radiologists annotated the tumors on the T1-weighted plain MR images.Subsequently,a three-layer ANN was constructed using image features as inputs to predict MVI status in patients with HCC.Postoperative pathological examination is considered the gold standard for determining MVI.Receiver operating characteristic analysis was used to evaluate the effectiveness of the algorithm.RESULTS Using the bagging strategy to vote for 50 classifier classification results,a prediction model yielded an area under the curve(AUC)of 0.79.Moreover,correlation analysis revealed that alpha-fetoprotein values and tumor volume were not significantly correlated with the occurrence of MVI,whereas tumor sphericity was significantly correlated with MVI(P<0.01).CONCLUSION Analysis of variable correlations regarding MVI in tumors with diameters<3 cm should prioritize tumor sphericity.The ANN model demonstrated strong predictive MVI for patients with HCC(AUC=0.79).

Stand basal area modelling for Chinese fir plantations using an artificial neural network model

Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearity,outliers and noise in the data.The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses.According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province,a back-propagation artificial neural network model(BPANN)and a support vector machine model(SVM)for basal area of Chinese fir(Cunninghamia lanceolata)plantations were constructed using four kinds of prediction factors,including stand age,site index,surviving stem numbers and quadratic mean diameters.Artificial intelligence methods,especially SVM,could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models.SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.

Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

Application of remote sensing,an artificial neural network leaf area model,and a process-based simulation model to estimate carbon storage in Florida slash pine plantations.

Carbon sequestration in forests is of great interest due to concerns about global climate change.Carbon storage rates depend on ecosystem fluxes（photosynthesis and ecosystem respiration）,typically quantified as net ecosystem exchange（NEE）.Methods to estimate forest NEE without intensive site sampling are needed to accurately assess rates of carbon sequestration at stand-level and larger scales.We produced spatially-explicit estimates of NEE for 9 770 ha of slash pine（Pinus elliottii） plantations in North-Central Florida for a single year by coupling remote sensing-based estimates of leaf area index（LAI） with a process-based growth simulation model.LAI estimates produced from a neural-network modeling of ground plot and Landsat TM satellite data had a mean of 1.06（range 0-3.93,including forest edges）.Using the neural network LAI values as inputs,the slash pine simulation model（SPM2） estimates of NEE ranged from-5.52 to 11.06 Mg·ha^-1·a^-1with a mean of 3.47 Mg·ha^-1·a^-1Total carbon storage for the year was 33920 t,or about 3.5 tons per hectare.Both estimated LAI and NEE were highly sensitive to fertilization.

APPLICATION OF HIERARCHY ARTIFICIAL NEURAL NETWORK TO EVALUATE THE EXPLOITATIONCONDITIONS OF SURFACE MINING AREA

It always adopts the direct hierarchy analysis to value the exploitation conditions of surface mining areas. This way has some unavoidable shortcomings because it is mainly under the aid of experts and it is affected by the subjective thinking of the experts. This paper puts forwards a new approach that divides the whole exploitation conditions into sixteen subsidiary systems and each subsidiary system forms a neural network system. The whole decision system of exploitation conditions of surface mining areas is composed of sixteen subsidiary neural network systems. Each neural network is practiced with the data of the worksite, which is reasonable and scientific. This way will be a new decision approach for exploiting the surface mining areas.

A robust behavior of Feed Forward Back propagation algorithm of Artificial Neural Networks in the application of vertical electrical sounding data inversion

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters. In particular, the behavior of earth resembles the non- linearity applications. An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth. Artificial Neural Networks （ANN） perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used. The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network. The single- layer feed-forward neural network with the back propagation algorithm is chosen as one of the well- suited networks after comparing the results. Initially, certain synthetic data sets of all three-layer curves have been taken tk^r training the network, and the network is validated by the field datasets collected from Tuticorin Coastal Region （78°7＇30＂E and 8°48＇45＂N）, Tamil Nadu, India. The interpretation has been done successfully using the corresponding learning algorithm in the present study. With proper training of back propagation networks, it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network. The network is trained with more Vertical Electrical Sounding （VES） data, and this trained network is demon- strated by the field data. Groundwater table depth also has been modeled.

Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City,China

In this paper, the artificial neural network(ANN) model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the comprehensive, spatial and complex nature of urban land use. Through a preliminary calculation of the degree of intensive land use of the sample area, representative sample area selection and using the back propagation neural network model to train, the intensive land use level of each evaluation unit is finally determined in the study area. Results show that the method can effectively correct the errors caused by the limitations of the model itself and the determination of the ideal value and weights when the multifactor comprehensive evaluation is used alone. The ANN model can make the evaluation results more objective and practical. The evaluation results show a tendency of decreasing land use intensity from the core urban area to the periphery and the industrial functional area has relatively low land use intensity compared with other functional areas. Based on the evaluation results, some suggestions are put forward, such as transforming the mode of urban spatial expansion, strengthening the integration and potential exploitation of the land in the urban built-up area, and strengthening the control of the construction intensity of protected areas.

Classification of Cardiovascular Disease Using Feature Extraction and Artificial Neural Networks

Electrocardiogram (ECG) signals are used to identify cardiovascular disease. The availability of signal processing and neural networks techniques for processing ECG signals has inspired us to do research that consists of extracting features of an ECG signals to identify types of cardiovascular diseases. We distinguish between normal and abnormal ECG data using signal processing and neural networks toolboxes in Matlab. Data, which are downloaded from an ECG database, Physiobank, are used for training and testing the neural network. To distinguish normal and abnormal ECG with the significant accuracy, pattern recognition tools with NN is used. Feature Extraction method is also used to identify specific heart diseases. The diseases that were identified include Tachycardia, Bradycardia, first-degree Atrioventricular (AV), and second-degree Atrioventricular. Since ECG signals are very noisy, signal processing techniques are applied to remove the noise contamination. The heart rate of each signal is calculated by finding the distance between R-R intervals of the signal. The QRS complex is also used to detect Atrioventricular blocks. The algorithm successfully distinguished between normal and abnormal data as well as identifying the type of disease.

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate users.Moreover,the security design of the wireless structure is vulnerable to versatile attacks.For example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false messages.Althoughmany security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world applications.This paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks effectively.The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack.The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in theWLAN.The proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related work.The experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

A novel method for extracting and optimizing the complex permittivity of paper-based composites based on an artificial neural network model

Measuring the complex permittivity of ultrathin,flexible materials with a high loss tangent poses a substantial challenge with precision using conventional methods,and verifying the accuracy of test results remains difficult.In this study,we introduce a methodology based on a back-propagation artificial neural network(ANN)to extract the complex permittivity of paper-based composites(PBCs).PBCs are ultrathin and flexible materials exhibiting considerable complex permittivity and dielectric loss tangent.Given the absence of mature measurement methods for PBCs and a lack of sufficient data for ANN training,a mapping relationship is initially established between the complex permittivity of honeycomb-structured microwave-absorbing materials(HMAMs,composed of PBCs)and that of PBCs using simulated data.Leveraging the ANN model,the complex permittivity of PBCs can be extracted from that of HMAMs obtained using standard measurement.Subsequently,two published methods are cited to illustrate the accuracy and advancement of the results obtained using the proposed approach.Additionally,specific error analysis is conducted,attributing discrepancies to the conductivity of PBCs,the homogenization of HMAMs,and differences between the simulation model and actual objects.Finally,the proposed method is applied to optimize the cell length parameters of HMAMs for enhanced absorption performance.The conclusion discusses further improvements and areas for extended research.

Load Forecasting for Control of the Use of Transmission System for Electric Distribution Utilities

The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flows through the connection points between the distribution systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds along the contracted values, avoiding monetary penalties to the utility or unnecessary amounts of contracted flows that overrates the costumers. This question highlights the necessity of forecast the flows in these connection points in sufficient time to permit the operator to take decisions to avoid flows beyond the contracted ones. In this context, this work presents the development of a neural network based load flow forecaster, being tested two time-series neural models： support vector machines and Bayesian inference applied to multilayered perceptron. The models are applied to real data from a Brazilian distribution utility.

基于深度强化学习的复杂网络可扩展社区检测

针对复杂网络社区检测可扩展性不足与准确性不高的问题,提出一种复杂网络可扩展社区检测算法。该算法由两个阶段构成,第一阶段根据邻域度数方差检测网络中的候选社区中心,基于网络拓扑结构评估节点的相似性,基于相似性进行标签传播,建立网络的初始化社区;第二阶段基于深度强化学习对网络社区结构进行微调与优化,利用深度强化学习强大的感知能力与决策能力提高社区结构的准确性。实验结果表明,由该算法发现的网络社区获得了较高的准确性。

网络药理学在中药领域的应用和展望

目的/意义总结近年中药领域网络药理学研究成果,提出人工智能时代大规模生物医学数据解析方法,为中药网络药理学的发展趋势和未来应用提供思路与参考。方法/过程基于文献分析,阐述网络药理学研究流程及其在中药药效物质基础、中药药效作用机制、疾病分子作用机制分析等方面的研究进展。探讨以图神经网络为代表的人工智能方法在中药网络药理学中的应用与趋势。结果/结论将图神经网络引入中药网络药理学研究,借助人工智能模型,进一步丰富网络药理学研究方法,深度解析中药作用机制,为构建现代中药基础理论体系提供技术支撑。

基于深度学习的生物组织病理图像分析在海洋监测中的发展潜力及案例分析

生物组织病理指标可用于评价海洋生物健康,但在应用中存在效率低、成本高、主观性强等缺陷。将人工智能技术引入生物组织病理分析,可以发挥其高通量的图像分析优势,突破其在海洋生物健康评价和监测中的应用限制。该文通过对海洋生物组织健康评价指标、人工智能技术的图像分析应用以及利用人工智能开展组织病理图像处理的文献调研,提出基于深度学习的海洋动物组织病理图像分析思路,并以海洋贻贝作为模式生物进行技术开发。经过对贻贝鳃组织病理影像数据的训练、验证和预测等过程,确定Res-UNet深度学习模型可对贻贝在典型环境污染物胁迫下的病理损伤进行高效、准确定量,构建了一种能够自动化、高通量和弱主观性地分析海洋贻贝组织病理影像的工作流程,为海洋生物健康评价、海洋监测提供新思路与新技术。

基于LVQ神经网络的水果图像分割研究

由于传统边沿检测算子在水果颜色多样、亮度不均匀时,难以分割得到完整、无噪声的二值图像且依赖优化的阈值,本研究提出了一种基于LVQ神经网络的水果图像分割方案。首先将彩色图像转变为灰度图像;然后对Canny算子获得的边沿图像随机选取一些像素作为网络的学习监督信号,仅以灰度图像中相同位置像素3×3邻域的Kirsch算子梯度值作为输入,训练权值;最后重新将原灰度图像的Kirsch算子梯度值输入到训练好的网络中,获得封闭的边沿并填充得到二值图像。考察了14幅像素为640×480的水果图像,结果表明:网络在很宽广的阈值范围内(0.001~0.99)分割得到完整、一致的二值图像;面积误差最小为0.9%,最大为8.83%,不依赖于优化的阈值,不需要对原始图像滤波预处理。与没有阈值及滤波的算法相比,本方案的误差和时间复杂度均更低;与设置了阈值和/或滤波的算法相比,本方案与之相当,甚至效果更优。

Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks

In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is essential.This task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi Bennour.The study area is located in the Doukkala irrigated perimeter in Morocco.Satellite data can provide an alternative and fill this gap at a low cost.Models to predict SOM from a satellite image,whether linear or nonlinear,have shown considerable interest.This study aims to compare SOM prediction using Multiple Linear Regression(MLR)and Artificial Neural Networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the laboratory.An image at 15 m resolution(MSPAN)was produced from a 30 m resolution(MS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the MLR models predicted the SOM with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images,respectively.In contrast,the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images,respectively.Image pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the MLR and ANN models,respectively.

基于改进人工神经网络的人体姿态识别方法在人机交互医疗设备中的应用

为提升人机交互医疗设备对久坐不动、常年卧床等状态下人体的监测效果,在利用无线体域网建立人体姿态识别系统的基础上,设计了相应的改进人工神经网络与无线体域网系统进行融合,并将其应用于人机交互医疗设备中;结果表明,在HiEve数据集中,该方法于20次迭代时开始收敛,损失函数值为0.0112;在患者不同姿势的识别验证中,该方法下的人机交互医疗设备识别准确率均显著高于90%,并且耗时最短仅为23.16 s,具有较高的识别准确率和效率,为人体姿态识别及相关医疗设备的应用提供了更为可靠的技术参考。

Pan evaporation modeling in different agroclimatic zones using functional link artificial neural network

Pan evaporation is an important climatic variable for developing efficient water resource management strategies.In the past,many machine learning models are reported in the literature for pan evaporation modeling using the different combinationof available climatic variables.In order to develop a novel model with improved accuracy and reduced computational complexity,the functional link artificial neural network(FLANN)is chosen as an architecture to estimate daily pan evaporation in three agro-climatic zones(ACZs)of Chhattisgarh state in east-central India.Single neuron and single layer in its structure make it less complex as compared to other multilayer neural networks and neuro-fuzzy based hybrid models.Estimation results obtained with the FLANN model are compared with those obtained by multi-layer artificial neural networks(MLANN)and two empirical methods using the same raw data and corresponding features.Statistical indices like root mean square error(RMSE),mean absolute error(MAE)and efficiency factor(EF)is also computed to evaluate the model performance.It is demonstrated that pan evaporation estimates obtained with the proposed FLANN models provide an improved estimation of pan evaporation(RMSE=0.85 to 1.27 mm d^(-1),MAE=0.63 to 0.95 mm d^(-1) and EF=0.70 to 0.89)as compared to MLANN(RMSE=0.94 to 1.58 mm d^(-1),MAE=0.73 to 1.14 mm d^(-1) and EF=0.62 to 0.88)and empirical(RMSE=1.19 to 2.19 mm d^(-1),MAE=0.91 to 1.62 mm d^(-1) and EF=0.49 to 0.88)models in different ACZs.

基于非线性复杂测度的往复压缩机故障诊断

往复压缩机以多源非线性冲击振动信号为主,应用传统方法难以从振动信号中提取故障特征,为此提出一种基于非线性复杂测度的往复压缩机故障诊断方法。以气阀正常、阀片有缺口、阀片断裂及弹簧损坏4种状态下往复压缩机气阀振动信号为分析数据,在小波阈值降噪处理的基础上,采用均值符号化方法计算信号的归一化Lempel-Ziv复杂度(Lempel-Zivcomplexity,LZC)指标,分别给出各状态相应的LZC特征区间,利用BP人工神经网络对各状态信号的有效值特征、功率谱能量特征及LZC特征分别进行训练和测试,结果表明LZC更能准确区分不同状态的往复压缩机气阀故障,为往复压缩机故障诊断和维修决策提供了一种有效方法。