3D Motion Parameters Estimation Using MRF and Neural Network

Comparison of the Kriging and neural network methods for modeling foF2 maps over North China region

A Novel and Efficient Hybrid Method to Develop the Fragility Curves of Horizontally Curved Bridges

This article has been retracted by the publisher.

One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.

Multivariate outlier detection and remediation in geochemical databases

An inverse modeling has a possibility to retrieve the concentration of water constituents, such as chlorophyll (alpha) (Chl-(alpha) ), suspended matter (SS) and yellow substance (CDOM), in case 2 water from remotely sensed data. It turned out to be useful for mass processing of satellite data. Standard approach for inversion of radiative transfer needs long computation time, because iteration procedure is essential. On the other hand, Neural Network (NN) method is able to overcome this problem since it is consider to be a kind of non-linear multiple regression method, so that it is possible to retrieve the concentration of multiple water constituents by NN. The NN Method was applied to OCTS (Ocean Color and Temperature Scanner) data in the Yellow Sea and East China Sea to retrieve of chlorophyll a concentration, in organic suspended solids and yellow substances. The large mount of suspended Solids the Yellow Sea and the East China Sea were supplied from the Yellow River (Wei He) and the Yangtze River (Chang Jiang). The temporal and spatial distributions of chlorophyll (alpha) , inorganic mineral suspension and yellow substance were analyzed.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Non-playable Character (NPC) is one of the important characters in the game. An autonomous and adaptive NPC can adjust actions with player actions and environmental conditions. To determine the actions of the NPC, the previous researchers used the Neural Network method but there were weaknesses, namely the action produced was not in accordance with the desired so the accuracy was not good. This study overcomes the problem of poor accuracy by using the Logarithmic Learning for Generalized Classifier Neural Network (L-GCNN) method with 6 input parameters, NPC health, distance from players, other NPCs involved, attack power, number of NPCs and NPC levels. While the output is to attack itself, attack in groups and move away. For testing, this study was tested on RPG games. From the results of the experiments conducted, it shows that the L-GCNN method has better accuracy than the 3 methods compared to 7% better than NN and SVM and 8% better than RBFNN because in the L-GCNN method there is an encapsulation process that is data have the same class will. Whereas the L-GCNN training time is 30% longer than the NN method because on L-GCNN one neuron consists of one data where there are fewer NNs in the hidden layer.

QSAR study of 4-phenylpiperidine derivatives as μ opioid agonists by neural network method

ObjectiveConsidering the complex and nonlinear relationship between characteristic parameters of tumors (location and morphology) and lung dose‐volume parameters of patients with esophageal cancer (EPC) undergoing intensity‐modulated radiation therapy (IMRT), the model identification method of artificial neural network (ANN) was adopted to build the model to predict lung dose‐volume parameters under different characteristic parameters of tumors. The goal is to enhance the efficiency and quality of radiotherapy planning and reduce the risk of radiation‐induced lung injury (RILI) by providing pre‐planning dose predictions.Methods1). In the previous research work, a retrospective analysis was done on 103 cases of patients with EPC who were treated by radiotherapy, in which a linear regression model was employed and had proved the association between tumor morphology [including tumor relative length (L) and “tumor axial cross‐sectional area” (S)] and lung dose‐volume parameters (including V5, V10, V20 and V30 of lungs). 2). Building on earlier research, this study uses curve regression analysis to further explore the effect of tumor relative position (P) on lung dose‐volume parameters of patients with EPC undergoing IMRT. 3). A backpropagation (BP) neural network model was constructed with P, L, and S as inputs and lung dose‐volume parameters as outputs to establish their quantitative relationship. 4). Genetic algorithm (GA) was combined with BP neural network to optimize the initial weights and thresholds of BP neural network and avoid the model from falling into local optima, so as to improve the performance of BP neural network model.Results1). Quadratic regression models were applied to analyze the relationship of P with V5 and V10 of the lung, yielding adjusted R2 values of 0.177 and 0.081, respectively, with statistically significant regression coefficients (p < 0.01). The results suggest that as P increases, V5 and V10 initially increase, then decrease. Linear regression models were applied to analyze the relationship of P with V20 and V30 of the lung, yielding adjusted R2 values of 0.06 and 0.072, respectively, with significant regression coefficients (p < 0.01). The results show that both V20 and V30 decrease as P increases. 2). The finding of this study indicates that the modeling method of BP neural network can realize the prediction of lung dose‐volume parameters under different characteristic parameters of tumors. The GA‐BP model showed slight improvements in prediction accuracy (PA) and error index (EI) compared to the BP model alone. For both models, prediction accuracy is highest for V5, followed by V10 and V20, with V30 being the least accurate. Notably, both the BP and GA‐BP neural networks achieve outstanding recognition accuracy for V5, with EI and PA values of 0.121 and 88.61% for BP, and 0.113 and 89.00% for GA‐BP, respectively. 3). The re‐optimization of four test cases with under‐predicted whole‐lung V5 and V10 values resulted in substantial improvements over the original plans. Conclusion: The ANN model developed in this study can effectively predict lung dose‐volume parameters for patients with EPC undergoing IMRT. It provides pre‐planning guidance that enhances the efficiency and quality of radiotherapy planning and reducing the risk of RILI.

The momentum method has become the workhorse in the deep learning community. To theoretical understand its success, researchers put efforts in demystifying its convergence properties when optimizing neural networks. For the convex problem, it is well-known that the triple momentum (TM) method owns the fastest theoretical convergence rate among all first-order methods. However, there exists no theoretical convergence results about the TM method in solving the non-convex neural networks training problem, let alone its acceleration guarantee. In this paper, we focus on the training process of the TM method for a two-layer ReLU neural network. Inspired by the accurate characterization of the high-resolution dynamical system, we consider the high-resolution ordinary differential equation (ODE) of the TM method. Under the over-parameterized assumption, we derive that the original non-convex optimization problem can be transformed to a strongly convex task. By applying an appropriate Lyapunov function, we prove that the TM method can linearly converge to a global minimum. Compared to the heavy ball method and Nesterov’s accelerated gradient method, our result provides the first guarantee for the acceleration of the TM method in training neural networks. Through empirical experiments, the accelerated convergence of the TM method and the effect of over-parameterization are validated.

The article presents the issue of monitoring the sociopsychological state of society in the period of epidemics by means of neural network algorithms and methods. Publications of sociologists, psychologists, and philologists, who have created a number of methods for in-depth analysis of emotions and tonality of texts in the Internet media, including cognitive and interpretive decoding, are devoted to substantiating approaches and methods for studying the content of Internet content. The purpose of the study is to substantiate the methods and computer tools for studying the socio-psychological state of society in crisis situations, in particular epidemics, based on neural network technologies based on Internet resources. The article considers methodological approaches and particular methods of their computer implementation. It is shown that for the computer analysis of the psychological state of society in the context of the epidemic, it is necessary to adapt the methodology for designing neural network technologies, as well as systems for collecting and textual analysis of the content of electronic and Internet resources. An effective approach to creating such systems is embedding, which uses a dense vector representation of tokens in a multidimensional space, the dimension of which should be selected experimentally in the process of training and testing the developed artificial neural networks (ANN). For contextual neural network analysis, a multiclass-oriented ANN with regularization layers of the form “SpatialDropout1D”can be used. The neural network architecture can be built on fully connected layers with an activation function of the “ReLU” type. The scientific and applied significance of the results of neural network analysis based on Internet resources is the possibility of obtaining classified assessments and segmentation of target information about the psychological state of society during periods of epidemics. This information can be used to effectively counter information threats to society.

Deals with a neural network method for the machine recognition of mixed facial expressions by decomposing mixed facial expression into 2 or 3 components of 6 basic ones. The authors obtain the facial images, which show mixed facial expressions, from video tape recorded facial images and from the information of facial expressions in terms of the (x,y) coordinates of facial characteristic points. Then the position information of facial image is generated for 19 clients, and is used for the neural network training and recognition test. The recognition test is done by inputting the facial information, not being used in training the neural network, to the trained neural network. The recognition results obtained by the neural network are compared with those by humans. The neural network method is found to give a rather high agreement rate of about 70% compared with those obtained by humans. >

SUMMARYThe success of poultry meat production has been strongly related to improvements in growth and carcass yield, mainly by increasing breast proportion and reducing carcass fat. Conventional laboratory techniques for determining carcass composition are expensive, cumbersome and time consuming. These disadvantages have prompted a search for alternative methods. In this respect, the potential benefits from modelling growth are considerable. Neural networks (NNs) are a relatively new option for modelling growth in animal production systems. One self-organizing sub-model of artificial NN is the group method of data handling-type NN (GMDH-type NN). The present study aimed at applying the GMDH-type NNs to data from two studies with broilers in order to predict carcass energy (CEn, MJ/g) content and relative growth (g/g of body weight) of carcass components (carcass protein, breast muscle, leg and thigh muscles, carcass fat, abdominal fat, skin fat and visceral fat). The effective input variables involved in the prediction of CEn and carcass fat content using data from the first study were dietary metabolizable energy (ME, kJ/kg), crude protein (CP, g/kg of diet), fat (g/kg of diet) and crude fibre (CF, g/kg of diet). For data from the second study, the effective input variables involved in the prediction of carcass components were dietary ME (MJ/kg), CP (g/kg of diet), methionine (g/kg of diet), lysine (g/kg of diet) and body weight (kg). Quantitative examination of the goodness of fit, using R2 and error measurement indices, for the predictive models proposed by the GMDH-type NN revealed close agreement between observed and predicted values of CEn and carcass components.

The problem of automation of the processes of information transmission and processing in adaptive information systems is considered. An analysis of existing approaches to solving this problem showed the prospects of using neural network technologies. A neural network method for processing and transmitting information in adaptive information systems is formulated. The method includes a formalized description of a neural network data channel - a software tool for analysis, data processing and selection of data transfer protocol. The main stages of the proposed method are outlined: classification of the structures of the source data, their transformation, data processing, selection of the necessary protocol for transmitting information. Each of the stages is implemented through neural networks of various architectures. The theoretical rationale of the possibility of using the neural network method is given, obtained on the basis of the proof of a number of theorems. The novelty of the proposed method consists in the transition from an analytical solution of the problems of classification, processing and data transfer to an automated approach using machine learning technologies. The practical significance of the neural network method is to reduce the complexity of the implementation of information processing and transmission processes, to increase the level of automation in the organization of intermodular interaction. The implementation of the neural network method has been assessed using a number of software complexity assessment metrics. The application, virtues and failings of the developed method are analyzed.

SGDAT: An optimization method for binary neural networks