Internal leakage detection for feedwater heaters in power plants using neural networks

As interest in safety and performance of power plants becomes more serious and wide-ranging, the significance of research on turbine cycles has attracted more attention. This paper particularly focuses on thermal performance analysis under the conditions of internal leakages inside closed-type feedwater heaters (FWHs) and their diagnosis to identify the locations and to quantify leak rates. Internal leakage is regarded as flow movement through the isolated path but remaining inside the system boundary of a turbine cycle. For instance, leakages through the cracked tubes, tube-sheets, or pass partition plates in a FWH are internal leakages. Internal leakages impact not only plant efficiency, but also direct costs and/or even plant safety associated with the appropriate repairs. Some types of internal leakages are usually critical to get the parts fixed and back in a timely manner. The FWHs installed in a Korean standard nuclear power plant were investigated in this study. Three technical steps have been, then, conducted: (1) the detailed modeling of FWHs covering the leakage from tubes, tube-sheets, or pass partition plates using the simulation model, (2) thermal performance analysis under various leakage conditions, and (3) the development of a diagnosis model using a feed-forward neural network, which is the correlation between thermal performance indices and leakage conditions. Since the operational characteristics of FWHs are coupled with one another and/or with other neighbor components such as turbines or condensers, recognizing internal leakages is difficult with only an analytical model and instrumentation at the inlet and outlet of tube- and shell-sides. The proposed neural network-based correlation was successfully validated for test cases.     

Intelligent data structures selection using neural networks

It is well known that abstract data types represent the core for any software application, and a proper use of them is an essential requirement for developing a robust and efficient system. Data structures are essential in obtaining efficient algorithms, having a major importance in the software development process. Selecting and creating the appropriate data structure for implementing an abstract data type can greatly impact the performance and the efficiency of the software systems. It is not a trivial problem for a software developer, as it is hard to anticipate all the use scenarios of the deployed application, and a static selection before the system’s execution is, generally, not accurate. In this paper, we are focusing on the problem of dynamic selection of efficient data structures for abstract data types implementation using a supervised learning approach. In order to dynamically select the most suitable representation for an aggregate according to the software system’s current execution context, a neural network will be used. We experimentally evaluate the proposed technique on a case study, emphasizing the advantages of the proposed model in comparison with existing similar approaches.     

Dark formation detection using neural networks

Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in marine environment, as they are not affected by local weather conditions and cloudiness. Dark formations can be caused by man‐made actions (e.g. oil spills) or natural ocean phenomena (e.g. natural slicks and wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they dampen the capillary and short gravity sea waves. Thus, traditionally, dark formation detection is the first stage of the oil‐spill detection procedure and in most studies is performed manually or using a fixed size window in which a threshold value is adopted. In high‐resolution imagery, dark formation detection may fail due to the nonlinear behaviour of the pixel values contained in the dark formation and in the area around it. In this paper, we examine the ability of two feed‐forward neural network families, i.e. Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) networks, to detect dark formations in high‐resolution SAR images. The general objective of this paper is to test the potential of artificial neural networks for dark formation detection using SAR high‐resolution satellite images. Both the type and the architecture of the network are subjects of research. The inputs into the networks are the original SAR images. Each network is called to classify an area of the image as dark area or sea. The group of MLP networks can be recognized as the most suitable group for dark formation detection, as it presents reliable stable results for all the examined accuracies. Nevertheless, in terms of single topology, there is no an MLP topology that performs significantly better than the others.     

Output-model-based predictive control of unstable combustion systems using neural networks

A novel strategy for the active stabilisation of combustion systems is presented. The algorithm is comprised of three parts: an output model, an output predictor and a feedback controller. The output model which is established using neural networks is used to predict the output in order to overcome the time delay of the system, which is often very large compared with the sampling period. An output-feedback controller is introduced which uses the output of the predictor to suppress instability in the combustion process. The approach developed is first demonstrated using a simulated unstable combustor with six modes. Results are also presented showing its application to an experimental combustion facility using a loudspeaker actuation device.     

调节形态学运算及其神经网络实现

数学形态学是一门建立在集合论基础上的学科,为数字图像处理和分析提供了一种有效的工具.在分析传统的数学形态学基本运算的基础上,引入调节数学形态学运算的概念,然后讨论了调节形态学运算的神经网络实现,并给出了用于图像滤波的计算机仿真结果.该方法较之传统的数学形态学基本运算更为灵活.     

Intelligent sensor system for discrimination of material type using neural networks

Neural network based classification of material type even with the variation in the sensor parameter is investigated in this paper. The sensor is developed by means of a lightweight plunger probe and an optical mouse sensor. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The experiment is conducted to obtain the bouncing signals for plain surface of an objects kept at different distances from the probe. During the bouncing of the probe, time varying signals are generated from optical mouse that are recorded in data files on PC. Some dominant unique features are then extracted using signal processing tools to optimize neural network based classifier. The time and features of bouncing signal are related to the material type, and each material has a unique set of such properties. It is found that the sensor system is intelligent due to its ability to classify the material type even with the variation in the sensor parameter (distance between the sensor probe and plain objects). The classifiers are developed using two neural networks configurations, namely a well-known Multi-layer Perceptron Neural Networks (MLP NN), and Radial Basis Function Neural Networks (RBF NN). MLP NN and RBF NN models are designed to maximize accuracy under the constraints of minimum network dimension.The optimal parameters of MLP NN and RBF NN models based on various performance measures that include percentage classification accuracy (PCLA) on the testing data, and area under Receiver Operating Characteristics (ROC), and are determined. For the sensor data set, the PCLA of both the classifiers are found reasonable consistently in respect of rigorous testing using different data partitions. The areas under the ROC curves are close to unity. Performances of the two classifiers have been compared. It has been found that the RBF NN is more robust to noise, and epochs required for training are very less as compared to that for MLP NN.     

Rule extraction for fatty liver detection using neural networks

Neural Computing and Applications - Non-alcoholic fatty liver disease (NAFLD) is one of the most common diseases in the world. Recently the FibroScan device is used as a noninvasive, yet costly...     

Epileptic EEG detection using neural networks and post-classification

Electroencephalogram (EEG) has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. In most cases, identification of the epileptic EEG signal is done manually by skilled professionals, who are small in number. In this paper, we try to automate the detection process. We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet coefficients. A feed-forward backpropagating artificial neural network (ANN) is used for the classification. We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. Average specificity of 99.19%, sensitivity of 91.29% and selectivity of 91.14% are obtained.     

Goal distance estimation for automated planning using neural networks and support vector machines

Many of today’s most successful planners perform a forward heuristic search. The accuracy of the heuristic estimates and the cost of their computation determine the performance of the planner. Thanks to the efforts of researchers in the area of heuristic search planning, modern algorithms are able to generate high-quality estimates. In this paper we propose to learn heuristic functions using artificial neural networks and support vector machines. This approach can be used to learn standalone heuristic functions but also to improve standard planning heuristics. One of the most famous and successful variants for heuristic search planning is used by the Fast-Forward (FF) planner. We analyze the performance of standalone learned heuristics based on nature-inspired machine learning techniques and employ a comparison to the standard FF heuristic and other heuristic learning approaches. In the conducted experiments artificial neural networks and support vector machines were able to produce standalone heuristics of superior accuracy. Also, the resulting heuristics are computationally much more performant than related ones.     

Spoken keyword detection using autoassociative neural networks

Spoken keywords detection is essential to organize efficiently lots of hours of audio contents such as meetings, radio news, etc. These systems are developed with the purpose of indexing large audio databases or of detecting keywords in continuous speech streams. This paper addresses a new approach to spoken keyword detection using Autoassociative Neural Networks (AANN). The proposed work concerns the use of the distribution capturing ability of the Autoassociative neural network (AANN) for spoken keyword detection. It involves sliding a frame-based keyword template along the speech signal and using confidence score obtained from the normalized squared error of AANN to efficiently search for a match. This work formulates a new spoken keyword detection algorithm. The experimental results show that the proposed approach competes with the keyword detection methods reported in the literature and it is an alternative method to the existing key word detection methods.     

Object detection using pulse coupled neural networks

Describes an object detection system based on pulse coupled neural networks. The system is designed and implemented to illustrate the power, flexibility and potential the pulse coupled neural networks have in real-time image processing. In the preprocessing stage, a pulse coupled neural network suppresses noise by smoothing the input image. In the segmentation stage, a second pulse coupled neural-network iteratively segments the input image. During each iteration, with the help of a control module, the segmentation network deletes regions that do not satisfy the retention criteria from further processing and produces an improved segmentation of the retained image. In the final stage each group of connected regions that satisfies the detection criteria is identified as an instance of the object of interest.     

在认知中继网络中基于放大转发与最佳中继的最优功率分配算法

由于受认知无线电与中继通信技术的启发,提出了一种认知中继网络模型.该模型由源节点、目的节点、认知中继节点及主用户(primary user,PU)构成.认知中继节点以与PU共存的方式为源节点辅助传输信息到目的节点,只要保证其对PU通信造成的干扰在PU干扰门限值以下.假设源节点、目的节点和认知中继节点之间的瞬时信道边信息(channel side information,CSI)和认知中继节点到主用户之间的均值信道增益已知的前提下,研究该模型中的认知中继节点分别采用放大转发(amplify-and-forward,AF)和基于AF的中继选择(selection AF,S-AF)下的功率分配策略,该策略以最小化系统中断概率为目标,同时也满足认知中继节点的发射功率约束(包括总发射功率和个体发射功率约束)和对主用户的干扰功率约束.最后,通过数值仿真来验证推导出的功率分配策略.仿真结果表明:本文提出的最优功率分配策略,无论在AF,还是S-AF下,均能明显的改善系统的中断性能和平均吞吐量;同时在S-AF下最优分配策略可以得到更高的平均吞吐量,因此中断概率更小.     

Optimal power allocation for cognitive relay networks: amplify-and-forward versus selection relay

We consider a cognitive relay network which is defined by a source,a destination,and cognitive relay nodes and primary user nodes.In this network,a source is assisted by cognitive relay nodes which allow coexisting with primary user nodes by imposing severe constraints on the transmission power so that they operate below the noise floor of primary user nodes.In this paper,we mainly study the power allocation strategies of this system to minimize the outage probability subject to total and individual power c...     

Context-aware saliency detection for image retargeting using convolutional neural networks

Multimedia Tools and Applications - Image retargeting is the task of making images capable of being displayed on screens with different sizes. This work should be done so that high-level visual...     

LADAR target detection using morphological shared-weight neural networks

Morphological shared-weight neural networks (MSNN) combine the feature extraction capability of mathematical morphology with the function-mapping capability of neural networks in a single trainable architecture. The MSNN method has been previously demonstrated using a variety of imaging sensors, including TV, forward-looking infrared (FLIR) and synthetic aperture radar (SAR). In this paper, we provide experimental results with laser radar (LADAR). We present three sets of experiments. In the first set of experiments, we use the MSNN to detect different types of targets simultaneously. In the second set, we use the MSNN to detect only a particular type of target. In the third set, we test a novel scenario, referred to as the Sims scenario: we train the MSNN to recognize a particular type of target using very few examples. A detection rate of 86% with a reasonable number of false alarms was achieved in the first set of experiments and a detection rate of close to 100% with very few false alarms was achieved in the second and third sets of experiments. In all the experiments, a novel pre-processing method is used to create a pseudo-intensity images from the original LADAR range images.     

Short-term electric power load forecasting using feedforward neural networks

Abstract: This paper presents the results of a study on short‐term electric power load forecasting based on feedforward neural networks. The study investigates the design components that are critical in power load forecasting, which include the selection of the inputs and outputs from the data, the formation of the training and the testing sets, and the performance of the neural network models trained to forecast power load for the next hour and the next day. The experiments are used to identify the combination of the most significant parameters that can be used to form the inputs of the neural networks in order to reduce the prediction error. The prediction error is also reduced by predicting the difference between the power load of the next hour (day) and that of the present hour (day). This is a promising alternative to the commonly used approach of predicting the actual power load. The potential of the proposed method is revealed by its comparison with two existing approaches that utilize neural networks for electric power load forecasting.     

Collision detection for multiple robot manipulatyors by using orthogonal neural networks

This article discusses the application of orthogonal neural networks to detect collisions between multiple robot manipulators that work in an overlapped space. By applying an expansion/shrinkage algorithm, the problem of collision detection between arms is transformed into that among cylinders (or rectangular solids) and line segments. This mapping simplifies the collision detection problem and thus neural networks can be applied to solve it. The property of parallel processing enables neural networks to detect collisions rapidly. A single-layer orthogonal neural network is developed to avert the problems of conventional multilayer feedforward neural networks such as initial weights and the number of layers and processing elements. This orthogonal neural network can approximate various functions and is used to calculate forward solution and to detect collisions. An efficient neural network system for collision detection is also developed. © 1995 John Wiley & Sons, Inc.     

Joint relay selection and power allocation for amplify-and-forward two-path relaying networks

This paper presents a joint relay selection and power allocation scheme for amplify-and-forward two-path relaying networks,in which diferent relay nodes forward information symbols alternatively in adjacent time slots.Our approach is based on the maximization of the received signal-to-noise ratio under total power consumption by the transmission of the symbol.We show that in spite of inter-relay interferences,the maximization problem has a closed-form solution.Simulation results explicitly indicate that the performance of proposed approach outmatches the existing methods including equal power allocation and one-path relaying.     

宽带电力线通信网络最优中继选择算法

针对现有低压宽带电力线通信网络拓扑不均衡问题,提出一种宽带电力线通信网络最优中继选择算法.从入网申请节点到中央控制器所有路径中选择信噪比最高的路径,使节点选择最合理的中继节点;利用信标报文丢包率记录节点间通信状态,使节点分布更加均衡;以公有中继节点为顶端节点建立倒V型中转策略,提高数据传输效率.实验结果表明,该算法在平...