Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

基于感知评价的Internet语音终端流量控制

分析了影响因特网语音通信质量的主要因素，根据实时传输控制协议计算延迟、丢包率等网络参数，并利用RBF神经网络将参数映射为语音质量评分，根据评分值的变化动态地调整语音终端的编码速率，在保证一定语音质量的同时，缓解网络拥塞。实验证明了该方法的有效性。     

动态评价技术在塔河碳酸盐岩缝洞型油气藏中的应用

塔河油田奥陶系油气藏是大型碳酸盐岩溶洞型油气藏，其储渗空间主要为大小不同的溶洞、裂缝带、溶蚀孔隙等组成，该油气藏具有极强的非均质性，单纯用静态资料来认识这类油气藏是非常困难。文章提出利用生产动态资料和信息进行该类型油气藏研究的新思路，利用人工神经网络技术在处理非线性相关参数预测方面的优势，并以渗流理论为基础，结合试井成果，选用已知油井的产量、油嘴、油压、含水率、气油比、原油密度等6个开发动态参数作为样品输入数据，推导出影响油气藏开发的重要参数（地层系数）与生产信息的关系，建立了人工神经网络预测储层参数的结构模型。通过塔里木盆地塔河油气田实例研究，说明了利用动态信息评价油气藏技术在碳酸盐岩缝洞型油气藏储层预测和非均质性分析等方面具有较高的实用价值。     

人工神经网络预测煤的发热量

应用人工神经网络处理部分广东地区煤质分析数据,建立网络模型,预测出煤的发热量,经实测值与预测值比较,在一定的煤种范围内预测值与实测值比较接近。     

人工智能的原理及应用

介绍了人工智能的发展，并对改进的神经网络专家系统的优越性做了介绍，指出了神经网络专家系统广阔的应用前景及实现。     

电力系统谐波检测的现状与发展

准确、实时地对电力系统谐波进行检测有着重要的意义。本文根据电力系统谐波测量的基本方法，对近年来电力系统谐波检测的新方法进行了分析和评述。最后对电力系统的谐波测量进行了总结并提出了看法。     

Segmented polymer networks containing amino‐dendrimers

Polymer networks in which poly(propylene imine) dendrimers (Astramol?) are connected to each other by linear polytetrahydrofuran (polyTHF) segments, were prepared by two methods. The first method was a one‐step procedure in which bifunctionally living polyTHF, obtained by initiation of the THF polymerization with trifluoromethane sulfonic anhydride (triflic anhydride), was reacted with an amino‐dendrimer. This reaction was very fast but did not allow formation of the end products. The second method was a two‐step procedure. In a first step, living polyTHF, prepared with acryloyloxybutyl triflate as initiator, was grafted on an amino‐dendrimer, to form a star‐like, acrylate‐terminated polyTHF multi‐macromonomer with the dendrimer as core. In a second step, networks were obtained by Michael addition between the acrylate end‐groups and unreacted amino‐groups of the dendrimer. This cross‐linking reaction occurred spontaneously upon heating of the solution of the multi‐macromonomer with gelation times varying from a few minutes to a few hours, depending on the temperature and the composition of the prepolymers. With this method it was possible to prepare networks in the form of coatings or films. © 2003 Society of Chemical Industry     

A Study on Turbo—rotor Multi—fault Diagnosis Based on a Neural Network

The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine.As it has many excellent qualities,the neural network might be used to solve the problems of multi-fault diagnasis of a turbo-rotor system.First,the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing.Then.the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propaga-tion)neural network.According to the BP neural network problems,the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated.The results indicated that the parallel Bp neural network method could solve the turbo-rotor multi-fault diagnosis problems.     

Joint scheduling and power control supporting multicasting in wireless ad hoc networks

This paper addresses the problem of power control in a multihop wireless network supporting multicast traffic. We face the problem of forwarding packet traffic to multicast group members while meeting constraints on the signal-to-interference-plus-noise ratio (SINR) at the intended receivers. First, we present a distributed algorithm which, given the set of multicast senders and their corresponding receivers, provides an optimal solution when it exists, which minimizes the total transmit power. When no optimal solution can be found for the given set of multicast senders and receivers, we introduce a distributed, joint scheduling and power control algorithm which eliminates the weak connections and tries to maximize the number of successful multicast transmissions. The algorithm allows the other senders to solve the power control problem and minimize the total transmit power. We show that our distributed algorithm converges to the optimal solution when it exists, and performs close to centralized, heuristic algorithms that have been proposed to address the joint scheduling and power control problem.     

分布式无线传感器网络通信协议研究

在归纳分布式无线传感器网络研究进展的基础上，给出了综合分布式无线传感器网络技术各个方面的协议体系结构，并在新的体系结构下，系统地阐述了分布式无线传感器网络通信协议研究的进展，介绍了典型的应用案例和国外相关计划的研究情况。最后，指明了这一处于发展初期的研究领域的未来发展方向。