用基函数神经网络实现多阈值图象分割

本文介绍了一种用基函数神经网络实现多阈值图象分割的新方法。它从函数逼近的角度研究基于灰度直方图的多阈值分割问题，提出了一种模糊反向传播学习算法，采用该算法的高斯基函数网络能够准确检测直方图中包含的子区域和它们的分布函数，而且速度很快。实验表明本文的方法在实际图象分割中是有效的。     

AN SVAD ALGORITHM BASED ON FNNKD METHOD

The capacity of mobile communication system is improved by using Voice Activity Detection (VAD) technology. In this letter, a novel VAD algorithm, SVAD algorithm based on Fuzzy Neural Network Knowledge Discovery (FNNKD) method is proposed. The performance of SVAD algorithm is discussed and compared with traditional algorithm recommended by ITU G.729B in different situations. The simulation results show that the SVAD algorithm performs better.     

Creating awareness on bullying and cyberbullying among young people: Validating the effectiveness and design of the serious game Conectado

Serious games have proven to be educational tools with numerous positive effects, capable of promoting learning, changing behavior, or improving training and skills development, among other effects. Serious games can also be applied to address and prevent social problems. This study describes the experiments and analyses we have carried out to test the effectiveness of Conectado, a serious game created to prevent bullying and cyberbullying in schools by increasing awareness of the problem in players. We have used the results from these experiments to answer different research questions regarding the game’s acceptance and effectiveness, validating several characteristics of the game design. We have also studied the influence of players’ characteristics on the effect of the game, as well as the relationship between the in-game behaviors of players and their degree of awareness. We have verified a positive effect of the game in terms of an increase of awareness regarding bullying and cyberbullying for all target users, and have provided further insight into how this increase is related to different players’ characteristics and behaviors.     

一种新型基于环上带误差学习问题的认证密钥交换方案

利用格上判定带误差学习问题(Ring-DLWE)困难假设,该文基于Peikert的调和技术构造认证密钥交换方案。在标准模型下,该方案是CK模型中可证明安全的,并达到弱前向安全性(wPFS)。与现有的基于LWE的密钥交换方案相比,该方案使用平衡的密钥提取函数,因而保护共享会话密钥,同时因其基于格中困难问题,所以能抵抗量子攻击。     

Analog VLSI Circuits for Competitive Learning Networks

An investigation is made concerning implementations of competitive learning algorithms in analog VLSI circuits and systems. Analog and low power digital circuits for competitive learning are currently important for their applications in computationally-efficient speech and image compression by vector quantization, as required for example in portable multi-media terminals. A summary of competitive learning models is presented to indicate the type of VLSI computations required, and the effects of weight quantization are discussed. Analog circuit representations of computational primitives for learning and evaluation of distortion metrics are discussed. The present state of VLSI implementations of hard and soft competitive learning algorithms are described, as well as those for topological feature maps. Tolerance of learning algorithms to observed analog circuit properties is reported. New results are also presented from simulations of frequency-sensitive and soft competitive learning concerning sensitivity of these algorithms to precision in VLSI learning computations. Applications of these learning algorithms to unsupervised feature extraction and to vector quantization of speech and images are also described.     

LANN AND ITS HETEROASSOCIATIVE MEMORY PROPERTIES

This paper provides a new architecture of neural network, called loop architecture neural network(LANN), and its learning rules. One of its features distinguished from other network, such as Hopfield and bidirectional associative memories, is that it can perform the associative memory among multiple categories. Analysis and simulated results have proved that it is an effective network with excellent convergence.     

基于Web的协同学习策略及其应用

对网络教育的现状进行了分析，并针对目前网络教育中师生之间、学生之间缺乏有效的交流的缺点，提出了将协同学习策略应用在的基于Web的协同学习系统中的方法。使用多种同学习策略对协同学习过程进行控制，并且将学习评估与协同学习过程进行结合，以提高学生的学习效果。     

A hybrid disturbance observer for delivery drone’s oscillation suppression

This paper proposes a new hybrid disturbance observer (DOB) to help suppress disturbance to the control systems. The proposed hybrid DOB consists of three main parts: (1) an actual system, (2) a simulated system, and (3) a learning filter that connects the actual and simulated systems. The simulated system aims to replicate the actual system response, where it leverages a neural network model to predict the input disturbance and generate the predicted system response. Such system response is used to generate a learning signal through a learning filter; this learning signal is then added to the feedforward loop of the estimation framework to enhance the disturbance estimate and its suppression performance for the actual system. The proposed hybrid DOB is designed to advance the standard DOB structure with a learning-based feedforward compensation. While the proposed method does not modify the baseline controller, it is well suited to systems whose baseline controllers are difficult or impossible to be changed. Considering the delivery drones are subject to oscillations when dropping payloads, experimental tests with multiple payload dropping scenarios have been conducted using both the hybrid and standard DOB, where the compared results validate the effectiveness and advantages of the proposed hybrid DOB.     

关于职业院校PLC课程的教学质量提升策略分析

职业院校毕业的学生其在未来工作中的表现如何在很大程度上取决于职业院校的教学质量如何.本文所提出的一些办法可以调动学生学习的积极性和主动性,实现自主学习,在提升PLC课程教学质量的同时也保障学生良好的学习效果,希望能为今后职业院校PLC课程教学的深入发展奠定良好的基础.     

人工神经网络的容量、学习与计算复杂性

本文讨论了人工神经网络（ＡＮＮ）解决问题的能力，从广泛的角度讨论了容量问题，推广与学习问题，深入研究了ＡＮＮ通过学习解决问题的计算复杂性，以及解决实际问题时困难所在。