An adaptive algorithm based on the immune system and its application to adaptive noise neutralization

We propose an adaptive algorithm based on some features of the immune system (a selection-based mechanism compatible with Edelman’s selectionist principle, self/nonself reference, and negative/positive selection). The algorithm proceeds in three steps: diversity generation, establishment of self-tolerance, and memorizing nonself. This algorithm may typically be used to model the system of distributed agents where the system (the self) as well as the environment (the nonself) are unknown or cannot be modeled. An agent-based architecture based on the local memory hypothesis and a network-based architecture based on the network hypothesis are discussed. The agent-based architecture is elaborated with applications to an adaptive system where knowledge about the environment is not available. An adaptive noise neutralizer is formalized and simulated for a simple plant. Some part of this work has been presented at ICEC 1996, IROS 1996, and AROB 1999.     

Scalable human-robot interactions in active sensor networks

Decentralized sensor networks promise virtually unlimited scalability and can tolerate individual component failures. An experimental active sensor network that leverages environment-centric modes of human-robot interaction can keep up with a network's arbitrary growth. Spatially distributed sensors provide better coverage, faster response to dynamically changing environments, better survivability, and robustness to failure. Taking an extra step to a decentralized system provides further benefits of scalability, modularity, and performance. Our active sensor network is a collection of sensing platforms connected into a network. Some or all of the network components have actuators that we can control, making them, in this sense, active. A mobile robot with onboard sensors and a communication facility is an example of an active component. We investigate the scalability of an important aspect of an ASN: interaction with human operations.     

Processing of chemical sensor arrays with a biologically inspired model of olfactory coding

This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.     

免疫原理在网络攻击检测系统中的应用研究

针对网络攻击方式具有动态性和多样性等特点,提出一种基于免疫原理的网络攻击检测系统;系统根据免疫原理将网络正常行为定义为自体集,网络攻击行为定义为非自体集,通过免疫机制来检测自体集和非自体,从而实现对网络系统的保护;仿真结果表明,该系统弥补了传统网络攻击检测系统的缺陷,提高了网络攻击检测的正确率,降低了误报率低。     

Distributed multiple model estimation for jump Markov linear systems with missing measurements

This paper is concerned with distributed multiple model estimation for jump Markov linear systems with missing measurements over a sensor network. Two independent Markov chains are used to describe the switching of dynamic models and the missing of measurements, respectively. Under the assumption that each sensor can only communicate with its neighbours, a distributed filter is developed by applying the basic interacting multiple model (IMM) approach in the Bayesian estimation framework. To circumvent the difficulty of exponentially growing filters by exchanging local measurements between neighbouring sensors, the mode-conditioned estimates are exchanged instead of local measurements and the covariance intersection method is adopted to fuse mode-conditioned estimates. A multi-sensor manoeuvering target tracking example is provided to verify the effectiveness of the proposed filter.     

Rotation-invariant neural pattern recognition system withapplication to coin recognition

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.     

Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities

For the past decades there has been a rising interest for wireless sensor networks to obtain information about an environment. One interesting modality is that of audio, as it is highly informative for numerous applications including speech recognition, urban scene classification, city monitoring, machine listening and classifying domestic activities. However, as they operate at prohibitively high energy consumption, commercialisation of battery-powered wireless acoustic sensor networks has been limited. To increase the network’s lifetime, this paper explores the joint use of decision-level fusion and dynamic sensor activation. Hereby adopting a topology where processing – including feature extraction and classification – is performed on a dynamic set of sensor nodes that communicate classification outputs which are fused centrally. The main contribution of this paper is the comparison of decision-level fusion with different dynamic sensor activation strategies on the use case of automatically classifying domestic activities. Results indicate that using vector quantisation to encode the classification output, computed at each sensor node, can reduce the communication per classification output to 8 bit without loss of significant performance. As the cost for communication is reduced, local processing tends to dominate the overall energy budget. It is indicated that dynamic sensor activation, using a centralised approach, can reduce the average time a sensor node is active up to 20% by leveraging redundant information in the network. In terms of energy consumption, this resulted in an energy reduction of up to 80% as the cost for computation dominates the overall energy budget.     

Synchronous distributed load balancing on dynamic networks

In this paper, three distributed load-balancing algorithms for dynamic networks are investigated. Dynamic networks are networks in which the topology may change dynamically. The definition of a dynamic network is introduced and its graph model is presented. The main result of this study consists in proving the convergence toward the uniform load distribution of the diffusion algorithm on an arbitrary dynamic network despite communication link failures. We also give two adaptations of this algorithm (the GAE and the relaxed diffusion). Note that the hypotheses of our result are realistic and that for example the network does not have to be maintained connected. To study the behavior of these algorithms, we compare the load evolution by several simulations.     

Dynamic hierarchical control for distributed problem solving

Distributed problem solving (DPS) has become one of the central topics in AI. Much research has been concerned with finding an appropriate distributed control regime. We propose the concept of dynamic, hierarchical control (DHC) for distributed problem solving.

In many domains, DPS has a natural hierarchy or a subproblem hierarchy can be imposed by utilizing appropriate decomposition techniques. DHC aims at exploiting the power inherent in the hierarchical approach. It also enables the control of the problem solving process to fit the structure of a domain. This results in well-coordinated cooperation and coherent negotiation among distributed controllers.

We have used the DHC to perform plant combine production planning (PCPP). This task involves the activity of a network of production units, each with possibly different characteristics, collaborating to produce various items under dynamically changing conditions. We also describe the general structure of a single controller which adopts a blackboard and a knowledge source (KS) scheduling mechanism to carry out dynamic process execution.

The paper describes the results of our first solution to this problem. Finally, we discuss on-going research that aims at handling additional problems.     

CASA and LEAD: Adaptive Cyberinfrastructure for Real-Time Multiscale Weather Forecasting

Two closely linked projects aim to dramatically improve storm forecasting speed and accuracy. CASA is creating a distributed, collaborative, adaptive sensor network of low-power, high-resolution radars that respond to user needs. LEAD offers dynamic workflow orchestration and data management in a Web services framework designed to support on-demand, real-time, dynamically adaptive systems     

基于可变形卷积和自适应空间特征融合的硬币表面缺陷检测算法

针对硬币表面缺陷较小、形状多变且易与背景混淆而不易检出的问题,改进YOLOv3算法并提出基于可变形卷积和自适应空间特征融合的硬币表面缺陷检测算法DCA-YOLO。首先,由于缺陷形状的多变设计了3种在主干网络中不同位置添加可变形卷积模块的网络结构,通过卷积学习偏移量和调节参数来提高缺陷的提取能力;然后,使用自适应空间特征融合网络学习权重参数来调整不同尺度特征图中各像素点的贡献度以更好地适应不同尺度的目标;最后,改进先验锚框比例,动态调节类别权重,优化并对比网络性能,从而提出在主干网络输出特征进行多尺度融合的上采样前增加可变形卷积的模型网络。实验结果表明,在硬币缺陷数据集上,DCA-YOLO算法检测平均精度均值（mAP）接近于Faster-RCNN,达到了92.8%;而相较于YOLOv3,所提算法的检测速度基本持平,在检测mAP上提高了3.3个百分点,F1分数提升了3.2个百分点。     

基于改进DHCP的IP动态伪装设计与实现

通过IP伪装来迷惑攻击者,从而防止针对特定IP的数据流分析与嗅探。文章分析了Linux系统中IP伪装技术的不足,设计并实现了一种基于改进DHCP针对网络会话的IP动态伪装。改进DHCP通过伪装IP使用频率、客户机和IP描述及IP状态变化来管理分配伪装IP,通过主动探测来确保分配IP的安全性。客户机发起新的网络会话时,通过动态申请伪装IP并跟踪网络会话来实现IP动态伪装。     

An autonomy-oriented computing approach to community mining in distributed and dynamic networks

A network community refers to a special type of network structure that contains a group of nodes connected based on certain relationships or similar properties. Our ability to mine communities hidden inside networks will readily enable us to effectively understand and exploit such networks. So far, various methods and algorithms have been developed to perform the task of community mining, where it is often required that the networks are processed in a centralized manner, and their structures will not dynamically change. However, in the real world, many applications involve distributed and dynamically evolving networks, in which resources and controls are not only decentralized but also updated frequently. It would be difficult for the existing methods to deal with these types of networks since their global topological representations are either not available or too hard to obtain due to their huge size, decentralization, and/or dynamic updates. The aim of our work is to address the problem of mining communities from a distributed and dynamic network. It differs from the previous ones in that here we introduce the notion of self-organizing agent networks, and provide an autonomy-oriented computing (AOC) approach to distributed and incremental mining of network communities. The AOC-based method utilizes reactive agents that can collectively detect and update community structures in a distributed and dynamically evolving network, based only on their local views and interactions. While providing detailed formulations, we present the results of our systematic validations using real-world benchmark networks as well as synthetic networks that include a distributed intelligent Portable Digital Assistant (iPDA) network example.     

A distributed probabilistic system for adaptive regulation of imageprocessing parameters

A distributed optimization framework and its application to the regulation of the behavior of a network of interacting image processing algorithms are presented. The algorithm parameters used to regulate information extraction are explicitly represented as state variables associated with all network nodes. Nodes are also provided with message-passing procedures to represent dependences between parameter settings at adjacent levels. The regulation problem is defined as a joint-probability maximization of a conditional probabilistic measure evaluated over the space of possible configurations of the whole set of state variables (i.e., parameters). The global optimization problem is partitioned and solved in a distributed way, by considering local probabilistic measures for selecting and estimating the parameters related to specific algorithms used within the network. The problem representation allows a spatially varying tuning of parameters, depending on the different informative contents of the subareas of an image. An application of the proposed approach to an image processing problem is described. The processing chain chosen as an example consists of four modules. The first three algorithms correspond to network nodes. The topmost node is devoted to integrating information derived from applying different parameter settings to the algorithms of the chain. The nodes associated with data-transformation processes to be regulated are represented by an optical sensor and two filtering units (for edge-preserving and edge-extracting filterings), and a straight-segment detection module is used as an integration site.     

IQ-Paths: Predictably High Performance Data Streams Across Dynamic Network Overlays

Overlay networks are a key vehicle for delivering network and processing resources to high performance applications. For shared networks, however, to consistently deliver such resources at desired levels of performance, overlays must be managed at runtime, based on the continuous assessment and prediction of available distributed resources. Data-intensive applications, for example, must assess, predict, and judiciously use available network paths, and dynamically choose alternate or exploit concurrent paths. Otherwise, they cannot sustain the consistent levels of performance required by tasks like remote data visualization, online program steering, and remote access to high end devices. The multiplicity of data streams occurring in complex scientific workflows or in large-scale distributed collaborations exacerbate this problem, particularly when different streams have different performance requirements. This paper presents IQ-Paths, a set of techniques and their middleware realization that implement self-regulating overlay streams for data-intensive distributed applications. Self-regulation is based on (1) the dynamic and continuous assessment of the quality of each overlay path, (2) the use of online network monitoring and statistical analyses that provide probabilistic guarantees about available path bandwidth, loss rate, and RTT, and (3) self-management, via an efficient packet routing and scheduling algorithm that dynamically schedules data packets to different overlay paths in accordance with their available bandwidths. IQ-Paths offers probabilistic guarantees for application-level specifications of stream utility, based on statistical predictions of available network bandwidth. This affords applications with the ability, for instance, to send control or steering data across overlay paths that offer strong guarantees for future bandwidth vs. across less guaranteed paths. Experimental results presented in this paper use IQ-Paths to better handle the different kinds of data produced by two high performance applications and one multimedia application: (1) a data-driven interactive high performance code with user-defined utility requirements, (2) an adaptive overlay version of the popular Grid-FTP application, and (3) a MPEG-4 Fine-Grained Scalable layered video streaming.     

基于分布式电磁探测系统的无线网络动态聚类路由算法设计

针对地探领域的应用特点,借鉴无线传感器网络组网机制,设计了基于动态聚类的分布式电磁探测系统路由算法.给出了网路节点的数学模型,定义了网路中的评价函数,与传统算法的网络生命周期进行了对比,实验证明动态聚类路由算法能够有效地延长网络生命周期.     

A new approach to distributed fusion filtering for networked systems with random parameter matrices and correlated noises

This paper is concerned with the distributed filtering problem for a class of discrete-time stochastic systems over a sensor network with a given topology. The system presents the following main features: (i) random parameter matrices in both the state and observation equations are considered; and (ii) the process and measurement noises are one-step autocorrelated and two-step cross-correlated. The state estimation is performed in two stages. At the first stage, through an innovation approach, intermediate distributed least-squares linear filtering estimators are obtained at each sensor node by processing available output measurements not only from the sensor itself but also from its neighboring sensors according to the network topology. At the second stage, noting that at each sampling time not only the measurement but also an intermediate estimator is available at each sensor, attention is focused on the design of distributed filtering estimators as the least-squares matrix-weighted linear combination of the intermediate estimators within its neighborhood. The accuracy of both intermediate and distributed estimators, which is measured by the error covariance matrices, is examined by a numerical simulation example where a four-sensor network is considered. The example illustrates the applicability of the proposed results to a linear networked system with state-dependent multiplicative noise and different network-induced stochastic uncertainties in the measurements; more specifically, sensor gain degradation, missing measurements and multiplicative observation noises are considered as particular cases of the proposed observation model.     

Applications of artificial neural network on signal processing of optical fibre pH sensor based on bromophenol blue doped with sol–gel film

In this paper, the applications of artificial neural network (ANN) in signal processing of optical fibre pH sensor is presented. The pH sensor is developed based on the use of bromophenol blue (BPB) indicator immobilized in a sol–gel thin film as a sensing material. A three layer feed-forward network was used and the network training was performed using the back-propagation (BP) algorithm. Spectra generated from the pH sensor at several selected wavelengths are used as the input data for the ANN. The bromophenol blue indicator, which has a limited dynamic range of 3.00–5.50 pH units, was found to show higher pH dynamic range of 2.00–12.00 and with low calibration error after training with ANN. The enhanced ANN could be used to predict the new measurement spectra from unknown buffer solution with an average error of 0.06 pH units. Changes of ionic strength showed minor effect on the dynamic range of the sensor. The sensor also demonstrated good analytical performance with repeatability and reproducibility characters of the sensor yield relative standard deviation (R.S.D.) of 3.6 and 5.4%, respectively. Meanwhile the R.S.D. value for this photostability test is 2.4% and it demonstrated no hysteresis when the sensor was cycled from pH 2.00–12.00–2.00 (acid–base–acid region) of different pH. Performance tests demonstrated a response time of 15–150 s, depending on the pH and quantity of the immobilized indicator.     

Ada95分布系统模型的分析

本文详细分析了Ada95分布系统模型的语义,重点探讨了分布单元的构成、通信和库单元分类编译指示,最后给出了网络应用的例子。     

基于分布式多子网神经网络的可燃气体分析

根据催化传感器在不同的电场条件下具有不同气体检测灵敏度的特点,介绍了采用单一的热催化传感器在不同的电场强度下,通过分布式多子网神经网络对含未知气体的可燃混合气体进行分析的新方法。应用分布式神经网络,通过训练建立了信号识别的模型,并以3种混合气体为对象进行实验,结果证明了分析方法的可行性。实验表明：该网络在泛化能力与学习速度等均优于BP和RBF网络,其多子网、自动分解任务的特点尤其适用于复杂样本的学习,具有很好的应用前景。