Simulation of the oregonator model by using deterministic ARMS

We propose a deterministic method for simulating chemical reactions, deterministic abstract rewriting system on multisets (DARMS), which is based on the concept of mass action low. The feasibility and utility of DARMS are demonstrated by applying it to the oregonator, which is a well-known model of the Belousov-Zhabotinskii (BZ) reaction. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

A simple method for simulating biochemical reaction networks

We propose a simple method of simulating chemical reaction networks by using a multiset rewriting system: abstract rewriting system on multisets (ARMS). We simulate the oregonator model, a mathematical model of the Belousov-Zhabotinskii reaction, and we obtain the same behavior when the differential equations or the Gillespie method are used. We also compose a 1- and 2-dimensional cellular automata by using ARMS, and confirm that it shows the pattern forming that is observed in the BZ reaction. This work was presented in part at the First European Workshop on Artificial Life and Robotics, Vienna, Austria, July 12–13, 2007     

一个抽象机库中B方法的研究

B方法是支持规格说明到软件设计和执行的形式化方法。文章研究了B方法中一种抽象机库的实现模型和生成代码,对该方法的特点进行评述。验证了B的正确可靠的实现方法,充分利用B方法的精化原则,提高了B形式化方法的开发验证效率。     

Investigating the relationship between adverse events and infrastructure development in an active war theater using soft computing techniques

The purpose of this paper is to investigate the relationship between adverse events and infrastructure development investments in an active war theater by using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) where the accuracy of the predictions is directly beneficial from an economic and humanistic point of view. Fourteen developmental and economic improvement projects were selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded or hijacked, and the total number of adverse events has been estimated.The results obtained from analysis and testing demonstrate that ANN, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic project data. When the model accuracy was calculated based on the mean absolute percentage error (MAPE) for each of the models, ANN had better predictive accuracy than FIS and ANFIS models, as demonstrated by experimental results. For the purpose of allocating resources and developing regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater, with emphasis on predicting the occurrence of events. We conclude that the importance of infrastructure development projects varied based on the specific regions and time period.     

Realization of emergent behavior in collective autonomous mobile agents using an artificial neural network and a genetic algorithm

This paper proposes a pursuit system that utilizes the artificial life concept where autonomous mobile agents emulate the social behavior of animals and insects and realize their group behavior. Each agent contains sensors to perceive other agents in several directions, and decides its behavior based on the information obtained by these sensors. In this paper, a neural network is used for behavior decision controlling. The input of the neural network is decided by the existence of other agents, and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behavior adequately fit the goal and can express group behavior. The validity of the system is verified through simulation. Also in this paper, we have observed the agents emergent behavior during simulation.This paper was supported by WonKwang University in 2004.     

Finding an efficient rewriting of OLAP queries using materialized views in data warehouses

OLAP queries involve a lot of aggregations on a large amount of data in data warehouses. To process expensive OLAP queries efficiently, we propose a new method to rewrite a given OLAP query using various kinds of materialized views which already exist in data warehouses. We first define the normal forms of OLAP queries and materialized views based on the selection and aggregation granularities, which are derived from the lattice of dimension hierarchies. Conditions for usability of materialized views in rewriting a given query are specified by relationships between the components of their normal forms. We present a rewriting algorithm for OLAP queries that can effectively utilize materialized views having different selection granularities, selection regions, and aggregation granularities together. We also propose an algorithm to find a set of materialized views that results in a rewritten query which can be executed efficiently. We show the effectiveness and performance of the algorithm experimentally.     

An expert system interface for an identification program

This paper describes an expert system interface, named (ihs), for the interactive data analysis and system identification program Idpac. The interface works as an intelligent help system. The system is completely noninvasive and uses the previous command history to understand what the user is doing and gives help according to this. This way of monitoring the user's activities is called the command spy strategy. Scripts are used for representing procedural knowledge, and production rules for diagnostic knowledge. The system has been implemented and a knowledge database handling system identification with the maximum-likelihood method has been developed. An example run with the system is included.     

Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system

Due to the rapid development of globalization, which makes supply chain management more complicated, more companies are applying radio frequency identification (RFID), in warehouse management. The obvious advantages of RFID are its ability to scan at high-speed, its penetration and memory. In addition to recycling, use of a RFID system can also reduce business costs, by indentifying the position of goods and picking carts. This study proposes an artificial immune system (AIS)-based fuzzy neural network (FNN), to learn the relationship between the RFID signals and the picking cart’s position. Since the proposed network has the merits of both AIS and FNN, it is able to avoid falling into the local optimum and possesses a learning capability. The results of the evaluation of the model show that the proposed AIS-based FNN really can predict the picking cart position more precisely than conventional FNN and, unlike an artificial neural network, it is much easier to interpret the training results, since they are in the form of fuzzy IF–THEN rules.     

Defending against Wormhole Attack in MANET Using an Artificial Immune System

MANETs are mobile networks that are spontaneously deployed over a geographically limited area without requiring any pre-existing infrastructure. Typically, nodes are both autonomous and self-organized without requiring a central administration or a fixed network infrastructure. Due to their distributed nature, MANET is vulnerable to a specific routing misbehavior, called wormhole attack. In a wormhole attack, one malicious node tunnels packets from its location to the other malicious node. Such wormhole attacks result in a false route with fewer hop count. If the source node follows this fake route, malicious nodes have the option of delivering the packets or dropping them. This article aims at removing these attacks. For this purpose, it investigates the use of an Artificial Immune System (AIS) to defend against wormhole attack. The proposed approach learns rapidly how to detect and bypass the wormhole nodes without affecting the overall performance of the network. The proposed approach is evaluated in comparison with other existing solutions in terms of dropped packet count, packet loss ratio, throughput, packet delivery ratio, and end-to-end delay. A simulation result shows that the proposed approach offers better performance than other schemes defending against the wormhole attack.     

Technology roles and paths of influence in an ecosystem model of technology evolution

We propose a new conceptual model for understanding technology evolution that highlights dynamic and highly interdependent relationships among multiple technologies. We argue that, instead of considering technologies in isolation, technology evolution is best viewed as a dynamic system or ecosystem that includes a variety of interrelated technologies. By considering the interdependent nature of technology evolution, we identify three roles that technologies play within a technology ecosystem. These roles are components, products and applications, and support and infrastructure. Technologies within an ecosystem interact through these roles and impact each others’ evolution. We also classify types of interactions between technology roles, which we term paths of influence. We demonstrate the use of our proposed model through examples of wireless networking (Wi-Fi) technologies and a business mini-case on the digital music industry.

Effective recognition of MCCs in mammograms using an improved neural classifier

Computer-aided diagnosis is one of the most important engineering applications of artificial intelligence. In this paper, early detection of breast cancer through classification of microcalcification clusters from mammograms is emphasized. Although artificial neural network (ANN) has been widely applied in this area, the average accuracy achieved is only around 80% in terms of the area under the receiver operating characteristic curve A_z. This performance may become much worse when the training samples are imbalanced. As a result, an improved neural classifier is proposed, in which balanced learning with optimized decision making are introduced to enable effective learning from imbalanced samples. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from has been significantly improved. An average improvement of more than 10% in the measurements of F₁ score and A_z has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications.     

汽车制动复合材料智能设计系统研制

将人工神经网络的知识获取和表现方法引入专家系统,结合遗传算法,研制智能化的汽车制动材料计算机辅助设计系统。该系统能够从已有的试验数据中获取知识,利用基于人工神经网络的知识系统来模拟制动材料配方组分和摩擦学性能之间的映射关系,利用模糊匹配的方法实现新开发的配方和知识库中已有配方的匹配,利用遗传算法实现配方寻优过程,能够快速有效地设计符合用户要求的新型汽车制动材料配方。整个系统在Windows环境运行,具有良好的人机交互界面,便于调试、维护和移植。     

Face recognition using an NNSRM classifier in LDA subspace

NNSRM is an implementation of the structural risk minimization (SRM) principle using the nearest neighbor (NN) rule, and linear discriminant analysis (LDA) is a dimension-reducing method, which is usually used in classifications. This paper combines the two methods for face recognition. We first project the face images into a PCA subspace, then project the results into a much lower-dimensional LDA subspace, and then use an NNSRM classifier to recognize them in the LDA subspace. Experimental results demonstrate that the combined method can achieve a better performance than NN by selecting different distances and a comparable performance with SVM but costing less computational time.

Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.     

人工蜂群算法的无人机航路规划与平滑

航路规划是无人机(UAV)作战任务规划系统的关键组成部分,目标是在适当的时间内为UAV计算出最优或次最优的飞行航路.人工蜂群(ABC)算法是一种最新发展的模拟昆虫王国中蜜蜂群体寻找优良蜜源的群体智能优化算法.采用人工蜂群算法完成无人机的平滑航路规划,首先阐述了人工蜂群算法的基本原理,然后将无人机航路规划问题通过建模转换成为一个多维函数优化问题,利用人工蜂群算法的优势,找到多维函数的最优解,最后对优化后的航路进行了平滑,使UAV对规划后的航路可飞.仿真实验结果表明,此方法可有效规划出航路,且所规划的航路可飞.     

Neural networks for control in an artificial brain of a recognition and tracking system

This paper presents a new information-processing machine which is called the artificial brain (ABrain). It also considers the structure of artificial neural networks constructed in a Ricoh neurocomputer RN-2000 in the ABrain to track given trajectories which are produced in a micro-computer or by a light moved by hand in a recognition and tracking system. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita Japan, February 18–20, 1997     

Driving control of an AGV for an automated container terminal using an immunized PID controller based on cell-mediated immunity

In this article, an immunized PID (I-PID) controller using a cell-mediated immune algorithm (CMIA) is proposed to control an autonomous guided vehicle (AGV) more effectively. The proposed controller is based on the specific immune response of the biological immune system that is cell-mediated immunity. Moreover, we designed and manufactured an AGV system for an automated container terminal (ACT), and some experiments are shown to verify the performance of the proposed controller. The tracking error of the AGV driving was investigated for this purpose. As a result, the capabilities of realization and reliability were proved by comparing the response characteristics of the proposed I-PID controllers with those of conventional PID and neural network PID (NNPID) controllers.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

An analysis of landslide susceptibility zonation using a subjective geomorphic mapping and existing landslides

This study explores the possibility of creating landslide susceptibility mappings by using two types of data: (i) an existing subjective geomorphic mapping; and (ii) landslides already identified in the area analyzed. The analysis is conducted using a type of Artificial Neural Network (ANN) named Learning Vector Quantization. For the subjective geomorphic mapping various definitions of stability were considered/analyzed, some using a 2-class system and some using a 5-class system.The study concludes that mappings using an existing subjective geomorphic classification and based on two stability classes can be successfully replicated with the ANN-based approach. However, mappings based on existing landslides and on the 5-class system do not yield results sufficiently accurate for practical applications. Creation of landslide susceptibility mappings involved utilization of data of numerous types (numerical and class-type variables). This study also investigated various methods of data coding and identified the most appropriate method for this type of analysis.     

GSM模块在车辆监控系统无线通信中的应用

介绍了GPS车辆监控系统中运行车辆使用西门子TC35T GSM模块和监控中心之间通过语音和短消息方式进行无线通信的方法,给出了软硬件的设计原则与方法。实验表明此方法满足车辆监控的需要并具有性价比高、数据保密性好等特点。     

Performance investigation of ABC algorithm in multi-area power system with multiple interconnected generators

This paper presents an extensive study on the application of Artificial Bee Colony (ABC) algorithm for load frequency control (LFC) in multi-area power system with multiple interconnected generators. The LFC model incorporates various possible physical constraints and non-linearities such as generation rate constraint, time delay, dead zone and boiler. The ABC algorithm is used to find the optimum PID controller parameters. The tuning performance of the algorithm is comparatively investigated against different optimization technique such as evolutionary programming (EP), genetic algorithm (GA), gravitational search algorithm (GSA) and particle swarm optimization (PSO). The robustness analysis of the system is also evaluated by investigating the dynamic response of the controller with load demand at varying time step, tuning based on different performance criterion and by varying the load demand. The performance of the system is evaluated based on the settling time and maximum overshoot value of the frequency deviation response. The performance of ABC is also verified against an exhaustive search based on interval halving method. Despite employing a single controller for multiple interconnected generators, the optimized controller is able to successfully damp oscillations in the system response and regulate the area control error back to zero in minimal amount of time. The results indicate the superiority of the ABC algorithm’s search mechanism in finding the optimum set of PID controller’s gain.