An expert decision support system for network routing: a case study

The paper presents a case study of the development of an expert decision support system which uses simple heuristic methods for fast determination of routes for simultaneous signals in a transmission network of limited capacity. It illustrates how heuristic solutions can be embodied in a model-based DSS and how the standard decision support literature, although intuitively appealing, provides little practical assistance in system construction or classification     

An expert system for wide area network component configuration

Abstract: Many organizations today have an interest in communications networks, either as users of public networks or as operators of their own private networks. Thus, the management of communications networks has become an important issue in the communications industry. The network design task is fundamental to the whole notion of network management; however, with the rapid rate of change in network technology and the emergence of highly sophisticated network users, network design has become an increasingly complex problem.
The purpose of this paper is to describe a development effort which incorporates expert systems techniques to treat one aspect of the network design problem—the initial planning and design of a network prior to implementation. The research effort was concerned with developing an expert system for Amdahl Communications Products which could be used by sales engineers in designing wide area networks to meet specified customer requirements. The system treats both the topological design problem and the component configuration problem.     

School bus routing and scheduling: An expert system approach

This paper proposes an expert system approach to routing and scheduling school buses for a rural school system. The expert system is programmed in TURBO PROLOG for use on an IBM/XT and is applied to rural county school systems in Alabama.

The busing problem considered here consists of two components: routing and scheduling. The routing problem is concerned with the determination of a stop-to-stop route to be traversed to each school by each bus whereas the scheduling problem, the determination of times at all bus stops for each bus. A bus may be used for multiple runs. Each route is designed in such a way that the bus capacity, student riding time, school time window and road condition constraints are satisfied while attempting to minimize the number of buses required in operation, minimize the fleet travel time and balance the bus loads.

Conventionally, a predetermined algorithm is coded into computer programs for generating efficient routes and schedules. The user or route designer neither has any knowledge about the algorithm nor has any input of personal expertise into the solution process. As a result, a veteran designer is skeptical of the computer-generated routes and schedules. Moreover, non-quantifiable factors such as safety, preference and judgment are not taken into consideration in the traditional approach.

To alleviate these deficiencies, an expert system approach, which enables the expert knowledge to be kept separately from its execution, is utilized. This knowledge base contains factual knowledge such as road map, school locations, but capacities, stop locations, number of students at each stop, and drivers' homes. It also contains procedural knowledge such as heuristics for finding a route and scheduling multiple runs for a bus subject to various constraints. The inference engine or control program chooses the appropriate heuristics used in constructing efficient routes and schedules with respect to various objectives or goals. A user interface includes the graphic display of road maps and determined routes.     

An architecture of an intelligent system for routing user requests in a network of heterogeneous databases

We present a general purpose model for routing user requests, e.g. queries, in a network of autonomous heterogeneous databases. The database schemas and other information on the database nodes are used to construct a multi-level knowledge-base (MKB) that resides in various nodes. Access to the databases is not done by creating direct connections between the user and the nodes where the data are presumably located. Rather, the user approaches the network by contents via an intelligent system that utilizes the MKB in order to identify the nodes and databases where the most relevant information resides, and establishes access routes to those nodes.     

An expert system for sorting

Abstract: Sorting an internal list is an indispensable requirement in numerous data processing applications, and many algorithms have been devised for accomplishing those tasks. Furthermore, it is fairly simple to derive order of magnitude of effort measures for particular sorting strategies, and within classes of equivalent theoretical power, practice has shown which algorithms prevail in which circumstances. Thus there is a useful background for testing a system's ability to learn which problem solving technique should be applied in a given instance, for the expert knowledge is rather concise and structured and facilitates comparison to machine decisions. The paper describes the construction of such a system and analyzes the results.     

An expert system for fault diagnosis in internal combustion engines using probability neural network

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.     

An expert system for respirator selection

A Respirator Selection Expert System (RSES) is described which assists the user in selection of a respirator for a specific application. In addition to determining the minimum class of respirator which should be used by following the NIOSH Respirator Decision Logic, RSES also provides all necessary calculations, data for common industrial contaminants and on-line reference material.     

An expert system framework for a CIM based quality inspection system

With today's competitive marketplace moving toward a goal of lean, error free manufacturing a need for technological change in quality decision making has arisen. One approach to this new “quality” standard involves tying quality analysis to the theory of Computer Integrated Manufacturing (CIM). In this paper a model of such a system is presented including the application of an expert system to enhance dimensional tolerancing and data analysis in quality control. Our expert system will also serve a dual role as a technological link to a CIM environment through the use of IGES computer aided design data.     

面向BLE健康监护网络的负载均衡算法

为解决基于BLE Mesh的健康监护系统中数据传输不稳定,部分节点死亡速度过快的问题,提出一种负载均衡多径路由算法.根据节点特性构建动态剩余能量计算模型和节点移动状态概率计算模型,在上述模型的基础上考虑链路质量情况引入负载均衡链路状态指标指导路由建立.仿真结果表明,该算法具有更高的投递率和更好的稳定性,能有效延长网络的...     

Combining a neural network and a rule-based expert system for short-term load forecasting

A backpropagation neural network that used the output provided by a rule-based expert system was designed for short-term load forecasting. Extensive studies were performed on the effect of various factors such as learning rate and the number of hidden nodes. Load forecasting was performed on a Taiwan power system to demonstrate that the inclusion of the prediction from a rule-based expert system developed for a power system would improve the predictive capability of the neural network. The hourly power load for two typical days was evaluated, and for both days the inclusion of the rule-based expert system prediction as a network input significantly improved the neural network's prediction of power load. The predictive capability of the network was compared to the expert system as well as to a previously developed neural network. The proposed neural network provided improved predictive capability. In addition, the proposed combined approach converges much faster than both the conventional neural network and the rule-based expert system method.     

An expert system for structure damage assessment

An expert system called Sperill-II is introduced for the damage assessment of existing structures using the knowledge of experienced structural engineers. Fuzzy sets and Dempster and Shafer's theory are used in this inexact inference method.     

An expert system for general symbol recognition

An expert system for analysis and recognition of general symbols is introduced. The system uses the structural pattern recognition technique for modeling symbols by a set of straight lines referred to as segments. The system rotates, scales and thins the symbol, then extracts the symbol strokes. Each stroke is transferred into segments (straight lines). The system is shown to be able to map similar styles of the symbol to the same representation. When the system had some stored models for each symbol (an average of 97 models/symbol), the rejection rate was 16.1% and the recognition rate was 83.9% of which 95% was recognized correctly. The system is tested by 5726 handwritten characters from the Center of Excellence for Document Analysis and Recognition (CEDAR) database. The system is capable of learning new symbols by simply adding their models to the system knowledge base.     

An expert system for federal tax planning

Abstract: Recent developments in a subarea of computer science called artificial intelligence have included the creation of expert systems that are capable of solving difficult applications problems which require expert knowledge for their solution. Such expert systems have been found to be useful in a number of applications (e.g. medicine, biochemistry and mineral exploration). In this paper the author presents an expert system for solving problems concerning income and transfer tax planning for individuals In developing this system, a theoretical structure and a set of decision rules were specified and then programmed into a rule-based system that had previously been used for medical diagnosis (Mycin [1]) Once the system was developed, its problem-solving capabilities were refined and verified by a panel of tax experts using a blind verification procedure. This verification step demonstrated that an expert system could be developed in that domain.     

An expert system for manufacturing cell control

An expert control system was designed to control an unmanned manufacturing cell in order to meet the operational requirements of a cellular Manufacturing System (CMS). In this paper, a knowledge-based three-layer control concept was used to build the cell control system. This cell control system is built to include workers' experience and problem handling ability. The cell control algorithms and heuristics are based on the pull system control principle. A Petri net is used to generate the cell control algorithm. The structure of the control system and the application of the Petri net method will be demonstrated.     

An expert system methodology for aircraft-gate assignment

The aircraft-gate assignment problem is a significant concern in airline operations. This paper addresses the use of knowledge based expert systems to help solve the aircraft-gate assignment problem. The problem is described and the factors that need to be considered in aircraft-gate assignment are identified and delineated. The expert system's architecture is presented and its working is described.     

Harvesting of river flow energy for wireless sensor network technology

River courses play a vital role in preserving unpolluted ecosystems. On the other hand, networks of sensor nodes can be used to measure characteristic parameters in the environment such as temperature, pressure, humidity or the concentration of pollutants. In the framework of the EU FP7 project “GOLDFISH”, technical competences of a consortium of 11 institutions are hence employed in designing, manufacturing, validating and operating wireless sensors nodes for tracking pollution in remote rivers. The sensor network is composed of sensor clusters located underwater and gateways on the riverbank with long-distance communication links to the central management and monitoring station. Each sensor node is composed of active electronic devices that have to be constantly powered. Batteries can generally be used for this purpose, but problems may occur when they are to be recharged or replaced, especially in the case of large networks placed in scarcely accessible locations. State-of-the-art energy harvesting technologies can hence constitute a viable powering solution. The possibility to use different small-scale river flow energy harvesting principles is thoroughly studied in this work by the University of Rijeka GOLDFISH team: a miniaturized hydro-generator, a ‘piezoelectric eel’ and a hybrid solution of a rotating shaft plucking a piezoelectric beam. The first two concepts are validated experimentally in a flow channel and in real river conditions. The miniaturized hydro-generator with suitable power management electronics is finally embedded into the wireless sensor node deployed into the river, allowing the GSM transmission of collected data to be successfully performed.     

An expert system based on wavelet transform and radon neural network for pavement distress classification

Nowadays, pavement distresses classification becomes more important, as the computational power increases. Recently, multi-resolution analysis such as wavelet decompositions provides very good multi-resolution analytical tools for different scales of pavement analysis and distresses classification. In this paper an expert system is proposed for pavement distress classification. A radon neural network, based on wavelet transform expert system is used for increasing the effectiveness of the scale invariant feature extraction algorithm. Wavelet modulus is calculated and Radon transform is then applied to the wavelet modulus. The features and parameters of the peaks are finally used for training and testing the neural network. Experimental results demonstrate that the proposed expert system is an effective method for pavement distress classification. The test performances of this study show the advantages of proposed expert system: it is rapid, easy to operate, and have simple structure.     

An expert system for detection of breast cancer based on association rules and neural network

This paper presents an automatic diagnosis system for detecting breast cancer based on association rules (AR) and neural network (NN). In this study, AR is used for reducing the dimension of breast cancer database and NN is used for intelligent classification. The proposed AR + NN system performance is compared with NN model. The dimension of input feature space is reduced from nine to four by using AR. In test stage, 3-fold cross validation method was applied to the Wisconsin breast cancer database to evaluate the proposed system performances. The correct classification rate of proposed system is 95.6%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR + NN model can be used to obtain fast automatic diagnostic systems for other diseases.     

An expert system for design for robotic assembly

In recent years, due to the various advantages associated with automation and robotics, much work has been done in developing robotic systems for assembly operations. Since part design plays a major role in assembly, this paper deals with the design of parts for ease of robotic assembly. Considerable knowledge is available in the form of design for robotic assembly rules. In addition, a large amount of data is required for decisions regarding suitability of parts for robotic assembly. The implementation of design for robotic assembly rules would be much easier with the help of an expert system, which would guide the designer toward choosing the design alternative that can best facilitate ease of assembly from a robotic point of view.To this end, a prototype expert system for design for robotic assembly is developed and presented in this paper. The expert system was implemented as a production system, which consists of rules and Object-Attribute-Value (O-A-V) triplets to represent domain knowledge. In order to best utilize the domain specific knowledge, a state space search-based inference mechanism was employed. The implementation of the prototype system is illustrated with examples.