ARTIFICIAL INTELLIGENCE BASED EXPERT SYSTEMS: APPLICATIONS TO HORTICULTURE

An important aspect of the production of quality software is that of software assessment and certification. This involves the measurement of a finished software product using a set of recognised Software Assessment Units. It is essential that at the specification of a piece of software a set of relevant software attributes with a suitable subset of Assessment Units is identified. The set is used to determine whether or not the finished product meets an appropriate quality standard. It has been recognised that a Software Assessment Advisor (SAA) is a necessary tool to assist with this task. An SAA can help in determining the software attributes which should be measured and in selecting an appropriate set of Assessment Units. The SAA will use information concerning the categorisation and functions of a proposed software product, the user's requirements, available tools, and prior assessment experiences in these tasks. So far, no clear approach has been identified for the development of an SAA. The investigation of the modelling required by an SAA indicates that Expert Systems techniques could provide a useful step forward in this development. Expert Systems techniques are able to relate the information required by the certification procedure intelligently by means of the knowledge model and to support symbolic reasoning to advise the software certifier. Also an Expert System's capability for knowledge learning is significant with respect to the relatively young area of Software Assessment and Certification. This paper discusses the activities involved in the Assessment and Certification process, describes how an SAA can assist for the process and how an Expert Systems approach has been used in the development of a prototype SAA.

Decreasing resources and increasing complexity of horticultural crop production necessitate that new technologies be developed to transfer information to commercial producers. Expert systems (ES) have been cited as potential tools that can facilitate knowledge transfer. The definitions of an expert system, however, technically only indicates a computer program that simulates the thought processes of a human expert and, as such, does not supply all the facets necessary to assist commercial producers. The combination of databases, graphic capabilities, and textual information into a comprehensive program would provide a more complete package. To differentiate the two, we use the term decision support systems (DSS). The development, testing, and release of DSS, however, require greater commitment and interdisciplinary cooperation. Developing DSS fosters interstate, interregional, and international cooperation among researchers and extension personnel. Using systems developed in fruit production as examples, we outline the value of DSS to promote cooperation, the resources necessary to develop these systems; and the attitudinal change necessary to build the systems.

Knowledge-based operation and management of communications systems

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.

8 - Real-time fault-tolerant control systems

Diagnosing the vibrating behaviour of the PWR internal structures using expert systems techniques and algorithmic treatment

Intelligent process-control system design

As environmental regulations become more stringent, manufacturers are confronted with the of productdesign considering environmental impacts. Life Cycle Analysis (LCA) is a developing technique that attacks this problem. LCAs provide environmental information for decision making by consumers, manufacturers, and governments. These decisions significantly affect the ability to maximize source reduction, re-use, recyclability, and recycled content. LCAs can aid in diminishing the negative impact a product has on the environment and can be utilized as a partial solution of our nations' (worlds') growing waste management problem. To date, the application of LCAs has varied from product comparison to process development and improvement. The results from these analyses have differed because of differing assumptions and differing choices of boundary conditions. An appropriate Life Cycle Analysis should be consistent across products, locations, and especially across experimenters. The development of LCAs lends itself to computer-assisted design methods such as the techniques of Expert Systems, due to the large amount of information associated with successfully developing an LCA. In this paper, we will discuss using Hierarchical Classification, a specific task specific architecture expert systems approach, to organize the terms necessary in performing an LCA. Although not yet definitive, we believe our experience indicates that such approaches to organizing the vast knowledge necessary to accomplish an LCA may be a key factor in eventual successful development of comprehensive LCAs in industry.

Combining Expert Systems and Cad Techniques

The global scientific community is increasingly recognizing the urgent need to address the complex challenges facing the agricultural sector, particularly those arising from significant shifts in climatic conditions. In India, the rising demand for horticultural crops, both domestically and internationally, has compelled farmers to focus on producing high-value crops, particularly fruits and vegetables, in a cost-effective manner. This demand, coupled with the relatively shorter production cycles, potential for crop diversification, more efficient land use, and opportunities for integrating advanced technologies, has led to a growing preference among farmers for horticultural production over traditional staple food crops. This paper aims to analyze the multifaceted impact of climate change on food security and to assess its long-term effects on the production of horticultural crops in India. To achieve this, the study utilizes climatic and agricultural data from the 15 leading horticultural-producing states of India, spanning the period from 1987-88 to 2020-21. A Panel Autoregressive Distributed Lag (ARDL) model is employed to examine the long-term effects of climate change on horticultural production. Keeping in view the regional variability in geographic, climatic, and socio-economic conditions, the ARDL model is particularly suited to capture heterogeneity by incorporating multiple cross-sectional units. The factors influencing horticultural crop production are categorized into climate-related and non-climate-related variables. The results indicate that, in the long run, climate variables exhibit a significantly negative relationship with horticultural production, suggesting an unfavourable impact. In contrast, non-climatic factors such as the area under cultivation and the annual average wage rate are positively correlated with horticultural production and have a significant effect. Overall, the study concludes that, in the long term, climate change exerts a more substantial influence on horticultural crop production than other factors. This finding highlights the critical need for the adoption of climateresilient technologies in horticultural farming to mitigate the adverse effects of climate change and ensure sustainable production.

The aim of this paper is to present a Fuzzy Expert System Methodology (FESM), based on the Fuzzy Logic, Expert System techniques and multicriteria decision making theory, which can be used to calculate a unique Fuzzy Total Power Quality Index (FTPQI) for every measured point of the electric network (measurements for a specific time period are used). FTPQI of the specific point includes unified and concentrated information about its PQ level and could contribute to the evaluation of examined electric network nodes providing a ranking between them in terms of PQ using the corresponding FTPQI. This tool could be used by the system administrator giving a reliable and scientific way to check the total PQ of electric networks in general and at specified points and, also, providing the possibility of ad-hoc network improvement remedies. (6 pages)

Artificial intelligence and expert systems techniques, coupled with historical signal analysis and optimization methods, provide a novel approach to monitoring process dynamics and performance and learning knowledge about how to do a production job better based on past experience. An expert system-based supervisor is proposed to provide such a tool for a simple tank level control problem which adjusts the schedule for supply purchase to attempt to minimize the average cost of operation over time. This research provides a prototype for the applications of expert systems in real-time process supervision and control; however, the result is primitive. The underlying modeling and optimization methods only work for simple situations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The authors demonstrate how a pattern-recognition system can be applied to the interpretation of capacitance-voltage (C-V) curves on an MOS test structure. By intelligently sequencing additional measurements it is possible to accurately extract the maximum amount of information available from C-V and conductance-voltage (G-V) measurements. The expert system described, (CV-EXPERT), is completely integrated with the measurement, instrumentation, and control software and is thus able to call up a sequence of individually tailored tests for the MOS test structure under investigation. The prototype system is able to correctly identify a number of process faults, including a leaky oxide, as shown. Improvements that could be gained from developing rules to coordinate G-V, capacitance-time, and doping profile measurements simply by recognizing the important factors in the initial C-V measurement are illustrated. >

Abstract: On the basis of experience gained in the engineering industry, the present paper describes advantages and drawbacks encountered using the expert system technique. The constraints which real programs are subject to are explained using a configuration system as an example. The criteria which influence the choice of software and hardware are presented. Finally, aspects of the knowledge elicitation task as well as implementation topics are discussed.

There are many environmental applications which rely on the accurate identification of insects, plants, etc. The availability of personnel with sufficient identification and taxonomic skill is often a limitation and the production of identification keys has been one means of alleviating this problem. It is the purpose of this paper to show that expert system techniques can be employed to provide computer based keys that are both simpler to use and easier to construct and maintain. This paper describes the structure, development and use of such an expert system based identification key. Experience indicates that quicker and more accurate identifications are possible and that there are other less obvious benefits. In particular the system can be both easily modified and maintained and that the addition of new knowledge is a relatively simple operation.