An Intelligent User Friendly Interface for a Database Design Expert System

1989 ◽  
pp. 93-96
Author(s):  
J. Moses
Keyword(s):  
Expert System ◽  
Database Design ◽  
Design Expert ◽  
User Friendly

scholarly journals Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

2021 ◽  
Vol 13 (9) ◽  
pp. 4640
Author(s):  
Seung-Yeoun Choi ◽  
Sean-Hay Kim
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
High Performance ◽  
Problem Definition ◽  
Design Expert ◽  
Decision Support Process ◽  
High Performance Building

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

A generalized expert system for database design

1989 ◽  
Vol 15 (4) ◽  
pp. 479-491 ◽  
Author(s):  
A. Dogac ◽  
B. Yuruten ◽  
S. Spaccapietra
Keyword(s):  
Expert System ◽  
Database Design

scholarly journals Development of a Web Based Expert System for Rubber Crop Disease Diagnosis and Management

2018 ◽  
Vol 10 (3) ◽  
pp. 239-248
Author(s):  
S. Konyeha ◽  
F. A. Imouokhome
Keyword(s):  
Expert System ◽  
Disease Diagnosis ◽  
Field Extension ◽  
Rule Base ◽  
Destructive Effect ◽  
Web Based ◽  
Procedural Code ◽  
Base Engine ◽  
Crop Disease ◽  
User Friendly

An expert system imitates the decision–making adeptness of a human expert. They are intended to answer complicated questions characterized mainly as if–then rules instead of traditional procedural code. A major problem facing the cultivation of rubber (Hevea brasiliensis) in developing countries is the destructive effect of pathogens which result in about fifty percent (50%) loss in crop yield. This problem persists, due to a communication gap between agricultural researchers, such that field extension officers, and farmers are hampered by various operational and logistic challenges. This paper is an effort to bridge this gap, and hence features an expert system that can be accessed online by farmers.  The expert system allows farmers to select disease symptoms presented in categories from a JAVA based user friendly graphical interface. The output generated by the rule–base engine, diagnoses the diseases of the rubber crop, and suggests curative and preventive measures. The main source of information for developing the expert system’ knowledge–base was the Rubber Research Institute, Iyanomo, Edo State, Nigeria.

scholarly journals Gaudii: An Automated Graphic Design Expert System

2021 ◽  
Vol 24 (2) ◽  
pp. 1775-1780
Author(s):  
Carlos Glez-Morcillo ◽  
Victor Martin ◽  
David Vallejo Fernandez ◽  
Jose Castro-Schez ◽  
Javier Albusac
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Evolutionary Computation ◽  
Graphic Design ◽  
Expert Knowledge ◽  
Trial And Error ◽  
Design Elements ◽  
Design Expert ◽  
Novel Approach ◽  
Design Activities

Graphic design is the process of creating graphics to meet specific commercial needs based on knowledge of layout principles and esthetic concepts. This is usually an iterative trial and error process which requires a lot of time even for expert designers. This expert knowledge can be modelled, represented and used by a computer to perform design activities. This paper describes a novel approach named Gaudii (standing for "Intelligent Automated Graphic Design Generator") which utilizes principles and techniques known from the fields of Evolutionary Computation and Fuzzy Logic to automatically obtain design elements. Experimental results that demonstrate the potential of the proposed approach are presented in the area of poster design.

scholarly journals MODEL PENUNJANG KEPUTUSAN INVESTASI BAGI PENGEMBANGAN AGROINDUSTRI PERIKANAN

2016 ◽  
Vol 13 (3) ◽  
Author(s):  
Bambang Herry Purnomo
Keyword(s):  
Expert System ◽  
Fuzzy Expert System ◽  
Interface Model ◽  
User Friendly

Investasi mempunyai peranan penting dalam menumbuhkembangkan agroindustri perikanan. Model penunjang keputusan investasi diperlukan dalam rangka membantu pemerintah daerah mengambil keputusan investasi agroindustri perikanan secara cepat dan tepat. Kajian ini bertujuan merancangbangun Sistem Manajemen Ahli untuk menunjang keputusan investasi dengan menggunakan pendekatan wilayah bagi pengembangan agroindustri perikanan dan kelembagaannya.  Sistem terdiri dari 6 model, yaitu (1)  fuzzy expert system untuk menentukan wilayah pengembangan, (2)  model untuk menentukan komoditas perikanan unggulan, (3) model  untuk menentukan produk unggulan, (4) model untuk menentukan potensi bahan baku, (5) model   analisis kelayakan finansial proyek agroindustri, dan (6) model untuk menentukan bentuk kelembagaan bagi pengembangan usaha kecil perikanan. Verifikasi model dilakukan di Kabupaten Tuban, Jawa Timur. Contoh hasil verifikasi menunjukkan bahwa wilayah pengembangan yang terpilih adalah Kecamatan Palang dengan komoditas utamanya ikan teri dan produk unggulannya ikan teri asin. Potensi bahan baku ikan teri pada tahun 2010 yang dapat dimanfaatkan oleh proyek agroindustri di wilayah ini adalah 1067 ton. Kajian lebih lanjut yang disarankan adalah penggunaan model dinamis untuk menduga produksi ikan teri dan pengembangan interface model penunjang keputusan yang lebih fleksibel dan user friendly.

Constraint Absorption and Relaxation Using a Design History

1990 ◽  
Author(s):  
Edmund J. Meehan ◽  
David C. Brown
Keyword(s):  
Expert System ◽  
Design History ◽  
Design Constraints ◽  
Limited Ability ◽  
Design Expert ◽  
Redundant Constraints ◽  
Simple Mechanism ◽  
Routine Design

Abstract A simple mechanism has been implemented and demonstrated within a routine design expert system. It has a limited ability to adjust design constraints based on experience within the domain. The mechanism is activated in response to noticing an extended run of either constraint failures or constraint successes. It reasons about the validity of either relaxing or absorbing a constraint. This process is referred to as Constraint Absorption. We hope to improve the performance of the expert system by reducing the incidence of repeated constraint failure, and by eliminating redundant constraints.

USER-FRIENDLY EXPERT SYSTEM FOR TURBINE/GENERATOR VIBRATION DIAGNOSIS

1990 ◽  
pp. 63-68
Author(s):  
S. Matsumura ◽  
K. Kawai ◽  
A. Kamiya ◽  
T. Koi ◽  
K. Momoeda
Keyword(s):  
Expert System ◽  
Turbine Generator ◽  
User Friendly

An expert system for pneumatic design

1998 ◽  
Vol 12 (1) ◽  
pp. 3-11 ◽  
Author(s):  
HEUNG-YEOUL SHIN ◽  
JAE-WON LEE
Keyword(s):  
Expert System ◽  
Image Data ◽  
Work Load ◽  
Pneumatic System ◽  
Design System ◽  
Design Expert ◽  
Reasoning Strategy ◽  
Design Requirements ◽  
System Prototype ◽  
Configuration Information

One of the biggest problems in pneumatic system design is over design. Thus, the results are excessive in costs of the initial investment and it requires too much energy. This article describes the development of expert system based pneumatic design system, PNEUDES (PNEUmatic Design Expert System), prototype that enables the user the optimal design of pneumatic system. Once the design requirements such as cylinder type and work load, etc. are input to the system, optimal cylinder specifications with standardized order-based size, valves types, and necessary accessories are all determined. Also the configuration information such as the connectivity among components and cylinder image data are supplied to the user. It can also help the novice of pneumatic design. The rule-based reasoning approach is used as a reasoning strategy with Intelligent Rule Element shell.

Two-Way Slab Design Expert System

1999 ◽  
Vol 4 (3) ◽  
pp. 122-123
Author(s):  
Anatol Longinow ◽  
Y. L. Mo ◽  
K. H. Shih
Keyword(s):  
Expert System ◽  
Design Expert
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