In this research, we try to show the major features of prolog which make it
a strong expressive language about writing the expert systems and the
traditional languages lacks them as Pascal language. We also provide expert
system, the purpose of it
is the Inventory Control by apply the Fixed-Order
Quantity Model, and we clearified concept of the static and dynamic data in
Prolog. Finally, we compared between the databases in prolog and some of
their quires with Access and SQL.
Expert systems are considered as one of the main applications of artificial
intelligence, which are known as knowledge based systems. And the expert
systems are computer applications which embody some non-algorithmic
expertise for solving certain types of problems. For example, the problems
which provide advice, analysis, classification, diagnostic, explanation, teaching,
or designing…etc.
we constructed a continuation predictor- corrector algorithm that
solves constrained optimization problems. Smooth penalty functions combined
with numerical continuation, along with the use of the expanded Lagrangian
system, were essential compone
nts of the algorithm. An improvement of this
algorithm was published, which dealt with the linear algebra in the
corrector part of the algorithm.
إن اختبار قابلية قسمة عدد صحيح موجب N أكبر من الواحد، على عدد صحيح موجب Q أكبر من الواحد، يتحدد تبعًا للعدد Q. فلكل عدد Q رائزه الخاص ( ١ ).
لكن هذا البحث فكرة جديدة مبتكرة تهدف إلى إيجاد قاعدة عامة لاختبار قابلية قسمة ( 2 ).
Various methods have been developed to measure the location of physical objects
on a landscape with high positional accuracy. A new method that has been gaining
popularity is the Airborne Light Detection and Ranging (LiDAR). LiDAR works by
scan
ning a landscape (the combination of ground, buildings, vegetation, etc.,) by
multiple passes. In each scan (pass), pulses of laser light are emitted from an
airborne platform and their return time is measured, thus enabling the range from
the point of emission to the landscape to be determined. The product of airborne
laser scanning is a cloud of points located in a 3D space. ALS is capable of
delivering clouds of very dense and accurate points that represent the landscape in a
relatively short time. However, in spite of the ability to measure objects with high
positional accuracy, the automatic detection and interpretation of individual objects
in landscapes remains a challenge. An example of such a challenge is the
classification of the cloud points produced by ALS. The classification of LiDAR
cloud points consists first of all of assigning the points as either object points or
bare ground ones. The points labeled object points are then further classified as
either buildings or vegetation. As a measurement technique, LiDAR is highly
promising, research has been conducted here to automate the detection of bare
ground, buildings and vegetation in LiDAR cloud points.
In this Research, we describe a new automated scheme that utilizes the so-called
“Edge Topology based Iterative Segmentation” (ETIS) model to classify the
LiDAR points as ground and objects points. First ground seed points based on
edges topology are to be selected and then the initial DTM is to be constructed, the
second step is an iterative densification of the DTM using a cloud point
segmentation method based on local slope parameter. General ground point
filtering parameters have been used was achieved in this method, instead of scene-
wise optimization of the parameters, in a way that many groups of benchmark
datasets have been without changing the thresholds values. Data provided by the
International Society for Photogrammetry and Remote Sensing (ISPRS)
commission, have been used to compare the performance of ETIS. The new method
is also tested against the 16 other publicized filtering methods. The results indicat
that the proposed method is capable of producing a high fidelity terrain model.
