21,159 research outputs found
Development of accident prediction model by using artificial neural network (ANN)
Statistical or crash prediction model have frequently been used in highway
safety studies. They can be used in identify major contributing factors or establish
relationship between crashes and explanatory accident variables. The
measurements to prevent accident are from the speed reduction, widening the
roads, speed enforcement, or construct the road divider, or other else. Therefore,
the purpose of this study is to develop an accident prediction model at federal road
FT 050 Batu Pahat to Kluang. The study process involves the identification of
accident blackspot locations, establishment of general patterns of accident, analysis
of the factors involved, site studies, and development of accident prediction model
using Artificial Neural Network (ANN) applied software which named
NeuroShell2. The significant of the variables that are selected from these accident
factors are checked to ensure the developed model can give a good prediction
results. The performance of neural network is evaluated by using the Mean
Absolute Percentage Error (MAPE). The study result showed that the best neural
network for accident prediction model at federal road FT 050 is 4-10-1 with 0.1
learning rate and 0.2 momentum rate. This network model contains the lowest
value of MAPE and highest value of linear correlation, r which is 0.8986. This
study has established the accident point weightage as the rank of the blackspot
section by kilometer along the FT 050 road (km 1 – km 103). Several main
accident factors also have been determined along this road, and after all the data
gained, it has successfully analyzed by using artificial neural network
Pembangunan dan penilaian modul berbantukan komputer bagi subjek pemasaran : Politeknik Port Dickson
Kajian ini bertujuan membangunkan Modul Berbantukan Komputer (MBK) bagi
subjek Pemasaran. MBK ini dibangunkan dengan menggunakan pensian AutoPlay
Media dan Flash MX. Sampel kajian ini terdiri daripada 30 orang pelajar Diploma
Pemasaran di Politeknik Port Dickson. Data dikumpulkan melalui kaedah soal
selidik dan dianalisis berdasarkan kekerpan, peratusan dan skor min dengan
menggunakan perisian Statistical Package For Social Sciene (SPSS) versi 11.0.
Dapatan kajian menunjukkan penilaian terhadap pembagunan MBK di dalam proses
P&P adalah tinggi. Ini bermakna MBK ini sesuai digunakan di Politeknik Port
Dickson di dalam proses P&P
A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning
Real world combinatorial optimization problems such as scheduling are
typically too complex to solve with exact methods. Additionally, the problems
often have to observe vaguely specified constraints of different importance,
the available data may be uncertain, and compromises between antagonistic
criteria may be necessary. We present a combination of approximate reasoning
based constraints and iterative optimization based heuristics that help to
model and solve such problems in a framework of C++ software libraries called
StarFLIP++. While initially developed to schedule continuous caster units in
steel plants, we present in this paper results from reusing the library
components in a shift scheduling system for the workforce of an industrial
production plant.Comment: 33 pages, 9 figures; for a project overview see
http://www.dbai.tuwien.ac.at/proj/StarFLIP
Hierarchical fuzzy logic based approach for object tracking
In this paper a novel tracking approach based on fuzzy concepts is introduced. A methodology for both single and multiple object tracking is presented. The aim of this methodology is to use these concepts as a tool to, while maintaining the needed accuracy, reduce the complexity usually involved in object tracking problems. Several dynamic fuzzy sets are constructed according to both kinematic and non-kinematic properties that distinguish the object to be tracked. Meanwhile kinematic related fuzzy sets model the object's motion pattern, the non-kinematic fuzzy sets model the object's appearance. The tracking task is performed through the fusion of these fuzzy models by means of an inference engine. This way, object detection and matching steps are performed exclusively using inference rules on fuzzy sets. In the multiple object methodology, each object is associated with a confidence degree and a hierarchical implementation is performed based on that confidence degree.info:eu-repo/semantics/publishedVersio
Hierarchical distributed model predictive control based on fuzzy negotiation
This work presents a hierarchical distributed model predictive control approach for multiple agents with cooperative negotiations based on fuzzy inference. Specifically, a fuzzy-based two-layer control architecture is proposed. In the lower control layer, there are pairwise negotiations between agents according to the couplings and the communication network. The resulting pairwise control sequences are sent to a coordinator in the upper control layer, which merges them to compute the final ones. Furthermore, conditions to guarantee feasibility and stability in the closed-loop system are provided. The proposed control algorithm has been tested on an eightcoupled tank plant via simulation
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
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