Passenger Flow Prediction Based on Newly Adopted Algorithms

Passenger flow forecasting is an essential part of transportation systems. Neural networks in the transportation field have been applied to passenger demand prediction. In this paper, we developed two hybrid methods, known as parlimentary optimization algorithm-artificial neural network (POA-ANN), and intelligent water drops algorithm-ANN (IWD algorithm-ANN). In addition, we applied the proposed algorithms to illustrate the effect of precise prediction for passenger queues. We mainly focus on predicting passenger demand by comparing the genetic algorithm-ANN (GA-ANN) with POA-ANN and IWD-ANN. The results of prediction methods suggest that both POA-ANN and IWD-ANN provide a better forecasting performance, which is obtained via mean square error (MSE), than GA-ANN in the field of passenger flow prediction. This study illustrates that the newly adopted algorithms exhibit good performance for passenger prediction.     

The effect of additives on the oxidation stability of biodiesel

In the present study, it was aimed to examine the degradation of biodiesel and the changes that occur in the cold flow characteristics of biodiesel obtained from safflower oil as a result of the modification of the production process steps that leads to a state in which the process is not realized completely despite the addition of antioxidant additives that stabilize the cold flow characteristics of the fuel and the effects of the modification of the production process steps on fuel characteristics. The study is focused on the modification of the production process. Some samples showed very rapid decomposition in the first 3 hours but the rest took nearly 3 days to reach 200 microsiemens. But then the derivative this value due to the rate of oxidation 0.04 h showed.     

Using RFID for the management of pharmaceutical inventory — system optimization and shrinkage control

Motivated by a case study at a radiology practice, we analyze the incremental benefits of RFID technology over barcodes for managing pharmaceutical inventories. Unlike barcode technology, RFID enables accurate real-time visibility, which in turn enables several process improvements. We analyze the impact of automatic counting and discuss the system redesign critical to optimizing the inventory policy and eliminating shrinkage. We show that continuous review is superior to periodic review whenever accurate real-time information is available at no additional cost. We explain how RFID-enabled strategies vary with inventory parameters and provide a cost-benefit analysis for the implementation of RFID for the radiology practice.     

The effect of inappropriate dead time settings on the regulation of unevenness of material implemented by the automatic control: an application in the textile spinning industry

In this paper, we analyze the efficiency of regulation of unevenness material implemented by automatic control in case of inappropriate dead time settings. In this analysis, we consider various degrees of inaccurate dead time settings, different sizes, and occurring frequencies of the faults on the material. The results obtained indicate that in some cases, the regulation can still continue even in case of quite high degree of inaccurate dead time settings. However, the results obtained indicate also that the evenness of material after the regulation process can be worse than before regulation process in some cases. The work is carried out using simulation methodology and by considering the autoleveler equipment used as an automatic control system in the textile industry.     

An adaptive normal estimation method for scanned point clouds with sharp features

Normal estimation is an essential task for scanned point clouds in various CAD/CAM applications. Many existing methods are unable to reliably estimate normals for points around sharp features since the neighborhood employed for the normal estimation would enclose points belonging to different surface patches across the sharp feature. To address this challenging issue, a robust normal estimation method is developed in order to effectively establish a proper neighborhood for each point in the scanned point cloud. In particular, for a point near sharp features, an anisotropic neighborhood is formed to only enclose neighboring points located on the same surface patch as the point. Neighboring points on the other surface patches are discarded. The developed method has been demonstrated to be robust towards noise and outliers in the scanned point cloud and capable of dealing with sparse point clouds. Some parameters are involved in the developed method. An automatic procedure is devised to adaptively evaluate the values of these parameters according to the varying local geometry. Numerous case studies using both synthetic and measured point cloud data have been carried out to compare the reliability and robustness of the proposed method against various existing methods.     

On a multivariable extension of Jacobi matrix polynomials

The classical Jacobi matrix polynomials only for commutative matrices were first studied by Defez et al. [E. Defez, L. Jódar, A. Law. Jacobi matrix differential equation, polynomial solutions and their properties, Comput. Math. Appl. 48 (2004) 789–803]. The main aim of this paper is to construct a multivariable extension with the help of the classical Jacobi matrix polynomials (JMPs). Generating matrix functions and recurrence relations satisfied by these multivariable matrix polynomials are derived. Furthermore, general families of multilinear and multilateral generating matrix functions are obtained and their applications are presented.     

Type-2 fuzzy model based controller design for neutralization processes

In this study, an inverse controller based on a type-2 fuzzy model control design strategy is introduced and this main controller is embedded within an internal model control structure. Then, the overall proposed control structure is implemented in a pH neutralization experimental setup. The inverse fuzzy control signal generation is handled as an optimization problem and solved at each sampling time in an online manner. Although, inverse fuzzy model controllers may produce perfect control in perfect model match case and/or non-existence of disturbances, this open loop control would not be sufficient in the case of modeling mismatches or disturbances. Therefore, an internal model control structure is proposed to compensate these errors in order to overcome this deficiency where the basic controller is an inverse type-2 fuzzy model. This feature improves the closed-loop performance to disturbance rejection as shown through the real-time control of the pH neutralization process. Experimental results demonstrate the superiority of the inverse type-2 fuzzy model controller structure compared to the inverse type-1 fuzzy model controller and conventional control structures.     

An intelligent process planning system for prismatic parts using STEP features

This paper presents an intelligent process planning system using STEP features (ST-FeatCAPP) for prismatic parts. The system maps a STEP AP224 XML data file, without using a complex feature recognition process, and produces the corresponding machining operations to generate the process plan and corresponding STEP-NC in XML format. It carries out several stages of process planning such as operations selection, tool selection, machining parameters determination, machine tools selection and setup planning. A hybrid approach of most recent techniques (neural networks, fuzzy logic and rule-based) of artificial intelligence is used as the inference engine of the developed system. An object-oriented approach is used in the definition and implementation of the system. An example part is tested and the corresponding process plan is presented to demonstrate and verify the proposed CAPP system. The paper thus suggests a new feature-based intelligent CAPP system for avoiding complex feature recognition and knowledge acquisition problems.