The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system

Mode choice modeling is probably the most important element of transportation planning. It affects the general efficiency of travel and the allocation of resources. The development of mode choice models has recently witnessed significant advances in many fields, such as passenger and freight transport. A large number of mathematical models have been used to model the traveler’s choice of mode and destination and the shipper’s choice of mode, shipment size and supply market, among others. Such models are not only becoming almost intractable but also data intensive, difficult to calibrate and update, and intransferable. These models cover a wide range of mathematical complexity and accuracy. This paper describes a new approach to mode choice of intercity freight transport modeling using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating non-linear relationships among different variables. The adaptive neuro-fuzzy inference system model is tested on the freight transport market in Turkey, Germany, France and Austria by using information on the freight flows and their attributes. The ANNs and ANFIS models are more successful in the representation of the non-linear behavior of mode choice of intercity freight transport compared to the classical models.     

An Objective Deghosting Quality Metric for HDR Images

Reconstructing high dynamic range (HDR) images of a complex scene involving moving objects and dynamic backgrounds is prone to artifacts. A large number of methods have been proposed that attempt to alleviate these artifacts, known as HDR deghosting algorithms. Currently, the quality of these algorithms are judged by subjective evaluations, which are tedious to conduct and get quickly outdated as new algorithms are proposed on a rapid basis. In this paper, we propose an objective metric which aims to simplify this process. Our metric takes a stack of input exposures and the deghosting result and produces a set of artifact maps for different types of artifacts. These artifact maps can be combined to yield a single quality score. We performed a subjective experiment involving 52 subjects and 16 different scenes to validate the agreement of our quality scores with subjective judgements and observed a concordance of almost 80%. Our metric also enables a novel application that we call as hybrid deghosting, in which the output of different deghosting algorithms are combined to obtain a superior deghosting result.     

An integrated approach to concept evaluation in a new product development

A new product development (NPD) process can be thought as a comprehensive process in which the design is progressively detailed through a series of phases. At the end of each phase a design review is held to approve the design and release or not it to the next level. As one of these phases, concept selection aiming to select the most appropriate concept for further development, is conducted earlier in the process. As the further development progresses on a selected concept, it becomes more difficult to make design changes in terms of cost and schedule dimensions, and therefore, selecting the best concept among a set of available alternatives has been an important issue for companies. On the other hand, in the presence of many alternatives and selection criteria, the selection problem becomes a multiple-criteria decision making concept selection problem. To solve this problem, in this work, an integrated approach bringing two popular methods together: the modified technique for order preference by similarity to ideal solution (TOPSIS) and the analytical network process (ANP). The ANP method is used to determine the relative weights of a set of quantitative and qualitative evaluation criteria, as the modified TOPSIS method utilized to rank competing concept alternatives. In addition, a real example is presented to demonstrate the effectiveness and applicability of the proposed approach for potential practitioners and readers.     

Mobile multi-view object image search

High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.     

A comprehensive study on mid-level representation and ensemble learning for emotional analysis of video material

Multimedia Tools and Applications - In today’s society where audio-visual content such as professionally edited and user-generated videos is ubiquitous, automatic analysis of this content is...     

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.     

Quantum chemical studies on protonation of some substituted thiazole derivatives

The acidity constants, pK(a) values for protonation of some substituted thiazole derivatives were calculated by using AM1 and PM3 basis sets of semi-empirical methods and B3LYP/6-31G(d) basis sets of density functional theory (DFT) calculated physical and thermodynamic parameters. Correlation search among the experimental and calculated acidity constants, pK(a) values, revealed that the best correlation exist between the experimental and ab initio calculated pK(a) values with a regression of R(2)=0.98.     

Efficient storage of healthcare data in XML-based smart cards

In this paper we present storage structures for efficiently processing XML path queries on healthcare data stored in smart cards using XML format. In the design of the storage structures that we present, properties of healthcare data and fundamental types of queries on the healthcare data are taken into account as well as the constraints of the smart cards. We compare the space and time efficiency of the proposed storage structures for various types of queries.     

基于COM与N层B/S结构的SNMP网管研究与实现*

分析了传统单一C/S结构的不足以及新型N层B/S结构的优越性,阐述了基于Web技术的网络管理是未来网络管理的发展趋势,结合现代软件开发中的软件复用理论,提出并设计实现了一种基于COM技术与N层B/S结构的SNMP网管系统模型,着重叙述了该系统中所使用的关键技术与设计要点。     

快速移植的微型呼叫中心设计

介绍了如何构建微型呼叫中心的系统设计及组成,给出了相应的系统结构,并对微型呼叫中心核心部分的设计进行了描述。该设计方案的优点是投资少,软件开发和扩展均较容易,实施快速,特别适合接入线路不多的微型呼叫中心。