This first chapter of Part IV of the book begins a discussion of optimization within the context of its application in modelling and simulation projects. This chapter provides a broad overview of some of the important notions in this area of study. It is noted that the main feature is the specification of a criterion function, J, whose value is dependent on a specified parameter vector, p, of dimension m. The objective of the optimization process is to locate a value for p which yields a minimum value for J (the alternative of seeking a maximum value for J can be accommodated by undertaking the minimization of −J). The simplest case of the problem occurs when the search space (namely the domain of admissible values allowed for p) is the entire space of real valued m-vectors. Constraints on the allowable values for p are common and this understandably introduces complexity in search procedures. A common occurrence within the modelling and simulation context is the case where only integer values are allowed for some, or possibly all, components of p. A common approach for classifying optimization methods relates to their dependence upon the gradient of the criterion function, J. Thus there are gradient dependent methods and heuristic methods; i.e., methods that do not depend of gradient information. Examples of both of these categories are provided.
In this paper, we present a new technique for low resolution face recognition using Hu Li moment invariants. The new technique can handle the issue of low resolution images very efficiently by the virtue of thermal face characteristics. The new technique will be tested on a new database comprising of images of different expressions, and were taken within different time-lapse. Experiments have resulted to almost consistent recognition rates. The proposed technique offers outstanding discriminability and performs efficiently, with an average recognition rate of ~94% over the various resolutions.
Lack of excellent quality culture becomes a deep crisis for most Chinese manufacturing enterprises and the obstacles for the quality improvement of their products. How to cultivate an effective quality culture and manage it well within an organization is a key question challenges the manufacturing enterprises. As there is lack of relative research on this issue, and the results of the only researches have been confined to extensive frameworks, this paper, based on the methodology of Grounded Theory, using three-level coding analysis techniques, analyzes in depth the quality culture management practices of eight representative Chinese manufacturing enterprises, and abstracts from those cases a quality culture management model and its measures. This study also analyzes the functions of the measures in developing different dimensions of quality culture. The results of this paper provide both theoretical and experiential reference for more enterprises facing the quality culture challenges.
Vulnerabilities in both hardware and software have exposed them to the lack of managing programs securely in the computational environment, giving hackers the means to conduct side channel attacks with intention to steal sensitive information, including secret encryption keys. Current techniques enable attackers to exploit vulnerabilities at the micro-architecture level to build side channels. A typical example is the use of the Flush+Reload technique in the Meltdown attack [1]. This paper proposes the detection of malicious loop activities within the Flush+Reload programs through the introduction of a new classification technique. Most current detection models, approach the side channel attacks, by relying on the correlation between attacker and victim programs through the use of machine learning algorithms. This paper differs from such models. It solely analyse the malicious loop activities inside the Flush+Reload attack program and does not seek to synchronise victim and attacker programs. The model proposed has the ability to classify Flush+Reload attacks with a level of accuracy approaching 99% for native and 96% for cloud systems without increasing the cost of detection in a cloud systems above that in native systems.
This paper describes the use of the intelligent cooperative system for autonomous vehicle network to manage the ravel flow in the urban roads. The intelligent cooperative system aims to reduce the amount of traffic congestion in road networks, and their negative effects, such as delays, waiting time, driver stress, air and noise pollution, and the blocking of emergency vehicles. The proposed algorithm is very successful for two reasons. The intelligent cooperative system in autonomous vehicle network uses performance analysis based on statistical measurement error to improve the accuracy of the forecast model, and the ability to communicate with others vehicles to update its local information based on quality of experience (QoE). The term QoE is defined and relates to how end users perceive the quality of an application or service. QoE is a new element that can play an important role in improving road traffic congestion, especially under abnormal conditions. The data collection to assess driver satisfaction was based on a questionnaire given to drivers, on road traffic management, and on channel demand data in base stations.
