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.
In this paper, a novel approach for selection of relevant features in SAR-ATR is proposed. The main concern of all studies in this filed is the accuracy. For this reason, many researchers have worked on feature extraction phase. Just a few studies focus on feature selection stage. The goal of working on feature selection is twofold. Firstly, the dimensionality of feature space can be reduced and secondary the accuracy can be further improved by eliminating the redundant features. Random Forest is the technique that can be easily implemented over the alternative algorithms such as Genetic Algorithms to SAR-ATR. The easy and fast implementation are the main advantages over the alternative methods. The experimental results show that by selecting just a few features, the accuracy is reaches to saturation.
Many companies are making the move to a cloudbased environment for data storage and management. Having their data in the cloud has many benefits in that it may help the company move forward and innovate. The embracing of cloud-based services by corporates, businesses, and people, has helped to usher in a paradigm shift in the people-data-service relationship. While most steps in the evolution of technology center around "development" and "productivity", given the changing scenario of threats and cybercrime, security in the cloud needs to be thoroughly analyzed, researched, and mitigated, even if it cannot be completely eliminated or avoided. However, cloud-based environments are not completely safe from attacks. Criminals are always looking for ways to make money through malicious activity. Cyber Security is already one of the fastest growing fields in the modern world and the number of incidents that occur on a daily basis are continuing evidence of its necessity. Systems security of all types have been addressed in similar ways but cloud-base environments offer quite a few unique threats that force professionals to become creative when preparing mitigation techniques. This paper introduce a cloud computing security analysis survey where we list out some of the grave security threats that the Workload Distribution and Resource Pooling Architecture in Cloud Systems model faces, and some mitigation techniques to encounter them.
Predictive maintenance is very important towards industrial economy by improving equipment efficiency, reliability and reducing downtime. In recent years, abundant of data of rotating equipment is readily available from various sources. However, these data are not being utilized and analyzed for improving maintenance performance. This requires advanced techniques to analyze a variety of data in order to transform into relevant information. Most problems with a lot of parameters involved were not being specific to analyze the contribution of motor failure. Therefore, this research proposed an efficient data analysis using Principle Component Analysis (PCA) in determining the most influential factor to the failure of the industrial motor. The result will show the parameters that influence the motor failure. This finding can be used as a guideline for predictive maintenance in order to mitigate the risk of the plant shutdown.
Mergers between mobile network operators involve merging their respective networks. Though this may represent a chance to optimize the network structure, merging may not represent the cost-optimal solution. In this paper, we compare two different evolution paths, where the networks to be merged are separately upgraded to cover the whole traffic demand or a single network is optimized as the result of the merger (Build vs Merge). Our preliminary analyses show that the Merge approach may lead to 50% higher costs, due in particular to the high costs borne to switch off redundant access points. Any Build vs Merge decision should therefore consider the sunk costs due to the inherited networks, as well as the possible benefits associated to a merger.
When very detailed simulation of geometrically complex objects is needed, finite element models are often the best choice due to their description of spatial and temporal behavior of the modelled object. The drawback of finite element models is their large number of parameters. This makes it difficult to match a model to a real measured object by process identification methods. In this paper this problem is addressed by new software that is compatible to ANSYS as the probably most known finite element software.
Measurement of consistency in the decisions made by observers or raters is an important problem in clinical medicine. Chance corrected agreement coefficients such as the Cohen and Fleiss Kappas are commonly used for this purpose, though the way that they estimate the probability of agreement ' by chance' has been strongly questioned. Alternatives have been proposed, such as the Aickin Alpha coefficient and the Gwet AC(1) and AC(2) coefficients which are gaining currency. A well known paradox illustrates deficiencies of the Kappa coefficients which, it is claimed, are remedied by an approach which grades the subjects according to their probability of being hard to score. The AC(1) and AC(2) coefficients result from the application of this grading to the Brennan-Prediger coefficient which may be considered a simplified form of Kappa. This paper questions the rationale of the hardness probability used by AC(1) and proposes an alternative approach that may be applied to weighted and unweighted multi-rater Cohen and Fleiss Kappas and also Intra-Class Correlation (ICC) coefficients.
Object detection and classification in Artificial Neural Networks (ANN) can play an important part in finding solutions for various tasks that are considered critical or time consuming if executed by humans. Also, object detection can be used in several applications and domains. One of which is physical security, where many illegal items, that should not pass through checkpoints, exist. In this sense, smart X-ray scanners can help in detecting any restricted object like weapons, knives or even drugs within a bag or held by an individual. This paper aims to provide a first step towards smart scanners where the MultiLayer Perceptron (MLP) is used to classify and detect two different types of illegal objects. Moreover, training of the network is performed using the Back Propagation algorithm, one of the most widely used algorithms in the MLP.
Recent interest in the optical behavior of bulk semiconductor based alloy heterostructures [1-3] has led to this work that simulates the effect of optical illumination on the DC parameters of 100 nm InAlAs/InGaAs High Electron Mobility Transistor (HEMT). This paper uses a C-interpreter function in the Silvaco Device Simulator that gives the flexibility of defining the photogeneration rate as a position/time dependent function. The modeling of photogeneration rate as a function of position in the device helps us to analyze the effect of illumination in the different regions of the device and its impact on the device parameters. The simulation results suggest an improvement in the device performance under illumination predicting possible applications in high frequency optoelectronics.
Scheduling is a process that allocates resources to the competing tasks. The distribution of the resources to the various tasks forms a job. The aggregation of the tasks within the job, determine the optimal utilization of the resources and minimal completion time and costs. This paper demonstrates the scheduling problem in the grid applications, considering single-grid and multi-grid environments. Petri Net is used for modeling the system, and the models are implemented with the GPenSIM tool. Also, this paper presents an algorithm for scheduling in multi-grid, in which the modules give the right to each other to use one others resources without causing delays. The novelty of this algorithm is that while it is easy to implement, it is also efficient as it minimizes the total processing time of the jobs.
The public safety network adopted the long term evolution(LTE) to overcome bandwidth limitations and service quality. In disaster, the mission critical push to talk(MCPTT) service may become slow due to LTE traffic capacity limitations. There is a need to change the service priority in real time according to the MCPTT priority. We propose that the dynamic resource scheduling algorithms according to service priority. LTE radio resources could be allocated by MCPTT user service priority. The result of algorithms is increased 75% download speed than previous method.
A capsule is a combination of multiple neurons, which designed to analyze specific feature representations in an image, a capsule network resembles with CNN multiple layer network model, in which convolutional and ReLu function is followed by max pooling which help in reducing the input values for processing. However, we observed that for each capsule in layer l to layer (l + 1), we need to calculate a prediction vector, whose magnitude defines the detected feature in an image and the orientation define the pose of the object in an image. Now calculating the prediction vector for each capsule in the lower layer is prolonged. This is because, in capsule network, we are not securing the gained knowledge for future use. In this paper, we are introducing a protocol named as ICRP (Inter Capsule Routing Protocol), which uses FDM (Feature Detection Matrix) to store the learned information at every iteration in the capsule network. This reduces the processing time for feature detection in an image gradually, by searching the similar feature in FDM before processing the network.
Photogrammetry based 3D model creation provides a new opportunty to survey and to evaluate natural and arificial surface formations. The photos needed for 3D model building can be taken using fixed wing aircraft or multicopters. These machines are more economical and are capable of more precise route tracking than big machines operated by human personnel. Depending on the size of the area (0.5 to 10km 2 ), the survey can be finished within an hour. The volume calculation method is introduced in the paper through the example of an actual, operating mine. Previous, mainly manual but competent analytical tasks, which demand engineering work, can be partly replaced agorithmically. The method may provide more accurate volume results than a conventional geodetic survey, as it builds a model with 10 cm resolution of any examined area or object. Another advantage is that the results are ready within a few hours after beginning the survey, it is not necessary to wait days for accurate volume data.
This paper discusses the problems faced by the organisations who are running domain specific 4GL systems to deploy their core business logic. Given the fact that it is often not realistic to find new engineers for these not-widespread languages, the paper proposes a method to extract useful artefacts from 4GL systems which have the data stored in XML like format such as Uniface system. In this work, the authors show how to use Encapsulated Document Object Model to read Uniface XML and scan the content to extract the custom code. In addition, this paper introduces how to restore the code schema and visualise it.
the revolution of the Internet of Things (IoT) will change our perception of computing moving into the next decade, simplifying everyday tasks, to a simple tap on a smart device. This paper will primarily look at a Smart University utilising automation and IoT to improve its sustainability and use for Students and Staff. Research areas to be considered; students count, environment management i.e. lights, blinds, and temperature, this paper will identify some currently available products and software available on the market which could make this possible. A consideration towards the future scalability of the smart university and to the potential of automating other areas will also be discussed. In addition to the research, a model of a smart university will be demonstrated using network simulation software known as QualNet. This will show how the network performs under simulation conditions.
In cancer classification, selection of genes that highly contribute to the classification process becomes essential due to the problem of 'curse of dimensionality' associated with microarray based gene expression data. Biogeography-Based Optimization (BBO) is a population based evolutionary computation technique successfully applied to many application domains and proved to deliver optimal solutions. This work proposes a gene selection method named as BBBOFS, by applying Binary Biogeography-Based Optimization (BBBO). The selected genes are used for cancer classification using the Artificial Neural Network (ANN) classifier. The proposed method is validated through experiments on standard gene expression dataset benchmarks. Results demonstrate that the proposed method is better than the related works from literature in terms of classification accuracy and selected gene count.
