[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

This work proposes an innovative infrastructure of secure scenario which operates in a wireless-mobile 6G network for managing big data (BD) on smart buildings (SBs). Count on the rapid growth of telecommunication field new challenges arise. Furthermore, a new type of wireless network infrastructure, the sixth generation (6G), provides all the benefits of its past versions and also improves some issues which its predecessors had. In addition, relative technologies to the telecommunications filed, such as Internet of Things, cloud computing (CC) and edge computing (EC), can operate through a 6G wireless network. Take into account all these, we propose a scenario that try to combine the functions of the Internet of Things with CC, EC and BD in order to achieve a Smart and Secure environment. The major purpose of this work is to create a novel and secure cache decision system (CDS) in a wireless network that operates over an SB, which will offer the users safer and efficient environment for browsing the Internet, sharing and managing large-scale data in the fog. This CDS consisted of two types of servers, one cloud server and one edge server. In order to come up with our proposal, we study related cache scenarios systems which are listed, presented, and compared in this work.

IoT-Based Big Data Secure Management in the Fog Over a 6G Wireless Network

People’s need for healthcare capacity has become increasingly critical as the elderly population continues to grow in most communities. Approximately 25–47% of seniors fall annually, and early detection of poor balance can significantly reduce their risk. Automated fall detection with big data analytics is key to maintaining the safety of the elderly in smart cities. Visible image systems (VIS) in smart buildings, on the other hand, visible image systems (VIS) in smart buildings may compromise the privacy of seniors by enabling technologies for intelligent big data analytics (IBDA). Thermal imaging (TI) is less obtrusive than visual imaging and can be used in combination with machine vision to perform a wide range of IBDAs. In this study, we present a novel two-step method for detecting falls in TI frames using deep learning (DL). As the first step, tracking tools are used to locate people’s locations. A novel modified deep transfer learning (TL) technique is used to classify the trajectory created by the tracking approach for people who are at risk of falling. Fall detection by the IBDA will be connected to the Internet of medical things (IoMT) and used as smart technology in the process of big data-assisted pervasive surveillance and health analytics. According to an analysis of the publicly available thermal fall dataset, our method outperforms traditional fall detection methods, with an average error of less than 3%. Additionally, IoMT platforms facilitate data processing, real-time monitoring and healthcare management. Our smart scheme for using big data analytics to enable intelligent decisions is compatible with the various spaces and provides a comfortable and safe environment for current and future elderly people. 

Deep Transfer Learning-Based Fall Detection Approach Using IoMT-Enabled Thermal Imaging-Assisted Pervasive Surveillance and Big Health Data

The considerable move towards the use of renewable energy resources has been provided by the digitization of energy systems with the help of virtual power plants (VPPs). However, due to the coincidence of this move with the introduction of new technologies in information and communications, joining these systems raises concerns about the privacy of personal data. The only real-world approach widely used in this case is to anonymize or pseudonymize the information associated with individuals in data received from distributed measurement devices. In this paper, we propose the method of classifying received data packets into different flows and assigning different access levels for each flow. This method makes data pseudonymous. Before this step, the received data, which has a different format, is unionized. To implement this idea, a tuple space flow classification algorithm is parallelized on a CPU cluster using MPI and OpenMP according to different scenarios. The CPU cluster consists of one head node and two computational nodes for packet classification operations. In this research, two scenarios have been used to run the CPU algorithm in parallel. The first scenario uses MPI and the second scenario uses a combination of MPI and OpenMP libraries. Also, the Tuple Space algorithm has been implemented on the computing systems using the mentioned libraries in the form of two scenarios using OpenMP and MPI. According to our results, the increase in the number of processor cores is linearly correlated with the increase in the speed of classification. Furthermore, while MPI uses more memory than OpenMP, it helps to achieve a higher speed of classification. In the combined method, the maximum speed of flow classification can be achieved if the number of processes and threads is equal to the number of processor cores. In other words, when the sum of processes and threads does not outnumber CPU cores, the least classification time and memory usage can be achieved. 

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High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

Proposing a Constrained-GSA for the Vehicle Routing Problem

Voice transmission over wireless edge networks is a conventional, cost-effective technology to transmit voice calls over 802.11 edge networks. In this type of service, Session Initiating Protocol (SIP) is responsible for initiating sessions. Research has shown that the call control algorithm that controls SIP sessions can also guarantee the quality of the medium during audio data transmission. In spite of decision-making in wireless access points and changing the parameters of the medium access sublayer (MAC), the existing algorithms are complicated and cannot adequately ensure service quality and efficient use of system resources. Therefore, the purpose of this study is to propose a new algorithm that enables a SIP server to decide to admit or reject an incoming call, in concise time. This algorithm considers the dynamic parameters of the network such as the location of node, channel busyness rate and real-time traffic percentage of the channel without changing the settings of MAC sublayer. The location of each system depending on the access point that transmits the call has a great impact on how the required service is provided. Implementation of this location-aware method on a real network testbed is indicative of remarkable superiority over the recently proposed methods in terms of service quality as measured by parameters, such as response time, call duration, loss-rate, and delay in real-time packets.

Mohammad Reza Khosravi

Papers

Deep Transfer Learning-Based Fall Detection Approach Using IoMT-Enabled Thermal Imaging-Assisted Pervasive Surveillance and Big Health Data

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

Proposing a Constrained-GSA for the Vehicle Routing Problem

Efficient resource-aware control on SIP servers in 802.11n wireless edge networks

Improving the Scientific Influence of International Journals: A Guideline for Guest Editors of Current Medical Imaging Reviews