Presents Performance Analysis Research Articles

Evaluating Dimensionality Reduction Methods for the Detection of Industrial IoT Attacks in Edge Computing

Edge computing is essential for 6G mobile networks for improving reliability, reducing data rates and latency, and enhancing mobile connectivity. Edge computing is also meant to meet the increasing demands of the Internet of Things (IoT)/ Internet of Everything (IoE). In these approaches, IIoT systems necessitate precision, reliability, and scalability, while vulnerabilities in IIoT systems may lead to financial losses and safety hazards. To tackle this, Edge AI/ML-based IDSs provide adaptability and robustness for IIoT security challenges. These solutions improve security levels, threat detection rates, and response times. However, main issues such as limited resources and the accuracy of attack detection rate are challenged nowadays. In this paper, we present contributions in proposing an intrusion detection system (IDS) for edge devices deploying a lightweight deep neural network to detect IIoT attacks. We present performance analysis of the typical dimensionality reduction methods and balancing data features of the Edge-IIoT dataset to get higher performance metrics than other state-of-the-art studies.

Renovation Analysis of a Socialist Modernism Office Building–Case Study

Socialist modernist architecture in the Balkan has left a valuable legacy of remarkable buildings from this century, which brought a mixture of new experiences and local traditions. Unfortunately, after the 1990s, many of these buildings have been neglected, improperly treated, or abandoned. This paper focuses on the iconic socialist modernist office building “Rilindja” in Prishtina, Kosovo, which was built in 1979. In 2010, the building envelope was changed in the name of revitalization, without considering the energy aspect. The study aims to present energy performance analysis through cost-optimal renovation measures of the building envelope while restoring the architectural values before the last renovation. Using Archicad and EcoDesigner STAR, a BIM model was created, and energy analysis was conducted. The findings indicate that eighteen proposed energy efficiency measures can achieve a reduction in energy consumption for heating and cooling of more than 80% over the life cycle period of 20 years. The comparison before and after renovation considering restoring and preserving the inherent values, while integrating energy efficiency measures was necessary, as there is a lack of such studies in the general literature. Finally, the potential of restoring the collective memory through cost-optimal analysis is emphasized as an approach for similar cases.

Thermodynamic and economic analysis of a deployable and scalable process to recover Monomer-Grade styrene from waste polystyrene

Less than 5% of polystyrene is recycled, motivating a search for energy efficient and economical methods for polystyrene recycling that can be deployed at scale. One option is chemical recycling, consisting of thermal depolymerization and purification to produce monomer-grade styrene (>99%) and other co-products. Thermal depolymerization and distillation are readily scalable, well-established technologies; however, to be considered practical, they must be thermodynamically efficient, economically feasible, and environmentally responsible. Accordingly, mass and energy balances of a pyrolysis reactor for thermal depolymerization and two distillation columns to separate styrene from α-methyl styrene, styrene dimer, toluene, and ethyl benzene co-products, were simulated using ASPEN to evaluate thermodynamic and economic feasibility. These simulations indicate that monomer-grade styrene can be recovered with energy inputs <10MJ/kg, comparable to the energy content of pyrolysis co-products. Thermodynamic sensitivity analysis indicates the scope to reduce these values and enhance the robustness of the predictions. A probabilistic economic analysis of multiple scenarios combined with detailed sensitivity analysis indicates that the cost for recycled styrene is approximately twice the historical market value of fossil-derived styrene when styrene costs are fixed at 15% of the total product cost or less than the historical value when feedstock costs are assumed to be zero. A Monte Carlo and Net Present Value-based economic performance analysis indicates that chemical recycling is economically viable for scenarios assuming realistic feedstock costs. Furthermore, the CO2 abatement cost is roughly $1.5 per ton of averted CO2, relative to a pyrolysis process system to produce fuels. As much as 60% of all polystyrene used today could be replaced by chemically recycled styrene, thus quantifying the potential benefits of this readily scalable approach.

Performance analysis of frequency selective reflectors based on rectangular loop pair for wideband applications

This paper presents performance analysis of single layered bandstop frequency selective surfaces (FSSs) for C-band and ultra-wideband (UWB) frequency ranges respectively. These FSSs are based on polarization dependent (PD) single rectangular loop pair (RLP). RLP arrays in square and rectangular grids are developed and simulated to evaluate signal rejection and reflection for incident transverse electric (TE) and transverse magnetic (TM) waves. Further, orthogonal patterns are printed back-to-back (BTB) on dielectric substrate to study wave propagation and effect on wave polarization. Non-linearities in the reflection phase of BTB arrays are then mitigated by printing orthogonal FSS arrays on the same side of the dielectric substrate. The equivalent circuit models (ECMs) of these FSSs are also presented to predict their transmission characteristics. Later, two FSSs with wideband and ultra-wideband rejection capability are developed and experiments are carried out. The array of merged RLP arranged in square grid exhibits stopband from 4.5 GHz to 8.4 GHz with polarization independence (PI) and it is angularly stable while merged array of RLP arranged in rectangular grid exhibit ultra-wide stopband from 2.3 GHz to 11.5 GHz but it is not angularly stable. The measured transmission responses of all FSSs proposed here are close to simulated results.

Demystifying disposition effect: past, present and future

PurposeThe disposition effect remains one of the most significant investor behavior puzzles. This study aims to consolidate the knowledge, explore current dynamics, elicit trends and offer future research directions to demystify the disposition effect.Design/methodology/approachThis study applies the hybrid review method. It first used bibliometric analysis (212 documents), followed by content analysis (54 articles) to analyze the breadth and depth of literature on the disposition effect.FindingsThis study presents performance analysis and science mapping. It identifies five main research streams: evidence, implications and mitigation techniques; theoretical explanations; investor biases and hedonic framing; attributes, beliefs and preferences; and implications for asset pricing and market efficiency. This study further offers future research directions for disposition effect research.Research limitations/implicationsThis study deploys sequential bibliometric and content analysis. A meta-analysis of quantitative articles could provide specific insights regarding the disposition effect. Besides, this study is based on Scopus-indexed journals only.Practical implicationsThis study benefits investors and portfolio managers as they learn effective ways to guard against the disposition effect. Policymakers may tweak tax laws to incentivize long-term holding, and regulators can run investor education campaigns to minimize the disposition effect’s consequences effectively.Originality/valueTo the best of the authors’ knowledge, this is probably the first hybrid review of high-quality, contemporary articles on the disposition effect that offers science mapping, research streams, future research directions and a succinct summary of theories, contexts, characteristics and methods deployed in the field of research.

Performance Analysis of Dual Axis Solar Tracker, Single Axis Solar Tracker and Fixed Array Configuration

Solar energy has become popular source of renewable energy owing to its cheap electricity generation and relatively low construction cost. With integration of solar PV tracking system, the electricity generation has boomed in its operating lifetime. This paper present performance analysis of dual axis solar tracker, single axis solar tracker and fixed array configuration in terms of energy generation and efficiency. Three solar experimental setups are designed, fabricated and tested. The experiment was conducted in Thimphu (27.415647N, 89.621183E) and obtained power generation data from 15-21 of March 2022. The sun tracking and data logging is performed by Arduino Uno microcontroller. The data obtained is analyzed to obtain results using Microsoft excel.

Performance analysis of distribution system under different loading conditions using test bench

This paper presents performance analysis of power distribution system using distribution test bench. Distribution system is the part of power system in which significant portion of the power is utilized in it. The losses occur in distribution system mainly due to inherent characteristics of the number of components in the system. In this wok, we analyse the performance of distribution system having 4 feeders having different lengths supplying resistive and combination of resistive and inductive loads. The analysis includes the voltage regulation, power loss, efficiency for different loading conditions. Also shown are the improvement of terminal voltage of the feeders and receiving end power with compensation. Finally the inferences are drawn and suggestions are proposed to improve the performance.

RETRACTED: Performance analysis of machine learning algorithms in heart disease prediction

This work presents performance analysis of machine learning algorithms such as logistic regression, naive bayes, decision tree, k nearest neighbour, random forest, support vector machine, and extreme gradient boosting in heart disease prediction. Machine learning algorithms are implemented in python using Scikit learn library in Jupiter notebook. Experiments are conducted by training and testing machine learning algorithms using kaggle heart disease dataset under six test cases. Performance of machine learning algorithms are evaluated using accuracy, precision, recall, F1 score and ROC as metrics. Results show random forest reported high accuracy, precision, recall, F1 score and ROC in heart disease prediction compared to other machine learning algorithms in all six test cases. Results show RF is effective in heart disease prediction in Case 3 with 80% train data and 20% test data.

A New Secure Lightweight Authentication Protocol for NFC mobile Payment

As mobile applications grow, securing these applications become an important factor for their success. Especially, when these applications are related to financial transactions. Nowadays, mobile payment that is based on NFC technology is considered one of these important topics. In this paper, we propose A New Secure and Lightweight Authentication Protocol for NFC mobile Payment (NSLA) protocol. NSLA protocol presents a new method to update the users’ identities and the valid session keys, which preserves the privacy and ensures the integrity of the system. The presented performance analysis shows that NSLA protocol satisfies low computation overhead. Moreover, the security analysis proves that NSLA protocol has an immunity against replay attack, brute force attack, denial of service attack, and others types of attacks.

Performance analysis of optical extra-WBAN links based on realistic user mobility modeling

We address the features channel characterization and performance evaluation for wireless body-area networks (WBANs) in medical applications during a walk scenario using optical wireless transmission. More specifically, we focus on optical extra-WBAN uplink communication between a central coordinator node (CN) placed on the patient’s body and an access point (AP) in a typical hospital room. To characterize the optical wireless channel, we use a Monte Carlo ray tracing-based method and take into account the effects of body shadowing and mobility based on realistic models, in contrast to the previous simplistic models considered in the literature. Using this approach, we derive the dynamic behavior of the channel DC gain for different configurations of the CNs and APs. Furthermore, based on the obtained results, we develop a statistical channel model based on kernel density estimation, which we use to investigate the impact of CN and AP placement on the communication link parameters. Also, based on the outage probability criterion, we discuss the link performance and further analyze the improvement in performance achieved through spatial diversity, i.e., by using multiple APs in the room, for different photodetector types, under different background noise conditions. The presented results show that CN placement and user’s local and global mobility significantly impact the performance of extra-WBAN links, which can nevertheless be reduced using spatial diversity. Finally, the presented performance analysis shows that a single AP equipped with an avalanche photodiode photodetector allows an acceptable link performance for low-to-moderate background noise conditions, whereas multiple APs equipped with PIN photodetectors should be used in the case of moderate-to-strong background noise.

Cooperative Mode Switching-Based Cognitive NOMA With Transmit Antenna and User Selection

The paper presents performance analysis of a multiuser Cooperative Non-Orthogonal Multiple Access (CNOMA) network that is underlay to a primary network. A selected secondary Full-Duplex (FD) Near User (NU) cooperates with a selected secondary Far User (FU) by apportioning the Interference Temperature Limit (ITL) with an ITL Apportioning Parameter (ITLAP). This Cooperative Mode (CM) is exercised only when NU meets its own performance criteria, else the network switches to a Non-Cooperative Mode (NCM). The network is therefore made adaptive in such a way that performance of the NU is same as in a network without the FU. Performance of the network is further improved using Maximum Transmit Power-based Transmit Antenna Selection (MTP-TAS), Best NU-Best FU (BNBF) selection, and optimal power allocation, thereby also motivating NOMA even in power-constrained underlay environments where transmit powers are not always high. The selection mechanisms are designed in a way that additional overheads for Channel State Information (CSI) estimation and feedback are not imposed. Analytical expressions for outage probability are presented for both end users, assuming only statistical knowledge of channel gains under the assumption of imperfect Successive Interference Cancellation (SIC) at the NU. Outage minimizing optimal values of the NOMA Power Allocation Parameter (NPAP) and ITLAP are derived that minimize FU outage. Simulations validate the derived expressions, and demonstrate superiority of the proposed scheme over FD NU-based conventional NOMA as well as conventional OMA signalling.

스윕을 가지는 축류 팬의 성능예측 방법

The present study provides a performance analysis method of forward swept axial flow fan. The present performance analysis method of forward swept axial flow fan incorporates the radial equilibrium equation formulated along swept-blade coordinate with spanwise pressure loss and flow deviation models. The pressure losses in fan are categorized into blade profile, secondary flow, end-wall boundary layer and tip clearance losses, and they are calculated by the corresponding correlations expressed in terms of the flow variables defined on swept-blade coordinate. The flow deviation at fan blade outlet is also calculated by the correlation model considering on- and off-design effects. The present analysis method predicts overall performance characteristics by the mass-weighted averaging of spanwise flow calculation results. For the verification of the present fan performance prediction method, various forward-swept fan models with different design specifications and operation conditions are considered and their prediction results are compared with the test and the CFD results. The comparisons between the present calculation and the test/C FD results show very good agreements in f low field and overall fan performance predictions.

BER performance of unmanned aerial vehicle assisted spatial modulation system in Rician channels

Unmanned aerial vehicle (UAV) is an emerging technology with high mobility and flexible reconfiguration. In this paper, an UAV-assisted spatial modulation (SM) system with amplify-and-forward (AF) protocol is presented, and the corresponding bit error rate (BER) performance is investigated over Rician fading channels, where the UAV is employed as a half-duplex relay and remains stationary over a given area, and the path loss exponent and Rician factor depend on the position of the UAV. Based on the imperfect channel state information (CSI), the effective signal-to-noise ratio (SNR) of the system is firstly derived. Then, with this result, we analyze the BER performance of the system, and deduce an upper bound of average BER for evaluation. On these bases, the asymptotic performance under large SNR is analyzed, and asymptotic expression of BER bound is derived to attain the diversity gain which is zero for imperfect CSI and one for perfect CSI. Simulation results demonstrate the effectiveness of the presented performance analysis, and reveal that the degradation arisen from estimation error and multiple antennas at the transmitter in BER performance is serious while the improvement brought by spatial diversity at the receiver is relatively modest. Specifically, the increase of 0.04 in estimation error variance results in the diminution of 12.5dB in performance gain, and the use of eight transmit antennas is 8dB worse than two transmit antennas. At the receiver, the increase in performance gain offered by going from ND=1 to ND=6 is about 4dB. Furthermore, according to the simulation, the optimal BER performance can be reached by adjusting the UAV position and power allocation factor. At last, the open issues and challenges focusing on both the deployment and architecture of this system are highlighted.

Sport Performance Analysis with a Focus on Racket Sports: A Review

Athletes, both professional and amateur, are always looking for ways to improve their performance. With the introduction and increasing availability of modern technologies and smart devices arose the need to measure and analyze performance, but likewise, the use of these innovations as a competitive advantage also arose. Scientific publications reflect the wide range of available approaches and technologies, as well as the growing interest in various sports. As a result, we concentrated on a systematic review of publications that presented performance analysis tools and methods in all sports, with a final focus on racket sports. Clarivate Analytics’ Web of Science (WoS) and Elsevier Inc.’s SCOPUS databases were searched for 1147 studies that conducted performance analysis and sports research and were published in English. The data in the systematic review are current, up until 18 May 2021. A general review was performed on 759 items, and then 65 racket sports publications were thoroughly scrutinized. We concentrated on performance data, data collection and analysis tools, performance analysis methods, and software. We also talked about performance prediction. In performance research, we have identified specific approaches for specific sports as well as key countries. We are also considering expanding performance analysis in to E-sports in the future.

Measuring the Performance Efficiency of State Bank of India and HDFC Bank using Balanced Score Card

&lt;p&gt;The research article presents performance analysis of the State Bank of India Ltd and the HDFC Bank Ltd using Balanced Score Card (BSC) developed by Robert Kaplan and David Norton. It attempts an analysis to develop a deep understanding of the concept of BSC as a tool for strategy mapping and measurement of performance of a public sector bank and a private sector bank as mentioned elsewhere. BSC is a performance evaluation system not only based upon financial parameters, but also on non-financial aspects like customer indicators, internal business processes and employee learning and growth. Actual performance data from years 2009 to 2019 and business indicators of the year 2020 of the Banks are used for this purpose. The BSC model was developed to address the problems and limitations of relying only on financial measures for strategy formulation and measuring organizational performance. The study revealed that Gross NPA of SBI has increased while that of HDFC Bank has reduced during the period of study. Obviously the ROA is also better for HDFC Bank compared to that of SBI. It is inferred that HDFC Bank has garnered more net profit than SBI during the period of study. HDFC Bank has shown better performance compared to SBI according to the current study. The implication of the study is that both the banks have to visit all the areas of their business to ensure growth in financial performance, better customer service, efficient internal processes and higher learning and growth in the future years. This could ensure better profit management and risk management.&lt;/p&gt;

Analyzing fusion of regularization techniques in the deep learning‐based intrusion detection system

The surge of constantly evolving network attacks can be addressed by designing an effective and efficient Intrusion Detection System (IDS). Various Deep Learning (DL) techniques have been used for designing intelligent IDS. However, DL techniques face an issue of overfitting because of complex network structure and high-dimensional data sets. Dropout and regularization are two competently perceived concepts of DL used for handling overfitting issue to enhance the performance of DL techniques. In this paper, we aim to apply fusion of various regularization techniques, namely, L1, L2, and elastic net regularization, with dropout regularization technique, for analyzing and enhancing the performance of Deep Neural Network (DNN)-based IDS. Experiments are performed using NSL-KDD, UNSW_NB-15, and CIC-IDS-2017 data sets. The value of dropout probability is derived using GridSearchCV-based hyperparameter optimization technique. Moreover, the paper also implements state-of-the-art Machine Learning techniques for the performance comparison. Apart from DNN, we have also presented performance analysis of various DL techniques, namely, Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, and Convolutional Neural Network using a fusion of regularization techniques for intrusion detection and classification. The empirical study shows that among the techniques implemented, dropout has proved to be more effective compared with L1, L2, and elastic net regularization. Moreover, fusion of dropout with other regularization techniques achieved better results compared with L1 regularization, L2 regularization, and elastic net regularization, individually. The techniques implemented for DNN-based IDS are also statistically tested using the Wilcoxon signed-rank test.

Migration of CMSWEB cluster at CERN to Kubernetes: a comprehensive study

The Compact Muon Solenoid (CMS) experiment heavily relies on the CMSWEB cluster to host critical services for its operational needs. The cluster is deployed on virtual machines (VMs) from the CERN OpenStack cloud and is manually maintained by operators and developers. The release cycle is composed of several steps, from building RPMs to their deployment, validation, and integration tests. To enhance the sustainability of the CMSWEB cluster, CMS decided to migrate its cluster to a containerized solution based on Docker and orchestrated with Kubernetes (K8s). This allows us to significantly speed up the release upgrade cycle, follow the end-to-end deployment procedure, and reduce operational cost. In this paper, we give an overview of the CMSWEB VM cluster and the issues we discovered during this migration. We discuss the architecture and the implementation strategy in the CMSWEB Kubernetes cluster. Even though Kubernetes provides horizontal pod autoscaling based on CPUs and memory, in this paper, we provide details of horizontal pod autoscaling based on the custom metrics of CMSWEB services. We also discuss automated deployment procedure based on the best practices of continuous integration/continuous deployment (CI/CD) workflows. We present performance analysis between Kubernetes and VM based CMSWEB deployments. Finally, we describe various issues found during the implementation in Kubernetes and report on lessons learned during the migration process.

Performance analysis of Terahertz communication link for cooperative Wireless Body Area Network

This paper presents performance analysis of Terahertz communication link for non-cooperative and cooperative Wireless Body Area Network (WBAN) considering a human model operating at Terahertz frequency (0.1–10.0 THz). Link parameter metrics like Signal to Noise ratio (SNR), Bit Error Rate (BER) and outage probability have been evaluated and revealed better performance of Terahertz link in cooperative WBAN compared to non-cooperative scenario by exhibiting better SNR, lower BER and lower outage probability. In the considered scenario for the cooperative link, amplify and forward (AF) scheme has been critically compared with decode and forward (DF) scheme and it has been observed that Terahertz link for decode and forward scheme exhibits better performance in terms of SNR, BER and outage probability by 5.68%, 24.51% and 80.9% respectively in comparison to amplify and forward scheme. By increasing the number of relays, a considerable decrease in outage probability has also been observed.

ENHANCED V-BLAST PERFORMANCE IN MIMO WIRELESS COMMUNICATION SYSTEMS

We have presented performance analysis of E-V-BLAST and according to this analysis, we found some useful characteristics of the E-V-BLAST algorithm for performance and complexity trade-off. Increasing S is applying ML detection with depth T for some most error likely signals. Increasing T is improving the ML detection with depth T. Increasing T will not affect so much to the performance of E-V-BLAST at high SNR. The importance of increasing T depends on S. Recently, a new detection algorithm for the Vertical Bell Laboratories Layered Space Time (V-BLAST) system, which we call Enhanced-V-BLAST, or simply E-V-BLAST, was proposed. In the present paper, we analyze the performance of E-V-BLAST as a function of the two inherent adjustable parameters of E-V-BLAST. On the basis of this analysis, we obtain some useful characteristics of the E-V-BLAST algorithm which allow one to achieve the desired performance and complexity trade-off.

Performance Analysis of Different Solar Models Based on the Solar cell Parameters

In this paper author present performance analysis between solar models based on accuracy, temperature, solar irradiance, resistances, output voltage and ideality factor of solar cell. In past literature various model of solar cell are presented, like one diode model and double diode model. This paper describes a comparative analysis between one-diode, two-diode and three-diode models of PV cell. To examine the accuracy of each model from information given through the past researcher, this paper differentiates the current-voltage (I-V) and power-voltage (P-V) characteristics performance at standard test condition (STC). The utmost frequently used models are one-diode and two-diode models, as they offer healthier relationships with a real solar cell. Authors compared different solar cell models based on above parameter and found that one-diode model provides supplementary accurate result as related to two-diode model for high intensity solar irradiation. While two-diode model is further correct for short intensity solar irradiation.