Performance Evaluation Of Networks Research Articles

HPETC: History Priority Enhanced Tensor Completion for Network Distance Measurement

In network distance measurement, how to estimate the whole network distance data from partially observed samples has attracted lots of attention because of its significance for network performance evaluation. Matrix completion becomes the most effective approach. However, the two-dimension matrix can only capture the spatial features in the network distance data while ignoring the temporal features. To conquer the problem, few recent studies begin to model the network distance data as a three-dimension tensor and propose tensor completion approaches for distance estimation. Although promising, existing tensor completion approaches still suffer the problem of low recovery accuracy and high measurement cost because they ignore the history priority information. To fully utilize both spatial and temporal features hidden in the distance data, this paper formulates a novel History Priority Enhanced Tensor Completion (HPETC) for distance estimation as a weighted tensor nuclear norm minimization problem where the weight is defined based on the history subspaces information. To solve the weighted tensor nuclear norm minimization problem, we firstly transform it into a factorization-based Frobenius norm minimization problem to avoid costly T-SVD computations, and then propose an iterative algorithm to solve the transformed problem. We further derive a theoretical sampling bound that is lower than the existing sampling bound, thus leads a lower measurement cost. We demonstrate the effectiveness of the proposed algorithm by conducting extensive experiments using two real network distance datasets. The result shows that the proposed algorithm can not only improve the estimation accuracy but also reduce the sampling complexity compared to the state-of-the-art approaches.

Mathematical Analysis and Performance Evaluation of the Cell-Free mMIMO Networks Based on Cognitive IRSs

Mathematical Analysis and Performance Evaluation of the Cell-Free mMIMO Networks Based on Cognitive IRSs

Sensitivity analysis and performance evaluation of neural networks for predicting forest stand volume - A case study: District 2, Kacha, Guilan province, Iran

Sensitivity analysis and performance evaluation of neural networks for predicting forest stand volume - A case study: District 2, Kacha, Guilan province, Iran

Performance Evaluation and Analysis of Urban-Suburban 5G Cellular Networks

5G is the fifth-generation technology standard for the new generation of cellular networks. Combining 5G and millimeter waves (mmWave) gives tremendous capacity and even lower latency, allowing you to fully enjoy the 5G experience. 5G is the successor to the fourth generation (4G) which provides high-speed networks to support traffic capacity, higher throughput, and network efficiency as well as supporting massive applications, especially internet-of-things (IoT) and machine-to-machine areas. Therefore, performance evaluation and analysis of such systems is a critical research task that needs to be conducted by researchers. In this paper, a new model structure of an urban-suburban environment in a 5G network formed of seven cells with a central urban cell (Hot spot) surrounded by six suburban cells is introduced. With the proposed model, the end-user can have continuous connectivity under different propagation environments. Based on the suggested model, the related capacity bounds are derived and the performance of 5G network is studied via a simulation considering different parameters that affect the performance such as the non-orthogonality factor, the load concentration in both urban and suburban areas, the height of the mobile, the height of the base station, the radius, and the distance between base stations. Blocking probability and bandwidth utilization are the main two performance measures that are studied, however, the effect of the above parameters on the system capacity is also introduced. The provided numerical results that are based on a network-level call admission control algorithm reveal the fact that the investigated parameters have a major influence on the network performance. Therefore, the outcome of this research can be a very useful tool to be considered by mobile operators in the network planning of 5G.

Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.

Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

Performance evaluation of communication networks for networked microgrids

The main goal of cyber-physical power systems (CPPSs) is to provide seamless collaboration between the physical and cyber systems, bridging disciplines, such as communication networks and control systems. Controlling the operation of Microgrids (MGs) as a cyber-physical power system must include both the physical and cyber layers. Generally, the microgrid control center MGCC is employed for reliable operation management between the different local controllers within the networked microgrids (NMGs). Therefore, an excessive number of communication channels between the MGCC and the local controllers will exist, affecting the control performance of the NMGs. This paper focuses on developing an innovative communication network emulation model for standalone DC networked microgrids using a Network Simulator (NS3), miming the communication infrastructure to control the networked microgrid system. Through the real-time simulation, the performance of the proposed communication network model was evaluated by employing two transmission protocols, User Datagram Protocol (UDP) and Transmission Control Protocol (TCP), to facilitate the data exchange between the MGCC and the local controllers in each MG while operating under different traffic loads. The comparative results of using these protocols in terms of the key performance indicators (KPIs) of the communication performance metrics, such as the mean delay, percentage of packet loss, and transmitted and received bitrate, reveal notable insights into their suitability in data transmission for controlling and managing the operation of the NMGs.

Performance evaluation of generative adversarial networks for generating mugshot images from text description

The process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification.

Hydraulic and Utility Performance Evaluation of Ataye Town Water Distribution Network, Ethiopia: case for small towns in developing countries

Evaluating hydraulic and utility performance of water distribution network is a way to check the functionality degree of a system. Water CAD software was used in developing a model and evaluating the hydraulic performance of water distribution system of Ataye Town, Amhara Region, Ethiopia. Both steady state and extended period simulation analysis were carried out to determine hydraulic parameters (pressure and velocity). The model was calibrated using Darwin Calibrator and validity was checked by both correlation coefficient and scatter plot. The utility performance was also evaluated using international water association performance indicators. The simulated result for steady state analysis showed that 68.7% of the nodes operated within optimum adopted pressure (15-60 meter) and 93% of the distribution pipes had a velocity of <0.6 m/s which was a minimum adopted velocity. For extended period simulation 34.3% during maximum demand time and 16.8% during minimum consumption hours had pressure <15meters. 10.8% had negative pressure during maximum demand hours located at Sudan, Selama, and Zigba sefer and 14 nodes (8.4%) during low consumption time had a pressure greater than 60meter, which was a maximum adopted pressure. The areas of high pressure were located at around mosque, Worku Hotel and Hamus Gebaya. The distribution system performs within the adopted pressure at minimum demand time (74.7%) and maximum consumption hours (65.1%). 23.5% of the systems had a velocity of 0.6-2 m/s and the rest had a velocity less than 0.6m/s during high demand time. During low consumption hours, 100% of the system velocity was estimated to be <0.6m/s. Based on the IWA performance indicators, water utility of Ataye Town was evaluated and had low, technical, financial, personnel, and environmental performances. Finally, the distribution system was modified and optimized by providing alternative connections with pressure reducing valves and changing pipe diameter to improve the hydraulic performance and reduce system disruption of the Town.
 Keywords: Extended period simulation; Hydraulic performance; IWA Utility performance; Steady state analysis; Water CAD

Performance Evaluation of Artificial Neural Networks and Support Vector Regression in Tunneling-Induced Settlement Prediction Incorporating Umbrella Arch Method Characteristics

Performance Evaluation of Artificial Neural Networks and Support Vector Regression in Tunneling-Induced Settlement Prediction Incorporating Umbrella Arch Method Characteristics

Performance evaluation of artificial neural network and hybrid artificial neural network based genetic algorithm models for global horizontal irradiance forecasting

The output of photovoltaic (PV) systems is highly dependent on Global Horizontal Irradiance (GHI). Thus, accurate prediction of GHI is essential to meet increasing energy demands, stabilise the electric grid system and mitigate climate change. The main objective of this study is to accurately model and forecast GHI at Albion, Mauritius for a time step of every 15 min using the Artificial Neural Network (ANN) and hybrid Artificial Neural Network based Genetic Algorithm (ANN-GA) techniques. Ground-based measurement (GBM) data, collected every 15 min for a winter month was checked for stationarity and normalised to enhance its quality. Only strongly correlated input variables were selected to minimise uncertainties in forecasts. Special emphasis is given to short-term forecasting with a relatively small dataset size. This work is repeated for 30 min and 1 h time scales. The study is further validated using satellite data for a different location (Curepipe) in Mauritius. The performance evaluation over different statistical metrics indicated that the ANN model has the best capabilities for GHI forecasting, regardless of the location. The highest quality forecasts from the ANN technique resulted in values of 0.9999 for correlation coefficient (r), 0.9999 for coefficient of determination (R2), 0.1537 W/m2 for Mean Absolute Error (MAE), 0.0641 W/m2 for Mean Square Error (MSE) and 0.2532 W/m2 for Root Mean Square Error (RMSE). The best ANN technique outperformed the strongest hybrid ANN-GA technique for every measured performance indicator.

Artificial Intelligence-Enabled 5G Network Performance Evaluation With Fine Granularity and High Accuracy

Artificial Intelligence-Enabled 5G Network Performance Evaluation With Fine Granularity and High Accuracy

Performance Evaluation of Multilayer Perceptron Neural Network and Adaptive Neuro-Fuzzy Inference Systems for Reservoir Operation Optimization: a Case Study of Cheffia Reservoir, Algeria

Artificial Intelligence based prediction has wide applications, including hydrology, water resources management and particularly reservoir operation. Thus, two black box models based on Artificial Neural Networks and Fuzzy Logic methods are implemented and tested in forecasting reservoir operation; the first is a Multilayer Perceptron Neural Network and the second is an Adaptive Neuro-Fuzzy Inference System combining the two methods. The developed models consist of predicting evaporation, inflows and reservoir storage from their historical records and that, with aim of providing the best fit between predicted and observed values and of improving operating rules on storage and releases. The performance of achieved results demonstrated the pertinence of Artificial Neural Networks and fuzzy Logic methods in predicting cyclical state variables, such as evaporation and storage, while the prediction of inflows to reservoir generally gave better results compared to other research works available in open literature on one hand. On other hand, it is deduced from testing different prediction models that these methods are unable to predict random variables.

The study of shadowing effect for LTE and 5G networks in suburban environment

Abstract The presence of obstacles in the propagation path is a critical factor in air-to-ground (AG) communication. The behavior of wireless signal propagation depends on several variables, such as frequency, building height, elevation angle, and street design. This paper aims to compare the three established line of sight (LOS) probability model based on actual site data, including the building geometry in suburban environment. The comparison between these three models using the site data provide a guideline for selecting the LOS probability model based on the optimistic and pessimistic predictions. The shadowing loss was evaluated at frequencies 2 and 3.5 GHz with an elevation angle of 20° in two suburban locations at Universiti Tun Hussein Onn Malaysia. Three prediction models, ITU-R P.1410-5, Holis and Pechac, and Pang et al., available in the literature were used to identify and compare the line-of-sight probability. By focusing on the shadowing model in suburban area, the guideline for optimizing LOS communications or navigation in these challenging environments can be developed. The finding highlights the importance of considering building height in AG communication for network performance evaluation and design.

A comprehensive approach in post-earthquake blockage prediction of urban road network and emergency resilience optimization

This study presents a probabilistic method for predicting urban road blockages due to the collapse of roadside buildings and develops network restoration planning through resilience optimization. Resilience analysis of a blocked network has faced two challenges: (1) blocked paths opening time estimation; and (2) network time-varying functionality evaluation. Probabilistic models, including earthquake magnitude, fault rupture location, seismic intensity, damage, debris, blockage, and debris evacuation models are utilized to determine the probability distribution of blocked paths opening time considering relevant uncertainties. A generalized network functionality metric is developed to evaluate network performance considering emergency multi-nodes such as medical centers. The optimization algorithm based on Simulated Annealing is extended for network restoration by assuming multi-location for resources pre-positioning. Three restoration scenarios are defined to identify required resources for network recovery with different restoration objectives. The proposed methodology is implemented in Region 2 of the Tehran metropolis. The results demonstrate that scenario (II) gives the highest number of required resources, so it is suitable where adequate resources are available. Scenario (III) predicts the least number of required resources; hence, it is appropriate where sufficient resources are not accessible. Subsequently, scenario (I) is an intermediate approach to determine the required resources with the lowest computational effort.

Performance evaluation of distribution and low-voltage networks under direct lightning flashes with paralleled triac-surge arrester

Performance evaluation of distribution and low-voltage networks under direct lightning flashes with paralleled triac-surge arrester

LGNN: a novel linear graph neural network algorithm.

The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the existing graph neural network framework often uses methods based on spatial domain or spectral domain to capture network structure features. This process captures the local structural characteristics of graph data, and the convolution process has a large amount of calculation. It is necessary to use multi-channel or deep neural network structure to achieve the goal of modeling the high-order structural characteristics of the network. Therefore, this paper proposes a linear graph neural network framework [Linear Graph Neural Network (LGNN)] with superior performance. The model first preprocesses the input graph, and uses symmetric normalization and feature normalization to remove deviations in the structure and features. Then, by designing a high-order adjacency matrix propagation mechanism, LGNN enables nodes to iteratively aggregate and learn the feature information of high-order neighbors. After obtaining the node representation of the network structure, LGNN uses a simple linear mapping to maintain computational efficiency and obtain the final node representation. The experimental results show that the performance of the LGNN algorithm in some tasks is slightly worse than that of the existing mainstream graph neural network algorithms, but it shows or exceeds the machine learning performance of the existing algorithms in most graph neural network performance evaluation tasks, especially on sparse networks.

Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection.

Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including RNNs and artificial neural networks (ANNs), hold great promise, their effectiveness heavily relies on the initial values assigned to hyperparameters, which are crucial for their performance for concrete assignment. To tune RNN performance, the selection of hyperparameters is approached as a typical optimization problem, and metaheuristic algorithms are employed to further enhance the process. The modified hybrid sine cosine algorithm has been developed and used to further improve hyperparameter optimization. To facilitate testing, publicly available real-world EEG data is utilized. A dataset is constructed using captured data from healthy and archived data from patients confirmed to be affected by epilepsy, as well as data captured during an active seizure. Two experiments have been conducted using generated dataset. In the first experiment, models were tasked with the detection of anomalous EEG activity. The second experiment required models to segment normal, anomalous activity as well as detect occurrences of seizures from EEG data. Considering the modest sample size (one second of data, 158 data points) used for classification models demonstrated decent outcomes. Obtained outcomes are compared with those generated by other cutting-edge metaheuristics and rigid statistical validation, as well as results' interpretation is performed.

Performance Evaluation CSMA/CA in MAC Layer and Markov Gaussian Memory Impulse Noise Channels in PHY Layer

There is memory impulse noise in urban outdoor/indoor mobile channels, underwater acoustic channels, powerline communications (PLC) channels, etc. The prior works about network performance evaluation in impulse noise channels considered both the impulse noise in the physical (PHY) layer and CSMA/CA in the media access control (MAC) layer but they used simpler memoryless impulse noise model. In this paper, we propose to use 3-D Markov chain and power iteration method to analyze the throughput of CSMA/CA protocols with finite queue and more general memory impulse noise. The numerical results show that when the impulse noise is strong (impulse noise to Gaussian noise ratio R=150), the normalized throughput would be significantly lower than the case without impulse noise (R=0). Therefore, throughput analysis considering the impulse noise effect is essential.

Ultrasonic characterization of small defects based on Res-ViT and unsupervised domain adaptation

This paper investigated the application of deep neural networks and domain adaptation for ultrasonic characterization of elliptical defects that are small and inclined. Based on performance evaluation of deep residual network (ResNet) and vision transformer (ViT), we proposed a novel Res-ViT architecture which fuses deep representative features of both models. Furthermore, we developed an unsupervised domain adaptation method to minimize the distance between the source and target domains, which is measured by maximum mean discrepancy. This approach serves to improve the generalizability of the proposed Res-ViT model in noisy environments. Simulation studies were performed at various noise levels to evaluate robustness of different deep neural networks. The proposed Res-ViT model was shown to reduce the characterization uncertainty of various defect parameters, including size, angle, and aspect ratio. Experiments were performed on three elliptical defects which have large orientation angles of 60∘ relative to the array direction. The proposed method achieved a 61% reduction in the root-mean-square error (RMSE) of defect size compared to a benchmark approach, which is based on principal component analysis and the nearest neighbor method.

LTE Network Performance Evaluation: A Comparative Study of Planned and Deployed Networks

LTE Network Performance Evaluation: A Comparative Study of Planned and Deployed Networks