Traditional Least Research Articles

Comparative analysis of heat transfer prediction for falling film evaporation on the horizontal tube based on machine learning methods

Precise prediction of heat transfer for falling film evaporation is necessary for the horizontal tube falling film evaporator design. In this study, models to predict the heat transfer are compared, which are developed by traditional Least Square method (LSM) and Machine Learning (ML) methods, including Support Vector Regression (SVR), Artificial Neural Network (ANN) and Random Forest Regression (RFR). The training data is obtained by a series of experiments, containing 510 experimental data of fresh water and seawater of salinities from 30 to 60 g/kg. The experiments were conducted with the test tube diameter of 19, 25.4 and 38 mm and with the heat flux of 7.7–12 kW/m2. All ML models behave better performances than the LSM model and the SVR model is suggested as the best model to predict the heat transfer, considering the prediction accuracy and generalization ability comprehensively. SHapley Additive exPlanations (SHAP) is used to explain the relationships between Nusselt number and input variables, which also helps to resolve controversial issues about the effects of spray density and tube diameter on the heat transfer. The importance of variables is ranked, in which the tube pitch and seawater salinity are regarded as the most influential variables. Besides, the limitations and performance of models are analyzed in terms of both dataset and algorithms.

Construction and optimization of non-parametric analysis model for meter coefficients via back propagation neural network

This study addresses the drawbacks of traditional methods used in meter coefficient analysis, which are low accuracy and long processing time. A new method based on non-parametric analysis using the Back Propagation (BP) neural network is proposed to overcome these limitations. The study explores the classification and pattern recognition capabilities of the BP neural network by analyzing its non-parametric model and optimization methods. For model construction, the study uses the United Kingdom Domestic Appliance-Level Electricity dataset’s meter readings and related data for training and testing the proposed model. The non-parametric analysis model is used for data pre-processing, feature extraction, and normalization to obtain the training and testing datasets. Experimental tests compare the proposed non-parametric analysis model based on the BP neural network with the traditional Least Squares Method (LSM). The results demonstrate that the proposed model significantly improves the accuracy indicators such as mean absolute error (MAE) and mean relative error (MRE) when compared with the LSM method. The proposed model achieves an MAE of 0.025 and an MRE of 1.32% in the testing dataset, while the LSM method has an MAE of 0.043 and an MRE of 2.56% in the same dataset. Therefore, the proposed non-parametric analysis model based on the BP neural network can achieve higher accuracy in meter coefficient analysis when compared with the traditional LSM method. This study provides a novel non-parametric analysis method with practical reference value for the electricity industry in energy metering and load forecasting.

Data Aided Channel Estimation for MIMO-OFDM Wireless Systems Using Reliable Carriers

Wireless channel estimation is one of the challenging problems in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) wireless systems. The MIMO-OFDM exploits the spatial resources and increases the reliability and capacity of wireless systems. However, the performance of these systems depends on accurate channel estimation since the receivers require perfect Channel State Information (CSI) for coherent signal detection. Thus, wireless channel estimation is a necessary component of OFDM systems. In this article, we have proposed an algorithm for MIMO-OFDM systems that combines pilot symbols with reliable data symbols for channel estimation. The reliable data symbols serve as virtual pilots and enhance the spectral efficiency. The proposed Data Aided Channel Estimation (DACE) algorithm eliminates the requirement of any additional resources such as excessive number of training symbols to attain the desired performance. Also, it outperforms the traditional Least Square (LS) and Linear Minimum Mean Square Error (LMMSE) methods for channel estimation in terms of Mean Square Error (MSE) and Bit Error Rate (BER) performance of the system.

Clock-offset computation method of space network aided by satellite clock prediction

A novel network clock-offset resolution method for space network is proposed in the paper. In the process of clock-offset resolution based on the network inter-node measurement data, the prediction of satellite clock result is used as the priori information. A double weighted control is made with the consideration of the point clock performance and the measurement performance, and better measurement error suppression effect is achieved. The results of simulation experiment indicated that network clock-offset resolution result with better stability and accuracy is achieved compared with the traditional Least Squares method in different scenarios.

A soft sensing method of billet surface temperature based on ILGSSA-LSSVM

It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of the billet quality. This paper proposes a sparrow search algorithm to optimize the Least Square Support Vector Machine (LSSVM) model for surface temperature prediction of the billet, which is further improved by Logistic Chaotic Mapping and Golden Sine Algorithm (Improve Logistic Golden Sine Sparrow Search Algorithm LSSVM, short name ILGSSA-LSSVM). Using the Improved Logistic Chaos Mapping and Golden Sine Algorithm to find the optimal initial sparrow population, the value of penalty factor gamma and kernel parameter sigma for LSSVM are calculated. Global optimization method is adopted to find the optimal parameter combination, so that the negative influence of randomly initializing parameters on the prediction accuracy would be reduced. Our proposed ILGSSA-LSSVM soft sensing model is compared respectively with traditional Least Square Support Vector Machine, BP neural network and Gray Wolf optimized Least Square Support Vector Machine, results show that proposed model outperformed the others. Experiments show that the maximum error of ILGSA-LSSVM soft sensing model is 3.85733 °C, minimum error is 0.0174 °C, average error is 0.05805 °C, and generally outperformed other comparison models.

A Variable Step Size Gradient-Descent TLS Algorithm for Efficient DOA Estimation

The direction-of-arrival (DOA) estimation model of this brief is based on deviation compensation model with input noise, and the performance of the traditional Least mean square (LMS) adaptive algorithm shows poor performance. Instead, total least squares (TLS) algorithm is widely used in models which contains input noise. Therefore, we present TLS algorithm for DOA estimation, which is used to update weight coefficient by searching the peak of the spatial spectrum to estimate the direction of the angle of arrival. Due to the unsatisfactory DOA estimation performance on fixed step algorithms, a variable step-size gradient descent total least-squares (VSS-GDTLS) is proposed. The variable step size strategy is derived by applying the instantaneous augmented weight vector and the estimated signal power. Moreover, the convergence of the proposed algorithm is analyzed. Finally, simulation results show the superiority of the VSS-GDTLS algorithm than GDTLS and the other adaptive algorithms.

Trajectory tracking under compound noise environments based on weighted total least squares and forney-style factor graph

In compound noise environments, both additive noises and multiplicative noises influence the Time-of-arrival-based (TOA-based) distance measurements, which challenge the source node localization and trajectory tracking in practical sensor networks. In order to realize trajectory tracking under such noise conditions, in this paper, a compound noise parameter estimation algorithm based on Weighted Total Least Squares (WTLS) and a source node tracking algorithm based on Forney-style Factor Graph (FFG) are presented. Four parameters of the compound noise model are estimated based on ranging observations between position-known anchor nodes, and the trajectory tracking is achieved by applying a noise pre-standardized procedure in the FFG. Monte-Carlo simulations evaluate the performance of the proposed system, and the proposed noise parameter estimation algorithm performs better than the traditional Least Square (LS) method and the proposed trajectory tracking algorithm is realizable and robust.

Channel Estimation for MIMO Systems

Abstract: A combination of multiple-input multiple-output (MIMO) systems and orthogonal frequency division multiplexing (OFDM) technologies can be employed in modern wireless communication systems to achieve high data rates and improved spectrum efficiency. For multiple input multiple output (MIMO) systems, this paper provides a Rayleigh fading channel estimation technique based on pilot carriers. The channel is estimated using traditional Least Square (LS) and Minimum Mean Square (MMSE) estimation techniques. The MIMO-OFDM system's performance is measured using the Bit Error Rate (BER) and Mean Square Error (MSE) levels. Keywords: MIMO, MMSE, Channel estimation, BER, OFDM

Effectiveness Evaluation of Replacement Policies for On-Chip Caches in Multiprocessors

A Cache plays a vital role in improving performance in the multicore environment, especially the Last Level Cache (LLC). The improvements in performance are based on the block size, associativity, and replacement policies. Most of the papers concentrate on traditional Least Recently Used (LRU) based replacement policies for their replacement decisions. Unfortunately, the replacement decisions do not enhance performance of the cache as expected. An enhanced modified Pseudo LRU policy is proposed, which is an approximation of LRU. The proposed methodology uses counters to enhance the confidence of replacement decisions based on the history of the replaceable blocks in cache. It is very clear from the Simulation results that the replacement scheme proposed exhibits better performance improvement in terms of miss ratio of about 3% and energy efficiency of about 2% on an average.

Blind Source Separation For Full-Duplex Systems: Potential and Challenges

Full-duplex communications systems that transmit and receive simultaneously suffer self-interference due to the mixing of the transmitted signal and the weaker received signal at the same node. The problem becomes compounded in Multi-Input Multi-Output (MIMO) systems, where considerable overhead is dedicated to training. In this article, we discuss using blind source separation techniques, namely Independent Component Analysis (ICA) to reduce training overhead in MIMO in-band full-duplex wireless communication systems. Practical limitations are discussed and experimental results that compare ICA to traditional Least Square approaches are presented, showing the superiority of ICA, especially in low SNR regimes.

Novel Multifold Secured System by Combining Multimodal Mask Steganography and Naive Based Random Visual Cryptography System for Digital Communication

With increase in growth of data and digital threat, demand of securing the data communicated over the internet is an essential play in the digital world. In the vision of digitalizing services with the next generation of security to the sensitive data transmitted over the internet by hiding the existence of the data using next generation cryptography by fusing cryptography techniques is one the major technique adopted. With this the aim in traditional Least Significant Bit (LSB) is one of the widely used technique. Where the secret message or image are placed in the cover image in the least significant bits of RGB Channels resulting in a stego image. But the drawback is, on suspecting the differences in the pixels of original and stegoimage in the secret data embedded can be guessed and extracted by attacker. The Proposed visual crypto-mask steganography method overcomes this drawback and support good payload capacity with multi modal approach of embedding biometrics, resulting in ∞ PSNR. The authenticated person face and fingerprint information is transmitted in a cover image and mask image (magic sheet) using proposed steganography and is combined with Random Visual Crypto Technique. Which results in enhanced and advance visual crypto steganography secured model in communicating sensitive (biometric features) information over the internet. Where the complete information cannot be extracted using only cover image. Mask image (magic sheet) is used along with cover image that reveals the secret data in the receiving end.

An adaptive multi-level caching strategy for Distributed Database System

The Internet of Things (IoT) has become a promising technology for addressing societal and industrial challenges by connecting geo-distributed and diverse devices to create smart systems worldwide. Nowadays, Distributed Database Systems (DDBS) are widely used in Internet of Things (IoT) frameworks to improve performance of massive data access. However, most recent caching techniques focus on collaborative caching and neglect the unbalanced workloads on hot data nodes, which may potentially become a bottleneck of latency-sensitive IoT services. To tackle this issue, we propose an adaptive multi-level caching strategy to solve the performance bottleneck caused by hot data access and therefore improve data access performance in DDBS. The essential idea of our strategy is to dynamically adjust cache resource allocation and cache size in different data nodes according to realtime access rate. In this way, more resources could be allocated to hot data node to speed up queries and eliminate the bottleneck. In addition, instead of the traditional Least Recently Used (LRU) algorithm, an efficient cache replacement algorithm is also proposed and implemented in our DDBS. The testbed experimental results indicate that the performance of DDBS with our adaptive multi-level caching strategy can be significantly improved by 20% compared with traditional strategies.

On the scheduling of operations in a chat contact center

Unlike calls, the chat channel allows a contact center agent to simultaneously work with many customers. The benefit of this flexibility can be however challenged by a new abandonment feature during service. In this context, new flow routing questions arise. How many chats should an agent serve? Which agent should be selected? Or may a chat be served by more than one agent? We aim to answer these questions so as to find the best trade-off between time spent in service, queueing delay and abandonment. For this purpose, we determine conditions under which the traditional Least Busy First or Most Busy First policies are optimal for agent selection. Using a dynamic programming analysis, we prove that a state-dependent threshold reservation policy is optimal when a chat can be handed to other agents without loss of efficiency. This analysis reveals that reservation is useful as it allows to maintain of sufficient agent productivity and abandonment from the queue which in turn reduce the system’s congestion. In addition, we show the closeness between the optimal policy and the easier-to-implement fixed capacity policy if this fixed capacity is optimized. When a chat cannot be handed to other agents, we develop a dispatching policy improvement algorithm based on the explicit computation of the relative value function for an initial policy with fixed probabilities. The dispatching policy significantly outperforms non state-dependent ones, as it partly compensates the agents’ inability to share a chat service.

The Channel Estimation and Modeling in High Altitude Platform Station Wireless Communication Dynamic Network

In order to analyze the channel estimation performance of near space high altitude platform station (HAPS) in wireless communication system, the structure and formation of HAPS are studied in this paper. The traditional Least Squares (LS) channel estimation method and Singular Value Decomposition-Linear Minimum Mean-Squared (SVD-LMMS) channel estimation method are compared and investigated. A novel channel estimation method and model are proposed. The channel estimation performance of HAPS is studied deeply. The simulation and theoretical analysis results show that the performance of the proposed method is better than the traditional methods. The lower Bit Error Rate (BER) and higher Signal Noise Ratio (SNR) can be obtained by the proposed method compared with the LS and SVD-LMMS methods.

Support Vector Machine Regression and Artificial Neural Network for Channel Estimation of LTE Downlink in High-Mobility Environments

In this paper we apply and assess the performance of support vector machine regression (SVR) and artificial neural network (ANN) channel estimation algorithms to the reference signal structure standardized for LTE Downlink system. SVR and ANN where applied to estimate real channel environment such as vehicular A channel defined by the International Telecommunications Union (ITU) in the presence of nonlinear impulsive noise.The proposed algorithms use the information provided by the received reference symbols to estimate the total frequency response of the time variant multipath fading channel in two phases. In the first phase, each method learns to adapt to the channel variations, and in the second phase it predicts all the channel frequency responses. Finally, in order to evaluate the capabilities of the designed channel estimators, we provide performance of SVR and ANN, which is compared with traditional Least Squares (LS) and Decision Feedback (DF). The simulation results show that SVRhas a better accuracy than other estimation techniques.

A smart force platform using artificial neural networks

The human body may interact with structures and these interactions are developed through the application of contact forces, for instance when walking. The aim of this paper is to propose a new methodology using Artificial Neural Network (ANN) for calibrating a force platform in order to reduce the uncertainties in the values of estimated vertical Ground Reaction Force and the positioning of the applied force in the human gait. Force platforms have been used to evaluate the pattern of human applied forces and to fit models for the interaction between pedestrians and structures. Linear relation assumptions between input and output are common in traditional Least Mean Square methods used in calibration. Some discrepancies due to nonlinearities in the experimental setup (looseness, wear, support settlements, electromagnetic noise, etc.) may harm the overall fitting. Literature has shown that nonlinear models, like ANN, can better handle this. During the calibration, the input data to the ANN were the reference voltages applied to the Wheatstone bridge, while the output data were the values of the standard weights applied in the force platform in defined sites. Supervised training based on k-fold cross validation was used to check the ANN generalization. The use of ANN shows significant improvements for the measured variables, leading to better results for predicted values with low uncertainty when compared to the results of a simple traditional calibration using Least Mean Squares.

Hybrid multi-tenant cache management for virtualized ISP networks

In recent years, Internet Service Providers (ISPs) have started to deploy Telco Content Delivery Networks (Telco CDNs) to reduce the pressure on their network resources. The deployment of Telco CDNs results in reduced ISP bandwidth utilization and improved service quality by bringing the content closer to the end-users. Furthermore, virtualization of storage and networking resources can open up new business models by enabling the ISP to simultaneously lease its Telco CDN infrastructure to multiple third parties. Previous work has shown that multi-tenant proactive resource allocation and content placement can significantly reduce the load on the ISP network. However, the performance of this approach strongly depends on the prediction accuracy for future content requests. In this paper, a hybrid cache management approach is proposed where proactive content placement and traditional reactive caching strategies are combined. In this way, content placement and server selection can be optimized across tenants and users, based on predicted content popularity and the geographical distribution of requests, while simultaneously providing reactivity to unexpected changes in the request pattern. Based on a Video-on-Demand (VoD) production request trace, it is shown that the total hit ratio can be increased by 43% while using 5% less bandwidth compared to the traditional Least Recently Used (LRU) caching strategy. Furthermore, the proposed approach requires 39% less migration overhead compared to the proactive placement approach we previously proposed in Claeys et al. (2014b) and achieves a hit ratio increase of 19% and bandwidth usage reduction of 7% in the evaluated VoD scenarios and topology.

A low-power-oriented cache design for multicore processors

This paper proposed a Low-Power oriented cache Design( LPD) of Level 2( L2) cache for multi-core processors. LPD considered three different ways to reduce the power consumption while promising the best performance: Low Power oriented Hybrid cache Partition algorithm( LPHP), Cache Reconfiguration Algorithm( CRA), and Way-Prediction based on L2 cache Partition algorithm( WPP-L2). LPHP and CRA closed the columns that were not in use dynamically.WPP-L2 predicted one appropriate way before cache accesses, which could save the access time, so as to save power. These three methods of LPD saved power consumption by 20. 5%, 17% and 64. 6% on average over the traditional Least Recently Used( LRU) strategy with improvement of the throughput and little influence on the runtime of programs. The experimental results show that this method can reduce the power of multi-core processors significantly and maintain the system performance.

Time-Delay Neural Network for Smart MIMO Channel Estimation in Downlink 4G-LTE-Advance System

Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a new flat radio-network architecture and significant increase in spectrum efficiency. In this paper, main focus on throughput performance analysis of robust MIMO channel estimators for Downlink Long Term Evolution-Advance (DL LTE-A)-4G system using three Artificial Neural Networks: Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN) and Time-Delay neural network (TDNN) are adopted to train the constructed neural networks‟ models separately using Back-Propagation Algorithm. The methods use the information received by the received reference symbols to estimate the total frequency response of the channel in two important phases. In the first phase, the proposed ANN based method learns to adapt to the channel variations, and in the second phase, it estimates the MIMO channel matrix and try to improve throughput of LTE. The performance of the estimation methods is evaluated by simulations in Vienna LTE-A DL Link Level Simulator. Performance of the proposed channel estimator, Time-Delay neural network (TDNN) is compared with traditional Least Square (LS) algorithm and ANN based other estimators for Closed Loop Spatial Multiplexing (CLSM) Single User Multi-input Multi-output (MIMO-2×2 and 4×4) in terms of throughput. Simulation result shows TDNN gives better performance than other ANN based estimations methods and LS.

Assessing performance of Bayesian state-space models fit to Argos satellite telemetry locations processed with Kalman filtering.

Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to “true” GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6±5.6 km) was nearly half that of LS estimates (11.6±8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales’ behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.