Rain Attenuation Prediction Model for Lagos at Millimeter Wave Bands

Abstract Lagos, Nigeria (6.35°N, 3.2°E), is a coastal station in the rain forest area of southwestern Nigeria with an altitude of 38 m. Since most communication now takes place above the X band because of congestion of lower bands, it was necessary to look into ways of maximizing X-band usage. There are inadequate data for use in rain propagation studies at microwave frequencies, and even less so at millimeter wave bands where most of the signal depolarization and fading has been discovered to exist. The proposed model is a modification of the International Telecommunication Union–Radio Communication Sector (ITU-R) model combined with locally obtained regression coefficients for estimating specific attenuation as proposed by G. Olalere Ajayi. The Dissanayake, Allnutt, and Haidara (DAH), Simple Attenuation Model (SAM), and ITU-R attenuation prediction models were investigated along with the proposed model. The ITU-R model was observed to produce the best results at 40 GHz, with percentage error values of 0.61%, 0.55%, and 0.49% at 0.1%, 0.01%, and 0.001% of the time, respectively. In comparison, the proposed prediction model showed good performance at 20-GHz down-link frequency, with percentage error values of 3.6%, 3.3%, and 2.9% at 0.1%, 0.01%, and 0.001% of the time, respectively. The obtained results also showed good agreement with other similar works in the open literature. The results presented in this work are valuable for the design and planning of a satellite link in the tropical regions.

Download Full-text

A line-of-sight channel model for the 100–450 gigahertz frequency band

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01974-8 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Joonas Kokkoniemi ◽

Janne Lehtomäki ◽

Markku Juntti

Keyword(s):

Channel Model ◽

Full Range ◽

Line Of Sight ◽

Absorption Loss ◽

Radio Communication ◽

International Telecommunication Union ◽

International Telecommunication ◽

Fifth Generation ◽

Proposed Model ◽

Actual Response

AbstractThis paper documents a simple parametric polynomial line-of-sight channel model for 100–450 GHz band. The band comprises two popular beyond fifth generation (B5G) frequency bands, namely, the D band (110–170 GHz) and the low-THz band (around 275–325 GHz). The main focus herein is to derive a simple, compact, and accurate molecular absorption loss model for the 100–450 GHz band. The derived model relies on simple absorption line shape functions that are fitted to the actual response given by complex but exact database approach. The model is also reducible for particular sub-bands within the full range of 100–450 GHz, further simplifying the absorption loss estimate. The proposed model is shown to be very accurate by benchmarking it against the exact response and the similar models given by International Telecommunication Union Radio Communication Sector. The loss is shown to be within ±2 dBs from the exact response for one kilometer link in highly humid environment. Therefore, its accuracy is even much better in the case of usually considered shorter range future B5G wireless systems.

Download Full-text

A New Click-Through Rates Prediction Model Based on Deep&Cross Network

Algorithms ◽

10.3390/a13120342 ◽

2020 ◽

Vol 13 (12) ◽

pp. 342

Author(s):

Guojing Huang ◽

Qingliang Chen ◽

Congjian Deng

Keyword(s):

Neural Networks ◽

Prediction Model ◽

Deep Neural Networks ◽

Prediction Models ◽

State Of The Art ◽

Online Advertising ◽

Optimization Technique ◽

Proposed Model ◽

Great Progress ◽

Online Advertisement

With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, the CTR model can predict the user’s click probability of an online advertisement. Recently, great progress has been made with the introduction of Deep Neural Networks (DNN) into CTR. In order to further advance the DNN-based CTR prediction models, this paper introduces a new model of FO-FTRL-DCN, based on the prestigious model of Deep&Cross Network (DCN) augmented with the latest optimization technique of Follow The Regularized Leader (FTRL) for DNN. The extensive comparative experiments on the iPinYou datasets show that the proposed model has outperformed other state-of-the-art baselines, with better generalization across different datasets in the benchmark.

Download Full-text

An Influence Prediction Model for Microblog Entries on Public Health Emergencies

Data and Information Management ◽

10.2478/dim-2018-0013 ◽

2019 ◽

Vol 3 (2) ◽

pp. 102-115 ◽

Cited By ~ 1

Author(s):

Lu An ◽

Xingyue Yi ◽

Yuxin Han ◽

Gang Li

Keyword(s):

Public Health ◽

Prediction Model ◽

Latent Dirichlet Allocation ◽

Prediction Models ◽

Data Sets ◽

Allocation Model ◽

Latent Dirichlet Allocation Model ◽

Public Health Emergencies ◽

Proposed Model ◽

The Individual

Abstract This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

Download Full-text

Driver Lane-Changing Behavior Prediction Based on Deep Learning

Journal of Advanced Transportation ◽

10.1155/2021/6676092 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Cheng Wei ◽

Fei Hui ◽

Asad J. Khattak

Keyword(s):

Neural Network ◽

Prediction Model ◽

Traffic Safety ◽

Prediction Models ◽

Time Window ◽

Driving Safety ◽

Physiological Data ◽

Lane Changing ◽

Proposed Model ◽

Neural Network Prediction

A correct lane-changing plays a crucial role in traffic safety. Predicting the lane-changing behavior of a driver can improve the driving safety significantly. In this paper, a hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neural network (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction. The dynamic time window is proposed to extract the lane-changing features which include driver physiological data, vehicle kinematics data, and driver kinematics data. The effectiveness of the proposed model is validated through the experiments in real traffic scenarios. Besides, the proposed model is compared with five prediction models, and the results show that the proposed prediction model can effectively predict the lane-changing behavior more accurate and earlier than the other models. The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average.

Download Full-text

An Influence Prediction Model for Microblog Entries on Public Health Emergencies

Data and Information Management ◽

10.2478/dim-2018-00013 ◽

2019 ◽

Vol 0 (0) ◽

Author(s):

Lu An ◽

Xingyue Yi ◽

Yuxin Han ◽

Gang Li

Keyword(s):

Public Health ◽

Prediction Model ◽

Latent Dirichlet Allocation ◽

Prediction Models ◽

Data Sets ◽

Allocation Model ◽

Latent Dirichlet Allocation Model ◽

Public Health Emergencies ◽

Proposed Model ◽

The Individual

Download Full-text

A Hybridly Optimized LSTM-Based Data Flow Prediction Model for Dependable Online Ticketing

Wireless Communications and Mobile Computing ◽

10.1155/2021/9951607 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Chunmei Fan ◽

Jiansheng Zhu ◽

Haroon Elahi ◽

Lipeng Yang ◽

Beibei Li

Keyword(s):

Service Delivery ◽

Network Flow ◽

Prediction Models ◽

Short Term Memory ◽

Real Data ◽

Communication Technologies ◽

Absolute Error ◽

Percentage Error ◽

Fifth Generation ◽

Proposed Model

Fifth-generation (5G) communication technologies and artificial intelligence enable the design and deployment of sophisticated solutions for enhanced user experience and superior network-based service delivery. However, the performance of the systems offering 5G-based services depends on various factors. In this paper, we consider the case of the online railway ticketing system in China that serves the needs of hundreds of millions of people daily. This system’s online access rates vary over time, and fluctuations are experienced, affecting its overall dependability and service quality. We use long short-term memory network, particle swarm optimization, and differential evolution to construct DP-LSTM—a hybridly optimized model to predict network flow for dependable and quality-enhanced service delivery. We evaluate the proposed model using real data collected over six months from the “12306 online ticketing” system. We compare the performance of the proposed model with mainstream network traffic prediction models. We use mean absolute percentage error, mean absolute error, and root mean square error for performance evaluation. Experimental results show the superiority of the proposed model.

Download Full-text

Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

Mathematics ◽

10.3390/math9161886 ◽

2021 ◽

Vol 9 (16) ◽

pp. 1886

Author(s):

Michal Pavlicko ◽

Marek Durica ◽

Jaroslav Mazanec

Keyword(s):

Prediction Model ◽

Central Europe ◽

Financial Distress ◽

Prediction Models ◽

Value Added ◽

Ensemble Model ◽

Financial Distress Prediction ◽

Proposed Model ◽

Distress Prediction ◽

Visegrad Group

The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial health of the company and its possible future development. Therefore, the main aim of the paper is ensemble model creation for financial distress prediction. This model is created using the real data on more than 550,000 companies from Central Europe, which were collected from the Amadeus database. The model was trained and validated using 27 selected financial variables from 2016 to predict the financial distress statement in 2017. Five variables were selected as significant predictors in the model: current ratio, return on equity, return on assets, debt ratio, and net working capital. Then, the proposed model performance was evaluated using the values of the variables and the state of the companies in 2017 to predict financial status in 2018. The results demonstrate that the proposed hybrid model created by combining methods, namely RobustBoost, CART, and k-NN with optimised structure, achieves better prediction results than using one of the methods alone. Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models. The proposed model serves as a one-year-ahead prediction model and can be directly used in the practice of the companies as the universal tool for estimation of the threat of financial distress not only in Central Europe but also in other countries. The value-added of the prediction model is its interpretability and high-performance accuracy.

Download Full-text

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

Mathematical Problems in Engineering ◽

10.1155/2017/8513652 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 25

Author(s):

Guohui Li ◽

Songling Zhang ◽

Hong Yang

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Extreme Point ◽

Prediction Models ◽

Original Data ◽

Symmetric Mode ◽

Intrinsic Mode Functions ◽

Model Based ◽

Mode Decomposition ◽

Proposed Model

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

Download Full-text

Development of a Prediction Model for Tractor Axle Torque during Tillage Operation

Applied Sciences ◽

10.3390/app10124195 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4195 ◽

Cited By ~ 2

Author(s):

Wan-Soo Kim ◽

Yong-Joo Kim ◽

Seung-Yun Baek ◽

Seung-Min Baek ◽

Yeon-Soo Kim ◽

...

Keyword(s):

Prediction Model ◽

Measurement System ◽

Engine Speed ◽

Prediction Models ◽

Travel Speed ◽

Percentage Error ◽

Engine Torque ◽

Load Measurement ◽

Tillage Operation ◽

Tillage Depth

In general, the tractor axle torque is used as an indicator for making various decisions when engineers perform transmission fatigue life analysis, optimal design, and accelerated life testing. Since the existing axle torque measurement method requires an expensive torque sensor, an alternative method is required. Therefore, the aim of this study is to develop a prediction model for the tractor axle torque during tillage operation that can replace expensive axle torque sensors. A prediction model was proposed through regression analysis using key variables affecting the tractor axle torque. The engine torque, engine speed, tillage depth, slip ratio, and travel speed were selected as explanatory variables. In order to collect explanatory and dependent variable data, a load measurement system was developed, and a field experiment was performed on moldboard plow tillage using a tractor with a load measurement system. A total of eight axle torque prediction regression models were proposed using the measured calibration dataset. The adjusted coefficient of determination (R2) of the proposed regression model showed a range of 0.271 to 0.925. Among them, the prediction model E showed an adjusted R2 of 0.925. All of the prediction models were verified using a validation set. All of the axle torque prediction models showed an mean absolute percentage error (MAPE) of less than 2.8%. In particular, Model E, adopting engine torque, engine speed, and travel speed as variables, and Model H, adopting engine torque, tillage depth and travel speed as variables, showed MAPEs of 1.19 and 1.30%, respectively. Therefore, it was found that the proposed prediction models are applicable to actual axle torque prediction.

Download Full-text

An Improved Photosynthesis Prediction Model Based on Artificial Neural Networks Intended for Cucumber Growth Control

Applied Engineering in Agriculture ◽

10.13031/aea.12634 ◽

2018 ◽

Vol 34 (5) ◽

pp. 769-787 ◽

Cited By ~ 1

Author(s):

Pingping Xin ◽

Haihui Zhang ◽

Jin Hu ◽

Zhiyong Wang ◽

Zhen Zhang

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Prediction Model ◽

Photosynthetic Rate ◽

Prediction Models ◽

Ann Model ◽

Rate Prediction ◽

Model Based ◽

Proposed Model ◽

Artificial Neural

Abstract. The existing photosynthetic rate prediction models consider only a single growing season. However, a photosynthetic rate prediction model intended for full growth of crops is needed. Therefore, a photosynthetic rate prediction model based on artificial neural networks (ANN), which establishes the prediction of the entire photosynthetic process, is presented in this article. The proposed model was developed using the multi-factor photosynthetic rate data obtained by experiments on cucumber seedlings and flowering stage. The ANN model was trained with the Levenberg-Marquardt (LM) training algorithm. In contrast to the single-phase photosynthetic rate prediction models, in the proposed model a fusion of parameters of all growing stages was applied, whereat all growing parameters were merged into one six-dimensional input signal (temperature, CO2 concentration, light intensity, relative humidity, chlorophyll content, and growth stage). Verification of model accuracy and performance has shown that merging of growing parameters has obvious advantage. Moreover, the proposed model satisfied the requirement in terms of training error. In addition, the determination correlation between measured and estimated values was 0.9517, thus, good correlation and estimation were achieved. Besides, the test average absolute error was 1.1454, which proves a high accuracy of the proposed model. Therefore, the proposed prediction model can provide the theoretical basis for the facilities light regulation and technical support. Keywords: Artificial neural networks, Cucumber, Full growth period, Photosynthetic rate, Prediction model.

Download Full-text