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Symbol Detection Based on Back Tracking Search algorithm in MIMO-NOMA Systems

Computer Systems Science & Engineering 2022年第2期40卷 795-804页

作者： M.Nuri Seyman Bandirma Onyedi Eylul University Enginering and Natural Sciences FacultyBandirma10200Turkey

One of the most important methods used to cope with multipath fading effects,which cause the symbol to be received incorrectly in wireless communication systems,is the use of multiple transceiver antenna *** combining the multi-input multi-output(MIMO)antenna structure with non-orthogonal multiple access(NOMA),which is a new multiplexing method,the fading effects of the channels are not only reduced but also high data rate transmission is ***,when the maximum likelihood(ml)algorithm that has high performance on coherent detection,is used as a symbol detector in MIMO NOMA systems,the computational complexity of the system increases due to higher-order constellations and antenna *** a result,the implementation of this algorithm will be *** this study,the backtracking search algorithm(BSA)is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA *** emphasize the efficiency of the proposed algorithm,simulations have been made for the system with various antenna *** can be seen from the obtained results,a considerable reduction in complexity has occurred using BSA compared to the ml algorithm,also the bit error performance of the system is increased compared to other algorithms.

关键词： MIMO-NOMA ml algorithm heuristic BSA algorithm

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STBC Recognition for OFDM Transmissions: Channel Decoder Aided algorithm

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IEEE COMMUNICATIONS LETTERS 2022年第7期26卷 1658-1662页

作者： Marey, Mohamed Mostafa, Hala Alshebeili, Saleh A. Dobre, Octavia A. Prince Sultan Univ Coll Engn Smart Syst Engn Lab Riyadh 11586 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia King Saud Univ Dept Elect Engn Riyadh 11451 Saudi Arabia Memorial Univ Dept Elect & Comp Engn St John NL A1C 5S7 Canada

Recently, the process of space-time block coding (STBC) recognition has found applications in a wide range of wireless transmissions. Numerous investigations on this topic have been developed for single-carrier transmissions and a few studies have been introduced for orthogonal frequency division multiplexing (OFDM) transmissions. The previous research work in the literature has focused on exploiting the space-time redundancy to blindly estimate the type of STBC signal. In this work, we propose a new approach to design a STBC recognizer for OFDM transmissions by taking benefits from the a posteriori probabilities provided by a channel decoder. The mathematical treatments have shown that the exact maximum-likelihood (ml) solution to this problem is computationally demanding in the absence of a closed form. Therefore, we settle for the expectation-maximization (EM) algorithm as a low-complexity procedure to perform STBC recognition. In addition, we develop a channel estimation algorithm to be integrated into the proposed recognition algorithm. Simulation results are presented to confirm the theoretical analysis, with exhibiting superior recognition performance over the state-of-the-art algorithms.

关键词： Symbols OFDM Signal processing algorithms Codes Channel estimation Wireless communication Maximum likelihood estimation Signal recognition STBCs ml algorithm

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Analytic Solutions Help Predict Sucker-Rod-Pump Failure

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JPT, Journal of Petroleum Technology 2022年第3期74卷 62-64页

作者： Carpenter, Chris JPT Technology Editor

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200825, “Failure Prediction for SRP Using Analytic Solutions,” by Viorica Sirghii, SPE, Rudolf Konrad Fruhwirth, and Herbert Hofstaetter, University of Leoben, et al. The paper has not been peer reviewed. A method for prediction of sucker-rod-pump (SRP) failure based on improved, completely connected perceptron (ICCP) artificial neural networks (ANNs) is presented. Results are compared with historical wellbore data. The validity of the ANN model is proved to meet SRP failure prediction with a ±5% error, helping identify and prevent potential damage, reduce costs and risks, and optimize *** Diagnosis. To evaluate the performance of SRPs, dynamometer cards are used. The load on the top rod is measured and plotted in relation to the polished rod position as the pumping unit moves through each stroke cycle. The result is a surface card that illustrates the polished rod load vs. position (downhole diagnostics).To determine above-pump liquid accumulation, pump-filling efficiency, and displacement, the well’s producing and lifting efficiencies can be analyzed and quantified. Depending on the results, operational and equipment changes can be made to improve efficiency and production optimization. How the plunger picks up, holds, and releases the fluid load at each stroke can be seen in a dynamometer card’s shape. The most-common and most-harmful pump states are fluid pound, gas interference, and tagging. When the pump is not completely full, the rod string is damaged because of an excessive number of strokes.

关键词： beam pumping neural network accuracy descriptor machine learning dynamometer card ml algorithm artificial lift system upstream oil & gas skewness Artificial Lift Systems Beam and related pumping techniques Information Management and Systems

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Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2022年第3期7卷 1-20页

作者： Uddin, Md Nasir Yu, Kequan Li, Ling-zhi Ye, Junhong Tafsirojjaman, T. Alhaddad, Wael Tongji Univ Dept Disaster Mitigat Struct Collage Civil Engn Siping Rd 1239 Shanghai 200092 Peoples R China Univ Southern Queensland Sch Civil Engn & Surveying Toowoomba Qld Australia Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China

Shear failure in reinforced concrete (RC) beams with a brittle nature is a serious safety concern. Due to the inadequate description of the phenomenology of shear resistance (the shear behavior of RC beams), several of the existing shear design equations for RC beams with stirrups have high uncertainty. Therefore, the predicted models with higher accuracy and lower variability are critical for the shear design of RC beams with stirrups. To predict the ultimate shear strength of RC beams with stirrups, machine learning (ml)-based models are proposed in the present research. The models were created using a database of 201 experimental RC beams with stirrups gathered from earlier investigations for training and testing of the ml method, with 70% of the data being used for model training and the rest for testing. The performance of suggested models was evaluated using statistical comparisons between experimental results and state-of-the-art current shear design models (ACI 318-08, Canadian code, GB 510010-2010, NZS 3101, BNBC 2015). The suggested machine learning-based models are consistent with experimentally observed shear strength and current predictive models, but they are more accurate and impartial. To understand the model very well, sensitivity analysis is determining as input values for a specific variable affect the outcomes of a mathematical model. To compare the results with different machine learning models in training and testing R-2, RMSE and MSE are also established. Finally, proposed ml models such as gradient boost regressor and random forest give higher accuracy to evaluate the shear strength of the reinforcement concrete beam using stirrups.

关键词： RC beam with stirrups Shear capacity ml algorithm Standard building code Sensitivity analysis

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Role of machine learning in changing social and business eco-system - a qualitative study to explore the factors contributing to competitive advantage during COVID pandemic

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WORLD JOURNAL OF ENGINEERING 2022年第2期19卷 238-243页

作者： Sanil, Hishan S. Singh, Deepmala Raj, K. Bhavana Choubey, Somya Bhasin, Narinder Kumar Kumar Yadav, Ranjeeta Gulati, Kamal Univ Teknol Malaysia Azman Hashim Int Business Sch Kuala Lumpur Malaysia LBEF Kathmandu Nepal Inst Publ Enterprise Hyderabad India Manipal Univ Jaipur Dept Commerce Jaipur Rajasthan India Amity Univ Asibas Noida India Amity Univ Noida India ABES Engn Coll Ghaziabad India

Purpose "Machine learning (ml)" in business aids in increasing company scalability and boosting company operations for businesses all over the world. "Artificial intelligence (AI)" technologies and several "ml" algorithms have grown in prominence in the business analytics sector. In the era of a huge quantum of data being generated by the virtue of the integration of the various software with the business operations, the relevance of "ml" is continuously increasing. As a result, companies may now profit from knowing how companies may use "ml" and incorporating it into their own operations. "ml" derives useful results from the data to address very dynamic and difficult social and business problems. ml helps in establishing a system that learns automatically and produces results in less time and effort, allowing machines to discover. ml is developing at a breakneck pace, fuelled mostly by new computer technology to competitive advantages during the COVID pandemic. Design/methodology/approach For firms all around the world, "ml" in business aids in expanding scalability and boosting operations. In the field of business analytics, artificial intelligence (AI) and machine learning (ml) algorithms have become increasingly popular. The importance of "ml" is growing in an era when a massive amount of data is generated as a result of the integration of various applications with company activities. As a result, businesses can now benefit from understanding how other businesses are using "ml" and adopting it into their own operations. In order to handle very dynamic and demanding societal and business challenges, machine learning (ml) extracts valuable results from data. Machine learning (ml) aids in the development of a system that learns automatically and generates outcomes with less time and effort, allowing machines to discover. ml is progressing at a dizzying pace, fueled primarily by new computer technology and used to gain competitive advantages during the COVID pandemi

关键词： Data Artificial intelligence Machine learning COVID Data-rich ml algorithm

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A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

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SENSORS 2023年第5期23卷 2545页

作者： Yaro, Abdulmalik Shehu Maly, Filip Prazak, Pavel Univ Hradec Kralove Fac Informat & Management Dept Informat & Quantitat Methods Hradec Kralove 50003 Czech Republic Ahmadu Bello Univ Dept Elect & Telecommun Engn Zaria 810106 Nigeria

A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ml) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system's localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user's instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers' suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS.

关键词： indoor localization RSS fingerprinting ml algorithm

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Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation

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RESPIRATORY RESEARCH 2021年第1期22卷 1-12页

作者： Chen, Chaojin Yang, Dong Gao, Shilong Zhang, Yihan Chen, Liubing Wang, Bohan Mo, Zihan Yang, Yang Hei, Ziqing Zhou, Shaoli Sun Yat Sen Univ Dept Anesthesiol Affiliated Hosp 3 600 Tianhe Rd Guangzhou 10630 Guangdong Peoples R China Guangzhou AID Cloud Technol Co LTD Guangzhou Peoples R China Sun Yat Sen Univ Dept Informat Affiliated Hosp 3 Guangzhou Peoples R China Sun Yat Sen Univ Dept Hepat Surg Affiliated Hosp 3 600 Tianhe Rd Guangzhou 510630 Guangdong Peoples R China Sun Yat Sen Univ Liver Transplantat Ctr Affiliated Hosp 3 600 Tianhe Rd Guangzhou 510630 Guangdong Peoples R China

Background: Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ml) methods. Methods: Data of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ml models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model. Results: 591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality (P<0.05). Among the six ml models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%;specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na+, TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time. Conclusion: Our study firstly demonstrated that the XGBoost model with 14 common variables might predict postoperative pneumonia in OLT patients.

关键词： Liver transplantation Postoperative pneumonia Machine learning Postoperative pulmonary complications Disease prediction Risk factors Early intervention Deep learning ml algorithm Extreme gradient boosting

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A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping

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INFORMATION 2023年第4期14卷

作者： Solimani, Firozeh Cardellicchio, Angelo Nitti, Massimiliano Lako, Alfred Dimauro, Giovanni Reno, Vito Natl Res Council Italy Inst Intelligent Ind Technol & Syst Adv Mfg Via Amendola 122 D-O I-70126 Bari Italy Polytech Univ Tirana Fac Civil Engn Blvd Deshmoret & Kombit 4 Tirana 1000 Albania Univ Bari Dept Comp Sci Via E Orabona 4 I-70125 Bari Italy

Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ml) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed;for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.

关键词： high-throughput phenotyping platform sensor plant traits DL ml algorithm image acquisition technique

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An Improved OFDM Timing Synchronization algorithm with Variable Length of Cyclic Prefix 6th

An Improved OFDM Timing Synchronization Algorithm with Varia...

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6th International Conference on Communications, Signal Processing, and Systems (ICCSPS)

作者： Li, Yanping Song, Qichao Gao, Hui Wu, Dongyan Zhang, Lingzhi Heilongjiang Inst Technol Harbin Heilongjiang Peoples R China

The influence of the length of cyclic prefix to ml algorithm synchronization performance has been studied. On the basis, an improved algorithm with variable length of cyclic prefix is proposed. The proposed algorithm ... 详细信息

ISBN: (纸本)9789811065712;9789811065705

关键词： Cyclic prefix ml algorithm Vary with length Timing synchronization OFDM

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Machine learning an alternate technique to estimate the state of charge of energy storage devices

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ELECTRONICS LETTERS 2017年第25期53卷 1665-1666页

作者： Zahid, Taimoor Xu, Kun Li, Weimin Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Acad Sci Jining Inst Adv Technol Jining Peoples R China

State of charge (SOC) estimation plays a critical role in the operation of an electric vehicle (EV) power battery. In this Letter, the authors propose machine learning (ml) algorithms as alternate to the existing filtering algorithms used for SOC estimation of an EV battery. The SOC estimation approach is evaluated by the simulation experiments in advanced vehicle simulator (ADVISOR). For the modelling of ml algorithms, the input parameters that affect the SOC estimation are battery current, battery module temperature, power out of the battery (available and requested), battery power loss and heat removed from the battery. Training and testing stages of the models are carried out using the data collected from ADVISOR. As the drive cycle conditions provided by ADVISOR are universal therefore present method is applicable to all kinds of batteries used in EVs including lithium ion, nickel metal hydride and lead acid batteries. Thus, the proposed models for SOC estimation provide an alternative approach in SOC estimation.

关键词： electric vehicle power battery electrical engineering computing learning (artificial intelligence) nickel metal hydride batteries ADVISOR drive cycle condition lithium ion batteries Electrical engineering computing machine learning algorithm lead acid batteries advanced vehicle simulator ml algorithm SOC estimation power out EV power battery energy storage devices battery module temperature Transportation battery powered vehicles battery power loss state-of-charge estimation Knowledge engineering techniques

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