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1 – 10 of over 1000Feng Qian, Yongsheng Tu, Chenyu Hou and Bin Cao
Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods…
Abstract
Purpose
Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.
Design/methodology/approach
This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.
Findings
Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.
Originality/value
At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.
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Yan Li, Ming K. Lim, Weiqing Xiong, Xingjun Huang, Yuhe Shi and Songyi Wang
Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental…
Abstract
Purpose
Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.
Design/methodology/approach
This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.
Findings
The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.
Research limitations/implications
The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.
Originality/value
In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.
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Manikandan R. and Raja Singh R.
The purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor…
Abstract
Purpose
The purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor (IGBT) faults has become an essential topic of study. Demand for sustainable energy sources has been prompted by rising environmental pollution and energy requirements. Renewable energy has been identified as a viable substitute for conventional fossil fuel energy generation. Because of its rapid installation time and adaptable expenditure for construction scale, wind energy has emerged as a great energy resource. Power converter failure is particularly significant for the reliable operation of wind power conversion systems because it not only has a high yearly fault rate but also a prolonged downtime. The power converters will continue to operate even after the failure, especially the open-circuit fault, endangering their other parts and impairing their functionality.
Design/methodology/approach
The most widely used signal processing methods for locating open-switch faults in power devices are the short-time Fourier transform and wavelet transform (WT) – based on time–frequency analysis. To increase their effectiveness, these methods necessitate the intensive use of computational resources. This study suggests a fault detection technique using empirical mode decomposition (EMD) that examines the phase currents from a power inverter. Furthermore, the intrinsic mode function’s relative energy entropy (REE) and simple logical operations are used to locate IGBT open switch failures.
Findings
The presented scheme successfully locates and detects 21 various classes of IGBT faults that could arise in a two-level three-phase voltage source inverter (VSI). To verify the efficacy of the proposed fault diagnosis (FD) scheme, the test is performed under various operating conditions of the power converter and induction motor load. The proposed method outperforms existing FD schemes in the literature in terms of fault coverage and robustness.
Originality/value
This study introduces an EMD–IMF–REE-based FD method for VSIs in wind turbine systems, which enhances the effectiveness and robustness of the FD method.
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Pietro Miglioranza, Andrea Scanu, Giuseppe Simionato, Nicholas Sinigaglia and America Califano
Climate-induced damage is a pressing problem for the preservation of cultural properties. Their physical deterioration is often the cumulative effect of different environmental…
Abstract
Purpose
Climate-induced damage is a pressing problem for the preservation of cultural properties. Their physical deterioration is often the cumulative effect of different environmental hazards of variable intensity. Among these, fluctuations of temperature and relative humidity may cause nonrecoverable physical changes in building envelopes and artifacts made of hygroscopic materials, such as wood. Microclimatic fluctuations may be caused by several factors, including the presence of many visitors within the historical building. Within this framework, the current work is focused on detecting events taking place in two Norwegian stave churches, by identifying the fluctuations in temperature and relative humidity caused by the presence of people attending the public events.
Design/methodology/approach
The identification of such fluctuations and, so, of the presence of people within the churches has been carried out through three different methods. The first is an unsupervised clustering algorithm here termed “density peak,” the second is a supervised deep learning model based on a standard convolutional neural network (CNN) and the third is a novel ad hoc engineering feature approach “unexpected mixing ratio (UMR) peak.”
Findings
While the first two methods may have some instabilities (in terms of precision, recall and normal mutual information [NMI]), the last one shows a promising performance in the detection of microclimatic fluctuations induced by the presence of visitors.
Originality/value
The novelty of this work stands in using both well-established and in-house ad hoc machine learning algorithms in the field of heritage science, proving that these smart approaches could be of extreme usefulness and could lead to quick data analyses, if used properly.
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Nazia Wahid, Nosheen Fatima Warraich and Muzammil Tahira
Assessing the research performance of researchers offers inducement toward excellence in research. This study aims to analyze the research productivity of the most prolific…
Abstract
Purpose
Assessing the research performance of researchers offers inducement toward excellence in research. This study aims to analyze the research productivity of the most prolific authors of Pakistan considering their trends toward publications, citations and collaboration.
Design/methodology/approach
Top 100 authors from the top 10 Pakistani universities from Web of Science over the 10 years with the rigorous data cleaning process were selected. Scientometric analysis techniques were carried out to evaluate the research profile of these authors.
Findings
The findings revealed that majority of the productive authors were male working in the position of Professor in the physical sciences area. The publications and citations gradually increase with time. They preferred to collaborate for their publications, while first authorship publications were found less in number. Moreover, the propensity to collaborate at the international level increases double-fold from the first five years to the next five years period. In addition, the position of the authors was explored among different performance metrics. The finding exhibits variation in the ranking of authors among them. The impact of numbers of authors, funding status, publication of articles, presence of collaboration and international collaboration on the dependent variable and citation count was insignificant. However, the publication of review papers has a significant impact on the citation counts.
Practical implications
Findings have significant implications for policymakers to make maximum opportunities for researchers to strengthen linkages for collaboration and increase the funding prospects.
Originality/value
Studies on this topic are scarce, and therefore, this study provides useful recommendations to researchers and institutes to improve research productivity.
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Xiangdi Yue, Yihuan Zhang, Jiawei Chen, Junxin Chen, Xuanyi Zhou and Miaolei He
In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and…
Abstract
Purpose
In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.
Design/methodology/approach
This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.
Findings
This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.
Originality/value
To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.
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Mengdi Zhang, Aoxiang Chen, Zhiheng Zhao and George Q. Huang
This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows…
Abstract
Purpose
This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations.
Design/methodology/approach
The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs.
Findings
This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization.
Research limitations/implications
This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work.
Practical implications
This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making.
Originality/value
This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.
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Eminda Ishan De Silva, Gayithri Niluka Kuruppu and Sandun Dassanayake
The non-fungible token (NFT) market had undergone dramatic growth and a sudden decline during 2021–2022. The market experienced a surge in prices in late 2021 and early 2022, with…
Abstract
Purpose
The non-fungible token (NFT) market had undergone dramatic growth and a sudden decline during 2021–2022. The market experienced a surge in prices in late 2021 and early 2022, with NFTs being sold at inflated prices. Despite this, by April 2022, the market underwent a correction, and the prices of NFTs returned to more reasonable levels. This can be a result of imitating the actions or judgments of a larger group, which is not systematically proven yet. Therefore, this study systematically investigates the applicability of herding behavior in the NFT market.
Design/methodology/approach
This research employs cross-sectional absolute deviation (CSAD) of returns and ordinary least squares (OLS) to test herding behavior with moving time windows of 10, 20 and 30 days based on the sales data collected from public interface of OpenSea between July 1, 2021 and June 30, 2022. Additionally, NFT-related keyword usage analysis is done for the detected herding periods.
Findings
As per the results of the data analyzed, herding behavior was evidenced using 10-, 20- and 30-day time windows from July 1, 2021 to June 30, 2022because of media movement. The findings revealed that this behavior was present and aligned with the overall behavior of the market.
Originality/value
This study introduces CSAD to examine herding behavior patterns within the NFT market. Complementing this method, keyword count-based analysis is employed to identify the underlying causes of herding behavior. Through this comprehensive approach, this study not only uncovers the roots of herding behavior but also offers an assessment of the time windows during which it occurs, considering the plausible socioeconomic contexts that influence these trends.
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Gopal Kumar, Felix T.S. Chan and Mohit Goswami
The coronavirus (COVID-19) is the worst pandemic in recent memory in terms of its economic and social impacts. Deadly second wave of COVID-19 in India shook the country and…
Abstract
Purpose
The coronavirus (COVID-19) is the worst pandemic in recent memory in terms of its economic and social impacts. Deadly second wave of COVID-19 in India shook the country and reshaped the ways organizations functions and societies behave. Medical infrastructure was unaffordable and unsupportive which created high distress in the Indian society, especially for poor. At this juncture, some pharmaceutical firms made a unique social investment when they reduced price of drugs used to treat COVID-19 patients. This study aims to examine how the market and the society respond to the price reduction announcement during the psychological distress of COVID-19.
Design/methodology/approach
Market reactions have been analyzed by conducting an event study on stock market data and visual analytics-based sentiment analysis on Twitter data.
Findings
Overall, this study finds positive abnormal returns on the day and around the day of event. Interestingly, this study finds that returns during the time of high distress are significantly higher. Sentiment analysis conveys that net sentiment is favorable to the pharmaceutical firms around the day of event and it sustains more during the time of high distress.
Originality/value
This study is unique in contributing to the business and industrial management literature by highlighting market reactions to social responsibility of business during the time of psychological distress in emerging economies.
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Gang Yu, Zhiqiang Li, Ruochen Zeng, Yucong Jin, Min Hu and Vijayan Sugumaran
Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due…
Abstract
Purpose
Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.
Design/methodology/approach
Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.
Findings
The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.
Originality/value
This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.
Details