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    Research Reports from Warsaw University of Life Sciences Provide New Insights in to Machine Learning (Custom Loss Functions in XGBoost Algorithm for Enhanced Cri tical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard)

    29-30页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news reporting out of Warsaw, Poland, by NewsRx ed itors, research stated, "The advancement of machine learning in industrial appli cations has necessitated the development of tailored solutions to address specif ic challenges, particularly in multi-class classification tasks." Our news editors obtained a quote from the research from Warsaw University of Li fe Sciences: "This study delves into the customization of loss functions within the eXtreme Gradient Boosting (XGBoost) algorithm, which is a critical step in e nhancing the algorithm's performance for specific applications. Our research is motivated by the need for precision and efficiency in the industrial domain, whe re the implications of misclassification can be substantial. We focus on the dri ll-wear analysis of melaminefaced chipboard, a common material in furniture pro duction, to demonstrate the impact of custom loss functions. The paper explores several variants of Weighted Softmax Loss Functions, including Edge Penalty and Adaptive Weighted Softmax Loss, to address the challenges of class imbalance and the heightened importance of accurately classifying edge classes. Our findings reveal that these custom loss functions significantly reduce critical errors in classification without compromising the overall accuracy of the model. This rese arch not only contributes to the field of industrial machine learning by providi ng a nuanced approach to loss function customization but also underscores the im portance of context-specific adaptations in machine learning algorithms."
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    Shanghai Maritime University Researchers Update Current Data on Support Vector M achines (Rolling bearing fault diagnosis based on RQA with STD and WOA-SVM)

    29-29页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on are presented in a new r eport. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stated, "A rolling bearing fault diagnosis method based on Recursive Quantitative Analysis (RQA) combined with time domain feature extraction and Whale Optimization Algorithm Support Vector Machine (WOA-SVM) is proposed." Our news editors obtained a quote from the research from Shanghai Maritime Unive rsity: "Firstly, the recurrence graph of the vibration signal is drawn, and the nonlinear feature parameters in the recurrence graph combined with Standard Devi ation (STD) are extracted by recursive quantitative analysis method to generate feature vectors; after that, in order to construct the optimal support vector ma chine model, the Whale Optimization Algorithm is used to optimize the c and g pa rameters. Finally, both Recursive Quantitative Analysis and standard deviation a re combined with the WOA-SVM model to perform fault diagnosis of rolling bearing s. The rolling bearing datasets from Case Western Reserve University and Jiangna n University were used for example analysis, and the fault identification accura cy reached 100 % and 95.00%, respectively. Compared to other methods, the method proposed in this paper has higher diagnostic accuracy and wide practical applicability, and the risk of accidents can be reduced thro ugh accurate fault diagnosis, which is also important for safety and environment al policies." According to the news editors, the research concluded: "This research originated in the field of mechanical fault diagnosis to solve the problem of fault diagno sis of rolling bearings in industrial production, it builds on previous research and explores new methods and techniques to fill some gaps in the field of mecha nical fault diagnosis."
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    Reports from Departamento de Informatica y Ciencias de la Computacion Advance Kn owledge in Machine Learning (Real-time impulse response: a methodology based on Machine Learning approaches for a rapid impulse response generation for real-tim e ...)

    30-31页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news originating from Quito, Ecuador, by NewsRx correspondents, research stated, "Simulation of high-definition binaural room im pulse responses using conventional approaches involves a significant amount of c omputational resources, resulting in high computational time, making these appro aches incapable of performing realtime high quality acoustic virtual reality. T his research implemented a methodology for the rapid impulse response generation using the position of a moving listener inside a fixed sound field." Our news correspondents obtained a quote from the research from Departamento de Informatica y Ciencias de la Computacion: "The rapid generation of the impulse r esponse is performed using its representative compressed dimension, with a small er dimension than the original impulse response, learned by variational autoenco ders and long short-term memory neural networks. First, the methodology selects a representative number of impulse responses covering the area of interest using a reliable room acoustic simulator. Second, it generates a dataset with suffici ent impulse responses uniformly distributed through a data augmentation approach using a modified bilinear interpolation from the impulse responses previously s imulated. Third, it applies an unsupervised model to positionally cluster the im pulse responses to reduce the variability of the impulse responses in the given environment. Fourth, it splits the impulse response into time segments and gener ates a dataset per segment and cluster. Fifth, it trains a variational autocoder with a long short-term memory neural network model for each time segment cluste r of impulse responses to infer the correspondent compressed impulse response pa rt. In summary, the impulse response is generated by assigning the current liste ner position to the corresponding cluster and executing the decoders of the vari ational autoencoders with long short-term memory, trained previously. The findin gs are encouraging; the normalized mean absolute error of the impulse responses gathered by the interpolator and the impulse responses generated by the proposed model is less than 15% in the 88% of impulse respon ses reserved for testing."
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    New Findings from Linyi University Describe Advances in Machine Learning (An Ada ptive Machine Learning Framework for Multi-Scenes Road Surface Weather Condition Monitoring)

    31-32页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting from Shandong, Peopl e's Republic of China, by NewsRx journalists, research stated, "Timely road surf ace condition (RSC) monitoring and maintenance significantly influences road saf ety." Our news editors obtained a quote from the research from Linyi University: "The current RSC relies on fixed road surveillance cameras and in-vehicle cameras. Ho wever, the fixed camera demands higher precision, while the in-vehicle camera re quires higher timeliness. To address these challenges, this paper introduces an adaptive machine learning framework for simultaneous road surface detection on b oth device types. Initially, a convolutional neural network -based differentiati on module identifies image sources. Subsequently, an adaptive algorithm switchin g mechanism leads to the development of two algorithms improved upon the real-ti me object detection algorithms. At last, extensive experiments with datasets col lected from Ontario, Canada and Iowa U.S. validate the framework."
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    Findings on Machine Learning Reported by Investigators at Northeastern Universit y (Rapid Detection of Iron Ore Grades Based On Fractional-order Derivative Spect roscopy and Machine Learning)

    32-33页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating from Shenyang, People's Republic of China, by NewsRx correspondents, research stated, "The time-consumin g nature of chemical testing techniques makes them lag behind mineral processing . Therefore, this article combines visible-infrared reflectance spectroscopy wit h machine learning (ML) algorithms to achieve rapid detection of iron ore grades and meet the requirements of mining production." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from Northeastern University , "First, the standard normal variate (SNV) and de-trending (DT) are used to eli minate noise and baseline drift in the original spectral data. Then, extraneous signals are removed using direct orthogonal signal correction (DOSC). In additio n, fractional-order derivative (FOD) is performed on the DOSC spectrum to furthe r amplify the spectral details. To extract spectral features and reduce the spec tral dimension, a multilayer incremental extreme learning machine autoencoder (M IELM-AE) is proposed in this article. MIELM-AE can automatically match the optim al number of network nodes and network layers to minimize the reconstruction err or. The experimental results show that the Pearson correlation coefficient ( R-2 ) of the extreme learning machine (ELM) built using MIELM-AE improves from 0.71 5 to 0.821, compared with the ELM built without the dimensionality reduction met hod. To increase the measurement accuracy, this article uses Tikhonov regulariza tion and truncated singular value decomposition (TSVD) to alleviate the ill-cond itioned matrix of the hidden layer of the ELM and uses the incremental method to match the optimal network nodes. Finally, double-regularization incremental ELM (DRIELM) is proposed in this article."
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    Researchers from Guangdong University of Technology Detail New Studies and Findi ngs in the Area of Machine Learning (Machine Learning for Infrared Spectral Clas sification of Polyvinyl Butyrals With Identical Chemical Groups: an Example for ...)

    33-34页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting out of Guangzhou, People's Republi c of China, by NewsRx editors, research stated, "Machine learning (ML) is extens ively applied in chemistry, particularly in vibrational spectroscopy. However, f ew teaching examples effectively demonstrate the capabilities of ML in classifyi ng polymeric materials, exhibiting subtle spectral differences that elude visual discrimination." Financial supporters for this research include Fenghua Co. Ltd, Fenghua Co. Ltd. . Our news journalists obtained a quote from the research from the Guangdong Unive rsity of Technology, "This study presents a teaching example specifically tailor ed for undergraduate students to acquire the skills necessary to employ ML model s in the classification of infrared spectral data from different types of polyvi nyl butyrals (PVBs). The course encompasses fundamental knowledge of PVB structu re and synthesis, a comprehensive spectral analysis workflow for constructing cl assification models, specific data processing techniques, and practical implemen tation of a student-synthesized PVB product in a laboratory demonstration. Asses sment of students' knowledge acquisition is conducted through assignments, and s tudent attitudes toward this course via submitted self-reflection surveys are di scussed. This study underscores the efficacy of classroom examples in developing students' abilities and fostering their interest in amalgamating chemistry and artificial intelligence."
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    New Artificial Intelligence Study Findings Have Been Reported from La Trobe Univ ersity (Learning from artificial intelligence researchers about international bu siness implications)

    34-35页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting out of La Trob e University by NewsRx editors, research stated, "Artificial intelligence is a d ynamic and emerging form of technological innovation that has numerous ramificat ions for international business managers." The news reporters obtained a quote from the research from La Trobe University: "The aim of this article is to obtain commentary from researchers about the role artificial intelligence will play in the global arena. This includes asking que stions about how it will affect internationalization processes and whether it wi ll lead to more international collaboration." According to the news editors, the research concluded: "Well-known researchers p rovide advice on what international business managers should do in terms of stay ing competitive but also how they can integrate learning from artificial intelli gence into their business operations. Lastly, suggestions for future research re garding the interplay between international business and artificial intelligence are provided." For more information on this research see: Learning from artificial intelligence researchers about international business implications. Thunderbird International Business Review, 2024. The publisher for Thunderbird International Business Review is Wiley.
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    Data from Zhejiang University Update Knowledge in Technology (The DMF: Fault Dia gnosis of Diaphragm Pumps Based on Deep Learning and Multi-Source Information Fu sion)

    35-35页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on technology have b een published. According to news reporting out of Hangzhou, People's Republic of China, by NewsRx editors, research stated, "Effective fault diagnosis for diaph ragm pumps is crucial." Financial supporters for this research include Natural Science Foundation of Sha ndong Province; Open Foundation of State Key Laboratory of Compressor Technology . The news correspondents obtained a quote from the research from Zhejiang Univers ity: "This paper proposes a diaphragm pump fault diagnosis method based on deep learning and multi-source information fusion (DMF). The time-domain features, fr equency-domain features, and modulation features are extracted from the vibratio n signals from eight different positions. After feature enhancement and data pre processing, the features are input into auto encoders (AE), convolutional neural networks (CNN), and support vector machines (SVM) to obtain the diagnostic resu lts. The results indicate that the DMF method achieves a fault diagnosis accurac y of 99.98%, which is on average 9.09% higher than us ing a single diagnostic model."
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    Kobe University Researcher Adds New Findings in the Area of Machine Learning (Id entifying Key Issues in Integration of Autonomous Ships in Container Ports: A Ma chine-Learning-Based Systematic Literature Review)

    36-36页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news originating from Kobe, Japan, by NewsRx corr espondents, research stated, "Autonomous ships have the potential to increase op erational efficiency and reduce carbon footprints through technology and innovat ion." Funders for this research include Jsps Kakenhi; Danish Agency For Higher Educati on And Science: International Network Grant: Global Ports And Shipping. The news editors obtained a quote from the research from Kobe University: "Howev er, there is no comprehensive literature review of all the different types of pa pers related to autonomous ships, especially with regard to their integration wi th ports. This paper takes a systematic review approach to extract and summarize the main topics related to autonomous ships in the fields of container shipping and port management. A machine learning method is used to extract the main topi cs from more than 2000 journal publications indexed in WoS and Scopus. The resea rch findings highlight key issues related to technology, cybersecurity, data gov ernance, regulations, and legal frameworks, providing a different perspective co mpared to human manual reviews of papers." According to the news editors, the research concluded: "Our search results confi rm several recommendations. First, from a technological perspective, it is advis ed to increase support for the research and development of autonomous underwater vehicles and unmanned aerial vehicles, establish safety standards, mandate test ing of wave model evaluation systems, and promote international standardization. Second, from a cyber-physical systems perspective, efforts should be made to st rengthen logistics and supply chains for autonomous ships, establish data govern ance protocols, enforce strict control over IoT device data, and strengthen cybe rsecurity measures. Third, from an environmental perspective, measures should be implemented to address the environmental impact of autonomous ships. This can b e achieved by promoting international agreements from a global societal standpoi nt and clarifying the legal framework regarding liability in the event of accide nts."
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    Study Findings on Machine Learning Discussed by Researchers at Graduate Universi ty of Advanced Technology (Vulnerability of the rip current phenomenon in marine environments using machine learning models)

    37-37页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting from Kerman, I ran, by NewsRx journalists, research stated, "Hidden and perilous rip currents a re one of the primary factors leading to drownings of beach swimmers. By identif ying the coastal areas with the highest likelihood of generating rip currents, i t becomes possible to prevent fatalities and mitigate economic losses associated with these hazardous currents." Financial supporters for this research include National Natural Science Foundati on of China; Graduate University of Advanced Technology; Guangdong Ocean Univers ity. The news journalists obtained a quote from the research from Graduate University of Advanced Technology: "Rip currents are characterized as streams of water mov ing towards the open sea, forming within the area where waves break, due to vari ations in wave-induced radiation stresses and pressure along the coastline. This study utilized nine different Machine Learning (ML) models, including M5 Model Tree (MT), Multivariate Adaptive Regression Spline (MARS), Gene Expression Progr amming (GEP), Evolutionary Polynomial Regression (EPR), Random Forest (RF), Supp ort Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Stacked ML models, to estimate the Relative Tide Range (RTR) va lues for 50 southern beaches in China. Through this approach, we gathered a reli able dataset from prior research conducted on the southern coast of China. In th is study, two parameters, namely dimensionless fall velocity parameter (O) and t ide range (TR) are used to predict the vulnerability of rip current event. The r esults of the AI models were assessed by various statistical analyses (Correlati on of Coefficient [R], Root Mean Square Er ror [RMSE], violin diagram, heatmap, and t aylor diagram) for training and testing stages. Accordingly, the MARS model exhi bited superior performance compared to other AI models in accurately predicting the RTR value."
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