首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    Studies from Lomonosov Moscow State University Further Understanding of Machine Learning (Generating Synthetic Images of Gamma-ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks)

    77-78页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news originating from Moscow, Russia, by NewsRx correspondents, research stated, “In recent years, machine learning techniques have seen huge adoption in astronomy applications. In this work, we discuss the generation of realistic synthetic images of gamma-ray events, similar to those captured by imaging atmospheric Cherenkov telescopes (IACTs), using the generative model called a conditional generative adversarial network (cGAN).” Financial support for this research came from Russian Science Foundation (RSF). Our news journalists obtained a quote from the research from Lomonosov Moscow State University, “The significant advantage of the cGAN technique is the much faster generation of new images compared to standard Monte Carlo simulations. However, to use cGAN-generated images in a real IACT experiment, we need to ensure that these images are statistically indistinguishable from those generated by the Monte Carlo method. In this work, we present the results of a study comparing the parameters of cGAN-generated image samples with the parameters of image samples obtained using Monte Carlo simulation. The comparison is made using the so-called Hillas parameters, which constitute a set of geometric features of the event image widely employed in gamma-ray astronomy. Our study demonstrates that the key point lies in the proper preparation of the training set for the neural network. A properly trained cGAN not only excels at generating individual images but also accurately reproduces the Hillas parameters for the entire sample of generated images.”
      原文链接:
    • NETL
    • NSTL
    • Pleiades Publishing Inc

    Data from Federal University Broaden Understanding of Machine Learning (Models for predicting coffee yield by chemical characteristics of soil and leaves using Machine Learning)

    78-79页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news originating from Federal University by NewsRx editors, the research stated, “Coffee farming constitutes a substantial economic resource. This crop represents a source of income for several countries due to the high consumption of coffee drinks worldwide.” The news journalists obtained a quote from the research from Federal University: “Precise management of coffee crops involves collecting crop attributes (soil, plant), mapping, and applying inputs according to the plant’s needs. This differentiated management is Precision Coffee Growing stands out for its increased yield and sustainability. Thus, this research aimed to predict yield in coffee plantations by applying machine learning methodologies to soil and plant attributes. The data was obtained in a field of 54.6ha during two consecutive seasons, applying varied fertilization rates according to recommendations of soil attributes maps. Furthermore, monitoring leaf analysis maps seeks to establish a correlation between input parameters and yield prediction. The machine learning models obtained from this data efficiently predicted coffee yield. The best model demonstrated predictive fit results of 0.86 Pearson correlation. Soil chemical attributes did not interfere with the prediction models, indicating that this analysis can be dispensed with when applying these models.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Skolkovo Institute of Science and Technology Researchers Release New Data on Machine Learning (Machine learning-based infant crying interpretation)

    79-80页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news originating from Moscow, Russia, by NewsRx editors, the research stated, “Crying is an inevitable character trait that occurs throughout the growth of infants, under conditions where the caregiver may have difficulty interpreting the underlying cause of the cry. Crying can be treated as an audio signal that carries a message about the infant’s state, such as discomfort, hunger, and sickness.” Our news correspondents obtained a quote from the research from Skolkovo Institute of Science and Technology: “The primary infant caregiver requires traditional ways of understanding these feelings. Failing to understand them correctly can cause severe problems. Several methods attempt to solve this problem; however, proper audio feature representation and classifiers are necessary for better results. This study uses time-, frequency-, and time-frequency-domain feature representations to gain in-depth information from the data. The time-domain features include zero-crossing rate (ZCR) and root mean square (RMS), the frequency-domain feature includes the Mel-spectrogram, and the time-frequency-domain feature includes Mel-frequency cepstral coefficients (MFCCs). Moreover, time-series imaging algorithms are applied to transform 20 MFCC features into images using different algorithms: Gramian angular difference fields, Gramian angular summation fields, Markov transition fields, recurrence plots, and RGB GAF. Then, these features are provided to different machine learning classifiers, such as decision tree, random forest, K nearest neighbors, and bagging. The use of MFCCs, ZCR, and RMS as features achieved high performance, outperforming state of the art (SOTA).”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    New Findings from University of Stuttgart Update Understanding of Machine Learning (Machine Learning Based Spray Process Quantification)

    80-81页
    查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting originating in Stuttgart, Germany, by NewsRx journalists, research stated, “Droplet size distribution and liquid loading are two of the most important properties to characterise spray processes. For example, nitrogen oxide (NOx) formation of liquid fuel-operated combustion systems is dominated by mixture homogenisation that is heavily dependent on atomization.” Financial supporters for this research include German Research Foundation (DFG), German Aerospace Centre (DLR), Stuttgart Center for Simulation Science (SimTech). The news reporters obtained a quote from the research from the University of Stuttgart, “While phase Doppler interferometry (PDI) is the technique of choice to measure droplet size distribution, shadowgraphy enables the detection of liquid bodies of arbitrary shape, e.g. ligaments, and thus can be used to delineate primary atomization. For this purpose, a data analysis tool is developed that enables a quantitative description of droplet size and the primary break-up process simultaneously. In this context, machine learning-based (ML) object detection provides a generally applicable approach that is used here to characterise a pressure swirl water spray. The deep neural networks are trained using synthetic training data with physics-informed domain randomisation. Two different network architectures (namely Mask R-CNN and SparseInst), backbones (ResNet50 and ResNet101) and several different training approaches are evaluated. The inference accuracy of the ML models is validated against PDI measurements in terms of droplet size distribution. The best-performing ML model provides a robust detection method for a wide range of measurement conditions. The validated ML model is used to characterise the primary break-up to delineate the effect of aerodynamic forces on the atomization of a hollow cone pressure swirl spray in high momentum gaseous co-flow.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Findings from University of Bordeaux Has Provided New Data on Machine Learning (The Explanatory Role of Machine Learning In Molecular Biology)

    81-81页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting from Bordeaux, France, by NewsRx editors, the research stated, “The philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding.” The news correspondents obtained a quote from the research from the University of Bordeaux, “Focusing on the field of molecular biology, I argue that in practice machine learning is often used with explanatory aims. More specifically, I argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Researchers at Guangxi University Target Robotics (Vibration Suppression of Welding Robot Based On Chaos-regression Tree Dynamic Model)

    82-82页
    查看更多>>摘要：A new study on Robotics is now available. According to news originating from Nanning, People’s Republic of China, by NewsRx correspondents, research stated, “A torque feedforward vibration suppression tactic, premised on the chaotic-regression tree dynamic model, is proposed to enhance the precision of motion in welding robots during low-velocity movement and to curtail robot oscillation. Chaotic theory is employed to scrutinize the nonlinear characteristics inherent in joint torque amid the low-velocity operation of welding robots.” Financial supporters for this research include Guangxi Science and Technology Major Project, Guangxi Science and Technology Major Program. Our news journalists obtained a quote from the research from Guangxi University, “A holistic approach is adopted toward the non-rigid body dynamics component of the joint torque across all joints. By merging ordered fitting with unordered regression tree techniques, the robot kinematic model is derived. The parameters for phase space reconstruction are identified through autocorrelation and pseudo-nearest neighbor methods, improving nonlinear dynamic prediction accuracy via the phase space reconstruction process. To boost the tracking precision of specific motion segments within the trajectory planning, a torque feedforward compensation control algorithm paired with trajectory planning is proposed. Experiments were conducted in low-velocity welding on a 6R welding robot platform, revealing that the improved torque compensation strategy proposed reduces the average position error by 25.2% in comparison with traditional torque compensation tactics.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Recent Findings from Hangzhou Dianzi University Provides New Insights into Robotics (A Regularization-patching Dual Quaternion Optimization Method for Solving the Hand-eye Calibration Problem)

    83-83页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting out of Hangzhou, People’s Republic of China, by NewsRx editors, research stated, “The hand-eye calibration problem is an important application problem in robot research. Based on the 2-norm of dual quaternion vectors, we propose a new dual quaternion optimization method for the hand-eye calibration problem.” Financial support for this research came from Natural Science Foundation of Zhejiang Province. Our news journalists obtained a quote from the research from Hangzhou Dianzi University, “The dual quaternion optimization problem is decomposed to two quaternion optimization subproblems. The first quaternion optimization subproblem governs the rotation of the robot hand. It can be solved efficiently by the eigenvalue decomposition or singular value decomposition. If the optimal value of the first quaternion optimization subproblem is zero, then the system is rotationwise noiseless, i.e., there exists a ‘perfect’ robot hand motion which meets all the testing poses rotationwise exactly. In this case, we apply the regularization technique for solving the second subproblem to minimize the distance of the translation. Otherwise we apply the patching technique to solve the second quaternion optimization subproblem. Then solving the second quaternion optimization subproblem turns out to be solving a quadratically constrained quadratic program. In this way, we give a complete description for the solution set of hand-eye calibration problems. This is new in the hand-eye calibration literature.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Investigators at Department of Computer Sciences and Engineering Detail Findings in Artificial Intelligence (An Efficient Cyber Threat Prediction Using a Novel Artificial Intelligence Technique)

    84-84页
    查看更多>>摘要：Current study results on Artificial Intelligence have been published. According to news originating from Uttar Pradesh, India, by NewsRx correspondents, research stated, “Digital applications are ruling today’s world with their advancement. However, offering security for that digital application is an important and complex task.” Our news journalists obtained a quote from the research from the Department of Computer Sciences and Engineering, “Several detection-based security models have existed in the Artificial Intelligence (AI) vision. Still, the problem in threat detection has not ended because of the unique behavior of the different attacks. So, the present research has introduced a novel Cuttlefish-based Peephole Long Short Term Memory (CbPLSTM) model proposed for predicting the cyber threat from the data defends against attacks. Initially, data were preprocessed by removing noise from the data using the noise filtering function. Then, the refined data is imported to the classification layer of the CbP-LSTM for performing the feature extraction and attack prediction tasks. Moreover, the proposed CbP-LSTM model was implemented in the Python tool with several performance metrics, whereas the parameters were calculated, such as accuracy, precision, Recall, and F-score.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Findings on Machine Learning Detailed by Investigators at Russian Academy of Sciences (Prediction of Drug-like Compounds Solubility In Supercritical Carbon Dioxide: a Comparative Study Between Classical Density Functional Theory and Machine ...)

    85-86页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting out of Ivanovo, Russia, by NewsRx editors, research stated, “Supercritical carbon dioxide (scCO(2)) plays an essential role in various technological procedures, making the solubility of drugs in scCO(2) a crucial aspect of the drug formulation process. This study focuses on utilizing theoretical approaches to predict the solubility of drug-like compounds in scCO2 in order to select the optimum parameters for subsequent experimental procedures.” Financial supporters for this research include Russian Science Foundation (RSF), Russian Science Foundation (RSF). Our news journalists obtained a quote from the research from the Russian Academy of Sciences, “Several machine learning models were developed and compared with a previously established theoretical approach based on classical density functional theory (cDFT). The CatBoost model, utilizing alvaDesc descriptors, demonstrated reasonably accurate predictions for the solubility of 187 drugs (AARD = 1.8%). Meanwhile, the CatBoost model, incorporating CDK descriptors and melting points of drugs as input parameters, exhibited satisfactory accuracy (AARD = 14.3%) in extrapolating predictions for new compounds. Comparing the results between the machine learning approach and the cDFT-based one revealed, on average, a higher accuracy and faster prediction speed for the former. However, cDFT demonstrated a more physical behavior of solubility isotherms compared with the machine learning models. This was particularly evident when the ML models struggled to accurately extrapolate solubility values beyond the experimental range of parameters in the supercritical state.”
      原文链接:
    • NETL
    • NSTL
    • Amer Chemical Soc

    Research on Robotics Published by Researchers at University of Kragujevac (Algorithm for optimal path planning of a robotic lawnmower)

    86-87页
    查看更多>>摘要：Current study results on robotics have been published. According to news reporting out of Cacak, Serbia, by NewsRx editors, research stated, “Horticultural lawns include areas of land that are covered with sown grass in urban areas and require periodic mowing in order to best preserve their aesthetic and functional purpose.” Financial supporters for this research include Ministry of Education, Science And Technological Development of The Republic of Serbia. The news reporters obtained a quote from the research from University of Kragujevac: “This activity may require significant involvement of human and material resources, especially in large public areas such as sports fields, parks and picnic areas. Thanks to the progress of computer technology, this tiresome routine work is gradually being taken over by robotic mowers that can bring significant savings in human and material resources. In this paper, the problem of robotic lawnmowers related to path planning in the working environment is presented. Commercial solutions to date are mostly based on choosing a random straight line path until the mower reaches a lawn boundary or obstacle, after which it turns in the opposite direction and continues mowing in an arbitrary direction.”
      原文链接:
    • NETL
    • NSTL