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    Findings on Robotics Reported by Investigators at Shanghai Jiao Tong University (Real-time Interpolation With Low-pass Filtering for Five-axis Hybrid Machining Robots)

    48-49页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "This paper proposes a new interpolation approach for five-axis hybrid robots to enhance the surface quality and efficiency of machining processes. Hybrid robots have the potential to manufacture large, complex structural parts with high flexibility and a favorable load-to-weight ratio." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Shanghai Jiao Tong University, "To interpolate G01 commands, the proposed method implements finite impulse response (FIR) filters rather than splines. These filters smoothly synchronize the motions of tool center points (TCPs) and tool orientation vectors (TOVs), and are designed for each linear segment online with geometric programming while considering joint constraints. Additionally, the adjacent linear segments are locally blended with bounded geometric errors under kinematic constraints. The proposed method generates time-optimal trajectories with jerk-limited joint motions in real-time, making it more effective than current interpolation methods."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Data on Machine Learning Reported by Researchers at Anhui University of Technology (Machine Learning Prediction of Pyrolytic Sulfur Migration Based On Coal Compositions)

    49-50页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Anhui, People's Republic of China, by NewsRx journalists, research stated, "Understanding the sulfur migration during pyrolysis of coals especially high -sulfur coals is important. However, structural complexity and diversity of coals make it face huge challenge." Financial supporters for this research include National Natural Science Foundation of China (NSFC), Key Project of Scien- tific Research Plan of Anhui Province, Provincial Innova- tive Group for Processing & Clean Utilization of Coal Resource. The news reporters obtained a quote from the research from the Anhui University of Technology, "In this study, a predictive model for morphological sulfur migration was developed using machine learning based on proximate analysis, ultimate analysis, sulfur forms of raw coal, ash composition, and blending ratio of coal. Three algorithms, i.e., Random Forest, XGBoost, and LightGBM were introduced and compared. The results show that six features are sufficient to accurately predict the products (R-2 ＞0.9, RMSE ＜3.01%). LightGBM model has the advantages of better accuracy, generalization, efficiency, and performance, and Hyperopt has a higher upper limit than Grid-search. H content has a significant effect on S content in chars (St,d(char)) and increasing H content from 5.0-5.3 wt% facilitates desulfurization. In addition, CaO, K2O and Fe2O3 also have remarkable effects on St,d(char). Higher H and volatile contents have a greater effect on thiophene removal in char."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Robotics Findings Reported from Harbin University (Research On the Compliant Control of Electrohydraulic Servo Drive Force/position Switching for a Lower Limb Exoskeleton Robot)

    50-51页
    查看更多>>摘要：Investigators discuss new findings in Robotics. According to news reporting originating from Harbin, People's Republic of China, by NewsRx correspondents, research stated, "In order to improve the flexibility of the foot landing of a lower limb exoskeleton robot based on an electro-hydraulic servo drive and to reduce its impact with the ground, an active compliance control method for force/position switching based on fuzzy control is proposed. According to the mathematical model of each component of the electro-hydraulic servo system of the core drive unit of the lower limb exoskeleton robot, the transfer functions of the position control system and the force control system are obtained respectively, and then its specific working characteristics are studied." Our news editors obtained a quote from the research from Harbin University, "Before the feet hit the ground, the position servo control system under the action of a fuzzy controller is used to achieve the movement of legs in free and unconstrained space, and the moment the foot touches the ground, the system is switched to a force servo control system to precisely control the output force, thereby reducing the rigid impact between the feet. In the meantime, the validity of the designed switching method and controller is verified by the joint simulation of MATLAB and AMESIM."
      原文链接:
    • NETL
    • NSTL
    • Univ Zagreb Fac Mechanical Engineering & Naval Architecture

    Data from Ahsanullah University of Science and Technology Provide New Insights into Machine Learning (Properties Prediction of Composites Based On Machine Learning Models: a Focus On Statistical Index Approaches)

    51-52页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting originating in Dhaka, Bangladesh, by NewsRx journalists, research stated, "Composites have a wide range of applications across various industries due to their high strength-to-weight ratio, corrosion resistance, durability, versatility, and lightweight structures. However, manufacturing reinforced composites and the various tests they undergo for their appropriate applications are extensive and expensive." Financial supporters for this research include National Natural Science Foundation of China-Shandong Joint Fund, Science and Technology-based Small and Medium-sized Enterprise Innovation Ability Improvement Project of Shandong Province. The news reporters obtained a quote from the research from the Ahsanullah University of Science and Technology, "Because of this, many researchers have employed the machine learning (ML) technique to evaluate the significance of the process parameters and predict the properties for effective composite design and their widespread applications. Therefore, this study critically reviewed and compared the different ML models applied to predict the mechanical, thermal, tribological, acoustic, and electrical properties of different reinforced composites. ML models, their appropriate methods, database size and source, training and testing data, input and output parameters, and statistical index are also summarized. In addition, the performance evaluation of ML models and statistical indexes of different property predictions is critically analyzed based on several models' training and testing scores, which may help select appropriate ML models to predict reinforced composite properties."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Shibaura Institute of Technology Researcher Details Findings in Machine Learning (Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection)

    52-53页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting out of Tokyo, Japan, by NewsRx editors, research stated, "The objective of this study was to investigate the liquefaction resistance of chemically improved sandy soils in a straightforward and accurate manner." Our news correspondents obtained a quote from the research from Shibaura Institute of Technology: "Using only the existing experimental databases and artificial intelligence, the goal was to predict the experimental results as supporting information before performing the physical experiments. Emphasis was placed on the significance of data from 20 loading cycles of cyclic undrained triaxial tests to determine the liquefaction resistance and the contribution of each explanatory variable. Different combinations of explanatory variables were considered. Regarding the predictive model, it was observed that a case with the liquefaction resistance ratio as the dependent variable and other parameters as explanatory variables yielded favorable results. In terms of exploring combinations of explanatory variables, it was found advantageous to include all the variables, as doing so consistently resulted in a high coefficient of determination." According to the news reporters, the research concluded: "The inclusion of the liquefaction resistance ratio in the training data was found to improve the predictive accuracy. In addition, the results obtained when using a linear model for the prediction suggested the potential to accurately predict the liquefaction resistance using historical data."
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Reports Outline Artificial Intelligence Research from University of Limpopo (An analysis of the international and European Union legal instruments for holding artificial intelligence accountable)

    53-53页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news originating from the University of Limpopo by NewsRx correspondents, research stated, "Despite being applauded as a great technological breakthrough of the current century, Artificial Intelligence (AI) technology and its operations keep attracting condemnations because of the failure by most countries to regulate and hold AI accountable." Our news correspondents obtained a quote from the research from University of Limpopo: "This assertion is made against the backdrop that mostly, AI perform functions and activities just like human beings, as such, AI is prone to make mistakes which might even negatively impact human beings and violate human rights. Mistake calls for accountability. This paper accentuates that even if there are no clear provisions in some country's statute books, there are existing international and European Union legal instruments for regulating and holding AI accountable should it erred." According to the news reporters, the research concluded: "Methodologically, using literature review research approach, this paper highlights and discusses selected but salient international and European legal instruments which have direct and indirect impacts on AI, especially pertaining to regulation, liability and accountability."
      原文链接:
    • NETL
    • NSTL
    • Editura Ase

    Findings from Hindustan Institute of Technology and Science Yields New Findings on Machine Learning (Mems Fault-tolerant Machine Learning Algorithm Assisted Attitude Estimation for Fixed-wing Uavs)

    54-55页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Chennai, India, by NewsRx journalists, research stated, "The utilization of Micro-electromechanical Systems (MEMS) sensors is widespread for directly detecting attitude angles, such as Accelerometer, Gyro, and Magnetometer readings. However, these MEMS sensors are prone to flaws, leading to inaccurate estimates of attitude angles and, consequently, causing UAVs to lose control." Financial support for this research came from Deanship of Scientific Research (DSR) , King Abdulaziz University, Jeddah. The news correspondents obtained a quote from the research from the Hindustan Institute of Technology and Science, "Given that UAVs are operated remotely over long distances, ensuring accurate attitude estimates becomes crucial. This study aims to address this issue by employing machine learning algorithms (MLA). These algorithms were trained and evaluated to overcome the problem by predicting missing data from a malfunctioning MEMS sensor using the available data from other MEMS sensors. To calculate the attitude angles, the study utilizes the Extended Kalman Filter (EKF) technique. Furthermore, a novel fault-tolerant machine learning-aided estimation algorithm has been proposed specifically for estimating the attitude angles (phi, theta, psi) of fixed-wing UAVs. The significance of this research becomes even more prominent when considering the occurrence of MEMS sensor failure. In such cases, the machine learning algorithm plays a crucial role as it has been pre-trained specifically to handle these scenarios. The algorithm is equipped with the ability to effectively address and mitigate the challenges posed by MEMS sensor failures. By leveraging its pre-existing knowledge and learned patterns, the algorithm can accu-rately predict missing data caused by malfunctioning MEMS sensors. This capability proves invaluable in ensuring the reliable estimation of attitude angles, even in the face of sensor failures."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Naval Medical University Reports Findings in Heart Failure (Uncovering hub genes and immunological characteristics for heart failure utilizing RRA, WGCNA and Machine learning)

    55-55页
    查看更多>>摘要：New research on Heart Disorders and Diseases - Heart Failure is the subject of a report. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Heart failure (HF) is a major public health issue with high mortality and morbidity. This study aimed to find potential diagnostic markers for HF by the combination of bioinformatics analysis and machine learning, as well as analyze the role of immune infiltration in the pathological process of HF." Our news editors obtained a quote from the research from Naval Medical University, "The gene expression profiles of 124 HF patients and 135 nonfailing donors (NFDs) were obtained from six datasets in the NCBI Gene Expression Omnibus (GEO) public database. We applied robust rank aggregation (RRA) and weighted gene co-expression network analysis (WGCNA) method to identify critical genes in HF. To discover novel diagnostic markers in HF, three machine learning methods were employed, including best subset regression, regularization technique, and support vector machine-recursive feature elimination (SVM-RFE). Besides, immune infiltration was investigated in HF by single-sample gene set enrichment analysis (ssGSEA). Combining RRA with WGCNA method, we recognized 39 critical genes associated with HF. Through integrating three machine learning methods, FCN3 and SMOC2 were determined as novel diagnostic markers in HF. Differences in immune infiltration signature were also found between HF patients and NFDs. Moreover, we explored the potential associations between two diagnostic markers and immune response in the pathogenesis of HF."
      原文链接:
    • NETL
    • NSTL

    Studies from School of Basic and Applied Sciences Reveal New Findings on Machine Learning [Utilization of Natural Zeolite (Scolecite) To Reduce Arsenic Contamination of Water In Relation To Machine Learning Approach]

    56-56页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news originating from Haryana, India, by NewsRx correspondents, research stated, "Among heavy metals, arsenic contamination in water resources is a major concern due to its harmful effects on human health as slow poison behaviour affecting many states of India and at global level. The present study is about efficiency of Scolecite (CaAl2Si3O10.3H2O), a natural zeolite mineral, to remove arsenic contamination from water." Our news journalists obtained a quote from the research from the School of Basic and Applied Sciences, "The arsenic-contaminated water samples were collected from both industrial areas and non-industrial areas which include Singrauli industrial sites of Madhya Pradesh/Uttar Pradesh, Jalangi Block and various thermal power station areas of West Bengal, Kaudikasa-Rajnandgaon district of Chhattisgarh and Kakching area of Manipur. After conducting rigorous experimental studies, it was observed that the collected water samples had been reduced up to below 10 ppb within the permissible limit of WHO in 7 days with different quantities of scolecite (as 0.5 g/50 ml, 2.5 g/50 ml, 5 g/50 ml and 10 g/100 ml). The reduction of arsenic and the absorbing properties were identified as negative charge developed on the crystal face of Si3Al3 in scolecite. The XRD analysis of filtrate, which remained after filtration of samples prior to chemical analysis for arsenic concentration, is that specific hkl faces (i.e. 111, 040, 132, 400 and 240) are more affected in increase of pH in scolecite-treated water samples and hence play a major role in arsenic removal."
      原文链接:
    • NETL
    • NSTL

    Recent Findings in Machine Learning Described by a Researcher from Gazi University (Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods)

    57-57页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news originating from Gazi University by NewsRx editors, the research stated, "Accurate fault detection in high-voltage direct current (HVDC) transmission lines plays a pivotal role in enhancing operational efficiency, reducing costs, and ensuring grid reliability." The news correspondents obtained a quote from the research from Gazi University: "This research aims to develop a cost-effective and high-performance fault detection solution for HVDC systems. The primary objective is to accurately identify and localize faults within the power system. In pursuit of this goal, the paper presents a comparative analysis of current and voltage characteristics between the rectifier and inverter sides of the HVDC transmission system and their associated alternating current (AC) counterparts under various fault conditions. Voltage and current features are extracted and optimized using a metaheuristic approach, specifically Harris Hawk's optimization method. Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. With a substantial volume of data employed for learning and training, the Harris Hawks optimization method exhibits faster convergence compared to other metaheuristic methods examined in this study." According to the news reporters, the research concluded: "The research findings are applied to simulate diverse fault types and unknown fault locations at multiple system points. Evaluating the fault detection system's effectiveness, quantified through metrics such as specificity, accuracy, F1 score, and sensitivity, yields remarkable results, with percentages of 99.01%, 98.69%, 98.64%, and 98.67%, respectively. This research underscores the critical role of accurate fault detection in HVDC systems, offering valuable insights into optimizing grid performance and reliability."
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi