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

    University of Nottingham Reports Findings in Machine Learning (Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review)

    76-77页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject of a report. According to news reporting from Semenyih, Malaysia, by NewsRx journalists, research stated, "Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process." The news correspondents obtained a quote from the research from the University of Nottingham, "Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700 mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R (0.9986) than conventional algorithms and could improve the optimization model of AD."
      原文链接:
    • NETL
    • NSTL

    New Findings in Robotics Described from Arizona State University (Local Navigation-like Functions for Safe Robot Navigation In Bounded Domains With Unknown Convex Obstacles)

    77-78页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics is the subject of a report. According to news originating from Tempe, Arizona, by NewsRx correspondents, research stated, "In this paper, we propose a controller that stabilizes a holonomic robot with single-integrator dynamics to a target position in a bounded domain, while preventing collisions with convex obstacles. We assume that the robot can measure its own position and heading in a global coordinate frame, as well as its relative position vector to the closest point on each obstacle in its sensing range." Our news journalists obtained a quote from the research from Arizona State University, "The robot has no information about the locations and shapes of the obstacles. We define regions around the boundaries of the obstacles and the domain within which the robot can sense these boundaries, and we associate each region with a virtual potential field that we call a local navigation-like function (NLF), which is only a function of the robot's position and its distance from the corresponding boundary. We also define an NLF for the remaining free space of the domain, and we identify the critical points of the NLFs. Then, we propose a switching control law that drives the robot along the negative gradient of the NLF for the obstacle that is currently closest, or the NLF for the remaining free space if no obstacle is detected. We derive a conservative upper bound on the tunable parameter of the NLFs that guarantees the absence of locally stable equilibrium points, which can trap the robot, if the obstacles' boundaries satisfy a minimum curvature condition. We also analyze the convergence and collision avoidance properties of the switching control law and, using a Lyapunov argument, prove that the robot safely navigates around the obstacles and converges asymptotically to the target position."
      原文链接:
    • NETL
    • NSTL

    Studies from University of Technology Sydney Yield New Information about Machine Learning (Applications of Machine Learning In Antibody Discovery, Process Development, Manufacturing and Formulation: Current Trends, Challenges, and Opportunities)

    78-79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 Machine Learning are discussed in a new report. According to news originating from Sydney, Australia, by NewsRx correspondents, research stated, "While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biologics, hindering the enormous potential for bioprocesses automation from their development to manufacturing. However, the adoption of ML-based models instead of conventional multivariate data analysis methods is significantly increasing due to the accumulation of large-scale production data." Financial support for this research came from Australian Research Council. Our news journalists obtained a quote from the research from the University of Technology Sydney, "This trend is primarily driven by the real-time monitoring of process variables and quality attributes of biopharmaceutical products through the implementation of advanced process analytical technologies. Given the complexity and multidimensionality of a bioproduct design, bioprocess development, and product manufacturing data, ML-based approaches are increasingly being employed to achieve accurate, flexible, and high-performing predictive models to address the problems of analytics, monitoring, and control within the biopharma field. This paper aims to provide a comprehensive review of the current applications of ML solutions in the design, monitoring, control, and optimisation of upstream, downstream, and product formulation processes of monoclonal antibodies. Finally, this paper thoroughly discusses the main challenges related to the bioprocesses themselves, process data, and the use of machine learning models in monoclonal antibody process development and manufacturing."
      原文链接:
    • NETL
    • NSTL

    Guangxi University Reports Findings in Robotics (3d Vision Technologies for a Self-developed Structural External Crack Damage Recognition Robot)

    79-80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 Robotics. According to news reporting out of Nanning, People's Republic of China, by NewsRx editors, research stated, "Persistent cracking and progressive damage can weaken the operational performance of structures such as bridges, dams, and concrete buildings. Consequently, research into automated, high-precision crack detection methods remains pivotal within the realm of structural health monitoring (SHM)." Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), National Natural Science Foundation of Guangxi Province, China Postdoctoral Science Foundation, Systematic Project of Guangxi Key Laboratory of Disaster Prevention and Engineering Safety. Our news journalists obtained a quote from the research from Guangxi University, "Presently, scholars predominantly rely on two-dimensional (2D) image-based algorithms for crack detection. However, these methods commonly struggle to accurately locate the three-dimensional (3D) coordinates of cracks on large structures and to extract the 3D contours of cracks. To address this challenge, this study proposes an automated 3D crack detection system for structures based on high-precision Light Detection and Ranging (LiDAR) and camera fusion. Firstly, precise registration of images and LiDAR point clouds was achieved through accurate extrinsic calibration of the sensors. Secondly, the lightweight MobileNetV2_DeepLabV3 crack semantic segmentation network was employed to detect and locate cracks. Finally, by automatically guiding the robotic arm, an industry-standard depth camera was able to capture high-precision 3D information about the crack at close observation points. Compared with the existing studies, this study emphasizes the extraction of high-precision 3D crack features and verifies the validity of the method by comparing the measurement results with those of the traditional method, demonstrating a remarkable measurement accuracy reaching sub-millimeter levels (0.1 mm)."
      原文链接:
    • NETL
    • NSTL

    Recent Findings from Faculty of Information Technology Has Provided New Information about Machine Learning [Vehicular Communication Using Federated Learning Empowered Chimp Optimization (Fleco) Algorithm]

    80-81页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject of a report. According to news reporting out of Lucknow, India, by NewsRx editors, research stated, "Recently in the field of vehicular communication, there has been a concentration of research on the integration of a vehicle-to-vehicle (V2V) network. With vehicle-to-vehicle (V2V) communication, users can directly exchange significant information with nearby vehicles." Our news journalists obtained a quote from the research from the Faculty of Information Technology, "Typically, automobiles tend to travel at higher speeds on highways compared to roads at intersections. As a result, it is necessary to have a reliable system in place that can effectively and securely facilitate communication. In recent times, scientists have developed different methods for distributing information. However, these systems have various issues such as latency, reliability, mobility, and communication cost. Consequently, this results in a lack of dependability for real-time communication. Therefore, this study introduces a novel approach to Federated Learning (FL) by including the Chimp Optimization Algorithm (ChOA). Federated Learning is an approach in the field of machine learning that enables multiple devices or nodes to collaboratively train a model without the need for data exchange. In the area of vehicular communication, utilization of Federated Learning can be employed to develop a predictive model that estimates the trajectory of nearby vehicles by utilizing collected data. The Chimp Optimization Algorithm (ChOA) is designed to improve the model's efficacy. The proposed method aims to enhance the accuracy of the model's predictions regarding the conduct of nearby vehicles, while also reducing the amount of data exchanged between vehicles, by combining Federated Learning and Chimp Optimization termed FLECO. This method has the potential to enhance vehicular communication effectiveness and security, while also improving road safety and traffic management. Federated Learning facilitates the group control of a machine learning (ML) system by vehicles through the adjustment of model parameters. To enhance the energy efficiency of the system, the implementation of resource allocation and an energy-efficient algorithm is employed for Federated Learning, which integrates power and time allocation methods. This paper conducts a comprehensive analysis of the impact of enabling re-routing capabilities on (i) the mobility of vehicles and (ii) Networks for predicting traffic. To achieve this, utilize the SUMO simulator for road traffic to generate vehicle trajectories. Subsequently, we evaluate the vehicular network's connectivity employing established graph metrics."
      原文链接:
    • NETL
    • NSTL

    University of Science & Technology of China Reports Findings in Androids (Separable amygdala activation patterns in the evaluations of robots)

    81-82页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics-Androids is the subject of a report. According to news reporting originating from Anhui, People's Republic of China, by NewsRx correspondents, research stated, "Given the increasing presence of robots in everyday environments and the significant challenge posed by social interactions with robots, it is crucial to gain a deeper understanding into the social evaluations of robots. One potentially effective approach to comprehend the fundamental processes underlying controlled and automatic evaluations of robots is to probe brain response to different perception levels of robot-related stimuli." Our news editors obtained a quote from the research from the University of Science & Technology of China, "Here, we investigate controlled and automatic evaluations of robots based on brain responses during viewing of suprathreshold (duration: 200 ms) and subthreshold (duration: 17 ms) humanoid robot stimuli. Our behavioral analysis revealed that despite participants' self-reported positive attitudes, they held negative implicit attitudes toward humanoid robots. Neuroimaging analysis indicated that subthreshold presentation of humanoid robot stimuli elicited significant activation in the left amygdala, which was associated with negative implicit attitudes. Conversely, no significant left amygdala activation was observed during suprathreshold presentation. Following successful attenuation of negative attitudes, the left amygdala response to subthreshold presentation of humanoid robot stimuli decreased, and this decrease correlated positively with the reduction in negative attitudes."
      原文链接:
    • NETL
    • NSTL

    Shanghai University of Engineering Science Researcher Updates Current Data on Robotics (A tightly-coupled LIDAR-IMU SLAM method for quadruped robots)

    82-83页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on robotics. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, "Aiming to address the issue of mapping failure resulting from unsmooth motion during SLAM (Simultaneous Localization and Mapping) performed by a quadruped robot, a tightly coupled SLAM algorithm that integrates LIDAR and IMU sensors is proposed in this paper." Funders for this research include Shanghai Science And Technology Innovation Action Plan High-tech Field Project. Our news editors obtained a quote from the research from Shanghai University of Engineering Science: "Firstly, the IMU information, after undergoing deviation correction, is utilized to remove point cloud distortion and serve as the initial value for point cloud registration. Subsequently, a registration algorithm based on Normal Distribution Transform (NDT) and sliding window is presented to ensure real-time positioning and accuracy. Then, an error function combining IMU and LIDAR is formulated using a factor graph, which iteratively optimizes position, attitude, and IMU deviation. Finally, loop closure detection based on Scan Context is introduced, and loop closure factors are incorporated into the factor graph to achieve effective mapping. An experimental platform is established to conduct accuracy and robustness comparison experiments."
      原文链接:
    • NETL
    • NSTL

    New Findings on Robotics from Beijing Institute of Technology Summarized (Multi-agent Policy Learning-based Path Planning for Autonomous Mobile Robots)

    83-84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 Robotics are discussed in a new report. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "The study addresses path planning problems for autonomous mobile robots (AMRs), considering their kinematics, where performance and responsiveness are often incompatible. This study proposes a multi-agent policy learning-based method to tackle this challenge in dynamic environments." Financial supporters for this research include National Key R&D Program of China, National Natural Science Foundation of China (NSFC), Beijing Institute of Technology (BIT) Research and Innovation Promoting Project. Our news journalists obtained a quote from the research from the Beijing Institute of Technology, "The proposed method features a centralized learning and decentralized execution-based path planning framework designed to meet performance and responsiveness requirements. The problem is modeled as a partial observation Markov Decision Process for policy learning while considering the kinematics using conventional neural networks. Then, an improved proximal policy optimization algorithm is developed with highlight experience replay that corrects failed experiences to speed up the learning processes. The experimental results show that the proposed method out-performs the baselines in both static and dynamic environments. The proposed method shortens the movement distance and time in static environments by about 29.1% and 5.7%, as well as in dynamic environments by about 21.1% and 20.4%, respectively."
      原文链接:
    • NETL
    • NSTL

    Findings from University of Notre Dame Has Provided New Data on Machine Learning (Autonomous Output-oriented Aerosol Jet Printing Enabled By Hybrid Machine Learning)

    84-85页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is the subject of a report. According to news reporting from Notre Dame, Indiana, by NewsRx journalists, research stated, "Additive manufacturing (AM) is rapidly revolutionizing modern manufacturing with recent progress in advanced printing methods and improved properties of printed materials. However, traditional AM methods are limited by their inputoriented nature, which demands tedious trial-and-error tuning of printing parameters to achieve desired output properties." Funders for this research include National Science Foundation (NSF), United States Department of Energy (DOE). The news correspondents obtained a quote from the research from the University of Notre Dame, "Here, an output-oriented artificial intelligence-integrated AM (AIAM) method is reported that enables an user to specify desired output properties while the printer autonomously discovers the optimal input printing parameters by integrating hybrid machine learning models and in situ measurements. Based on a predictive mapping between the input printing parameters and the output properties of interests established with <20 experiments designed by active learning, inverse design tasks are performed to intelligently generate the printing parameter settings that lead to desired outcomes using reinforcement learning. This method is demonstrated by autonomous aerosol jet printing (AJP) of conductive polymer films and achieving userdefined electrical resistances with an ultralow error of 3.7%."
      原文链接:
    • NETL
    • NSTL

    New Machine Learning Study Findings Reported from Norwegian University of Science and Technology (NTNU) (Evaluating the Generalizability and Transferability of Water Distribution Deterioration Models)

    85-86页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting from Trondheim, Norway, by NewsRx journalists, research stated, "Small utilities often lack the required amount of data to train machine learning-based models to predict pipe failures, and hence are unable to harness the possibilities and predictive power of machine learning. This study evaluates the generalizability and transferability of a machine learning model to see if small utilities can benefit from the data and models of other utilities." Financial support for this research came from Research Council of Norway. The news correspondents obtained a quote from the research from the Norwegian University of Science and Technology (NTNU), "Using nine Norwegian utilities' datasets, we trained nine global models (by merging multiple datasets) and nine local models (by utilizing each utility's dataset) using random survival forest. Several pre-processing techniques including addressing left-truncated break data and break data scarcity are also presented. The global models and three of the local models were tested to predict the pipe failure of the utilities which were not included in their training datasets. The results indicate that the global models can predict other utilities with sufficient accuracy while local models have some limitations. However, if a representative utility with a sufficiently large (and information rich) dataset is selected, its model can predict the other utility's pipe breaks as accurate as the global models. Furthermore, survival curves for defined cohorts as proxies for uncertainty, and variable importance show that pipes with and without previous breaks behave extremely different."
      原文链接:
    • NETL
    • NSTL