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    Seoul National University of Science and Technology Researcher Yields New Findin gs on Robotics (Localization of solar panel cleaning robot combining vision proc essing and extended Kalman filter)

    103-103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 ro botics. According to news reporting originating from Seoul National University o f Science and Technology by NewsRx correspondents, research stated, “In this stu dy, we introduce a method for estimating the position of a self-driving solar pa nel-cleaning mobile robot.” Financial supporters for this research include Korea Institute For Advancement o f Technology. Our news correspondents obtained a quote from the research from Seoul National U niversity of Science and Technology: “This estimation relies on line counts, typ ically 16 cm in panel width, obtained through image processing on the panel floo r, along with wheel encoder information and inertial sensor data. To achieve acc urate line counts, we introduce two adjusted threshold values and allow offsets in these values based on the robot’s speed. Additionally, inertial measurement u nit (IMU) signals assist in determining whether a line is horizontal or vertical , depending on the robot’s movement direction on the panel, utilizing the robot’ s heading angle and detected line angle. When the robot is positioned between li nes on the panel, more precise location estimation is necessary beyond simple li ne counts. To tackle this challenge, we integrate the extended Kalman filter wit h IMU data and encoder information, significantly enhancing position estimation. ”
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    Recent Research from Northeastern University Highlight Findings in Machine Learn ing (Senseoran: O-ran-based Radar Detection In the Cbrs Band)

    104-105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 Boston, Massachusetts, by News Rx journalists, research stated, “Open RAN (O-RAN) has the potential for revolut ionizing not only cellular communication but also spectrum sensing by carefully controlling uplink/downlink traffic in shared spectrum bands. In this paper, we present the design of SenseORAN, which detects the presence of radar pulses with in the Citizens Broadband Radio Service (CBRS) band.” Financial support for this research came from National Science Foundation (NSF).
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    First Hospital of China Medical University Reports Findings in Artificial Intell igence (Application and progress of artificial intelligence in radiation therapy dose prediction)

    104-104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 Artificial Intelligenc e is the subject of a report. According to news reporting from Shenyang, People’ s Republic of China, by NewsRx journalists, research stated, “Radiation therapy (RT) nowadays is a main treatment modality of cancer. To ensure the therapeutic efficacy of patients, accurate dose distribution is often required, which is a t ime-consuming and labor-intensive process.” The news correspondents obtained a quote from the research from the First Hospit al of China Medical University, “In addition, due to the differences in knowledg e and experience among participants and diverse institutions, the predicted dose are often inconsistent. In last several decades, artificial intelligence (AI) h as been applied in various aspects of RT, several products have been implemented in clinical practice and confirmed superiority.”
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    Researcher at University of Sydney Publishes New Study Findings on Machine Learn ing (Suppressing Beam Background and Fake Photons at Belle II using Machine Lear ning)

    105-106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 ar tificial intelligence. According to news reporting originating from the Universi ty of Sydney by NewsRx correspondents, research stated, “The Belle II experiment situated at the SuperKEKB energyasymmetric e+ e- collider began operation in 20 19.” The news correspondents obtained a quote from the research from University of Sy dney: “It has since recorded half of the data collected by its predecessor, and reached a world record instantaneous luminosity of 4.7 x 1034 cm-2s-1. For disti nguishing decays with missing energy from background events at Belle II, the res idual calorimeter energy measured by the electromagnetic calorimeter is an impor tant quantity. Ideally, calorimeter clusters due to beam backgrounds and fake ph otons should be excluded when the residual calorimeter energy is calculated, so identifying them during the analysis process is key. We present two new boosted decision tree classifiers that have been trained to identify such clusters at Be lle II and distinguish them from real photons originating from collision events at the interaction point.”
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    Investigators at University of the Western Cape Describe Findings in Machine Lea rning (Remote Sensing-based Land Use Land Cover Classification for the Heuningne s Catchment, Cape Agulhas, South Africa)

    106-107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 out of Cape Town, South Africa, by NewsRx editors, research stated, “The primary objective of this study was to eva luate the effectiveness of Sentinel 2 and machine-learning technique for classif ying seasonal land use land cover (LULC) changes on an annual basis, in the Heun ingnes Catchment in Cape Agulhas, South Africa. The study focused on July 2017, October 2017, March 2018, and July 2018, representing both dry and wet seasons w ithin the Catchment.” Our news journalists obtained a quote from the research from the University of t he Western Cape, “The study also assessed the rainfall and temperature variation s and how they link with these short-term changes in LULC. The classification re sults revealed a consistent increase in the extent of bare rock and soil cover f rom October 2017 to July 2018. The wet seasons of July 2017 and July 2018 exhibi ted the highest percentage of vegetation cover. The overall accuracy of the SVM classification ranged between 55 % and 75 %, with the wet seasons demonstrating higher overall accuracies of 75 %. The p erformance of SVM was evaluated using kappa statistics, which indicated a modera te to substantial level of agreement ranging from 0.43 to 0.69.”
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    Reports from Shenyang Aerospace University Advance Knowledge in Robotics (An Ima ge-Based Interactive Training Method of an Upper Limb Rehabilitation Robot)

    107-108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 robotics have been published. According to news reporting originating from Shenyang, People’s Republic of China, by NewsRx correspondents, research stated, “Aimed at the pro blem of human-machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a h uman-machine interactive control method based on an independently developed uppe r limb rehabilitation robot.” Funders for this research include Shenyang Aerospace University; Shandong Provin cial Key Research And Development Program. The news editors obtained a quote from the research from Shenyang Aerospace Univ ersity: “In this method, the camera is used as a sensor, the human skeleton mode l is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the huma n arm are extracted by depth estimation and spatial geometry, and then the realtime motion data are obtained, and the control instructions of the robot are gen erated from it to realise the real-time interactive control of the robot. This m ethod can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affect ed by environmental changes.”
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    Researchers from Huazhong University of Science and Technology Report Recent Fin dings in Robotics and Automation (Sr-livo: Lidarinertial- visual Odometry and Ma pping With Sweep Reconstruction)

    108-109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 Ro botics - Robotics and Automation. According to news reporting from Wuhan, People ’s Republic of China, by NewsRx journalists, research stated, “Existing LiDAR-in ertial-visual odometry and mapping (LIV-OAM) systems mainly utilize the LiDAR-in ertial odometry (LIO) module for structure reconstruction and the LiDAR-assisted visual-inertial odometry (VIO) module for color rendering. However, the perform ance of existing LiDAR-assisted VIO module doesn’t match the accuracy delivered by LIO systems in the scenarios containing rich textures and geometric structure s (i.e., without failure mode for both camera and LiDAR).” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from the Huazhong Uni versity of Science and Technology, “This letter introduces SR-LIVO, an advanced and novel LIV-OAM system employing sweep reconstruction to align reconstructed s weeps with image timestamps. This allows the LIO module to accurately determine states at all imaging moments, enhancing pose accuracy and processing efficiency . Experimental results on two public datasets demonstrate that: 1) our SR-LIVO o utperforms the existing state-of-the-art LIV-OAM systems in both pose accuracy, rendering performance and runtime efficiency; 2) In scenarios with rich textures and geometric structures, the LIO framework can provide more accurate pose than existing LiDAR-assisted VIO framework, and thus helps rendering.”
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    Recent Findings in Machine Learning Described by a Researcher from Chandigarh Un iversity (Investigation of melt flow index and tensile properties of dual metal reinforced polymer composites for 3D printing using machine learning approach: . ..)

    109-110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 reporting originating from Punjab, India, b y NewsRx correspondents, research stated, “This study investigates the enhanceme nt of mechanical properties of metal/polymer composites produced through fused d eposition modeling and the prediction of the ultimate tensile strength (UTS) by machine learning using a Classification and Regression Tree (CART).” Funders for this research include Deanship of Scientific Research, King Khalid U niversity. The news journalists obtained a quote from the research from Chandigarh Universi ty: “The composites, comprising 80% acrylonitrile butadiene styren e matrix and 10% each of aluminum (Al) and copper (Cu) fillers, we re subjected to a comprehensive exploration of printing parameters, including pr inting temperature, infill pattern, and infill density using the Taguchi method. The CART unveiled a hierarchical tree structure with four terminal nodes, each representing distinct subgroups of materials characterized by similar UTS proper ties. The predictors’ importance was assessed, highlighting their role in determ ining material strength. The model exhibited a high predictive power with an R-s quared value of 0.9154 on the training data and 0.8922 on the test data, demonst rating its efficacy in capturing variability. The optimal combination of paramet ers for maximizing UTS was a zigzag infill pattern, a printing temperature of 24 5 °C, and an infill density of 10%, which is associated with the hi ghest UTS of 680 N. The model’s reliability was confirmed through a paired t-tes t and test and confidence interval for two variances, revealing no significant d ifference between the observed and predicted UTS values.”
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    Patent Issued for Systems and methods for valuation of a vehicle (USPTO 11983745 )

    110-114页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Capital One Services LLC (McLean, Virg inia, United States) has been issued patent number 11983745, according to news r eporting originating out of Alexandria, Virginia, by NewsRx editors. The patent’s inventors are Chow, Chih-Hsiang (Coppell, TX, US), Dang, Steven (Pl ano, TX, US), Furlan, Elizabeth (Plano, TX, US). This patent was filed on August 6, 2021 and was published online on May 14, 2024 . From the background information supplied by the inventors, news correspondents o btained the following quote: “When determining to sell a vehicle, a vehicle owne r will traditionally either go to a dealership to perform a trade-in or try to s ell the vehicle via various resell vehicle websites, such as Carguru or Craigsli st. The valuation mechanism for the vehicle that is typically used is a service such as Kelly Blue Book, which asks general questions about one or more of the f ollowing: make/model, year, color, mileage, features, accessories and/or vehicle condition. Another way of determining the value of the vehicle is to identify t he various components of the vehicle that the vehicle can be separated into and resold on the market. Various solutions currently in the art, such as car-parts. com, require a large database of historical vehicle inventory and/or historical vehicle component inventory and that provide a limited assessment of the current vehicle component marketplace.”
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    Patent Issued for Combined transfer and storage device and manufacturing line fo r machining (USPTO 11980989)

    114-118页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A patent by the inventors Cheseaux, Ma thieu (Saillon, CH), Maret, Dylan (Martigny, CH), Mettan, Blaise (Evionnaz, CH), Vuadens, Samuel (Finhaut, CH), filed on May 27, 2022, was published online on M ay 14, 2024, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents. Patent number 11980989 is assigned to CHIRON Group SE (Tuttlingen, Germany). The following quote was obtained by the news editors from the background informa tion supplied by the inventors: “The present disclosure generally relates to pre cision machining with machine tools. According to certain embodiments, the prese nt disclosure relates to a manufacturing system for machining. According to furt her embodiments, the present disclosure relates to a combined transfer and stora ge device for machining. Further, the present disclosure relates to a manufactur ing line for machining, which comprises a combined transfer and storage device a nd a manufacturing system.
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