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Robotics & Machine Learning Daily News

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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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    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 Robotic tools and methods for operating the same (USPTO 119815 18)

    124-128页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – From Alexandria, Virginia, NewsRx jour nalists report that a patent by the inventors Diankov, Rosen Nikolaev (Tokyo, JP ), Matsuoka, Shintaro (Tokyo, JP), Mizoguchi, Hironori (Tokyo, JP), filed on Aug ust 2, 2021, was published online on May 14, 2024. The patent’s assignee for patent number 11981518 is MUJIN Inc. (Tokyo, Japan). News editors obtained the following quote from the background information suppli ed by the inventors: “Robots (e.g., machines configured to automatically/autonom ously execute physical actions) are now extensively used in many fields. Robots, for example, can be used to execute various tasks (e.g., manipulate or transfer an object) in manufacturing, packaging, transport and/or shipping, etc. In exec uting the tasks, robots can replicate human actions, thereby replacing or reduci ng human involvements that are otherwise required to perform dangerous or repeti tive tasks. Robots often lack the sophistication necessary to duplicate human se nsitivity and/or adaptability required for executing more complex tasks. For exa mple, robots often have difficulty gripping object(s) in certain sub-optimal loc ations or poses. Accordingly, there remains a need for improved robotic systems and techniques for transferring objects using a set of gripping tools.”
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    Researchers Submit Patent Application, 'Systems For Transitioning Telephony-Base d And In-Person Servicing Interactions To And From An Artificial Intelligence (A i) Chat Session', for Approval (USPTO 20240163372)

    128-132页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – From Washington, D.C., NewsRx journali sts report that a patent application by the inventors KARP, Scott (McLean, VA, U S); Kaushik, Deepak (Vienna, VA, US), filed on January 25, 2024, was made availa ble online on May 16, 2024. No assignee for this patent application has been made. News editors obtained the following quote from the background information suppli ed by the inventors: “Organizations that offer products and/or services associat ed with customer accounts have traditionally relied on in-person servicing at a brick-and-mortar location, call centers, IVR systems to interact with customers for account servicing. “In person servicing at a brick-and-mortar location and call centers staffed wit h human representatives can provide certain advantages, particularly for custome rs who wish to speak to a human. However, such staffing can be cost-prohibitive on the organization (and, in turn, the customers) and often results in long wait times for customers.
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    Patent Issued for Techniques for generating training data for machine learning e nabled image enhancement (USPTO 11983853)

    132-136页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Meta Plattforms Inc. (Menlo Park, Cali fornia, United States) has been issued patent number 11983853, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors. The patent’s inventors are Ozgirin, Ege (Cambridge, MA, US), Shen, Liying (Charl estown, MA, US), Yang, Haitao (Boston, MA, US), Zhu, Bo (Charlestown, MA, US). This patent was filed on November 2, 2020 and was published online on May 14, 20 24. From the background information supplied by the inventors, news correspondents o btained the following quote: “Images (e.g., digital images, video frames, etc.) may be captured by many different types of devices. For example, video recording devices, digital cameras, image sensors, medical imaging devices, electromagnet ic field sensing, and/or acoustic monitoring devices may be used to capture imag es. Captured images may be of poor quality as a result of the environment or con ditions in which the images were captured. For example, images captured in dark environments and/or under poor lighting conditions may be of poor quality, such that the majority of the image is largely dark and/or noisy. Captured images may also be of poor quality due to physical constraints of the device, such as devi ces that use low-cost and/or low-quality imaging sensors.
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    'Method, Apparatus, And Electronic Device For Controlling Legged Robot, Computer -Readable Storage Medium, Computer Program Product, And Legged Robot' in Patent Application Approval Process (USPTO 20240157555)

    136-139页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A patent application by the inventors CHI, Wanchao (Shenzhen, CN); WANG, Shuai (Shenzhen, CN); ZHANG, Jingfan (Shenzhe n, CN); ZHENG, Yu (Shenzhen, CN), filed on January 22, 2024, was made available online on May 16, 2024, according to news reporting originating from Washington, D.C., by NewsRx correspondents. This patent application has not been assigned to a company or institution. The following quote was obtained by the news editors from the background informa tion supplied by the inventors: “With the wide application of artificial intelli gence (AI) and legged robot technology in civilian and commercial fields, legged robots based on AI and the legged robot technology play an increasingly importa nt role in fields such as intelligent transportation and smart home, and also fa ce higher requirements.
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    Patent Issued for Robotic lawn mower with sensor for detecting relative movement between body parts of the lawn mower (USPTO 11980124)

    142-144页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting originatin g from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors La rsson, Svante (Eksjo, SE), Palm, Staffan (Hok, SE), Stark, Stefan (Huskvarna, SE ), filed on June 4, 2019, was published online on May 14, 2024. The assignee for this patent, patent number 11980124, is Husqvarna AB (Huskvarna , Sweden). Reporters obtained the following quote from the background information supplied by the inventors: “Self-propelled robotic lawnmowers of different configurations are available on the market today which are capable of cutting grass in areas i n an autonomous manner. Some robotic lawnmowers require a user to set up a borde r wire around a lawn that defines the area to be mowed. Such robotic lawnmowers use a sensor to locate the wire and thereby the boundary of the area to be trimm ed. As an alternative to, or in addition to, a sensor arranged to locate a wire, robotic lawnmowers may comprise other types of positioning units, for example a space-based satellite navigation system such as a Global Positioning System (GP S).
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