期刊,Robotics & Machine Learning Daily News 2024年卷Mar.8期_国家学术搜索

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Robotics & Machine Learning Daily News

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Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News

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University of Texas Tyler Researchers Publish Findings in Machine Learning (Appl ication of machine learning models to predict driver left turn destination lane choice behavior at urban intersections)

93-94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 from Tyler, Texas, by NewsRx jour nalists, research stated, "When there are multiple lanes to choose from downstre am of a turning movement, drivers should choose the innermost lane so that drive rs at other approaches of the intersection may make concurrent turning movements in the outermost lane(s)." Our news editors obtained a quote from the research from University of Texas Tyl er: "However, human drivers do not always choose the innermost lane, which could lead to crashes with other vehicles. Therefore, predicting human driver behavio rs is vital in reducing crashes, as the need to share the roadways with automate d vehicles (AVs) continues to grow. In this research, various machine learning m odels have been used to predict the left turn destination lane choice of human-d riven vehicles (HDVs) at urban intersections based on several quantifiable param eters. A total of 174 subject vehicles were extracted and analyzed in Los Angele s, California, and Atlanta, Georgia, using HDV trajectory data from the Next Gen eration SIMulation (NGSIM) database. Five machine learning techniques, namely bi nary logistic regression, k nearest neighbors, support vector machines, random f orest, and adaptive neuro-fuzzy inference system, were applied to the extracted data to predict the lane choice behavior of drivers. The k nearest neighbors mod el showed the most promising results for the evaluated data with a correct decis ion score of over 93 % for the unseen test data."

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Studies Conducted at National University of Defense Technology on Machine Learni ng Recently Reported (Analysis of the Combustion Modes In a Rocket-based Combine d Cycle Combustor Using Unsupervised Machine Learning Methodology)

94-95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 Ma chine Learning. According to news reporting originating from Changsha, People's Republic of China, by NewsRx correspondents, research stated, "Combustion mode a nalysis is essential to a rocket-based combined cycle (RBCC) combustor because i t may experience multiple combustion modes during the operation. In this study, a method based on an autoencoder and a K-means algorithm was proposed for combus tion mode analysis." Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of China (NSFC), Science and Technolo gy on Scramjet Laboratory. Our news editors obtained a quote from the research from the National University of Defense Technology, "Flame chemiluminescence images and schlieren images of three combustion modes observed in an RBCC combustor were used to evaluate this method. Two autoencoders that followed the same encoderdecoder architecture wer e developed separately to generate the latent space representations of flame che miluminescence images and schlieren images. In the latent space, the centroids a nd boundaries of different combustion modes were determined using the K-means al gorithm. Each autoencoder was trained using 750 images and tested using another 3000 images. The method achieved an accuracy up to 99% on both fla me chemiluminescence images and schlieren images. The images generated by the de coder suggested that the autoencoder captured the important features (e.g., prim ary reaction zone and shock wave) of the reacting flow field. The autoencoder de veloped for flame chemiluminescence images also successfully detected the combus tion mode transition during an ignition process, which suggested that it had the potential to monitor the combustion mode in a real time manner. However, the au toencoder failed on monitoring combustion mode transition when it came to the sc hlieren images because the optical access of the training data was not exactly t he same."

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Researcher from Xuchang Vocational Technical College Reports on Findings in Inte lligent Systems (An improved method for extracting essay tangency features in in telligent scoring of English essays)

95-96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 intelligent syste ms are discussed in a new report. According to news originating from Xuchang, Pe ople's Republic of China, by NewsRx editors, the research stated, "With the cont inuous improvement of teaching quality in China's colleges and universities, Eng lish teaching has received more and more attention, and the automatic scoring sy stem for English composition has begun to be gradually applied in English teachi ng in colleges and universities, but the system can only score compositions obje ctively, which is difficult to meet the actual teaching needs."

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New Machine Translation Study Results Reported from University of Manchester (Ne ural machine translation of clinical text: an empirical investigation into multi lingual pre-trained language models and transfer-learning)

96-97页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on machine translation are presented in a new report. According to news reporting from the University of Ma nchester by NewsRx journalists, research stated, "Clinical text and documents co ntain very rich information and knowledge in healthcare, and their processing us ing state-of-the-art language technology becomes very important for building int elligent systems for supporting healthcare and social good. This processing incl udes creating language understanding models and translating resources into other natural languages to share domain-specific cross-lingual knowledge." Financial supporters for this research include Nuffield Foundation; Ukri/epsrc. The news correspondents obtained a quote from the research from University of Ma nchester: "In this work, we conduct investigations on clinical text machine tran slation by examining multilingual neural network models using deep learning such as Transformer based structures. Furthermore, to address the language resource imbalance issue, we also carry out experiments using a transfer learning methodo logy based on massive multilingual pre-trained language models (MMPLMs). The exp erimental results on three sub-tasks including (1) clinical case (CC), (2) clini cal terminology (CT), and (3) ontological concept (OC) show that our models achi eved top-level performances in the ClinSpEn-2022 shared task on English-Spanish clinical domain data. Furthermore, our expert-based human evaluations demonstrat e that the small-sized pre-trained language model (PLM) outperformed the other t wo extra-large language models by a large margin in the clinical domain fine-tun ing, which finding was never reported in the field. Finally, the transfer learni ng method works well in our experimental setting using the WMT21fb model to acco mmodate a new language space Spanish that was not seen at the pre-training stage within WMT21fb itself, which deserves more exploitation for clinical knowledge transformation, e.g. to investigate into more languages. These research findings can shed some light on domain-specific machine translation development, especia lly in clinical and healthcare fields."

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Researchers from University College Dublin Report Findings in Machine Learning ( Machine Learning Driven Methodology for Enhanced Nylon Microplastic Detection an d Characterization)

97-98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 Machine Learning is now available. According to news reporting originating in Dublin, Ireland, by NewsR x journalists, research stated, "In recent years, the field of microplastic (MP) research has evolved significantly; however, the lack of a standardized detecti on methodology has led to incomparability across studies. Addressing this gap, o ur current study innovates a reliable MP detection system that synergizes sample processing, machine learning, and optical photothermal infrared (O-PTIR) spectr oscopy." Funders for this research include Science Foundation Ireland, Science Foundation Ireland. The news reporters obtained a quote from the research from University College Du blin, "This approach includes examining high-temperature filtration and alcohol treatment for reducing non-MP particles and utilizing a support vector machine ( SVM) classifier focused on key wavenumbers that could discriminate between nylon MPs and non-nylon MPs (1077, 1541, 1635, 1711 cm-1 were selected based on the f eature importance of SVM-Full wavenumber model) for enhanced MP identification. The SVM model built from key wavenumbers demonstrates a high accuracy rate of 91 .33%. Results show that alcohol treatment is effective in minimizin g non-MP particles, while filtration at 70 degrees C has limited impact. Additio nally, this method was applied to assess MPs released from commercial nylon teab ags, revealing an average release of 106 particles per teabag."

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Patent Application Titled "Methods,systems, And Apparatus For Use In Main Pipes Connected To Branch Conduit" Published Online (USPTO 20240060592)

98-102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 Washington, D.C., by NewsRx journalists, a patent application by the inve ntors Baxter, Rick (St. Louis, MO, US); Herrlich, Hermann (Hauppage, NY, US); Ko dadek, Robert (Hauppauge, NY, US); McKeefrey, Steven (Hauppauge, NY, US); Webste r, John (Hauppauge, NY, US), filed on October 4, 2023, was made available online on February 22, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: "Referring to FIG. 1A, in various pipe systems that carry flui d under pressure (e.g., municipal water systems, service water systems, industri al processes, etc.), it is common for a main pipe M to be fluidly coupled to one or more branch conduits C (e.g., user connections) at respective junctions J. F or example, in a water distribution system, a water main M can be coupled to a p lurality of corporation stops C that provide connections to water service lines. Over the life of a pipe system, it may become necessary to rehabilitate or repa ir the main pipe M.

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Symbiosis International (Deemed University) Researchers Further Understanding of Machine Learning (Safeguarding Critical Infrastructures: Machine Learning in Cy bersecurity)

102-103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 new report. According to news originating from Symbiosis I nternational (Deemed University) by NewsRx correspondents, research stated, "It has become essential to protect vital infrastructures from cyber threats in an a ge where technology permeates every aspect of our lives. This article examines h ow machine learning and cybersecurity interact, providing a thorough overview of how this dynamic synergy might strengthen the defence of critical systems and s ervices." The news correspondents obtained a quote from the research from Symbiosis Intern ational (Deemed University): "The hazards to public safety and national security from cyberattacks on vital infrastructures including electricity grids, transpo rtation networks, and healthcare systems are significant. Traditional security m ethods have failed to keep up with the increasingly sophisticated cyber threats. Machine learning offers a game-changing answer because of its ability to analys e big datasets and spot anomalies in real time. The goal of this study is to str engthen the defences of key infrastructures by applying machine learning algorit hms, such as CNN, LSTM, and deep reinforcement learning for anomaly algorithm. T hese algorithms can anticipate weaknesses and reduce possible breaches by using historical data and continuously adapting to new threats. The research also look s at issues with data privacy, algorithm transparency, and adversarial threats t hat arise when applying machine learning to cybersecurity. For machine learning technologies to be deployed successfully, these obstacles must be removed. Prote cting vital infrastructures is essential as we approach a day where connectivity is pervasive."

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Patent Issued for System for dynamic allocation of navigation tools based on lea rned user interaction (USPTO 11907522)

103-106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 Krishnamoort hy, Madhusudhanan (Chennai, IN), Rajesh, Madhumathi (Chennai, IN), filed on June 24, 2020, was published online on February 20, 2024, according to news reportin g originating from Alexandria, Virginia, by NewsRx correspondents. Patent number 11907522 is assigned to Bank of America Corporation (Charlotte, No rth Carolina, United States). The following quote was obtained by the news editors from the background informa tion supplied by the inventors: "Graphical User Interface (GUI) Design focuses o n anticipating what users might need to do and ensuring that the interface has n avigation tools that are easy to access, understand, and use to facilitate those actions. GUI brings together concepts from interaction design, visual design, a nd information architecture. However, the effectiveness of the GUI design to eac h user is highly subjective.

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Patent Application Titled "Regularizing Targets In Model Distillation Utilizing Past State Knowledge To Improve Teacher-Student Machine Learning Models" Publish ed Online (USPTO 20240062057)

107-110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 Washington, D.C., by NewsRx journalists, a patent application by the inve ntors Jandial, Surgan (Jammu, IN); Krishnamurthy, Balaji (Noida, IN); Puri, Nika ash (New Delhi, IN), filed on August 9, 2022, was made available online on Febru ary 22, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: "Recent years have seen an increase in hardware and software p latforms that compress and implement learning models. In particular, many conven tional systems utilize knowledge distillation to compress, miniaturize, and tran sfer the model parameters of a deeper and wider deep learning model, which requi re significant computational resources and time, to a more compact, resource-fri endly student machine learning model. Indeed, conventional systems often distill information of a high-capacity teacher network (i.e., a teacher machine learnin g model) to a low-capacity student network (i.e., a student machine learning mod el) with the intent that the student network will perform similar to the teacher network, but with less computational resources and time. In order to achieve th is, many conventional systems train a student machine learning model using a kno wledge distillation loss to emulate the behavior of a teacher machine learning m odel. Although many conventional systems utilize knowledge distillation to train compact student machine learning models, many of these conventional systems hav e a number of shortcomings, particularly with regards to efficiently and easily distilling knowledge from a teacher machine learning model to a student machine learning model to create a compact, yet accurate student machine learning model. "

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Patent Issued for Autonomous floor cleaner with drive wheel assembly (USPTO 1190 3541)

110-112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 Haverkamp, M atthew (Kentwood, MI, US), Johnson, Steven M. (Hudsonville, MI, US), VanTongeren, Todd R. (Ada, MI, US), filed on December 19, 2022, was published online on Feb ruary 20, 2024, according to news reporting originating from Alexandria, Virgini a, by NewsRx correspondents. Patent number 11903541 is assigned to BISSELL Inc. (Grand Rapids, Michigan, Unit ed States). The following quote was obtained by the news editors from the background informa tion supplied by the inventors: "Autonomous or robotic floor cleaners can move w ithout the assistance of a user or operator to clean a floor surface. For exampl e, the floor cleaner can be configured to vacuum or sweep dirt (including dust, hair, and other debris) into a collection bin carried on the floor cleaner. The floor cleaner can move randomly about a surface while cleaning the floor surface or use a mapping/navigation system for guided navigation about the surface. Som e floor cleaners are further configured to apply and extract liquid for wet clea ning of bare floors, carpets, rugs, and other floor surfaces. "Such floor cleaners include a drive system with one or more drive wheels for dr iving the floor cleaner across a surface to be cleaned. Hair and other dirt tend to become caught around the drive wheels, and over time can build up and preven t the drive wheels from functioning properly. A user must manually remove the bu ildup around the drive wheels, which is time consuming and undesirable. With wet cleaning robots, this problem is compounded by the hair and debris being wet, w hich makes the buildup harder to remove."

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