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    Investigators at Lancaster University Report Findings in Machine Learning (Machi ne Learning and the Work of the User)

    10-11页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting out of Lancaster, United Kingdom, by NewsRx editors, research stated, "This paper introduces the collection of the Journal on Machine Learning (ML) and the user. It provides a brief history of M L from the 1950's through to the current time, sketching the nature of the kinds of precursor AI techniques used in such things as expert systems right the way through to the emergence of ML and its tool sets, including deep learning." Our news journalists obtained a quote from the research from Lancaster Universit y, "It concludes with the ‘generative AI' used in such ML technologies as PaLM a nd GPT-3. The history highlights key changes and developments in ML, the especia l importance and limitations of deep learning, and the changing attitudes and ex pectations of users in an environment when ML can and often is oversold. The pap er then explores the ways CSCW research has addressed the social context of orga nisational systems and how the same can apply for ML tools and techniques." According to the news editors, the research concluded: "It urges research that f ocuses on the particular ways that ML comes to fit into ‘real world' collaborati ve work sites and hence speaks to the CSCW cannon." This research has been peer-reviewed.
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    Studies from Harbin Institute of Technology Reveal New Findings on Robotics (Des ign and Optimization of UAV Aerial Recovery System Based on Cable-Driven Paralle l Robot)

    11-11页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in robotics. According to news reporting out of Harbin, People's Republic of China, by NewsRx editors, research stated, "Aerial recovery and redeployment can effec tively increase the operating radius and the endurance of unmanned aerial vehicl es (UAVs)." Funders for this research include Foundation of Chinese State Key Laboratory of Robotics And Systems. The news journalists obtained a quote from the research from Harbin Institute of Technology: "However, the challenge lies in the effect of the aerodynamic force on the recovery system, and the existing roadbased and sea-based UAV recovery methods are no longer applicable. Inspired by the predatory behavior of net-cast ing spiders, this study introduces a cable-driven parallel robot (CDPR) for UAV aerial recovery, which utilizes an end-effector camera to detect the UAV's fligh t trajectory, and the CDPR dynamically adjusts its spatial position to intercept and recover the UAV. This paper establishes a comprehensive cable model, simult aneously considering the elasticity, mass, and aerodynamic force, and the static equilibrium equation for the CDPR is derived. The effects of the aerodynamic fo rce and cable tension on the spatial configuration of the cable are analyzed. Nu merical computations yield the CDPR's end-effector position error and cable-driv en power consumption at discrete spatial points, and the results show that the p osition error decreases but the power consumption increases with the increase in the cable tension lower limit (CTLL). To improve the comprehensive performance of the recovery system, a multi-objective optimization method is proposed, consi dering the error distribution, power consumption distribution, and safety distan ce."
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    Investigators from Beijing University of Civil Engineering and Architecture Targ et Robotics (Use of Cross-training In Human-robot Collaborative Rescue)

    12-12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 disc ussed in a new report. According to news reporting out of Beijing, People's Repu blic of China, by NewsRx editors, research stated, "Human-robot collaboration ha s been widely used in postdisaster investigation and rescue. Human-robot team tr aining is a good way to improve the team rescue efficiency and safety; two commo n training methods, namely, procedural training and cross-training, are explored in this study." Financial supporters for this research include National Natural Science Foundati on of China study, National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Beijing Univers ity of Civil Engineering and Architecture, "Currently, relatively few studies ha ve explored the impact of cross-training on humanrobot collaboration in rescue tasks. Cross-training will be novel to most rescuers and as such, an evaluation of cross-training in comparison with more conventional procedural training is wa rranted. This study investigated the effects of these two training methods on re scue performance, situation awareness and workload. Forty-two participants compl eted a path-planning and a photo-taking task in an unfamiliar simulated postdisa ster environment. The rescue performance results showed that cross-training meth od had significant advantages over procedural training for human-robot collabora tive rescue tasks. During the training process, compared with procedural trainin g, participants were more likely to achieve excellent photo-taking performance a fter cross-training; after training, the length of the route planned by the cros s-training group was significantly shorter than that of the procedural-training group. In addition, procedural-training marginal significantly increased the emo tion demand, which proves that cross-training can well control the emotions of t he operators and make them more involved in the rescue task. The study also foun d that arousal level increased significantly after the first cross-training sess ion, and decreased to the same level as procedural training after multiple sessi ons."
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    IRCCS Regina Elena National Cancer Institute Reports Findings in Chondrosarcoma (X-rays radiomics-based machine learning classification of atypical cartilaginou s tumour and high-grade chondrosarcoma of long bones)

    13-14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 Oncology - Chondrosarc oma is the subject of a report. According to news originating from Rome, Italy, by NewsRx correspondents, research stated, "Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with a ctive surveillance or curettage and wide resection. Our aim was to determine dia gnostic performance of X-rays radiomics-based machine learning for classificatio n of ACT and high-grade CS of long bones." Financial support for this research came from Associazione Italiana per la Ricer ca sul Cancro. Our news journalists obtained a quote from the research from IRCCS Regina Elena National Cancer Institute, "This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bo ne sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT , n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) coho rts, respectively, based on the date of surgery. At centre 2, the dataset consti tuted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmen tation was performed on frontal view X-rays, using MRI or CT for preliminary ide ntification of lesion margins. After image pre-processing, radiomic features wer e extracted. Dimensionality reduction included stability, coefficient of variati on, and mutual information analyses. In the training cohort, after class balanci ng, a machine learning classifier (Support Vector Machine) was automatically tun ed using nested 10-fold cross-validation. Then, it was tested on both the test c ohorts and compared to two musculoskeletal radiologists' performance using McNem ar's test. Five radiomic features (3 morphology, 2 texture) passed dimensionalit y reduction. After tuning on the training cohort (AUC = 0.75), the classifier ha d 80%, 83%, 79% and 80%, 89 %, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p 0.617 )."
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    New Data from Harbin Institute of Technology Illuminate Research in Robotics (En hancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classif ication Approach)

    14-14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 robotics is now availab le. According to news originating from Harbin, People's Republic of China, by Ne wsRx correspondents, research stated, "With the rise of electric vehicles, auton omous driving, and valet parking technologies, considerable research has been de dicated to automatic charging solutions. While the current focus lies on chargin g robot design and the visual positioning of charging ports, a notable gap exist s in addressing safety aspects during the charging plug-in process." Our news journalists obtained a quote from the research from Harbin Institute of Technology: "This study aims to bridge this gap by proposing a collision classi fication scheme for robot manipulators in automatic electric vehicle charging sc enarios. In situations with minimal visual positioning deviation, robots employ impedance control for effective insertion. Significant deviations may lead to po tential collisions with other vehicle parts, demanding discrimination through a global visual system. For moderate deviations, where a robot's end-effector enco unters difficulty in insertion, existing methods prove inadequate. To address th is, we propose a novel data-driven collision classification method, utilizing vi bration signals generated during collisions, integrating the robust light gradie nt boosting machine (LightGBM) algorithm. This approach effectively discerns the acceptability of collision contacts in scenarios involving moderate deviations. Considering the impact of passing vehicles introducing environmental noise, a n oise suppression module is introduced into the proposed collision classification method, leveraging empirical mode decomposition (EMD) to enhance its robustness in noisy charging scenarios."
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    Technical University of Kosice Researcher Describes Recent Advances in Robotics (Sensing of Continuum Robots: A Review)

    15-16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 robotics have been pr esented. According to news originating from Technical University of Kosice by Ne wsRx correspondents, research stated, "The field of continuum robotics is rapidl y developing. The development of new kinematic structures, locomotion principles and control strategies is driving the development of new types of sensors and s ensing methodologies." Financial supporters for this research include Vega. Our news editors obtained a quote from the research from Technical University of Kosice: "The sensing in continuum robots can be divided into shape perception a nd environment perception. The environment perception is focusing on sensing the interactions between the robot and environment. These sensors are often embedde d on an outer layer of the robots, so the interactions can be detected. The shap e perception is sensing the robot's shape using various principles. There are th ree main groups of sensors that use the properties of electricity, magnetism and optics to measure the shape of the continuum robots. The sensors based on measu ring the properties of electricity are often based on measuring the electrical r esistance or capacitance of the flexible sensor. Sensors based on magnetism use properties of permanent magnets or coils that are attached to the robot. Their m agnetic field, flux or other properties are then tracked, and shape reconstructi on can be performed. The last group of sensors is mostly based on leveraging the properties of traveling light through optical fibers. There are multiple object ives of this work. Objective number one is to clearly categorize the sensors and make a clear distinction between them."
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    Findings from Agricultural Engineering Research Institute Yields New Findings on Robotics (A Small Boat for Fish Feeding)

    15-15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 out of Giza, Egypt, by NewsRx editors, resea rch stated, "This study focuses on development of a solar-powered Arduino Mega-d riven robotic boat designed for efficient fish feeding in aquaculture. By integr ating IoT technology and a mobile app, the project aims to reduce labor costs an d improve feeding practices while addressing challenges associated with manual f eeding, which previ-ously failed to adequately cover the entire pond area." Our news journalists obtained a quote from the research from Agricultural Engine ering Research Institute, "The research specifically centers on Nile tilapia pro duction. Experimental results show that the robot emulates manual feeding with a n average response time of 1.82 s for feeding initiation and 2.17 s for movement initiation. It efficiently distributes feed across a 4000 m2 area in 9 min, out performing the manual method, which takes 32 min. Fish fed by robotic boat exhib it an 18.71 % weight gain compared to those fed by hand. The robot ic boat farm consumes a total of 11.480 kg of feed to produce 7.6 kg, while the hand-fed farm required 16.185 kg of feed to produce 6.5 kg. This results in feed conversion ratios of 1.51 and 2.49, respectively. The study finds an overall co st reduction of 46.13 % due to decreased labor hours."
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    New Robotics Study Findings Has Been Reported by a Researcher at University of M odena and Reggio Emilia (Towards the Legibility of Multi-Robot Systems)

    16-17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 originating from the University of Modena and Reggio Emilia by NewsRx correspondents, research stated, "Communication is cruc ial for human-robot collaborative tasks."The news editors obtained a quote from the research from University of Modena an d Reggio Emilia: "In this context, legibility studies movement as the means of i mplicit communication between robotic systems and a human observer. This concept has been explored mostly for manipulators and humanoid robots. In contrast, lit tle information is available in the literature about legibility of multi-robot s ystems or swarms, where simplicity and non-anthropomorphism of robots, along wit h the complexity of their interactions and aggregated behavior impose different challenges that are not encountered in single-robot scenarios. This paper invest igates legibility of multi-robot systems. Hence, we extend the definition of leg ibility, incorporating information about high-level goals in terms of the coordi nation objective of the group of robots, to previous results that focused solely on the legibility of spatial goals."
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    Study Results from Shri Ramdeobaba College of Engineering & Manage ment Update Understanding of Machine Learning (Machine-learning-assisted Blood P arameter Sensing Platform for Rapid Next Generation Biomedical and Healthcare Ap plications)

    17-18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 originating from Maharashtra, India , by NewsRx correspondents, research stated, "The pursuit of rapid diagnosis has resulted in considerable advances in blood parameter sensing technologies. As a dvances in technology, there may be challenges in equitable access for all indiv iduals due to economic constraints, advanced expertise, limited accessibility in particular places, or insufficient infrastructure." Financial support for this research came from RCOEM YFRF Scheme. Our news editors obtained a quote from the research from the Shri Ramdeobaba Col lege of Engineering & Management, "Hence, simple, cost efficient, benchtop biochemical blood-sensing platform was developed for detecting crucial blood parameters for multiple disease diagnosis. Colorimetric and image processi ng techniques is used to evaluate color intensity. CMOS image sensor is utilized to capture images to calculate optical density for sensing. The platform is ass essed with blood serum samples, including Albumin, Gamma Glutamyl Transferase, A lpha Amylase, Alkaline Phosphatase, Bilirubin, and Total Protein within clinical ly relevant limits. The platform had excellent Limits of Detection (LOD) for the se parameters, which are critical for diagnosing liver and kidney-related diseas es (0.27 g dl-1, 0.86 IU l-1, 1.24 IU l-1, 0.97 IU l-1, 0.24 mg dl-1, 0.35 g dl- 1, respectively). Machine learning (ML) algorithms were used to estimate targete d blood parameter concentrations from optical density readings, with 98.48% accuracy and reduced incubation time by nearly 80%. The proposed pl atform is compared to commercial analyzers, which demonstrate excellent accuracy and reproducibility with remarkable precision (0.03 to 0.71%CV)."
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    Tongji University Reports Findings in Machine Learning (The insightful water qua lity analysis and predictive model establishment via machine learning in dual-so urce drinking water distribution system)

    18-19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 th e subject of a report. According to news reporting originating from Shanghai, Pe ople's Republic of China, by NewsRx correspondents, research stated, "Dual-sourc e drinking water distribution systems (DWDS) over single-source water supply sys tems are becoming more practical in providing water for megacities. However, the more complex water supply problems are also generated, especially at the hydrau lic junction." Our news editors obtained a quote from the research from Tongji University, "Her ein, we have sampled for a one-year and analyzed the water quality at the hydrau lic junction of a dual-source DWDS. The results show that visible changes in dri nking water quality, including turbidity, pH, UV, DOC, residual chlorine, and tr ihalomethanes (TMHs), are observed at the sample point between 10 and 12 km to o ne drinking water plant. The average concentration of residual chlorine decrease s from 0.74 ± 0.05 mg/L to 0.31 ± 0.11 mg/L during the water supplied from 0 to 10 km and then increases to 0.75 ± 0.05 mg/L at the end of 22 km. Whereas the TH Ms shows an opposite trend, the concentration reaches to a peak level at hydraul ic junction area (10-12 km). According to parallel factor (PARAFAC) and high-per formance sizeexclusion chromatography (HPSEC) analysis, organic matters vary si gnificantly during water distribution, and tryptophan-like substances and amino acids are closely related to the level of THMs. The hydraulic junction area is c onfirmed to be located at 10-12 km based on the water quality variation. Further more, data-driven models are established by machine learning (ML) with test R2 h igher than 0.8 for THMs prediction. And the SHAP analysis explains the model res ults and identifies the positive (water temperature and water supply distance) a nd negative (residual chlorine and pH) key factors influencing the THMs formatio n." According to the news editors, the research concluded: "This study conducts a de ep understanding of water quality at the hydraulic junction areas and establishe s predictive models for THMs formation in dual-sources DWDS."
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