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 Chengdu, Peo ple's Republic of China, by NewsRx correspondents, research stated, "The High Re solution Transmission Electron Microscope (HRTEM) images provide valuable insigh ts into the atomic microstructure, dislocation patterns, defects, and phase char acteristics of materials. However, the current analysis and research of HRTEM im ages of crystal materials heavily rely on manual expertise, which is labor-inten sive and susceptible to subjective errors." Our news editors obtained a quote from the research from Southwest Petroleum Uni versity, "This study proposes a combined machine learning and deep learning appr oach to automatically partition the same phase regions in crystal HRTEM images. The entire image is traversed by a sliding window to compute the amplitude spect rum of the Fast Fourier Transform (FFT) in each window. The generated data is tr ansformed into a 4-dimensional (4D) format. Principal component analysis (PCA) o n this 4D data estimates the number of feature regions. Non-negative matrix fact orization (NMF) then decomposes the data into a coefficient matrix representing feature region distribution, and a feature matrix corresponding to the FFT magni tude spectra. Phase recognition based on deep learning enables identifying the p hase of each feature region, thereby achieving automatic segmentation and recogn ition of phase regions in HRTEM images of crystals. Experiments on zirconium and oxide nanoparticle HRTEM images demonstrate the proposed method achieve the con sistency of manual analysis."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on have been presented. According t o news originating from Giza, Egypt, by NewsRx editors, the research stated, "Su pport Vector Machine (SVM) is a robust Machine Learning (ML) algorithm used exte nsively in classification tasks." The news editors obtained a quote from the research from Nile University: "This work proposes a reconfigurable hardware implementation of the SVM classification algorithm for the linear and three kernel cases on FPGA. Efficient implementati ons of two generalization techniques, One-versus-All (OvA) and One-versus-One (O vO), to deal with multi-class problems are also realized on FPGA to overcome the binary nature of the SVM algorithm. The presented model is fully reconfigurable and can easily be adapted to any dataset with any number of classes or features . The results show that the proposed model excels in power efficiency, requires low area utilization, and reaches high performance up to 250.7 MHz. The two real ized generalization methods, OvO and OvA, offer a trade-off between accuracy and hardware cost. OvA provides lower accuracy than OvO and is more affected by the data imbalance problem, which becomes more dominant as the number of classes in creases; however, it is more resource-efficient than OvO."
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 Machine Learn ing have been published. According to news reporting originating in Sichuan, Peo ple's Republic of China, by NewsRx journalists, research stated, "Hydrogen produ ction for clean energy is gaining a foothold, notably through the gasification o f biomass. Machine learning aids in its accurate production predictions, yet its opaque nature limits explanation." Funders for this research include National Natural Science Foundation of China ( NSFC), Natural Science Foundation of Sichuan, Fundamental Research Funds for the Central Universities, Science and Technology Innovation Talent Program of Sichu an Provincial.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting out of Moscow, Russia, b y NewsRx editors, research stated, "Actual load identification is a most importa nt task solved in the course of (1) engineering inspections of steel structures, (2) the design of systems rising or restoring the bearing capacity of damaged s tructural frames, and (3) structural health monitoring. Actual load values are u sed to determine the stress-strain state (SSS) of a structure and accomplish var ious engineering objectives." Funders for this research include National Research Moscow State University of C ivil Engineering. Our news correspondents obtained a quote from the research from National Researc h Moscow State University of Civil Engineering: "Load identification can involve some uncertainty and require soft computing techniques. Towards this end, the a rticle presents an integrated method combining basic provisions of structural me chanics, machine learning, and artificial neural networks. This method involves decomposing structures into primitives, using machine learning data to make proj ections, and assembling structures to make final projections for steel frame str uctures subjected to elastic strain. Final projections serve to identify paramet ers of point forces and loads distributed along the length of rods. The process of identification means checking the difference between (1) weight coefficient m atrices applied to unit loads and (2) actual loads standardized using maximum lo ad values. Cases of neural network training and parameters identification are pr ovided for simple beams."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Gram-Negative Bacteria-Klebsiella pneumoniae is the subject of a report. According to news originati ng from Zhejiang, People's Republic of China, by NewsRx correspondents, research stated, "Klebsiella pneumoniae is a type of Gram-negative bacterium which can cause a range of infections in h uman. In recent years, an increasing number of strains of K. pneumoniae resistant to multiple antibiotics have emerged, posing a significant threat to public health." Our news journalists obtained a quote from the research from Hangzhou Dianzi Uni versity, "The protein function of this bacterium is not well known, thus a syste matic investigation of K. pneumoniae proteome is in urgent need. In this study, the protein functions of this bacter ia were re-annotated, and their function groups were analyzed. Moreover, three m achine learning models were built to identify novel virulence factors. Results s howed that the functions of 16 uncharacterized proteins were first annotated by sequence alignment. In addition, K. pneumoniae proteins share a high proportion of homology with Haemophilus influenzae and a low homology proportion with Chlamydia pneumoniae. By sequence analysis, 10 proteins were identified as potential drug targets fo r this bacterium. Our model achieved a high accuracy of 0.901 in the benchmark d ataset. By applying our models to K. pneumoniae, we identified 39 virulence factors in this pathogen."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Robotic s. According to news reporting originating from Vasteras, Sweden, by NewsRx corr espondents, research stated, "This paper analyzes the timing performance of a pe rsistent storage designed for distributed container-based architectures in indus trial control applications. The timing performance analysis is conducted using a n in-house simulator, which mirrors our testbed specifications." Funders for this research include Mlardalen University, European Union (EU), Vin nova. Our news editors obtained a quote from the research from Malardalen University, "The storage ensures data availability and consistency even in presence of fault s. The analysis considers four aspects: 1. placement strategy, 2. design options , 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failur e patterns. Comparison results also reveal that, while the method may underperfo rm current centralized solutions in fault-free conditions, it outperforms the ce ntralized solutions in failure scenario."
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 Learning are pre sented in a new report. According to news reporting originating from Hubei, Peop le's Republic of China, by NewsRx correspondents, research stated, "Traditional models of the soil -plant -atmosphere continuum (SPAC) system are physics -based and their practical application has been hindered by issues of high parameteriz ation, prior structural bias, and costly running. In this paper, we developed a machine learning modelling method based on the Gaussian process (GP) to avoid th ese difficulties of traditional modelling." Funders for this research include Priority Research and Development Projects for Ningxia, National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Wuhan University, "At t he same time, shortcomings of conventional machine learning in terms of interpre tability and mechanism representation were addressed. The innovative method cons isted of structuring a local GP group framework and improving kernel functions u sed in each local GP. The resultant model was endowed with advanced capacities, including representing interplays between subprocesses within the complex SPAC s ystem, representing nonstationary subprocess dynamics caused by crop growth stag e shifts, as well as automatically interpreting key low -order input interaction s and dominant input variables for each subprocess. The performance of our model was examined on synthetic SPAC system datasets that covered three different soi l conditions. Results demonstrated that its interpretations regarding subprocess mechanisms were robust across different soil conditions and consistent with dom ain knowledge. Compared to a conventional global GP model and two deep learning models (DNN and LSTM), our model performed significantly better not only in regu lar prediction experiments but also in generalization experiments that required models to be transferred across different conditions. Our study suggested that i t is promising to enable a machine learning model interpretable by improving its feature representation function."
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 reporting out of Shangqiu, Peop le's Republic of China, by NewsRx editors, research stated, "Increasing number o f sensor-centric communication and computer equipment installed inside cars for various purposes, such as vehicle monitoring, physical wiring reduction, driving efficiency, has made invehicle communication an essential part of today's driv ing environment. However, the relevant literature on cyber security for in-vehic le communication systems does not currently offer any targeted, workable solutio ns for in-vehicle cyber hazards." Financial support for this research came from Henan Province Science and Technol ogy Project-Research and Application of D2D-MEC Technology in Resource Allocati on Optimization of Heterogeneous Vehicle Networks.
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 reporting originating from Lausanne, Switzerland, by NewsR x correspondents, research stated, "Edible robots and robotic food-edible syst ems that perceive, process and act upon stimulation-could open a new range of opportunities in health care, environmental management and the promotion of heal thier eating habits. For example, they could enable precise drug delivery and in vivo health monitoring, deliver autonomously targeted nutrition in emergency si tuations, reduce waste in farming, facilitate wild animal vaccination and produc e novel gastronomical experiences." Financial support for this research came from European Union (EU).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on robotics are presented i n a new report. According to news reporting originating from Warsaw, Poland, by NewsRx correspondents, research stated, "The development and study of an optimal control method for the problem of controlling the formation of a group of mobil e robots is still a current and popular theme of work. However, there are few wo rks that take into account the issues of time synchronization of units in a dece ntralized group." Our news reporters obtained a quote from the research from Military University o f Technology: "The motivation for taking up this topic was the possibility of im proving the accuracy of the movement of a group of robots by including dynamic t ime synchronization in the control algorithm. The aim of this work was to develo p a two-layer synchronous motion control system for a decentralized group of mob ile robots. The system consists of a master layer and a sublayer. The sublayer o f the control system performs the task of tracking the reference trajectory usin g a single robot with a kinematic and dynamic controller. In this layer, the inp ut and output signals are linear and angular velocity. The master layer realizes the maintenance of the desired group formation and synchronization of robots du ring movement."
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