Improving the Effectiveness of Image Classification Structural Methods by Compressing the Description According to the Information Content Criterion

The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency.The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations.It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.The informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other descriptions.The developed method determines the relevance of the full description of the recognized object with the reduced description of the etalons.Several practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are considered.The results of the experimental modeling of the proposed methods for a database including images of museum jewelry are presented.The test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of view.The practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been researched.Modeling has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification accuracy.Reducing the descriptions by twenty times in the experiment leads to slightly decreased accuracy.The speed of the analysis increases in proportion to the degree of reduction.The use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent level of accuracy.

Investigations on High-Speed Flash Boiling Atomization of Fuel Based on Numerical Simulations

Flash boiling atomization(FBA)is a promising approach for enhancing spray atomization,which can generate a fine and more evenly distributed spray by increasing the fuel injection temperature or reducing the ambient pressure.However,when the outlet speed of the nozzle exceeds 400 m/s,investigating high-speed flash boiling atomization(HFBA)becomes quite challenging.This difficulty arises fromthe involvement ofmany complex physical processes and the requirement for a very fine mesh in numerical simulations.In this study,an HFBA model for gasoline direct injection(GDI)is established.This model incorporates primary and secondary atomization,as well as vaporization and boilingmodels,to describe the development process of the flash boiling spray.Compared to lowspeed FBA,these physical processes significantly impact HFBA.In this model,the Eulerian description is utilized for modeling the gas,and the Lagrangian description is applied to model the droplets,which effectively captures the movement of the droplets and avoids excessive mesh in the Eulerian coordinates.Under various conditions,numerical solutions of the Sauter mean diameter(SMD)for GDI show good agreement with experimental data,validating the proposed model’s performance.Simulations based on this HFBA model investigate the influences of fuel injection temperature and ambient pressure on the atomization process.Numerical analyses of the velocity field,temperature field,vapor mass fraction distribution,particle size distribution,and spray penetration length under different superheat degrees reveal that high injection temperature or low ambient pressure significantly affects the formation of small and dispersed droplet distribution.This effect is conducive to the refinement of spray particles and enhances atomization.

Method for detector description conversion from DD4hep to Filmbox

DD4hep serves as a generic detector description toolkit recommended for offline software development in next-generation high-energy physics(HEP)experiments.Conversely,Filmbox(FBX)stands out as a widely used 3D modeling file format within the 3D software industry.In this paper,we introduce a novel method that can automatically convert complex HEP detector geometries from DD4hep description into 3D models in the FBX format.The feasibility of this method was dem-onstrated by its application to the DD4hep description of the Compact Linear Collider detector and several sub-detectors of the super Tau-Charm facility and circular electron-positron collider experiments.The automatic DD4hep–FBX detector conversion interface provides convenience for further development of applications,such as detector design,simulation,visualization,data monitoring,and outreach,in HEP experiments.

Enhancing constitutive description and workability characterization of Mg alloy during hot deformation using machine learning-based Arrhenius-type model

Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy.However,the hot deformation of Mg alloy is highly sensitive to factors such as temperature,strain rate,and strain,leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy.To overcome the shortcomings of the conventional Arrhenius-type(AT)model,this study developed machine learning-based Arrhenius-type(ML-AT)models by combining the genetic algorithm(GA),particle swarm optimization(PSO),and artificial neural network(ANN).Results indicated that when describing the flow behavior of the AQ80 alloy,the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models.Moreover,an activation energy-processing(AEP)map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model.Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods,ultimately contributing to the precise determination of the optimum processing window.These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.

A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region

Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.

Trends in Event Understanding and Caption Generation/Reconstruction in Dense Video:A Review

Video description generates natural language sentences that describe the subject,verb,and objects of the targeted Video.The video description has been used to help visually impaired people to understand the content.It is also playing an essential role in devolving human-robot interaction.The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping.Deep learning is changing the shape of computer vision(CV)technologies and natural language processing(NLP).There are hundreds of deep learning models,datasets,and evaluations that can improve the gaps in current research.This article filled this gap by evaluating some state-of-the-art approaches,especially focusing on deep learning and machine learning for video caption in a dense environment.In this article,some classic techniques concerning the existing machine learning were reviewed.And provides deep learning models,a detail of benchmark datasets with their respective domains.This paper reviews various evaluation metrics,including Bilingual EvaluationUnderstudy(BLEU),Metric for Evaluation of Translation with Explicit Ordering(METEOR),WordMover’s Distance(WMD),and Recall-Oriented Understudy for Gisting Evaluation(ROUGE)with their pros and cons.Finally,this article listed some future directions and proposed work for context enhancement using key scene extraction with object detection in a particular frame.Especially,how to improve the context of video description by analyzing key frames detection through morphological image analysis.Additionally,the paper discusses a novel approach involving sentence reconstruction and context improvement through key frame object detection,which incorporates the fusion of large languagemodels for refining results.The ultimate results arise fromenhancing the generated text of the proposedmodel by improving the predicted text and isolating objects using various keyframes.These keyframes identify dense events occurring in the video sequence.

Enhancing Cross-Lingual Image Description: A Multimodal Approach for Semantic Relevance and Stylistic Alignment

Cross-lingual image description,the task of generating image captions in a target language from images and descriptions in a source language,is addressed in this study through a novel approach that combines neural network models and semantic matching techniques.Experiments conducted on the Flickr8k and AraImg2k benchmark datasets,featuring images and descriptions in English and Arabic,showcase remarkable performance improvements over state-of-the-art methods.Our model,equipped with the Image&Cross-Language Semantic Matching module and the Target Language Domain Evaluation module,significantly enhances the semantic relevance of generated image descriptions.For English-to-Arabic and Arabic-to-English cross-language image descriptions,our approach achieves a CIDEr score for English and Arabic of 87.9%and 81.7%,respectively,emphasizing the substantial contributions of our methodology.Comparative analyses with previous works further affirm the superior performance of our approach,and visual results underscore that our model generates image captions that are both semantically accurate and stylistically consistent with the target language.In summary,this study advances the field of cross-lingual image description,offering an effective solution for generating image captions across languages,with the potential to impact multilingual communication and accessibility.Future research directions include expanding to more languages and incorporating diverse visual and textual data sources.

Enhancing Image Description Generation through Deep Reinforcement Learning:Fusing Multiple Visual Features and Reward Mechanisms

Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually impaired.This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images.Our method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more closely.Our approach comprises three key architectures.Firstly,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature fusion.Secondly,the Transformer model is engaged to derive contextual semantic features from textual data.Finally,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward functions.Compared with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is 0.299.Consensus-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image description.Future research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems.

耦合拉格朗日-欧拉方法及其在海洋工程中的应用

Combining the strengths of Lagrangian and Eulerian descriptions,the coupled Lagrangian–Eulerian methods play an increasingly important role in various subjects.This work reviews their development and application in ocean engineering.Initially,we briefly outline the advantages and disadvantages of the Lagrangian and Eulerian descriptions and the main characteristics of the coupled Lagrangian–Eulerian approach.Then,following the developmental trajectory of these methods,the fundamental formulations and the frameworks of various approaches,including the arbitrary Lagrangian–Eulerian finite element method,the particle-in-cell method,the material point method,and the recently developed Lagrangian–Eulerian stabilized collocation method,are detailedly reviewed.In addition,the article reviews the research progress of these methods with applications in ocean hydrodynamics,focusing on free surface flows,numerical wave generation,wave overturning and breaking,interactions between waves and coastal structures,fluid–rigid body interactions,fluid–elastic body interactions,multiphase flow problems and visualization of ocean flows,etc.Furthermore,the latest research advancements in the numerical stability,accuracy,efficiency,and consistency of the coupled Lagrangian–Eulerian particle methods are reviewed;these advancements enable efficient and highly accurate simulation of complicated multiphysics problems in ocean and coastal engineering.By building on these works,the current challenges and future directions of the hybrid Lagrangian–Eulerian particle methods are summarized.

Rice University Rule to Determine the Number of Bins

This study aims to establish a rationale for the Rice University rule in determining the number of bins in a histogram. It is grounded in the Scott and Freedman-Diaconis rules. Additionally, the accuracy of the empirical histogram in reproducing the shape of the distribution is assessed with respect to three factors: the rule for determining the number of bins (square root, Sturges, Doane, Scott, Freedman-Diaconis, and Rice University), sample size, and distribution type. Three measures are utilized: the average distance between empirical and theoretical histograms, the level of recognition by an expert judge, and the accuracy index, which is composed of the two aforementioned measures. Mean comparisons are conducted with aligned rank transformation analysis of variance for three fixed-effects factors: sample size (20, 35, 50, 100, 200, 500, and 1000), distribution type (10 types), and empirical rule to determine the number of bins (6 rules). From the accuracy index, Rice’s rule improves with increasing sample size and is independent of distribution type. It outperforms the Friedman-Diaconis rule but falls short of Scott’s rule, except with the arcsine distribution. Its profile of means resembles the square root rule concerning distributions and Doane’s rule concerning sample sizes. These profiles differ from those of the Scott and Friedman-Diaconis rules, which resemble each other. Among the seven rules, Scott’s rule stands out in terms of accuracy, except for the arcsine distribution, and the square root rule is the least accurate.

Travel-Limiting Disabilities in the United States: Why Accessibility Matters?

This study evaluates the distribution of travel-limiting disabilities across genders and geographic locations in the United States. This study aims to describe and compare the socioeconomic and demographic variables of the people with and without travel-limiting disabilities across geographic locations and gender. The study further evaluates the trip purpose and impact of Covid-19 fourth wave pandemic on the use of public transit and travel to physical workplace for the people with and without travel-limiting disabilities across gender and geographic locations. The study uses the 2022 weighted National Household Travel Survey dataset and employs descriptive statistics. Results reaffirm the findings from previous literature that there are more people with travel-limiting disabilities in urban areas and among women. Over 50 percent of people aged 65 and above have a form of travel-limiting disabilities. The most trip for people with travel-limiting disabilities is made for shopping and medical purposes. Across all categories, rural areas, urban areas, male and female for the people without travel-limiting disabilities, COVID-19 fourth wave did not change the pattern of trips made to physical workplace as pre-COVID-19 era. This pattern is also observable for the people with travel-limiting disabilities in rural and urban areas. Females with travel-limiting disabilities reported making less trips to physical workplaces while male reported doing the same as before COVID-19 era. The study concludes that the quantification of travel-limiting disabilities across geographic location and gender is vital in disability study and could drive policy implementation for improved accessibility for the vulnerable population.

A Descriptive Translation Study of Chinese Translation of French Musical Roméo et Juliette

This paper presents a Descriptive Translation Study(DTS)analysis of the Chinese translation of the French musical adaptation of Romeo and Juliet,titled Roméo et Juliette:de la Haineàl’Amour.Romeo and Juliet,a timeless play by Shakespeare,has captivated audiences since its premiere in 1597 and has been adapted into various forms,including stage productions,films,musicals,and operas.The focus of this study is to analyze the Chinese translation of the French musical adaptation from a DTS perspective.DTS is an approach that aims to understand the translation process and its reception in the target culture.By examining language choices,cultural references,and adaptation strategies,this study seeks to shed light on how the Chinese translation of the French musical functions within the target culture and influences the reception and interpretation of the source text.This analysis is expected to gain insights into the challenges and strategies employed in translating a musical adaptation of Romeo and Juliet into Chinese.The findings of this study will contribute to the field of translation studies and provide a deeper understanding of the complexities involved in the translation of musical works.

Unified Description of the Three Stable Particles in Self-Action Allows Determination of Their Relative Masses

The Dirac equation γμ(δμ-eAμ)Ψ=mc2Ψ describes the bound states of the electron under the action of external potentials, Aμ. We assumed that the fundamental form of the Dirac equation γμ(δμ-Sμ)Ψ=0 should describe the stable particles (the electron, the proton and the dark-matter-particle (dmp)) bound to themselves under the action of their own potentials Sμ. The new equation reveals that self energy is consequence of self action, it also reveals that the spin angular momentum is consequence of the dynamic structure of the stable particles. The quantitative results are the determination of their relative masses as well as the determination of the electromagnetic coupling constant.

Modeling the Physical Parameters of Solenoids for MEMS Applications

The paper is devoted to study of the electrical parameters of the motion parts of the MEMS such as solenoids. The analytical background is given in order to describe the influence of the electrical field components on the forces, which are result of interaction of the electromagnetic (EM) field components with the parts of motion devices of MEMS. The given analytical formulas open the ability to calculate the self-inductance of the microsolenoids of the different kind, as well as the stored energy of such motion devices, that could be used for the modeling and optimization of parameters of running devices of MEMS such as actuators, sensors etc.

Exploring the Characteristics of Data Breaches: A Descriptive Analytic Study

Data breaches have massive consequences for companies, affecting them financially and undermining their reputation, which poses significant challenges to online security and the long-term viability of businesses. This study analyzes trends in data breaches in the United States, examining the frequency, causes, and magnitude of breaches across various industries. We document that data breaches are increasing, with hacking emerging as the leading cause. Our descriptive analyses explore factors influencing breaches, including security vulnerabilities, human error, and malicious attacks. The findings provide policymakers and businesses with actionable insights to bolster data security through proactive audits, patching, encryption, and response planning. By better understanding breach patterns and risk factors, organizations can take targeted steps to enhance protections and mitigate the potential damage of future incidents.

Describing a Baryon as a Composition of Bound Stated and Unbound Stated Sea-Quarks

We propose the revised description of a baryon as a composition of bound stated sea-quarks and unbound stated sea-quarks from the previously proposed description of baryon as a meson pair. The purpose of this article is to show the following two possibilities. The first one shows the qualitative explanation to support our description of a nucleon as a pair of pions and the second one is that it gives an explanation of ALICE results that the pTdependence of Λc+/D0ratio is 0.5. Each isospin group is constructed of both baryons and antibaryons. This way of construction is consistent with that of mesons. The results obtained are listed in tables. This shows that the generalized Gell-Mann-Nishijima relation equation holds under the condition that the baryon number is 0.

Natural Numbers and the Strong Goldbach Conjecture

This study introduces the representation of natural number sets as row vectors and pretends to offer a new perspective on the strong Goldbach conjecture. The natural numbers are restructured and expanded with the inclusion of the zero element as the source of a strong Goldbach conjecture reformulation. A prime Boolean vector is defined, pinpointing the positions of prime numbers within the odd number sequence. The natural unit primality is discussed in this context and transformed into a source of quantum-like indetermination. This approach allows for rephrasing the strong Goldbach conjecture, framed within a Boolean scalar product between the prime Boolean vector and its reverse. Throughout the discussion, other intriguing topics emerge and are thoroughly analyzed. A final description of two empirical algorithms is provided to prove the strong Goldbach conjecture.

Theory,technology and practice of shale gas three-dimensional development:A case study of Fuling shale gas field in Sichuan Basin,SW China

In the Jiaoshiba block of the Fuling shale gas field,the employed reserves and recovery factor by primary well pattern are low,no obvious barrier is found in the development layer series,and layered development is difficult.Based on the understanding of the main factors controlling shale gas enrichment and high production,the theory and technology of shale gas three-dimensional development,such as fine description and modeling of shale gas reservoir,optimization of three-dimensional development strategy,highly efficient drilling with dense well pattern,precision fracturing and real-time control,are discussed.Three-dimensional development refers to the application of optimal and fast drilling and volume fracturing technologies,depending upon the sedimentary characteristics,reservoir characteristics and sweet spot distribution of shale gas,to form"artificial gas reservoir"in a multidimensional space,so as to maximize the employed reserves,recovery factor and yield rate of shale gas development.In the research on shale gas three-dimensional development,the geological+engineering sweet spot description is fundamental,the collaborative optimization of natural fractures and artificial fractures is critical,and the improvement of speed and efficiency in drilling and fracturing engineering is the guarantee.Through the implementation of three-dimensional development,the overall recovery factor in the Jiaoshiba block has increased from 12.6%to 23.3%,providing an important support for the continuous and stable production of the Fuling shale gas field.

Effect of different drying methods on the taste and volatile compounds,sensory characteristics of Takifugu obscurus

Pufferfish is prone to deterioration due to abundant nutrients and high moisture content.Drying technology can extend the shelf life and enhance the flavor quality of aquatic products.The study investigated the effect of hot air drying(HAD),microwave vacuum drying(MVD)and hot air assisted radio frequency drying(HARFD)on the taste and volatile profiles of Takifugu obscurus.Different drying methods had significant influence on the color,rehydration,5’-nucleotides,free amino acids and volatile components(P<0.05).The results showed that HAD and HARFD could promote the flavor of T.obscurus by producing higher equivalent umami concentration(EUC)values,which were about two times of MVD group,and more pronounced pleasant odor according to sensory analysis.HAD is more appropriate for industrial application than HARFD and MVD considering the economic benefits.This study could provide a reference for the industrial application of drying T.obscurus.

An overview of efficient development practices at low permeability sandstone reservoirs in China

Low permeability sandstone reservoirs in China typically have more complicated geological conditions, pore structures, and flow characteristics as compared to medium-to-high-permeability sandstone reservoirs. Traditional geological and seepage theories, and engineering methods are not applicable to the development of these low permeability reservoirs, and wells drilled into them often produce oil and gas at very low rates. Recent breakthroughs in reservoir exploitation technology have greatly improved the productivity of low permeability reservoirs, making them the primary target for oil exploration and extraction in China. The development theories and practices applied to low permeability reservoirs in China are reviewed in this study— based on relevant geological and engineering practices, including drilling, fracturing, recovery, and surface engineering. A unique series of technological advances that aid the development of low permeability reservoirs in China are summarized here. This study may serve as a meaningful guide in achieving scale efficiency for the development of low permeability reservoirs.