High-level Analysis Research Articles

The on-orbit mission analysis of OTV based on DoDAF

PurposeThe purpose of this paper is to improve the efficiency of on-orbit operations through the top-level task design based on DoDAF. Based on the existing upper stage rocket technology, orbit transfer vehicles (OTVs) have developed rapidly in recent years. However, the lack of decision guidance based on overall task analysis requires integrating top-level analysis and bottom-level execution to achieve the smooth development of full-process tasks.Design/methodology/approachUsing the Department of Defense Architecture Framework (DoDAF) as a reference, this paper performs the top-level mission analysis modeling of the on-orbit rendezvous and capture of the OTV. Moreover, the typical operational view products are obtained, and the cooperative relations among the mission requirements, the system requirements, and the functional requirements are also analyzed.FindingsThe results show that the attitude of the OTV changes violently during the maneuver and rendezvous phases. In addition, the view products can be optimized based on the results.Originality/valueThe proposed DoDAF-based on-orbit task integration analysis method achieves the effective fusion of high-level analysis and bottom-level execution of OTV on-orbit rendezvous and capture task through the top-level task modeling, operation view generation and task relationship analysis. According to the requirements and constraints of the on-orbit rendezvous and capture task, the control instructions of the vehicle are efficiently generated under the DoDAF framework.

Reducing waste in garment factories by intelligent planning of optimal cutting orders

The design and production of apparel industry are a combination between the arts, the engineering, and the technology section. This paper enhances to find the optimal distribution of higher investment of raw materials by depending on artificial intelligence algorithms; the production orders in cut department with the studied company were reviewed. The waste rate, the number of layers, the number of markers for each executed order were studied and analyzed. Methodology of this study is examined under Genetic Algorithms for creating programs starting from a high level analysis of the problem by using code to express the problem. As a powerful optimization technique; The new scheduling method includes the movement of orders when comparing with the original process to reduce in the percentage of waste of fabric by 2.62%, As well as reducing the number of spreading layers by optimizing the cutting table from 56 to 25 layers, and reducing the number of markers from 6 to 3 markers. The method that gained could be applied in the cutting department and provide the extra knowledge in managing and handling the manufacturing system.

Automated Sports Player Performance Analysis: A Survey

Sports can not be said it is a mean for social cohesion by allowing people interaction disregarding their social status, age, etc. only but also it became the entertainment industry from governmental views. Consequently, a large amount of resources has been directed to sports to improve understanding, performance and presentation. Sports video analysis is a popular tool to capture low-level and high-level analysis. Sports analytics have several usage scenarios such as personalized video summary, player statistics and tactical analysis. In order to make the automatic sports video analysis realized, there is a necessity for player identification across sports. Identifying players in sports videos is a major research challenge due to low video resolution per player, camera perspective, the variation of the posture of player, illumination conditions, variety of sports fields and t-shirts. Here, we survey the most significant methods and problem formulations that have been proposed to address the problem of player identification.

Model-Driven AI for Games: Research Plan

The field of game AI is largely industry driven, lacking an agreed upon formalism for AI representation. Ad-hoc scripting languages, simple finite state machines, behaviour trees, and planners are employed, but not in a fashion adhering to any standard. As a result, reuse is sparse between games and formal analysis techniques are undeveloped. As research for a Ph.D. thesis, we propose to show that a layered Statechart-based AI is a suitable formalism for Game AI, enabling the use of model-driven development techniques such as reuse and high-level analysis including model-checking. The fundamentally modular nature of this approach leads naturally to reuse as a fundamental component of the design process. Supported by a clearly defined formalism, useful behavioural analyses become possible, such as testing reactions to various inputs at design time. We also explore transformations at the modelling level to enable procedural generation, allowing rapid deployment of varying AIs. Additionally, such a model allows for the generation of efficient code that can be directly inserted into games. Tool support for reuse, generation, and analysis will be developed, then employed in creating an industrial scale AI, proving that this formalism is appropriate for industrial use.

Maximizing Benefits to Nature and Society in Techno-Ecological Innovation for Water

Nature-based solutions (NbS) build upon the proven contribution of well-managed and diverse ecosystems to enhance resilience of human societies. They include alternatives to techno-industrial solutions that aim to enhance social-ecological integration by providing simultaneous benefits to nature (such as biodiversity protection and green/blue space) and society (such as ecosystem services and climate resiliency). Yet, many NbS exhibit aspects of a technological or engineered ecosystem integrated into nature; this techno-ecological coupling has not been widely considered. In this work, our aim is to investigate this coupling through a high-level and cross-disciplinary analysis of NbS for water security (quantity, quality, and/or water-related risk) across the spectrums of naturalness, biota scale, and benefits to nature and society. Within the limitations of our conceptual analysis, we highlight the clear gap between “nature” and “nature-based” for most NbS. We present a preliminary framework for advancing innovation efforts in NbS towards maximizing benefits to both nature and society, and offer examples in biophysical innovation and innovation to maximize techno-ecological synergies (TES).

Cancer and COVID-19 vaccines: a complex global picture.

Cancer and COVID-19 vaccines: a complex global picture.

The Analysis of Students' High Level Thinking Ability on Motion Systems Material on Covid Pandemic 19 at Madrasah Aliyah Medan City

This study aims to: (1) Know the description of higher order thinking skills in the material of motion systems in students at MAN 1 Medan City. (2) Knowing the description of high-order thinking skills in the material of motion systems in students at MAL UINSU Medan City. (3) Knowing the differences in higher-order thinking skills in the material of motion systems in students at MAN 1 Medan and MAL UINSU Medan. (4) Knowing the learning process of the motion system during the COVID 19 pandemic at MAN 1 Medan and at MAL UINSU Medan. This research method uses a quantitative research type with a descriptive approach. The results showed: (1) Students 'high-order thinking skills on motion system material in class XI MAN 1 Medan City had an average score of 84.83 in a good category. (2) Students' high-order thinking skills on movement system material in class XI MAL UINSU Medan City average score of 82.17 with good category. (3) (1) Students 'high-order thinking skills on motion system material in class XI MAN 1 Medan City with an average score of 84.83 in the good category, (2) Students' high-order thinking skills on motion system material in class XI MAL UINSU Medan City an average score of 82.17 with good category. (3) With the Sig (2-tailed) value of 0.048 is smaller than the significant levels of 0.05 and nt value> t table (2.125> 2,048). (4) The highest indicator of students 'high-order thinking skills in class XI MAN 1 Medan City is creating, while the highest indicator of students' high-order thinking skills in class XI MAL UINSU Medan City.

Experimental Testing of a Preview-Enabled Model Predictive Controller for Blade Pitch Control of Wind Turbines

Model predictive control (MPC) is a control method that involves determining the input to a dynamical system as the solution to an optimization problem that is solved online. In the wind turbine research literature, MPC has received considerable attention for its ability to handle both actuator constraints and preview disturbance information about the oncoming wind, which can be provided by a lidar scanner. However, while many studies simulate the wind turbine response under MPC, very few physical tests have been carried out, likely due in part to the difficulties associated with solving the MPC problem in real time. In this work, we implement MPC on an experimental, scaled wind turbine operating in a wind tunnel testbed, using an active grid to create reproducible wind sequences and a hot-wire anemometer to generate upstream wind measurements. To our knowledge, this work presents the first physical test of MPC for blade pitch control of a scaled wind turbine. We compare two MPC strategies: one including preview disturbance information and one without. Our results provide further evidence that feedforward control can improve wind turbine performance in transition and above-rated conditions without increasing actuation requirements, which we hope will encourage industry experimentation and uptake of feedforward control methods. We also provide a high-level analysis and interpretation of the computational performance of the chosen approach. This work builds upon the results of an earlier study, which considered unconstrained optimal blade pitch control.

An Adaptive Weighted Threshold Image Restoration Method Based on Wavelet Domain

Due to the limitation of imaging equipment, the influence of transmission medium and external environment, image quality degradation will inevitably occur in the process of generation, transmission and reception. These degradation not only worsens the visual effect of the image, but also makes the image lose a lot of useful information, which seriously affects image recognition, target detection and other high-level visual analysis. Wavelet analysis can extract useful information from image signal and meanwhile its profound wavelet basis can get adapted to signals of different properties. To better apply wavelet transform into image restoration domain, this paper according to the characteristics of wavelet transform, analyzes the method to select threshold function and the relationship within and between layers of wavelet coefficients, gets a proper threshold weight coefficient and propose an adaptive weighted threshold image restoration method based on wavelet domain, which makes smaller deviation and variance between the de-noised image and the original signal. The experiment result shows that the algorithm of this paper can obtain good subjective and objective image quality and effectively retain most detailed information of the image.

Pediatric literature trends: high-level analysis using text-mining.

Pediatric research is a diverse field that is constantly growing. Current machine learning advancements have prompted a technique termed text-mining. In text-mining, information is extracted from texts using algorithms. This technique can be applied to analyze trends and to investigate the dynamics in a research field. We aimed to use text-mining to provide a high-level analysis of pediatric literature over the past two decades. We retrieved all available MEDLINE/PubMed annual data sets until December 31, 2018. Included studies were categorized into topics using text-mining. Two hundred and twenty-five journals were categorized as Pediatrics, Perinatology, and Child Health based on Scimago ranking for medicine journals. We included 201,141 pediatric papers published between 1999 and 2018. The most frequently cited publications were clinical guidelines and meta-analyses. We found that there is a shift in the trend of topics. Epidemiological studies are gaining more publications while other topics are relatively decreasing. The topics in pediatric literature have shifted in the past two decades, reflecting changing trends in the field. Text-mining enables analysis of trends in publications and can serve as a high-level academic tool. Text-mining enables analysis of trends in publications and can serve as a high-level academic tool. This is the first study using text-mining techniques to analyze pediatric publications. Our findings indicate that text-mining techniques enable better understanding of trends in publications and should be implemented when analyzing research.

Significance driven inverse distance weighted filter to restore impulsive noise corrupted X-ray image

This paper presents a novel significance driven inverse distance weighted (SDIDW) filter for the impulsive noise removal in the X-ray images. The proposed SDIDW filter restores the noisy pixel using minimum number of nearest noise-free pixels to achieve good estimation while exhibiting low computational complexity. In the proposed filter, higher priority (weight) is given to nearest pixels compared to distant pixels and only sufficient nearest noise free pixels are determined to estimate the value of noisy pixel. A high level analysis of the computation complexity at varying noise density is done which shows that proposed SDIDW filter provides significant reduction in computation complexity over the adaptive median filters. Finally, the performance of the proposed filter is evaluated and compared over the state-of-the-art impulse noise removal techniques for varying noise density (wide range 10–90% and very high noise density range 91–99%). The experimental results on medical images demonstrate significant improvement in filtered images quality by the proposed filter over the state-of-the-art filters at each sample of noise density with small computational complexity.

Down type iso-singlet quarks at the HL-LHC and FCC-hh

We study the discovery potential of down type iso-singlet quarks, D, predicted by the E_6 GUT model in the {pprightarrow Dbar{D}rightarrow ZZdbar{d} rightarrow ell ^+ell ^-ell ^+ell ^- dbar{d}} channel, at the HL-LHC and FCC-hh colliders. The analysis is performed using a high level analysis description language and its runtime interpreter. The study shows that, using solely this channel, HL-LHC can discover D quarks up to a mass of 730 GeV whereas FCC-hh up to 2980 GeV with data collected in their complete run periods.

Time-Domain Adaptive Algorithms for Low- and High-Level Audio Information Processing

Abstract In this paper, we present a set of time-domain algorithms for the low- and high-level analysis of audio streams. These include spectral centroid, noisiness, and spectral spread for the low level, and dynamicity, heterogeneity, and complexity for the high level. The low-level algorithms provide a continuous measure of the features and can operate with short analysis frames. The high-level algorithms, on the other hand, are original designs informed both perceptually and by complexity theory for the analysis of musically meaningful information, both in short sounds or articulated streams with long-term nontrivial variations. These algorithms are suitable for the implementation of real-time audio analysis in diverse live performance setups that require the extraction of information from several streams at the same time. For example, the low-level algorithms can be deployed in large audio networks of adaptive agents, or in small-to-large ensembles for the analysis of various characteristics of the instruments for computer-assisted performance. Furthermore, the high-level algorithms can be implemented as part of fitness functions in music systems based on evolutionary algorithms that follow musically-informed criteria, or as analysis tools to assess the quality of some of the characteristics of a musical output. Musical applications of these algorithms can be found in a companion paper in this issue of Computer Music Journal: “Complex Adaptation in Audio Feedback Networks for the Synthesis of Music and Sounds.”

Athleteʼs Physical Fitness Prediction Model Algorithm and Index Optimization Analysis under the Environment of AI

With the rapid progress of network technology and computers, the Internet of Things has slowly entered peopleʼs lives and work. The Internet of Things can bring a lot of convenience to peopleʼs lives and work. People have been living in a networked era, and communications, computers, and network technologies are changing the entire human race and society. The extensive application of databases and computer networks, coupled with the use of advanced automatic data collection tools, has dramatically increased the amount of data that people have. There are many important information hidden behind the surge of data, and people hope to conduct higher-level analysis on it in order to make better use of these data. This article mainly introduces the prediction model algorithm and index optimization analysis of athletesʼ physical fitness under the Internet of things environment. This paper proposes an algorithm and index optimization method for the athletesʼ physical fitness prediction model in the Internet of Things environment, which is used to conduct athletesʼ fitness prediction model algorithm and index optimization experiments in the Internet of Things environment, and designs steps for athletesʼ physical fitness prediction in the Internet of Things environment to lay a solid foundation for related applications of athlete index optimization. The experimental results in this article show that the prediction accuracy rate of the professional group with the athleteʼs physical fitness prediction model and index optimization under the Internet of Things environment is higher than that of the control group, with a difference p < 0.001 .

From acoustic to linguistic analysis of temporal speech structure: Acousto-linguistic transformation during speech perception using speech quilts

Speech perception entails the mapping of the acoustic waveform to linguistic representations. For this transformation to succeed, the speech signal needs to be tracked over various temporal windows at high temporal precision in order to decode linguistic units ranging from phonemes (tens of milliseconds) to sentences (seconds). Here, we tested the hypothesis that cortical processing of speech-specific temporal structure is modulated by higher-level linguistic analysis. Using fMRI, we measured BOLD signal changes to 4 s long speech quilts with variable temporal structure (30, 120, 480, 960 ms segment lengths), as well as natural speech, created from a familiar (English) or foreign (Korean) language. We found evidence for the acoustic analysis of temporal speech properties in superior temporal sulcus (STS): the BOLD signal increased as a function of temporal speech structure in both familiar and foreign languages. However, activity in left inferior gyrus (IFG) revealed evidence for linguistic processing of temporal speech properties: the BOLD signal increased as a function of temporal speech structure only in familiar, but not in foreign speech. Network connectivity analyses suggested that left IFG modulates the processing of temporal speech structure in primary and non-primary auditory cortex, which in turn sensitizes the analysis of temporal speech structure in STS. The results thus suggest that acousto-linguistic transformation of temporal speech structure is achieved by a cortical network comprising primary and non-primary auditory cortex, STS, and left IFG.

Flipped classroom pedagogy in an online learning environment: A self-regulated introduction to information literacy threshold concepts

Online curriculums increase self-regulatory learning behaviors among students and facilitate knowledge transfer essential for deep learning to take place. Additionally, threshold concepts are best taught using a format that is designed for repeated point-of-need access by students. Pairing flipped classroom pedagogy, in which students engage with content independently prior to a synchronous class, with online learning objects intentionally designed to promote self-regulatory learning behaviors helps to build a strong foundation of information literacy. This paper discusses the decision of one library to move its information literacy curriculum to a fully online format in order to more effectively teach information literacy threshold concepts to students at all levels of college. The online curriculum was designed using both synchronous and asynchronous models of instruction and built upon the library's online learning pilot project with first year Communication Studies students. A high-level analysis of effective online pedagogy is offered in conjunction with a detailed look at the online course and content development process, librarians' training requirements, and the communication strategies used to inform campus stakeholders of the curricular change.

Writing a compelling integrated discussion: a guide for integrated discussions in article-based theses and dissertations.

Article-based theses and dissertations are increasingly being used in nursing and the health sciences as an alternate format to the traditional five-chapter monograph. A unique chapter in the article-based thesis is the integrated discussion, which differs in breadth and depth as compared to the discussion for a traditional thesis monograph or journal article. For many students and faculty, the integrated discussion is a challenging chapter to write, with minimal or no published guidance available. In this article, we offer a four-step approach with templates for planning and writing an integrated discussion. We also share several lessons learned with examples from published theses and dissertations. Writing an integrated discussion can be facilitated and written more efficiently by developing a clear and detailed outline of the chapter and broad discussion points prior to drafting the text, to achieve a higher-level synthesis, analysis, and interpretation of the overall significance of the thesis findings.

Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent.

Computer vision algorithms performance are near or superior to humans in the visual problems including object recognition (especially those of fine-grained categories), segmentation, and 3D object reconstruction from 2D views. Humans are, however, capable of higher-level image analyses. A clear example, involving theory of mind, is our ability to determine whether a perceived behavior or action was performed intentionally or not. In this paper, we derive an algorithm that can infer whether the behavior of an agent in a scene is intentional or unintentional based on its 3D kinematics, using the knowledge of self-propelled motion, Newtonian motion and their relationship. We show how the addition of this basic knowledge leads to a simple, unsupervised algorithm. To test the derived algorithm, we constructed three dedicated datasets from abstract geometric animation to realistic videos of agents performing intentional and non-intentional actions. Experiments on these datasets show that our algorithm can recognize whether an action is intentional or not, even without training data. The performance is comparable to various supervised baselines quantitatively, with sensible intentionality segmentation qualitatively.

Human Resource Case Analysis – The Students’ Perspective

Case analysis is a powerful teaching tool that requires students to demonstrate the higher-level application, analysis, evaluation, and synthesis skills essential to effective critical thinking. Human Resource Management (HRM) professionals must develop these skills and incorporate them into their day-to-day practices in serving organizations and the employees with whom they work. This paper presents HRM students’ perspectives about ten key factors essential to developing case analysis competencies essential to prepare them for successful careers.

Spatial-Spectral Structured Sparse Low-Rank Representation for Hyperspectral Image Super-Resolution.

Hyperspectral image super-resolution by fusing high-resolution multispectral image (HR-MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution spatial-spectral information of the scene. Existing methods mostly based on spectral unmixing and sparse representation are often developed from a low-level vision task perspective, they cannot sufficiently make use of the spatial and spectral priors available from higher-level analysis. To this issue, this paper proposes a novel HSI super-resolution method that fully considers the spatial/spectral subspace low-rank relationships between available HR-MSI/LR-HSI and latent HSI. Specifically, it relies on a new subspace clustering method named "structured sparse low-rank representation" (SSLRR), to represent the data samples as linear combinations of the bases in a given dictionary, where the sparse structure is induced by low-rank factorization for the affinity matrix. Then we exploit the proposed SSLRR model to learn the SSLRR along spatial/spectral domain from the MSI/HSI inputs. By using the learned spatial and spectral low-rank structures, we formulate the proposed HSI super-resolution model as a variational optimization problem, which can be readily solved by the ADMM algorithm. Compared with state-of-the-art hyperspectral super-resolution methods, the proposed method shows better performance on three benchmark datasets in terms of both visual and quantitative evaluation.