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2022 Systems and Information Engineering Design Symposium (SIEDS)最新文献

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Autonomous Vehicle Tracking and Collision Avoidance Using Adaptive Control Algorithms 基于自适应控制算法的自动驾驶车辆跟踪与避碰
Pub Date : 2022-04-28 DOI: 10.1109/sieds55548.2022.9799339
Qianhong Zhao, G. Tao
This paper studies the control problems of a vehicle passing an intersection: the designed controller can make the controlled vehicle pass the intersection quickly and avoid any collision. In this research, the state-space model of the vehicle dynamics, containing several uncertain parameters, is established. The adaptive control method is adopted to deal with the systems parameter uncertainties in such vehicle control problems. For this study, two adaptive control designs are developed to solve the problem: a baseline adaptive control design and an enhanced adaptive control design. Unlike the classic PI controller which can only make the vehicle track constant velocity trajectories, both two adaptive control designs can achieve asymptotic tracking of arbitrary vehicle velocity trajectories. The enhanced adaptive design can even further improve the system tracking performance.
本文研究了车辆通过交叉口的控制问题:所设计的控制器能使被控车辆快速通过交叉口并避免碰撞。在本研究中,建立了包含多个不确定参数的车辆动力学状态空间模型。在此类车辆控制问题中,采用自适应控制方法处理系统参数的不确定性。为了解决这个问题,本研究开发了两种自适应控制设计:基线自适应控制设计和增强自适应控制设计。与传统的PI控制器只能使车辆跟踪匀速轨迹不同,两种自适应控制设计都能实现任意车辆速度轨迹的渐近跟踪。增强的自适应设计可以进一步提高系统的跟踪性能。
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引用次数: 0
Investigating Disinformation Through the Lens of Mass Media: A System Design 通过大众传媒的镜头调查虚假信息:一个系统设计
Pub Date : 2022-04-28 DOI: 10.1109/sieds55548.2022.9799320
Luke Palmieri, Ekrem Kaya, Gowri Prathap, A. Korb, Saltuk Karahan, Hamdi Kavak
Mass media is a medium of communication with a significant impact on public opinion and perception of issues of global significance. This study is centered around developing a software system to detect and analyze disinformation efforts through mass media outlets and predict shifts in public opinion or reveal active campaigns. The developed system uses a multistep process to analyze and reveal anti-American sentiment in any country of interest, particularly US allies. We used Turkey as a use case to test our system. Turkey is an important country because it holds a critical role within NATO as a US ally and has recently had significant shifts in anti-American views. We collected mass media articles from various Turkish media outlets. The articles were translated to English and stored in the system database. Roughly 3,500 articles are being published and added to the database each month. Using this system, we were able to conduct both exploratory and targeted analyses. For instance, an Iranian disseminated Turkish language newspaper was found to have the most negative Anti-American sentiment. Additionally, we retrieved and analyzed news articles related to Turkey's S-400 missile purchase from Russia, proving our system has significant potential.
大众传媒是一种对公众舆论和对具有全球意义的问题的看法产生重大影响的传播媒介。这项研究的核心是开发一个软件系统,通过大众媒体渠道检测和分析虚假信息,预测公众舆论的变化或揭示积极的运动。发达的系统使用一个多步骤的过程来分析和揭示任何国家的反美情绪,特别是美国的盟友。我们使用土耳其作为用例来测试我们的系统。土耳其是一个重要的国家,因为它作为美国的盟友在北约中发挥着关键作用,最近在反美观点上发生了重大转变。我们收集了来自土耳其各媒体的大众媒体文章。这些文章被翻译成英文并存储在系统数据库中。每月大约有3500篇文章发表并被添加到数据库中。使用该系统,我们能够进行探索性和针对性的分析。例如,一份伊朗传播的土耳其语报纸被发现具有最负面的反美情绪。此外,我们检索并分析了有关土耳其从俄罗斯购买S-400导弹的新闻文章,证明了我们的系统具有巨大的潜力。
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引用次数: 0
Adaptive Control of Robot Manipulators in Varying Environments 机器人机械臂在不同环境下的自适应控制
Pub Date : 2022-04-28 DOI: 10.1109/sieds55548.2022.9799418
Jiacheng Chen, G. Tao
This paper studies the multiple-model based adaptive control of a robot manipulator moving in varying environments. The research problem is divided into two parts: the modeling of the system consisting of the robot manipulator and the varying environment, and the multiple-model based adaptive control of the robot manipulator. This paper considers the added mass, added moment of inertia, drag, and buoyancy as the environmental factors. In these dynamic models, the environmental factors and the mass, moment of inertia, and gravity of the robot are unknown parameters. By the linearity in the parameters property, we can write these parameters independently of the robot joint variables and thus can be estimated using an adaptive control law. After obtaining the system model, we adopt a multiple-model based adaptive control scheme. When the model of the robot changes, the parameter estimates can rapidly convert to a relatively closer one for the new true values. With a multiple-model based adaptive controller, the asymptotic tracking of the robot and the parameter boundedness are achieved, and the tracking is not disturbed by the variance of the environment parameters in the multiple model control case, which has better performance than the single model case.
研究了基于多模型的机器人机械手在不同环境下的自适应控制问题。研究问题分为两部分:由机械手和变化环境组成的系统建模和基于多模型的机械手自适应控制。本文考虑了附加质量、附加惯性矩、阻力和浮力等环境因素。在这些动力学模型中,环境因素以及机器人的质量、转动惯量和重力都是未知参数。利用参数的线性特性,我们可以独立于机器人关节变量来编写这些参数,从而可以使用自适应控制律进行估计。在得到系统模型后,采用了基于多模型的自适应控制方案。当机器人模型发生变化时,参数估计可以迅速转换为相对接近新的真值。采用基于多模型的自适应控制器,实现了机器人的渐近跟踪和参数有界性,并且在多模型控制情况下跟踪不受环境参数变化的干扰,比单模型控制情况具有更好的性能。
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引用次数: 2
Humanlikeness and Aesthetic Customization's Effect on Trust, Performance, and Affect 拟人与审美定制对信任、绩效和情感的影响
Pub Date : 2022-04-28 DOI: 10.1109/SIEDS55548.2022.9799376
Carly E. Gray, A.F. Chesser, A. Atchley, R. C. Smitherman, N. Tenhundfeld
Human-machine interactions have become a staple of people's daily lives through the use of mobile devices, robotics, and a myriad of smart technologies. Previous research has established that anthropomorphism can significantly affect subjective perceptions of, and interactions with, machines. Furthermore, the ability to customize digital tools has been shown to affect user preferences, video game enjoyment, and the efficacy of digital mental health interventions. This study examined whether the customization of a machine teammate could influence the performance of the human-machine team and generate an affective response on the part of the human teammate. To evaluate this premise, we developed a bomb-defusing task simulation using the Unity game engine wherein participants were randomly assigned to one of two (humanlike or machinelike) robot avatars or were given the ability to customize one. The customizable robot avatar allows the participant to select either a humanlike or machinelike robot and customize the color of the wheels and casing. The customization is aesthetic in nature and has no effect on the functionality of the robot. The game design incorporates a high-risk environment and uncertainty with respect to the bomb-defusing distance and required button presses to encourage cautious guidance of the robot. We predicted that the ability to customize the robot will increase performance and subjective measures of trust, affect, attachment, identification, immersion, and control. We also predicted that the humanlikeness of the robot would increase performance and our subjective measures. Finally, we expected to see a significant effect of customization and humanlikeness such that the customization and humanlikeness have an additive effect on performance and our subjective measures. The results of all analyses were nonsignificant. These results may help inform the design of such systems and address fears that customization could lead to over-empathizing with a machine teammate in a way that would reduce use in high-risk environments.
通过使用移动设备、机器人技术和无数智能技术,人机交互已经成为人们日常生活的主要内容。先前的研究已经证实,拟人化可以显著影响对机器的主观感知以及与机器的互动。此外,定制数字工具的能力已被证明会影响用户偏好、视频游戏乐趣和数字心理健康干预的效果。本研究考察了机器队友的定制是否会影响人机团队的表现,并产生人类队友的情感反应。为了评估这个前提,我们使用Unity游戏引擎开发了一个拆除炸弹的任务模拟,其中参与者被随机分配到两个(类人或类机器)机器人化身中的一个,或者被赋予自定义一个的能力。可定制的机器人化身允许参与者选择一个像人或像机器的机器人,并定制车轮和外壳的颜色。定制本质上是美学的,对机器人的功能没有影响。游戏设计包含高风险环境和拆除炸弹距离的不确定性,并要求按下按钮以鼓励机器人谨慎引导。我们预测,定制机器人的能力将提高信任、情感、依恋、识别、沉浸和控制的性能和主观衡量标准。我们还预测,与人类相似的机器人将提高性能和我们的主观衡量标准。最后,我们期望看到定制和人类相似性的显著影响,这样定制和人类相似性对性能和我们的主观度量具有附加效应。所有分析的结果都不显著。这些结果可能有助于为此类系统的设计提供信息,并解决定制可能导致过度同情机器队友的担忧,从而减少在高风险环境中的使用。
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引用次数: 0
An Analysis on the Factors Affecting Undergraduate Interdisciplinary Research Programs 影响大学生跨学科研究计划的因素分析
Pub Date : 2022-04-28 DOI: 10.1109/sieds55548.2022.9799329
Sheri Leder, Kristin Weger, Bryan L. Mesmer
Enhancing undergraduate programs with interdisciplinary collaboration is increasingly important to prepare students for the demands of the competitive multi-stakeholder market. In this study, the Interdisciplinary Undergraduate Experience (INCLUDE) program brings undergraduates from disciplines such as industrial and systems engineering, computer science, psychology, ethics, art, and marketing together and grants them the opportunity to network with practitioner mentors from large stakeholder organizations (e.g., NASA, Dynetics, AOA, the U.S. Army, and Navy) to solve a Grand Challenge. Although interdisciplinary teams are key to innovation, monodisciplinary programs tend to be divergent in nature and may not expose students to project teamwork. Problems arise when trying to foster effective outcomes for students who are unfamiliar with the skills necessary for self-managed teamwork. The purpose of this study is to share the results of exploratory qualitative research designed to better understand the challenges that senior undergraduates from two separate teams (Fall 2020 - Spring 2021 and Fall 2021 - Spring 2022) faced in the INCLUDE program at The University of Alabama in Huntsville (UAH). This analysis focuses on the individual, institutional, and gender factors that shaped student perceptions between the two focus groups who were tasked with different Grand Challenges and access to the same campus resources. Data sets from observations and interviews are used in this study. This approach, combined with extensive literature research, provides valuable insight into their perceptions and attitudes about interdisciplinary teamwork and possible conflict resolutions. The results are expected to reveal the complexity of interdisciplinary collaboration for educational researchers.
加强跨学科合作的本科课程,为学生准备好应对竞争激烈的多方利益相关者市场的需求变得越来越重要。在这项研究中,跨学科本科生体验(INCLUDE)项目将来自工业和系统工程、计算机科学、心理学、伦理学、艺术和市场营销等学科的本科生聚集在一起,并为他们提供与来自大型利益相关者组织(如NASA、dynamics、AOA、美国陆军和海军)的实践者导师建立联系的机会,以解决一个重大挑战。虽然跨学科的团队是创新的关键,但单学科的项目往往在本质上是发散的,可能不会让学生接触到项目团队合作。当学生不熟悉自我管理团队的必要技能时,试图培养有效的结果就会出现问题。本研究的目的是分享探索性定性研究的结果,旨在更好地理解来自两个独立团队(2020年秋季- 2021年春季和2021年秋季- 2022年春季)的高年级本科生在亨茨维尔阿拉巴马大学(UAH)的INCLUDE项目中面临的挑战。这一分析侧重于个人、机构和性别因素,这些因素影响了学生对两个焦点群体的看法,这两个焦点群体面临着不同的大挑战,并获得相同的校园资源。本研究使用了来自观察和访谈的数据集。这种方法与广泛的文献研究相结合,为他们对跨学科团队合作和可能的冲突解决方案的看法和态度提供了有价值的见解。研究结果有望为教育研究者揭示跨学科合作的复杂性。
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引用次数: 0
Democratizing Housing Affordability Data: Open Data and Data Journalism in Charlottesville, VA 住房负担能力数据民主化:弗吉尼亚州夏洛茨维尔的开放数据和数据新闻
Pub Date : 2022-04-28 DOI: 10.1109/sieds55548.2022.9799410
Spencer Bozsik, Xinlun Cheng, Malvika Kuncham, E. Mitchell
Charlottesville is an independent city in central Virginia currently undergoing a rezoning process in an attempt to address the local impact of the housing affordability crisis. Restrictive, low-density zoning and gentrification have led to the displacement of many income-constrained Charlottesville residents. Local journalists are the primary information sources for residents about the crisis, but public data quantifying crisis impacts are often inaccessible to the public and to local media due to the technical experience required for data access and analysis. As a result, residents seeking to understand the land-scape of housing in Charlottesville are not taking full advantage of the information contained within public datasets as rezoning discussions take place. The focus of this project is to create a public dashboard in cooperation with local journalists to display and contextualize Charlottesville housing affordability data. Figures included in the dashboard make use of publicly available data from the Census, Bureau of Labor Statistics, ALICE (Asset Limited, Income Constrained, Employed) Project, and Charlottesville Open Data Portal, which have been cleaned, joined, and aggregated for plotting. The dashboard displays maps that geographically visualize home rental and purchase prices and identify the locations of resources such as public transportation and grocery stores, animated bar charts representing historical demographic information from the Census, and line graphs of historical median home prices. Neighborhood development is a central focus. We hope this platform for contextualizing and communicating data through data journalism will support advocacy for affordable housing initiatives and encourage more data scientists to carry out similar projects highlighting trends in their own communities.
夏洛茨维尔是弗吉尼亚州中部的一个独立城市,目前正在进行重新规划过程,试图解决住房负担能力危机对当地的影响。限制性的低密度分区和中产阶级化导致夏洛茨维尔许多收入有限的居民流离失所。当地记者是居民关于危机的主要信息来源,但由于数据获取和分析所需的技术经验,公众和当地媒体往往无法获得量化危机影响的公共数据。因此,当重新分区讨论进行时,寻求了解夏洛茨维尔住房景观的居民并没有充分利用公共数据集中包含的信息。该项目的重点是与当地记者合作创建一个公共仪表板,以显示夏洛茨维尔的住房负担能力数据。仪表板中包含的数据使用了来自人口普查局、劳工统计局、ALICE(资产有限、收入受限、就业)项目和夏洛茨维尔开放数据门户的公开数据,这些数据已经过清理、连接和汇总,以便绘制。仪表板显示地理上可视化的房屋租赁和购买价格地图,并确定公共交通和杂货店等资源的位置,表示人口普查中历史人口统计信息的动画条形图,以及历史中位数房价的线形图。社区发展是一个中心焦点。我们希望这个通过数据新闻将数据背景化和交流的平台将支持对经济适用房倡议的倡导,并鼓励更多的数据科学家在他们自己的社区开展类似的项目,突出趋势。
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引用次数: 0
Time-Series Forecasting Energy Loads: A Case Study in Texas 时间序列预测能源负荷:以德克萨斯州为例
Pub Date : 2022-04-28 DOI: 10.1109/sieds55548.2022.9799332
R. Rice, K. North, G. Hansen, D. Pearson, Oliver Schaer, T. Sherman, Daniel Vassallo
Future predicted energy demand on the grid is a major factor that drives the prices of energy contracts on trading markets. Errors in forecasting are problematic for energy traders who buy and sell futures contracts on the expected price of energy: when decisions are made on inaccurate predictions, the market will be inefficient, leading to price volatility and investment losses. This paper proposes the use of an ensemble model of lasso and ridge regressions to predict energy loads. Specifically, the methodology is used to forecast hourly energy demand for up to forty-one hours in the future for the Electric Reliability Council of Texas (ERCOT). The features in the model include previous energy loads and time identifiers such as month, day, and hour of the prediction horizon. The methodology resulted in the creation of forty-one hourly models, each an ensemble of lasso and ridge regression models. The performance of the methodology is measured via out-of-sample data from ERCOT in 2020 against the ERCOT predictions for the same period.
未来电网预测的能源需求是驱动交易市场能源合约价格的主要因素。对于根据预期能源价格买卖期货合约的能源交易员来说,预测错误是一个问题:当根据不准确的预测做出决策时,市场将效率低下,导致价格波动和投资损失。本文提出使用套索回归和脊回归的集合模型来预测能量负荷。具体来说,该方法被用于预测德克萨斯州电力可靠性委员会(ERCOT)未来长达41小时的每小时能源需求。模型中的特征包括以前的能源负荷和时间标识符,如预测视界的月、日和小时。该方法产生了41个小时模型,每个模型都是套索回归模型和脊回归模型的集合。该方法的性能是通过2020年ERCOT的样本外数据与同期的ERCOT预测来衡量的。
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引用次数: 1
Linking Inmates to Mental Health Services by Matching Records Between Independent Data Sets 通过在独立数据集之间匹配记录,将囚犯与心理健康服务联系起来
Pub Date : 2022-04-28 DOI: 10.1109/sieds55548.2022.9799372
Aditi Jain, Amelia Norman, L. Alonzi, Michael C. Smith, Neal Goodloe, K. P. White
Officials in the United States correctional system have long been aware of the significant role that serious mental illness (SMI) plays in recidivism. In a 2011 study, Bronson reported that 68% of prison inmates with diagnosed SMI returned to custody at least once within 4 years, 8% higher than those without SMI [1]. This issue is especially prevalent in regional jails, where 63% of male inmates and 75% of female inmates in regional jails suffer from symptoms of serious mental illness every year, making immediate assistance to these individuals crucial [2]. In response, a team of University of Virginia (UVA) Systems Engineering students work in collaboration with an array of organizations in the Charlottesville-Albemarle region to identify and provide local jail inmates with the mental health services they need, and produce policy recommendations to improve conditions for individuals with SMI who are prone to exposure to the criminal justice system [3]. The current Capstone team consists of undergraduate UVA students who perform analysis using the data provided by the organizations, enabling the community to make informed decisions. However, these decisions are hindered because, since the data sets from different organizations are not linked with a unique identifier for individuals across the agencies that are responsible for the care and supervision of individuals suffering from SMI. This makes the matching of individuals between data sets difficult. This issue is exacerbated by recidivism, which results in multiple occurrences of similar (or identical) values, complicating typical record matching methods, which often rely on one-to-one matching methods. Moreover, the data include protected personal identifiers (PPI) and HIPPA protected data, which also restricts data sharing among the agencies. Thus, any effort to merge the data must adhere to applicable data security rules and non-disclosure agreements. To resolve these matching issues, we first condensed the reiterations of data within each dataset into one line per individual and included an internal consistency metric that reflects possible changes (i.e. preferred name, address, etc.) that could affect data matching. Then, we developed a matching algorithm using the Record Linkage package on Python that compares two data sets consisting of resident information from Region Ten Community Services (R10) and the Jail Management System (JMS) at the Albemarle-Charlottesville Regional Jail (ACRJ) [4]. As a result of this process, we identified over 95 additional matches and another 50 uncertain matches that required human spot-checking, which is an improvement of 10% to previous methods of record matching applied to the data set. Such results could have significant results to the Capstone team as well as to other fields of research, especially regarding medical, financial, or other forms of data that deal with changing data over time.
美国惩教系统的官员们早就意识到严重精神疾病(SMI)在累犯中所起的重要作用。在2011年的一项研究中,布朗森报告说,68%被诊断为重度精神分裂症的囚犯在4年内至少再次被拘留一次,比没有重度精神分裂症的囚犯高8%。这一问题在地区监狱中尤为普遍,每年有63%的男性囚犯和75%的女性囚犯患有严重的精神疾病症状,因此对这些人的立即援助至关重要。作为回应,弗吉尼亚大学(UVA)系统工程专业的学生团队与夏洛茨维尔-阿尔伯马尔地区的一系列组织合作,确定并为当地监狱囚犯提供他们所需的心理健康服务,并提出政策建议,以改善容易暴露于刑事司法系统的重度精神障碍患者的状况。目前的Capstone团队由弗吉尼亚大学的本科生组成,他们使用组织提供的数据进行分析,使社区能够做出明智的决策。然而,这些决定受到阻碍,因为来自不同组织的数据集没有与跨负责照顾和监督重度精神分裂症患者的机构的个人的唯一标识符联系起来。这使得数据集之间的个体匹配变得困难。累犯会导致多次出现相似(或相同)的值,从而使典型的记录匹配方法复杂化,这些方法通常依赖于一对一的匹配方法。此外,这些数据包括受保护的个人标识符(PPI)和HIPPA保护的数据,这也限制了机构之间的数据共享。因此,任何合并数据的努力都必须遵守适用的数据安全规则和保密协议。为了解决这些匹配问题,我们首先将每个数据集中的重复数据浓缩成一行,并包含一个内部一致性指标,反映可能影响数据匹配的可能变化(即首选名称,地址等)。然后,我们使用Python上的Record Linkage包开发了一个匹配算法,该算法比较了来自Albemarle-Charlottesville地区监狱(ACRJ)[4]的十区社区服务(R10)和监狱管理系统(JMS)的居民信息组成的两个数据集。这个过程的结果是,我们确定了超过95个额外的匹配和另外50个需要人工抽查的不确定匹配,这比以前应用于数据集的记录匹配方法提高了10%。这样的结果可能会对Capstone团队以及其他研究领域产生重大影响,特别是在医疗、金融或其他形式的数据方面,这些数据会随着时间的推移而变化。
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引用次数: 1
How wearable sensing can be used to monitor patient recovery following ACL reconstruction 如何使用可穿戴传感来监测ACL重建后患者的恢复情况
Pub Date : 2022-04-28 DOI: 10.1109/sieds55548.2022.9799422
K. Cox, Drew Hamrock, Sydney Lawrence, Sean Lynch, Jane Romness, Jonathan Saksvig, Alice Warner, Robert Gutierrez, Joe M. Hart, M. Boukhechba
Anterior Cruciate Ligament (ACL) reconstructions are among the most common sports medicine procedures performed in the world. Over 100,000 patients in the United States annually elect to have ACL reconstruction (ACLR) in hopes of returning to pre-injury level of activity. In the first two years following an ACLR, patients are at their highest risk for re-injury to both the repaired and contralateral knee. The overall incidence rate of an ACLR patient having to go through a second repair in 24 months is six times greater than someone who has never had an ACL tear. Early detection of functional deficits is vital to optimize post-operative rehabilitation and to restore normal movement patterns in patients, especially in those who are young with continued risk exposure from competitive sports. The decision about when to return to unrestricted physical activity or competitive sports has come under much scrutiny due to the lack of evidence-based criteria that have sufficient predictive value. Current methods of detection require unconventional movements which cannot be done in the early stages of recovery in fear of damaging the newly repaired ligament. The need for a precise, objective, and whole-body approach to movement evaluation is essential for the health and safety of patients recovering from ACLR. The objective of our research is to leverage sensing technologies to monitor patients post ACLR and investigate how body sensors can be used to aid medical decision-making regarding rehabilitation progressions. In our study, patient data, extracted from wearable sensors during several functional assessments, was used for multi-level analysis to extract features indicative of mobility and muscle activation. In conclusion of our pilot, we have identified key features effective in determining patient health post-ACLR and implemented these into a machine learning model to estimate the efficacy of lower-body wearable sensors as a means of assessing patient recovery.
前交叉韧带(ACL)重建是世界上最常见的运动医学手术之一。在美国,每年有超过10万名患者选择ACL重建(ACLR),希望能恢复到损伤前的活动水平。在ACLR术后的头两年,患者修复的膝关节和对侧膝关节再次损伤的风险最高。ACLR患者在24个月内进行第二次修复的总体发生率是从未发生过ACL撕裂的患者的6倍。早期发现功能缺陷对于优化术后康复和恢复患者的正常运动模式至关重要,特别是对于那些持续暴露于竞技体育风险的年轻人。由于缺乏具有足够预测价值的循证标准,关于何时恢复不受限制的体育活动或竞技体育的决定受到了严格审查。目前的检测方法需要非常规的运动,在恢复的早期阶段不能做,因为害怕损伤新修复的韧带。需要一种精确、客观和全身的运动评估方法对于ACLR患者康复的健康和安全至关重要。我们的研究目的是利用传感技术来监测ACLR后患者,并研究如何使用身体传感器来帮助有关康复进展的医疗决策。在我们的研究中,从可穿戴传感器中提取的患者数据在几次功能评估中被用于多层次分析,以提取指示活动和肌肉激活的特征。在我们的试验总结中,我们已经确定了确定aclr后患者健康状况的有效关键特征,并将这些特征实现到机器学习模型中,以估计下半身可穿戴传感器作为评估患者康复的一种手段的有效性。
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引用次数: 0
Building a Better Benchmark: Predicting Effects of Shopper Marketing on Sales 建立更好的基准:预测购物者营销对销售的影响
Pub Date : 2022-04-28 DOI: 10.1109/sieds55548.2022.9799392
P. Corbett, N. Keeley, Gabriella Belmarez, F. W. Blickle, Oliver Schaer
Companies with wide product portfolios and multiple retail channels often have difficulty quantifying the sales impact of marketing programs due to the large number of factors that potentially influence sales. The amount of data and complex modeling necessary to get such a promotional model off the ground creates a challenge, especially for firms executing a wide variety of promotions across many retailers. This challenge can stunt any efforts to make data-driven decisions regarding marketing spending. Our work explores marketing program data from a national consumer packaged goods (CPG) manufacturer and related product sales data from one of its retail partners. We build two separate models that provide a measure of incremental sales attributable to marketing programs at the brand level. The findings show that under certain conditions, organizations can achieve a useful promotional sales model with modest data inputs. Applying this approach, organizations can gain insights into the sales impact of their marketing spending, especially if they incorporate partner data, limit data streams and features, and incorporate program tactics. Our models can be used for descriptive as well as predictive analysis, thus allowing a CPG company to improve decision making that relies on forecasts of future sales.
拥有广泛产品组合和多种零售渠道的公司往往难以量化营销计划的销售影响,因为潜在影响销售的因素很多。建立这样的促销模型所必需的数据量和复杂的建模带来了挑战,特别是对于在许多零售商中执行各种促销活动的公司。这一挑战可能会阻碍有关营销支出的数据驱动决策的努力。我们的研究研究了来自一家全国性包装消费品(CPG)制造商的营销计划数据,以及来自其零售合作伙伴之一的相关产品销售数据。我们建立了两个独立的模型,以衡量品牌层面的营销计划所带来的增量销售。研究结果表明,在一定条件下,组织可以通过适度的数据输入实现有用的促销销售模型。应用这种方法,组织可以深入了解其营销支出的销售影响,特别是如果他们合并合作伙伴数据,限制数据流和功能,并合并计划策略。我们的模型既可以用于描述性分析,也可以用于预测性分析,从而使CPG公司能够根据对未来销售的预测来改进决策。
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2022 Systems and Information Engineering Design Symposium (SIEDS)
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