Matching Items (42)
Description作为世界工程机械发展的重要市场，中国本土培育了多个优秀的国际工程机械品牌。根据2020年KHL group发布的全球50强工程机械企业榜单，中资品牌代表性企业如三一重工、徐工机械跻身前5强，这是自2013年以来首次有中资品牌回归前5强，可见中国工程机械企业在世界范围内的影响力近年来大大提升。工程机械产品作为大宗商品，具有价值高、投资回报周期长的特点，因此其交易方式也与一般商品存在着明显的差异，以分期、融资、按揭等为主要交易形式的信用销售在工程机械产品交易中占据着重要的地位。采用信用销售，减少客户资金压力，扩大客户覆盖面，更有利于和客户建立长期互信的合作关系。尤其是在当下，当各主流品牌在产品质量、营销服务方面无法拉开明显差距的时候，信用销售的应用成为打造市场竞争力的重要组成部分。由此，对信用销售风险的管理不可避免地成为了各大工程机械企业普遍面临的课题。
一方面国内传统信用销售风险管理建立在征信系统信息和历史资信的基础上，存在着信息更新不及时、不全面的缺憾，无法对潜在的风险进行动态的评估和把控，对于成本高昂的工程机械产品而言，如果无法较为精准地预测到客户风险、及时采取预防性举措，其经营活动将会受到严重的制约；另一方面，当下工程机械企业积极开展国际化业务，然而国际市场信息不对称现象更加严重，这对企业国际化过程中的信用销售管理提出了严峻挑战。
本文拟借助现代化物联网技术，使用机器历史数据来对事中风险进行综合性评估，以期实现对信用销售风险的积极管理。三一重工易维讯系统、徐工汉云信息平台、中联重科云谷工业互联网平台ZValley OS均是工程机械物联网技术的积极实践，因此，研究物联网技术在工程机械企业信用销售风险管理中的应用，对行业的发展具有很强的实际意义。
本文以国内工程机械代表性企业A公司为研究对象，从交易双方的内外部风险出发，结合宏观层面，客户层面以及物联网维度信息构造风控模型，进一步探究信用销售风险大小背后的原因，借助机器学习技术，构建逾期预测模型，实时预测客户风险，并依照模型结果采取针对性的预防措施和对应策略，为工程机械行业在如何应用物联网技术提升客户信用销售风险管理提供了良好借鉴。
ContributorsLi, Qin (Author) / Zhu, David (Thesis advisor) / Li, Feng (Thesis advisor) / Sun, Tianshu (Committee member) / Arizona State University (Publisher)
Created2023
Description
Supply chain sustainability has become an increasingly important topic for corporations
due to consumer demands, regulatory requirements, and employee retention and productivity.
Since more and more stakeholders are beginning to care about sustainability, companies are
looking at how they can reduce their carbon footprint without it leading to higher costs. Although
sustainable supply chain operations are often associated with higher costs, new technology has
surfaced within the last decade that makes this association come into question. This paper serves as an investigation on whether or not implementation of recent technology will not only make for more sustainable supply chains, but also bring cost savings to a company. For the sake of simplicity, this paper analyzes the topic within the context of the consumer packaged goods (CPG) industry. The three categories of technology that were evaluated are artificial intelligence, Internet of Things, and data integration systems. Internship projects and/or published case studies and articles were examined to explore the relationship between the technology, supply chain sustainability, and costs. The findings of this paper indicate that recent technology offers companies innovative sustainability solutions to supply chains without sacrificing cost. This calls for CPG companies to invest in and implement technology that allows for more sustainable supply chains. Shying away from this because of cost concerns is no longer necessary.
ContributorsDixon, Logan (Author) / Printezis, Antonios (Thesis director) / Macias, Jeff (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2024-05
Description
Internet of Things (IoT) has become a popular topic in industry over the recent years, which describes an ecosystem of internet-connected devices or things that enrich the everyday life by improving our productivity and efficiency. The primary components of the IoT ecosystem are hardware, software and services. While the software and services of IoT system focus on data collection and processing to make decisions, the underlying hardware is responsible for sensing the information, preprocess and transmit it to the servers. Since the IoT ecosystem is still in infancy, there is a great need for rapid prototyping platforms that would help accelerate the hardware design process. However, depending on the target IoT application, different sensors are required to sense the signals such as heart-rate, temperature, pressure, acceleration, etc., and there is a great need for reconfigurable platforms that can prototype different sensor interfacing circuits.
This thesis primarily focuses on two important hardware aspects of an IoT system: (a) an FPAA based reconfigurable sensing front-end system and (b) an FPGA based reconfigurable processing system. To enable reconfiguration capability for any sensor type, Programmable ANalog Device Array (PANDA), a transistor-level analog reconfigurable platform is proposed. CAD tools required for implementation of front-end circuits on the platform are also developed. To demonstrate the capability of the platform on silicon, a small-scale array of 24×25 PANDA cells is fabricated in 65nm technology. Several analog circuit building blocks including amplifiers, bias circuits and filters are prototyped on the platform, which demonstrates the effectiveness of the platform for rapid prototyping IoT sensor interfaces.
IoT systems typically use machine learning algorithms that run on the servers to process the data in order to make decisions. Recently, embedded processors are being used to preprocess the data at the energy-constrained sensor node or at IoT gateway, which saves considerable energy for transmission and bandwidth. Using conventional CPU based systems for implementing the machine learning algorithms is not energy-efficient. Hence an FPGA based hardware accelerator is proposed and an optimization methodology is developed to maximize throughput of any convolutional neural network (CNN) based machine learning algorithm on a resource-constrained FPGA.
This thesis primarily focuses on two important hardware aspects of an IoT system: (a) an FPAA based reconfigurable sensing front-end system and (b) an FPGA based reconfigurable processing system. To enable reconfiguration capability for any sensor type, Programmable ANalog Device Array (PANDA), a transistor-level analog reconfigurable platform is proposed. CAD tools required for implementation of front-end circuits on the platform are also developed. To demonstrate the capability of the platform on silicon, a small-scale array of 24×25 PANDA cells is fabricated in 65nm technology. Several analog circuit building blocks including amplifiers, bias circuits and filters are prototyped on the platform, which demonstrates the effectiveness of the platform for rapid prototyping IoT sensor interfaces.
IoT systems typically use machine learning algorithms that run on the servers to process the data in order to make decisions. Recently, embedded processors are being used to preprocess the data at the energy-constrained sensor node or at IoT gateway, which saves considerable energy for transmission and bandwidth. Using conventional CPU based systems for implementing the machine learning algorithms is not energy-efficient. Hence an FPGA based hardware accelerator is proposed and an optimization methodology is developed to maximize throughput of any convolutional neural network (CNN) based machine learning algorithm on a resource-constrained FPGA.
ContributorsSuda, Naveen (Author) / Cao, Yu (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Yu, Shimeng (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2016
Description
Emerging from years of research and development, the Internet-of-Things (IoT) has finally paved its way into our daily lives. From smart home to Industry 4.0, IoT has been fundamentally transforming numerous domains with its unique superpower of interconnecting world-wide devices. However, the capability of IoT is largely constrained by the limited resources it can employ in various application scenarios, including computing power, network resource, dedicated hardware, etc. The situation is further exacerbated by the stringent quality-of-service (QoS) requirements of many IoT applications, such as delay, bandwidth, security, reliability, and more. This mismatch in resources and demands has greatly hindered the deployment and utilization of IoT services in many resource-intense and QoS-sensitive scenarios like autonomous driving and virtual reality.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments.
ContributorsYu, Ruozhou, Ph.D (Author) / Xue, Guoliang (Thesis advisor) / Huang, Dijiang (Committee member) / Sen, Arunabha (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
Environmental pollution has been one of the most challenging problems in modern society and more and more health issues are now linked to environmental pollution and especially, air pollution. Certain sensitive group like patients with asthma are highly influenced by the environmental air quality and knowledge of the daily air pollution exposure is of great importance for the management and prevention of asthma attack. Hence small form factor, real time, accurate, sensitive and easy to use portable devices for environmental monitoring are of great value.
Three novel image-based methods for quantitative real time environmental monitoring were introduced and the sensing principle, sensor performances were evaluated through simulation and field tests. The first sensing principle uses surface plasmon resonance (SPR) image and home-made molecular sieve (MS) column to realize real time chemical separation and detection. SPR is sensitive and non-specific, which makes it a desirable optical method for sensitive biological and chemical sensing, the miniaturized MS column provides small area footprint and makes it possible for SPR to record images of the whole column area. The innovative and system level integration approach provide a new way for simultaneous chemical separation and detection. The second sensor uses scattered laser light, Complementary metal-oxide-semiconductor (CMOS) imager and image processing to realize real-time particulate matter (PM) sensing. Complex but low latency algorithm was developed to obtain real time information for PM including PM number, size and size distribution. The third sensor uses gradient based colorimetric sensor, absorbance light signal and image processing to realize real-time Ozone sensing and achieved high sensitivity and substantially longer lifetime compared to conventional colorimetric sensors. The platform provides potential for multi-analyte integration and large-scale consumer use as wearable device.
The three projects provide novel, state-of-the-art and sensitive solutions for environmental and personal exposure monitoring. Moreover, the sensing platforms also provide tools for clinicians and epidemiologists to conduct large scale clinical studies on the adverse health effects of pollutants on various kinds of diseases.
Three novel image-based methods for quantitative real time environmental monitoring were introduced and the sensing principle, sensor performances were evaluated through simulation and field tests. The first sensing principle uses surface plasmon resonance (SPR) image and home-made molecular sieve (MS) column to realize real time chemical separation and detection. SPR is sensitive and non-specific, which makes it a desirable optical method for sensitive biological and chemical sensing, the miniaturized MS column provides small area footprint and makes it possible for SPR to record images of the whole column area. The innovative and system level integration approach provide a new way for simultaneous chemical separation and detection. The second sensor uses scattered laser light, Complementary metal-oxide-semiconductor (CMOS) imager and image processing to realize real-time particulate matter (PM) sensing. Complex but low latency algorithm was developed to obtain real time information for PM including PM number, size and size distribution. The third sensor uses gradient based colorimetric sensor, absorbance light signal and image processing to realize real-time Ozone sensing and achieved high sensitivity and substantially longer lifetime compared to conventional colorimetric sensors. The platform provides potential for multi-analyte integration and large-scale consumer use as wearable device.
The three projects provide novel, state-of-the-art and sensitive solutions for environmental and personal exposure monitoring. Moreover, the sensing platforms also provide tools for clinicians and epidemiologists to conduct large scale clinical studies on the adverse health effects of pollutants on various kinds of diseases.
ContributorsDu, Zijian (Author) / Tao, Nongjian (Thesis advisor) / Goryll, Michael (Committee member) / Herckes, Pierre (Committee member) / Tsow, Tsing (Committee member) / Arizona State University (Publisher)
Created2019
Description
Smart technology is now pervasive in society and has partnered with people on every level, yet its social and cultural implications are easily overlooked by the majority. In this thesis, I work on building a silent partnership between humans and smart technology and creating smart devices/systems as silent partners by revealing the complexity of smart technology and tackling the current issues of unilateral transparency, a lack of negotiation, and the dynamic of the sense of control. This work draws on varied fields such as critical cultural studies, science and technology studies (STS), media studies, information studies, sociology, psychology, and design and consists of three main themes: materiality, politics, and affect. In addition, I utilize theoretical frameworks such as posthumanism, actor-network theory (ANT), assemblage, materialism, and affect theory to analyze the underlying factors and relationships among human and nonhuman actors such as technology companies, governments, engineers, designers, users, as well as infrastructure, algorithms, and smart devices/systems. Finally, I offer four roles to rethink smart technology (an actor, a fluid, a peer, and a silent partner) and propose 15 design principles to redesign smart devices/systems as silent partners.
ContributorsLee, Yueh-Jung (Author) / Wise, John M (Thesis advisor) / Nadesan, Majia H (Committee member) / Wetmore, Jameson M. (Committee member) / Arizona State University (Publisher)
Created2020
Description
Suction stabilized floats have been implemented into a variety of applications such as supporting wind turbines in off-shore wind farms and for stabilizing cargo ships. This thesis proposes an alternative use for the technology in creating a system of suction stabilized floats equipped with real time location modules to help first responders establish a localized coordinate system to assist in rescues. The floats create a stabilized platform for each anchor module due to the inverse slack tank effect established by the inner water chamber. The design of the float has also been proven to be stable in most cases of amplitudes and frequencies ranging from 0 to 100 except for when the frequency ranges from 23 to 60 Hz for almost all values of the amplitude. The modules in the system form a coordinate grid based off the anchors that can track the location of a tag module within the range of the system using ultra-wideband communications. This method of location identification allows responders to use the system in GPS denied environments. The system can be accessed through an Android app with Bluetooth communications in close ranges or through internet of things (IoT) using a module as a listener, a Raspberry Pi and an internet source. The system has proven to identify the location of the tag in moderate ranges with an approximate accuracy of the tag location being 15 cm.
ContributorsDye, Michaela (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2020
Description
The broad deployment of time-synchronized continuous point-on-wave (CPoW) modules will enable electric power utilities to gain unprecedented insight into the behavior of their power system assets, loads, and distributed renewable generation in real time. By increasing the available level of detail visible to operators, serious fault events such as wildfire-inducing arc flashes, safety-jeopardizing transformer failures, and equipment-damaging power quality decline can be mitigated in a data-driven, systematic manner. In this research project, a time-synchronized micro-scale CPoW module was designed, constructed, and characterized. This inductively powered CPoW module, which operates wirelessly by using the current flowing through a typical distribution conductor as its power source and a wireless data link for communication, has been configured to measure instantaneous line current at high frequency (nominally 3,000 samples per second) with 12-bit resolution. The design process for this module is detailed in this study, including background research, individual block design and testing, printed circuit board (PCB) design, and final characterization of the system.
To validate the performance of this module, tests of power requirements, measurement accuracy, battery life, susceptibility to electromagnetic interference, and fault detection performance were performed. The results indicate that the design under investigation will satisfy the technical and physical constraints required for bulk deployment in an actual distribution network after manufacturing optimizations. After the test results were summarized, the future research and development activities needed to finalize this design for commercial deployment were identified and discussed.
ContributorsPatterson, John (Author) / Pal, Anamitra (Thesis advisor) / Ogras, Umit (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2021
Description
Machine learning (ML) and deep learning (DL) has become an intrinsic part of multiple fields. The ability to solve complex problems makes machine learning a panacea. In the last few years, there has been an explosion of data generation, which has greatly improvised machine learning models. But this comes with a cost of high computation, which invariably increases power usage and cost of the hardware. In this thesis we explore applications of ML techniques, applied to two completely different fields - arts, media and theater and urban climate research using low-cost and low-powered edge devices. The multi-modal chatbot uses different machine learning techniques: natural language processing (NLP) and computer vision (CV) to understand inputs of the user and accordingly perform in the play and interact with the audience. This system is also equipped with other interactive hardware setups like movable LED systems, together they provide an experiential theatrical play tailored to each user. I will discuss how I used edge devices to achieve this AI system which has created a new genre in theatrical play. I will then discuss MaRTiny, which is an AI-based bio-meteorological system that calculates mean radiant temperature (MRT), which is an important parameter for urban climate research. It is also equipped with a vision system that performs different machine learning tasks like pedestrian and shade detection. The entire system costs around $200 which can potentially replace the existing setup worth $20,000. I will further discuss how I overcame the inaccuracies in MRT value caused by the system, using machine learning methods. These projects although belonging to two very different fields, are implemented using edge devices and use similar ML techniques. In this thesis I will detail out different techniques that are shared between these two projects and how they can be used in several other applications using edge devices.
ContributorsKulkarni, Karthik Kashinath (Author) / Jayasuriya, Suren (Thesis advisor) / Middel, Ariane (Thesis advisor) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this update to the ESPBot, we have introduced new libraries for a small OLED display and a beeper. This functionality can be easily expanded to multiple beepers and displays, but requires more GPIO pins, or for the user to not use some of the infrared sensors or the ultrasonic sensor. We have also relocated some of the pins. The display can be updated to display 1 of 4 predefined shapes, or to display user-defined text. New shapes can be added by defining new methods within display.ino and calling the appropriate functions while parsing the JSON data in viple.ino. The beeper can be controlled by user-defined input to play any frequency for any amount of time. There is also a function added to play the happy birthday song. More songs can be added by defining new methods within beeper.ino and calling the appropriate functions while parsing the JSON data in viple.ino. More functionality can be added to allow the user to input a list of frequencies along with a list of time so the user can define their own songs or sequences on the fly.
ContributorsWelfert, Monica Michelle (Co-author) / Nguyen, Van (Co-author) / Chen, Yinong (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-12