SeungBeum Suh

Bio: SeungBeum Suh is an academic researcher from Korea Institute of Science and Technology. The author has contributed to research in topics: Mobile robot & Torque. The author has an hindex of 6, co-authored 20 publications receiving 227 citations. Previous affiliations of SeungBeum Suh include Kigali Institute of Science and Technology & Virginia Tech.

Nanoscale Bacteria‐Enabled Autonomous Drug Delivery System (NanoBEADS) Enhances Intratumoral Transport of Nanomedicine

Abstract: Cancer drug delivery remains a formidable challenge due to systemic toxicity and inadequate extravascular transport of nanotherapeutics to cells distal from blood vessels. It is hypothesized that, in absence of an external driving force, the Salmonella enterica serovar Typhimurium could be exploited for autonomous targeted delivery of nanotherapeutics to currently unreachable sites. To test the hypothesis, a nanoscale bacteria-enabled autonomous drug delivery system (NanoBEADS) is developed in which the functional capabilities of the tumor-targeting S. Typhimurium VNP20009 are interfaced with poly(lactic-co-glycolic acid) nanoparticles. The impact of nanoparticle conjugation is evaluated on NanoBEADS' invasion of cancer cells and intratumoral transport in 3D tumor spheroids in vitro, and biodistribution in a mammary tumor model in vivo. It is found that intercellular (between cells) self-replication and translocation are the dominant mechanisms of bacteria intratumoral penetration and that nanoparticle conjugation does not impede bacteria's intratumoral transport performance. Through the development of new transport metrics, it is demonstrated that NanoBEADS enhance nanoparticle retention and distribution in solid tumors by up to a remarkable 100-fold without requiring any externally applied driving force or control input. Such autonomous biohybrid systems could unlock a powerful new paradigm in cancer treatment by improving the therapeutic index of chemotherapeutic drugs and minimizing systemic side effects.

A Lidar-Based Decision-Making Method for Road Boundary Detection Using Multiple Kalman Filters

Abstract: In this paper, a novel decision-making method is proposed for autonomous mobile robot navigation in an urban area where global positioning system (GPS) measurements are unreliable. The proposed method uses lidar measurements of the road's surface to detect road boundaries. An interacting multiple model method is proposed to determine the existence of a curb based on a probability threshold and to accurately estimate the roadside curb position. The decision outcome is used to determine the source of references suitable for reliable and seamless navigation. The performance of the decision-making algorithm is verified through extensive experiments with a mobile robot autonomously navigating through campus roads with several intersections and unreliable GPS measurements. Our experimental results demonstrate the reliability and good tracking performance of the proposed algorithm for autonomous urban navigation.

Machine learning of robots in tourism and hospitality: interactive technology acceptance model (iTAM) – cutting edge

Abstract: The purpose of this study is to discuss how consumers accept advanced artificial intelligence (AI) robots in hospitality and tourism and provide a typology and conceptual framework to support future research on advanced robot applicability.,This research reviews current cases of AI use and technology acceptance model (TAM) studies and proposes a framework, interactive technology acceptance model (iTAM), to identify key determinants that stimulate consumer perceptions of advanced robot technology acceptance.,The main constructs and types of advanced robots were identified by reviewing TAM studies and AI robots that are currently used in the tourism and hospitality industry. This research found that as technologies tested in TAM studies have been improved by highly interactive systems, increased capability and a more user-friendly interface, examining perceived interactivity of technology has become more important for advanced robot acceptance models. The examples of advanced robot uses indicate that each machine learning application changes the robots’ task performance and interaction with consumers. Conducting experimental studies and measuring the interactivity of advanced robots are vital for future research.,To the authors’ knowledge, this is the first study on how consumers accept AI robots with machine learning applications in the tourism and hospitality industry. The iTAM framework provides fundamental constructs for future studies of what influences consumer acceptance of AI robots as innovative technology, and iTAM can be applied to empirical experiments and research to generate long-term strategies and specific tips to implement and manage various advanced robots.,旅游和酒店业机器人的机器学习：交互式技术接受模型（iTAM）–前沿研究,这项研究旨在讨论消费者如何在酒店和旅游业中接受先进的人工智能（AI）机器人, 并提供类型学和概念框架来支持有关先进机器人适用性的未来研究。,这项研究回顾了AI使用和技术接受模型（TAM）研究的当前案例, 并提出了一个框架, 即交互式技术接受模型（iTAM）, 以识别能够激发消费者对先进机器人技术接受程度的认知的关键因素。,通过回顾当前在旅游和酒店业中使用的TAM研究和AI机器人, 本文确定了高级机器人的主要结构和类型。研究发现, 由于TAM中的技术已通过高度互动的系统, 增强的功能以及更友好的用户界面进行了改进, 因此, 对于先进的机器人接受模型而言, 检查感知技术交互性已变得越来越重要。先进的机器人使用案例表明, 每个机器学习应用程序都会改变机器人的任务性能以及与消费者的互动。进行实验研究和测量高级机器人的交互性对于将来的研究来说是至关重要的方向。,这是关于消费者如何在旅游和酒店业中接受具有机器学习应用程序的AI机器人的首次研究。iTAM框架为将来的研究提供了基础结构, 以了解哪些因素会影响消费者对AI机器人作为创新技术的接受程度。iTAM亦可以用于实证实验和研究, 以提供实施和管理各种先进机器人的长期策略和具体技巧。,纸张类型概念纸,概念性文章,El Machine Learning (Aprendizaje Automatico) de robots en turismo y hoteleria: Modelo de Aceptacion de Tecnologia Interactiva (iTAM): tecnologia de punta,El objetivo de este estudio, es analizar la aceptacion que tienen los robots avanzados de Inteligencia Artificial (IA) por parte de los consumidores de hoteles y turismo y proporcionar una tipologia y un marco conceptual para apoyar la investigacion futura sobre la aplicabilidad avanzada de estos robots.,Esta investigacion, revisa los casos actuales de uso de IA y estudios del Modelo de Aceptacion de Tecnologia (TAM) y propone: el Modelo de Aceptacion de Tecnologia Interactiva (iTAM) para identificar los determinantes clave que estimulan las percepciones del consumidor sobre la aceptacion avanzada de la tecnologia de robots.,Los principales prototipos de robots avanzados se identificaron mediante la revision de los estudios TAM y de IA sobre robots que se utilizan actualmente en la industria del turismo y la hosteleria. Esta investigacion, encontro que a medida que las tecnologias testadas en los estudios TAM, se han mejorado mediante la incorporacion de sistemas altamente interactivos, aumentando la capacidad y mejorando la usabilidad de la interfaz; se ha vuelto mas importante examinar la interactividad percibida de la tecnologia para los modelos avanzados de aceptacion de robots. Los ejemplos de usos avanzados de robots, indican que cada aplicacion de aprendizaje automatico varia el rendimiento de la tarea de los robots y la interaccion con los consumidores. La realizacion de estudios experimentales y la medicion de la interactividad de los robots avanzados son vitales para futuras investigaciones.,Este es el primer estudio sobre como los consumidores del sector turistico y hotelero aceptan los robots de IA basado en aplicaciones de machine learning (aprendizaje automatico). El marco iTAM proporciona constructos fundamentales para futuros estudios sobre los factores que influyen en el consumidor a la hora de aceptar los robots de IA como tecnologia innovadora. iTAM se podria aplicar a experimentos empiricos e investigaciones con el objetivo de generar estrategias a largo plazo y consejos especificos para implementar y administrar varios robots avanzados.,Tipo de papel Papel conceptual

Bacterial chemotaxis-enabled autonomous sorting of nanoparticles of comparable sizes.

Abstract: High throughput sorting of micro/nanoparticles of similar sizes is of significant interest in many biological and chemical applications. In this work, we report a simple and cost-effective sorting technique for separation of similarly-sized particles of dissimilar surface properties within a diffusion-based microfluidic platform using chemotaxis in Escherichia coli bacteria. Differences in surface chemistry of two groups of similarly-sized nanoparticles in a mixture were exploited to selectively assemble one particle group onto motile E. coli, through either specific or non-specific adhesion, and separate them from the remaining particle group via chemotaxis of the attached bacteria. To enable optimal operation of the sorting platform, the chemotaxis behavior of E. coli bacteria in response to casamino acids, the chemoeffector of choice was first characterized. The chemical concentration gradient range within which the bacteria exhibit a positive chemotactic response was found to be within 0.25 × 10−7–1.0 × 10−3 g ml−1 mm−1. We demonstrate that at the optimum concentration gradient of 5.0 × 10−4 g ml−1 mm−1, a sorting efficiency of up to 81% at a throughput of 2.4 × 105 particles per min can be achieved. Sensitivity of the sorting efficiency to the adhesion mechanism and particle size in the range of 320–1040 nm was investigated.

Optimizing the restored chemotactic behavior of anticancer agent Salmonella enterica serovar Typhimurium VNP20009.

Abstract: Bacteria, including strains of Salmonella, have been researched and applied as therapeutic cancer agents for centuries. Salmonella are particularly of interest due to their facultative anaerobic nature, facilitating colonization of differentially oxygenated tumor regions. Additionally, Salmonella can be manipulated with relative ease, resulting in the ability to attenuate the pathogen or engineer vectors for drug delivery. It was recently discovered that the anti-cancer Salmonella enterica serovar Typhimurium strain VNP20009 is lacking in chemotactic ability, due to a non-synonymous single nucleotide polymorphism in cheY. Replacing the mutated copy of cheY with the wild-type sequence restored chemotaxis to 70% of the parental strain. We aimed to investigate further if chemotaxis of VNP20009 can be optimized. By restoring the gene msbB in VNP20009 cheY+, which confers attenuation by lipid A modification, we observed a 9% increase in swimming speed, 13% increase in swim plate performance, 19% increase in microfluidic device partitioning towards the attractant at the optimum concentration gradient, and mitigation of a non-motile cell subpopulation. We conclude that chemotaxis can be enhanced further but at the cost of changing one defining characteristic of VNP20009. A less compromised strain might be needed to employ for investigating bacterial chemotaxis in tumor interactions.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Kalman Filter for Robot Vision: A Survey

Abstract: Kalman filters have received much attention with the increasing demands for robotic automation. This paper briefly surveys the recent developments for robot vision. Among many factors that affect the performance of a robotic system, Kalman filters have made great contributions to vision perception. Kalman filters solve uncertainties in robot localization, navigation, following, tracking, motion control, estimation and prediction, visual servoing and manipulation, and structure reconstruction from a sequence of images. In the 50th anniversary, we have noticed that more than 20 kinds of Kalman filters have been developed so far. These include extended Kalman filters and unscented Kalman filters. In the last 30 years, about 800 publications have reported the capability of these filters in solving robot vision problems. Such problems encompass a rather wide application area, such as object modeling, robot control, target tracking, surveillance, search, recognition, and assembly, as well as robotic manipulation, localization, mapping, navigation, and exploration. These reports are summarized in this review to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed in an abstract level.

Review of mobile mapping and surveying technologies

Abstract: Mobile surveying is currently one of the most popular topics in the LiDAR industry. The collection of highly precise point cloud data is provided by laser scanning systems on moving platforms with an integrated navigation solution. The potential of LiDAR based mobile surveying technology is now well proven. This article introduces an analysis on the current performance of some outstanding mobile terrestrial laser scanning systems. In this work, an overview of the positioning, scanning and imaging devices integrated into these systems is also presented. As part of this study, a systematic comparison of the navigation and LiDAR specifications provided by the manufacturers is provided. Our review suggests that mobile laser scanning systems can mainly be divided into two categories (mapping and surveying) depending on their final purpose, accuracy, range and resolution requirements. A refined integrated analysis based on hardware components could be expected to cause further improvements on these results.

Bioengineered and biohybrid bacteria-based systems for drug delivery.

Abstract: The use of bacterial cells as agents of medical therapy has a long history. Research that was ignited over a century ago with the accidental infection of cancer patients has matured into a platform technology that offers the promise of opening up new potential frontiers in medical treatment. Bacterial cells exhibit unique characteristics that make them well-suited as smart drug delivery agents. Our ability to genetically manipulate the molecular machinery of these cells enables the customization of their therapeutic action as well as its precise tuning and spatio-temporal control, allowing for the design of unique, complex therapeutic functions, unmatched by current drug delivery systems. Early results have been promising, but there are still many important challenges that must be addressed. We present a review of promises and challenges of employing bioengineered bacteria in drug delivery systems and introduce the biohybrid design concept as a new additional paradigm in bacteria-based drug delivery.

Development of Autonomous Car—Part II: A Case Study on the Implementation of an Autonomous Driving System Based on Distributed Architecture

Abstract: Part I of this paper proposed a development process and a system platform for the development of autonomous cars based on a distributed system architecture. The proposed development methodology enabled the design and development of an autonomous car with benefits such as a reduction in computational complexity, fault-tolerant characteristics, and system modularity. In this paper (Part II), a case study of the proposed development methodology is addressed by showing the implementation process of an autonomous driving system. In order to describe the implementation process intuitively, core autonomous driving algorithms (localization, perception, planning, vehicle control, and system management) are briefly introduced and applied to the implementation of an autonomous driving system. We are able to examine the advantages of a distributed system architecture and the proposed development process by conducting a case study on the autonomous system implementation. The validity of the proposed methodology is proved through the autonomous car A1 that won the 2012 Autonomous Vehicle Competition in Korea with all missions completed.