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A Reference Architecture for Machine Learning-Based Autonomous Systems
http://doi.org/10.5626/JOK.2020.47.4.368
Autonomous computing is one of the essential factors for realizing the fourth industrial revolution and a future technology that provides capabilities of autonomous recognition, autonomous judgement, autonomous planning, and autonomous management with automatic systems. With the advent of various sensors and IoT devices, a rich set of context data can be acquired from the environment, and autonomous system technologies with human-machine interface (HMI) enabling the realization of an eco-system wherein a system itself can maintain its best quality by using the acquired context data. However, because of the highly complicated functional and non-functional requirements for realizing autonomous systems, developing such systems becomes more difficult and development productivity becomes much lower. In the paper, we present a reference architecture which can be commonly applied to autonomous systems. The proposed reference architecture includes architecture design, core components, main algorithm, and so on. The reference architecture forms a structural basis of the target system and can guarantee the overall quality and improve development efficiency by reusing the core structure of the reference architecture. Additionally, we apply the reference architecture to two autonomous systems and verify the applicability and practicability of the reference architecture.
Methods for Analyzing Preference Tendencies and Activity Patterns with Audio Contexts
Hyun Jung La, Moon Kwon Kim, Han Ter Jung, Soo Dong Kim
http://doi.org/10.5626/JOK.2018.45.4.348
Recently, there has been a trend for collecting various rich personal contexts, such as social network services (SNS), user created contents (UCC), and digital diaries. Because of this trend, much more attention to semantic analysis with personal contexts is being paid. The semantic analysis allows users to analyze and understand diverse aspects of their lives such as their lifestyle and quality of life which are not easily recognized by them. Hence, in this paper, we propose a process to infer semantics from personal contexts, more specifically, audio contexts. This paper is focused on proposing detailed algorithms for analyzing user’s preference tendencies and activity patterns. To evaluate the proposed methods, we apply them to developing a system, called Smart Diary System, which is used to analyze a user’s preference tendency and activity pattern from audio diaries, and we present experiment results with the system. We expect to use the proposed process and algorithms in various application domains such as personal secretary service, recommendation services, and advertising services.
A Prediction-based Dynamic Component Offloading Framework for Mobile Cloud Computing
http://doi.org/10.5626/JOK.2018.45.2.141
Nowadays, mobile computing has become a common computing paradigm that provides convenience to people’s daily life. More and more useful mobile applications’ appearance makes it possible for a user to manage personal schedule, enjoy entertainment, and do many useful activities. However, there are some inherent defects in a mobile device that battery constraints and bandwidth limitations. These drawbacks get a user into troubles when to run computationally intensive applications. As a remedy scheme, component offloading makes room for handling mentioned issues via migrating computationally intensive component to the cloud server. In this paper, we will present the predictive offloading method for efficient mobile cloud computing. At last, we will present experiment result for validating applicability and practicability of our proposal.
A Design of Effective Inference Methods and Their Application Guidelines for Supporting Various Medical Analytics Schemes
Moon Kwon Kim, Hyun Jung La, Soo Dong Kim
As a variety of personal medical devices appear, it is possible to acquire a large number of diverse medical contexts from the devices. There have been efforts to analyze the medical contexts via software applications. In this paper, we propose a generic model of medical analytics schemes that are used by medical experts, identify inference methods for realizing each medical analytics scheme, and present guidelines for applying the inference methods to the medical analytics schemes. Additionally, we develop a PoC inference system and analyze real medical contexts to diagnose relevant diseases so that we can validate the feasibility and effectiveness of the proposed medical analytics schemes and guidelines of applying inference methods.
A Conceptual Framework for Aging Diagnosis Using IoT Devices
Jae Yoo Lee, Jin Cheul Park, Soo Dong Kim
With the emergence of Internet-of-Things (IoT) computing, it has become possible to acquire users’ health-related contexts from various IoT devices and to diagnose their biological aging through analysis of the IoT health contexts. However, previous work on methods of aging diagnosis used a fixed list of aging diagnosis factors, making it difficult to handle the variability of users’ IoT health contexts and to dynamically adapt the addition and deletion of aging diagnosis factors. This paper proposes a design and methods for a dynamically adaptable aging diagnosis framework that acquires a set of IoT health contexts from various IoT devices based on a set of aging diagnosis factors of the user. By using the proposed aging diagnosis framework, aging diagnosis methods can be applied without considering the variability of IoT health contexts and aging diagnosis factors can be dynamically added and deleted.
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