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Boolean Control Network Based Modeling for Context-Aware System in Smart Home
M. Humayun Kabir,M. Robiul Hoque,Hyungyu Seo,Sung-Hyun Yang 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.4
Context-awareness is an important characteristic for smart home. Context-aware system reacts and adapts according to the changes in the domain environment. In this paper, we have presented a mathematical modeling used for a context-aware system based on Boolean control network with five nodes for smart home. This Boolean control network describes the relationship between the context elements (user, time, location, activity) and service states (morning call, normal, entertainment, sleeping, guarding). We expressed the dynamics of the state spaces by linear algebraic equation using semi-tensor product of matrices, which is effective to logical inference and control.
Implementation of Boolean Control Network Based Intelligent System in Smart Home
M. Humayun Kabir,M. Robiul Hoque,Sung-Hyun Yang 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.3
Smart home is a nondeterministic complex environment. Sensors and actuators network are involved in smart home to collect environmental information and to control the devices used by the user for comfort of life. Generally smart home system is implemented by logical rules which described the relation between each element (sensors and actuators). Controlling using logical rules is difficult. In this paper, we have represented an intelligent system for smart home by using Boolean Control Network. For easy control, we have used matrix expression of logic. The system is controlled in several states and in each state different device is operated through actuator network. Matlab based simulation work is done to show the state changes of the system. The result shows that using matrix expression it is easy to control the state of this system.
Machine Learning Based Adaptive Context-Aware System for Smart Home Environment
M. Humayun Kabir,M. Robiul Hoque,Hyungyu Seo,Sung-Hyun Yang 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.11
Context-awareness is the key element for building a smart home environment. The goal of a smart home is to predict the demand of home users and proactively provides the proper services by considering the user’s context information. Several methods are used in context-aware system to provide services. Machine learning based approaches are capable to make better prediction and adaptation than others. In this paper, we present machine learning based context-aware system which can provide service according to the trained model. Two effective learning algorithms: Back propagation Neural Network, and Temporal Differential (TD) class of reinforcement learning are used for prediction and adaptation respectively. This ap
Middleware-based Cooperative Context Dissemination for Smart Home Application
M. Robiul Hoque,M. Humayun Kabir,Dong-Hyuk Lim,Sung-Hyun Yang 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.10
A smart home service is provided by following several contexts of environment and user activities. Moreover, some contexts are common for different services. So, it is unwise to separately compute the same context for different services due to the processing time cost, here a context reusable mechanism can significantly reduce the context computing cost. For this purpose, middleware-based implementation is a good alternative. Middleware uses raw data through sensors and user profiles from database then generates context using these data, and finally conveys context to applications. In this paper, we propose a middleware architecture that shares context in a cooperative manner so that it can be reused among applications. Basically, this middleware generates a particular context at once for a time interval and disseminates among registered applications using a new mechanism. Measuring inequality of the context computing time over application-based implementation exposes the effectiveness of this middleware.
Development of a Smart Home Context-aware Application : A Machine Learning based Approach
M. Humayun Kabir,M. Robiul Hoque,Sung-Hyun Yang 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.1
Context-awareness is an important characteristic of smart home. Several methods are used in context-aware application to provide services. The main target of smart home is to predict the demand of home users and proactively provide the proper services by computing user’s context information. In this paper, we present a context-aware application which can provide service according to predefined choice of user. It uses Mahalanobis distance based k nearest neighbors classifier technique for inference of predefined service. We combine the features of supervised and unsupervised machine learning in the proposed application. This application can also adapt itself when the choice of user is changed by using Q-learning reinforcement learning algorithm.
M. Robiul Hoque,M. Humayun Kabir,Keshav Thapa,Sung-Hyun Yang 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.9
A context-aware system provides services based on the current context of the environment and user activities. So, context management and reasoning in context-aware systems are important tasks. A formal context model based on ontology can play a vital role in facilitating reasoning by formally representing domain knowledge. This paper presents an ontology-based reusable generic context model for context-aware systems. This model provides a context vocabulary and structure for contexts and their semantics which are essential for reasoning. We evaluate the effectiveness of this model for both ontology and rule-based reasoning in the smart home domain and the result we obtain is promising.
Development of an Edge-Based Algorithm for Moving-Object Detection Using Background Modeling
Shin, Won-Yong,Kabir, M. Humayun,Hoque, M. Robiul,Yang, Sung-Hyun The Korea Institute of Information and Commucation 2014 Journal of information and communication convergen Vol.12 No.3
Edges are a robust feature for object detection. In this paper, we present an edge-based background modeling method for the detection of moving objects. The edges in the image frames were mapped using robust Canny edge detector. Two edge maps were created and combined to calculate the ultimate moving-edge map. By selecting all the edge pixels of the current frame above the defined threshold of the ultimate moving edges, a temporary background-edge map was created. If the frequencies of the temporary background edge pixels for several frames were above the threshold, then those edge pixels were treated as background edge pixels. We conducted a performance comparison with previous works. The existing edge-based moving-object detection algorithms pose some difficulty due to the changes in background motion, object shape, illumination variation, and noises. The result of the performance evaluation shows that the proposed algorithm can detect moving objects efficiently in real-world scenarios.