Internet of Things (IoT) technology has been attracted lots of interests over the recent years, due to its applicability across the various domains. In particular, an IoT-based robot with artificial intelligence may be utilized in various fields of surveillance. In this paper, we propose an IoT platform with an intelligent surveillance robot using machine learning in order to overcome the limitations of the existing closed-circuit television (CCTV) which is installed fixed type. The IoT platform with a surveillance robot provides the smart monitoring as a role of active CCTV. The intelligent surveillance robot, which has been built with its own IoT server, and can carry out line tracing and acquire contextual information through the sensors to detect abnormal status in an environment. In addition, photos taken by its camera can be compared with stored images of normal state. If an abnormal status is detected, the manager receives an alarm via a smart phone. For user convenience, the client is provided with an app to control the robot remotely. In the case of image context processing it is useful to apply convolutional neural network (CNN)-based machine learning (ML), which is introduced for the precise detection and recognition of images or patterns, and from which can be expected a high performance of recognition. We designed the CNN model to support contextually-aware services of the IoT platform and to perform experiments for learning accuracy of the designed CNN model using dataset of images acquired from the robot. Experimental results showed that the accuracy of learning is over 0.98, which means that we achieved enhanced learning in image context recognition. The contribution of this paper is not only to implement an IoT platform with active CCTV robot but also to construct a CNN model for image-and-context-aware learning and intelligence enhancement of the proposed IoT platform. The proposed IoT platform, with an intelligent surveillance robot using machine learning, can be used to detect abnormal status in various industrial fields such as factory, smart farms, logistics warehouses, and public places.

In this paper, we propose an intelligent monitoring framework based on the Internet of Things (IoT) by applying a Recurrent Neural Network (RNN) for the predictive maintenance of a biobanking system. RNN, which is one of the deep learning models, is used for time series data. It is called a sequence model because it processes inputs and outputs in sequence units. The proposed framework measures the internal temperature of the cryogenic freezer and the temperature of each component simultaneously, monitors the internal temperatures of internal and middle layers in real time, sends the sensing temperature data to the server, and performs predictive learning. Thus, it is possible to support the intelligent predictive maintenance of the biobank by performing a time series data analysis of the temperature sensor using RNN. Among RNN methods, a simple RNN has a longer-term dependency problem; therefore, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which have higher learning performance, are selected. To support the intelligent predictive maintenance of the biobank, both the LSTM and GRU models were constructed, and comparative experiments were performed. The proposed system can ensure the safety of bio-resources by performing predictive maintenance using RNN and provide an accurate status of the biobank in real-time. In addition, before an abnormal situation occurs, it is possible to respond immediately to emergencies that may damage biological resources.

A fuzzy inference technique for real-time textile animation without integration at textile model based Mass-Spring model is introduced. Until now many techniques have used the Mass-Spring model to describe elastically deformable objects like textile. A textile object is able to represent as a deformable surface composed of spring and masses, the movement of textile surface which is analysed through the numerical integration by the fundamental law of dynamics such as Hooke's law. However, the integration methods have 'instability problems' if the explicit Euler's method is applied or 'large amounts of calculation' if the implicit Euler's method is applied. A simple and fast animation technique for Mass-Spring model of a textile with fuzzy inference is proposed. The stabilized simulation result is obtained the state of each mass-point in real-time for the n of mass-points by a relatively simple calculation.

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A fuzzy inference technique for real-time textile animation without integration at textile model based Mass Spring system is introduced. Until now many techniques have used the Mass-Spring model to describe elastically deformable objects like textile. A textile object is able to represent as a deformable surface composed of spring and masses, the movement of textile surface which is analyzed through the numerical integration by the fundamental law of dynamics such as Hooke's law. However, the integration methods have 'instability problems' if the explicit Euler's method is applied or 'large amounts of calculation' if the implicit Euler's method is applied. A simple and fast animation technique for Mass-Spring model of a textile with fuzzy inference is proposed. The stabilized simulation result is obtained the state of each mass-points in real time for the n of mass-points by a relatively simple calculation.

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