• Advanced Industrial SCIence
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• v.1 no.2
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• pp.24-29
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• 2022

Recently, smart farm research that applies IoT technology to various farms is being actively conducted to improve agricultural cooling power and minimize cost reduction. In particular, methods for automatically and remotely controlling environmental information data around smart farms through IoT devices are being studied. This paper proposes a processing model that can maintain an optimal growth environment by monitoring environmental information data collected from smart farms in real time based on machine learning. Since the proposed model uses machine learning technology, environmental information is grouped into multiple blockchains to enable continuous data collection through rich big data securing measures. In addition, the proposed model selectively (or binding) the collected environmental information data according to priority using weights and correlation indices. Finally, the proposed model allows us to extend the cost of processing environmental information to n-layer to a minimum so that we can process environmental information in real time.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.433-440
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• 2022

Recently, owing to global warming, average summer temperatures are increasing and the number of hot days is increasing is increasing, which leads to an increase in heat stroke. In particular, outdoor workers directly exposed to the heat are at higher risk of heat stroke; therefore, preventing heat-related illnesses and managing safety have become important. Although various wearable devices have been developed to prevent heat stroke for outdoor workers, applying various sensors to the safety helmets that workers must wear is an excellent alternative. In this study, we developed a smart helmet that measures various vital signs of the wearer such as body temperature, heart rate, and sweat rate; external environmental signals such as temperature and humidity; and movement signals of the wearer such as roll and pitch angles. The smart helmet can acquire the various data by connecting with a smartphone application. Environmental data can check the status of heat wave advisory, and the individual vital signs can monitor the health of workers. In addition, we developed an algorithm that classifies the risk of heat-related illness as normal and abnormal by inputting a set of vital signs of the wearer using a support vector machine technique, which is a machine learning technique that allows for rapid binary classification with high reliability. Furthermore, the classified results suggest that the safety manager can supervise the prevention of heat stroke by receiving feedback from the control system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.64-83
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• 2012

The number of smart phone users is rapidly growing due to recent increase in wireless Internet usage, development of a wide variety of applications, and activation of M2M (Machine to machine) services. Although the smart phone offers benefits of mobility and convenience, it also has serious security problems. To utilize M2M services in the smart phone, a flexible integrated authentication and access control facility is an essential requirement. To solve these problems, we propose a context-aware single sign-on and access control system that uses context-awareness, integrated authentication, access control, and an OSGi service platform in the smart phone environment. In addition, we recommend Fuzzy Logic and MAUT (Multi-Attribute Utility Theory) in handling diverse contexts properly as well as in determining the appropriate security level. We also propose a security system whose properties are flexible and convenient through a typical scenario in the smart phone environment. The proposed context-aware security system can provide a flexible, secure and seamless security service by adopting diverse contexts in the smart phone environment.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.79-86
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• 2021

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1321-1326
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• 2015

The amount of smart phones has increased exponentially. Due to the periodic release of high-performance smart phones and upgraded operating system, new smart phones become out-dated over 1 or 2 years. In order to solve environmental constraints of these smart phones, virtualization technology using Thin-Client terminal has been developed. However, in the case of Virtual Machine(VM), the applications associated with sensors and a GPS device can not run because they are not included. In this paper, by implementing the device driver for Android running in a virtual machine in the x86-based systems, it is to provide Android virtualization capabilities such as using the latest smart phones in the virtual machine environment. It would like to propose a method that the virtual device driver receives sensors and GPS information from the old Android smart phones(Thin-Client) that actually work and run as if the real device exists.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.155-165
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• 2020

In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.

• Journal of Drive and Control
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• v.17 no.3
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• pp.9-14
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• 2020

A special purpose machine with two manipulators and quadruped crawler system is being developed to work at disaster sites where it is intended to quickly respond in the initial stages after the event. In this study, a terrain recognition module is developed so that the above special purpose machine can quickly obtain ground information to help choose its path while recognizing objects in its way, this is intended to enhance the remote driver's limited situational awareness. Terrain recognition modules were developed for two tasks (real-time path guidance, precision terrain measurements). The real-time path guidance analyzes terrain and obstacles while moving, while the precision terrain measurement feature provides more accurate terrain information by precisely measuring the ground in front of the vehicle while stationary. In this study, an air-cooled sensor protection module was developed so that the terrain recognition module can continue its vital tasks in the event of exposure to foreign substances, including scattered dust, mist and rainfall, as well as high temperatures.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.73-79
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• 2021

In the wave of the 4th industrial revolution, smart factory is required in many factories. However, small and mid-sized companies (SMEs) still have aging machines and are having difficulties in the data collection stage, which is the basis of smart factories. This study proposes a low cost monitoring method by using an open source based technology that extracts data from the image of the facility control panel without the need for modification of existing facilities. The proposed method was tested and evaluated for forging facilities in automobile parts manufacturing plants through prototyping. As a result of the evaluation, it was confirmed that low-cost facility monitoring is possible, and it will help SMEs build smart factories.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.89-100
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• 2021

Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.760-761
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• 2019

Smart manufacturing is a process of integrating computer-related technologies in production and by doing so, achieving more efficient production management. The recent development of supercomputers has led to the broad utilization of artificial intelligence (AI) and machine learning techniques useful in predicting specific patterns. Despite the usefulness of AI and machine learning techniques in smart manufacturing processes, there are many fundamental issues with the direct deployment of these technologies related to data management. In this paper, we focus on solving the outlier detection issue in smart manufacturing applications. More specifically, we apply a state-of-the-art outlier detection technique, called Elliptic Envelope, to detect anomalies in simulation-based collected data.