• Steel and Composite Structures
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• v.50 no.6
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• pp.609-625
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• 2024

This paper presents an in-depth analysis of the nonlinear vibration of microbeams, with a particular emphasis on their application in sports monitoring systems. The research utilizes classical beam theory, modified couple stress theory, and von-Kármán nonlinear parameters to explore the behavior of microbeams. These microbeams are characterized by a non-uniform geometry, with materials that continuously change along the beam radius and a thickness that varies along the beam length. The main contribution lies in its exploration of the stability of smart sensors in sports structures, particularly those with non-uniform geometries. The research findings indicate that these non-uniform microbeams, when used in smart systems made of functionally graded temperature-dependent materials, can operate effectively in thermal environments. The smart system developed in this study demonstrates significant potential for use in sports applications, particularly in monitoring and gathering information. The insights gained from this research contribute to the understanding of the performance and optimization of microbeams in sports applications, particularly in the context of non-uniform geometries. This research, therefore, provides a foundation for the development of advanced, reliable, and efficient monitoring systems in sports applications.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1755-1760
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• 2016

Automation of greenhouses has proved to be extremely helpful in maximizing crop yields and minimizing labor costs. The optimum conditions for cultivating plants are regularly maintained by the use of programmed sensors and actuators with constant monitoring of the system. In this paper, we have designed a prototype of a smart greenhouse using Arduino microcontroller, simple yet improved in feedbacks and algorithms. Only three important microclimatic parameters namely moisture level, temperature and light are taken into consideration for the design of the system. Signals acquired from the sensors are first isolated and filtered to reduce noise before it is processed by Arduino. With the help of LabVIEW program, Time domain analysis and Fast Fourier Transform (FFT) of the acquired signals are done to analyze the waveform. Especially, for smoothing the outlying data digitally, Moving average algorithm is designed. With the implement of this algorithm, variations in the sensed data which could occur from rapidly changing environment or imprecise sensors, could be largely smoothed and stable output could be created. Also, actuators are controlled with constant feedbacks to ensure desired conditions are always met. Lastly, data is constantly acquired by the use of Data Acquisition Hardware and can be viewed through PC or Smart devices for monitoring purposes.

• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.311-320
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• 2022

Objective: To investigate the reliability and validity of static balance measurements using an acceleration sensor and a gyroscope sensor in smart phone inertial sensors. Design: Equivalent control group pretest-posttest. Methods: Subjects were forty five healthy adults aged twenty to fifty-years-old who had no disease that could affect the experiment. After pre-test, all participants wore a waist band with smart phone, and conducted six static balance measurements on the force plate twice for 35 seconds each. To investigate the test-retest reliability of both smart phone inertial sensors, we compared the intra-correlation coefficient (ICC 3, 1) between primary and secondary measurements with the calculated root mean scale-total data. To determine the validity of the two sensors, it was measured simultaneously with force plate, and the comparision was done by Pearson's correlation. Results: The test-retest reliability showed excellent correlation for acceleration sensor, and it also showed excellent to good correlation for gyroscope sensor(p<0.05). The concurrent validity of smartphone inertial sensors showed a mostly poor to fair correlation for tandem-stance and one-leg-stance (p<0.05) and unacceptable correlation for the other postures (p>0.05). The gyroscope sensor showed a fair correlation for most of the RMS-Total data, and the other data also showed poor to fair correlation (p<0.05). Conclusions: The result indicates that both acceleration sensor and gyroscope sensor has good reliability, and that compared to force plate, acceleration sensor has unacceptable or poor correlation, and gyroscope sensor has mostly fair correlation.

• Smart Structures and Systems
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• v.1 no.1
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• pp.103-120
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• 2005

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.197-202
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• 2016

In this paper, we propose optimal smart home security monitoring system. Proposeed optimal smart home security system using the three types of ultrasonic sensors were tested to obtain reliable data. and Using Raspberry Pi3, the smart home security system was implemented. In addition, It was verified through experiments optimal efficiency with a small amount compared to the conventional sensor of the home security system by the two ultrasonic sensors located in the optimal position. It was able to use two ultrasonic sensors to determine whether the intruder's highly efficient and reliable intrusion, and connect the servo motor at the bottom of the camera so you can shoot adjusted to the attacker's location to shoot the intruder's image. In addition, by using a Web server and stored the recorded image and two ultrasonic sensor data and provide a Web page for a user to monitor at all remote locations.

• Journal of Sensor Science and Technology
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• v.29 no.5
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• pp.293-302
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• 2020

Food safety has emerged as a growing concern for human health in recent times. Consuming contaminated food may lead to serious health problems, and therefore, a system for monitoring food freshness that is both non-detrimental to the quality of food and highly accurate is required to ensure that only high-quality fresh food packages are provided to the customers. This paper proposes a method to monitor and detect food quality using a compact smart sensor tag. The smart tag is composed of three ultra-low-power sensors, which monitor four major indicators of food freshness: temperature, humidity, and the concentrations of ammonia and hydrogen sulfide gases. An RF energy scavenging circuit is integrated into the smart sensor tag to harvest energy from radio waves at a high frequency of 13.56 MHz to supply sufficient power to the tag. Experimental results show that the proposed energy harvester can efficiently obtain energy at a distance of approximately 40 cm from a 4 W reader. In addition, the proposed smart sensor tag can operate without any battery, thereby eliminating the requirement of frequent battery replacement and consequently decreasing the cost. Meanwhile, the freshness of preserved pork is continuously monitored under two conditions--room temperature and refrigerator temperature--both of which are the most common temperatures under which food is generally stored. The food-monitoring experiments are conducted over a period of one week using the proposed battery-less tag. Based on the experimental results, the food assessment is classified into four categories: fresh, normal, low, and spoiled.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.76-81
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• 2017

This paper presents a low cost and flexible IoT enabled smart LED controller using Arduino that is used for emergency exit signs. The Internet of Things (IoT) is become a global network that put together physical objects using network communications for the purpose of inter-communication of devices, access information on internet, interaction with users as well as permanent connected environment. A crucial point in this paper, is underlined on the potential key points of applying the Arduino platform as low cost, easy to use microcontroller with combination of various sensors applied in IoT technology to facilitate and establishment of intelligent products. To demonstrate the feasibility and effectiveness of the system, devices such as LED strip, combination of various sensors, Arduino, power plug and ZigBee module have been integrated to setup smart emergency exit sign system. The general concept of the proposed system design discussed in this paper is all about the combination of various sensor such as smoke detector sensor, humidity, temperature sensor, glass break sensors as well as camera sensor that are connected to the main controller (Arduino) for the purpose of communicating with LED exit signs displayer and dedicated PC monitors from integrated system monitoring (controller room) through gateway devices using Zig bee module. A critical appraisal of the approach in the area concludes the paper.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.259-264
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• 2016

In this paper, we proposed an arduino-based smart green house management system model and implemented it. The proposed system consists of control unit composed of sensors and arduino, agent program controlling the green house, and web applications providing user interfaces. The control unit transmits data of sensors such as temperature, humidity, illuminance, moisture, etc. to the agent program, and then the agent saves the data in its database. In reverse, control data are transmitted from agent program to control unit. Users can monitor sensed data of green houses and control actuators remotely using web. Plus, smart green house management is available by context awareness and autonomous control functions of the proposed system.

• Smart Structures and Systems
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• v.15 no.3
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• pp.683-698
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• 2015

Railroad bridges form an integral part of railway infrastructure throughout the world. To accommodate increased axel loads, train speeds, and greater volumes of freight traffic, in the presence of changing structural conditions, the load carrying capacity and serviceability of existing bridges must be assessed. One way is through system identification of in-service railroad bridges. To dates, numerous researchers have reported system identification studies with a large portion of their applications being highway bridges. Moreover, most of those models are calibrated at global level, while only a few studies applications have used globally and locally calibrated model. To reach the global and local calibration, both ambient vibration tests and controlled tests need to be performed. Thus, an approach for system identification of a railroad bridge that can be used to assess the bridge in global and local sense is needed. This study presents system identification of a railroad bridge using free vibration data. Wireless smart sensors are employed and provided a portable way to collect data that is then used to determine bridge frequencies and mode shapes. Subsequently, a calibrated finite element model of the bridge provides global and local information of the bridge. The ability of the model to simulate local responses is validated by comparing predicted and measured strain in one of the diagonal members of the truss. This research demonstrates the potential of using measured field data to perform model calibration in a simple and practical manner that will lead to better understanding the state of railroad bridges.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.4
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• pp.103-110
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• 2018

Services that enhance user convenience by using various IoT devices are increasing with the development of Internet of Things(IoT) technology. Also, since the price of IoT sensors has become cheaper, companies providing services by collecting and utilizing data from various sensors are increasing. The smart IoT home system is a representative use case that improves the user convenience by using IoT devices. To improve user convenience of Smart IoT home system, this paper proposes a method for the control of related devices based on data analysis. Internal environment measurement data collected from IoT sensors, device control data collected from device control actuators, and user judgment data are learned to predict the current home state and control devices. Especially, differently from previous approaches, it uses deep neural network to analyze the data to determine the inner state of the home and provide information for maintaining the optimal inner environment. In the experiment, we compared the results of the long-term measured data with the inferred data and analyzed the discrimination performance of the proposed method.