• Journal of Sensor Science and Technology
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• v.14 no.4
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• pp.237-243
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• 2005

Strain gauge type load cell is used widely as weight sensor. However, it has problems such as noise, power consumption, high cost and big size. Semiconductor type piezoresistive pressure sensor is practically used in recent for low hysteresis, good linearity, small size, light weight and strong on vibration. In this paper, we have fabricated the piezoresistive pressure sensor and packaged the miniature weight sensor. We packaged the miniature weight sensor by flip-chip bonding between die and PCB for durability, because the weight sensor is directly contacted on a physical solid distinct from air and oil pressure. We measured the characteristics of the weight sensor, which had the output of $10{\sim}80$ mV on the weight range of $0{\sim}2$ kg. In the result, we could fabricate the weight sensor with an accuracy of 3 %FSO linearity.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.262-267
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• 2000

This paper describes a weight in motion(WIM) sensor to measure the weight of a vehicle in motion. The main sensing element of the WIM sensor is the PVDF(Polyvinylidene fluoride) film that shows rapid response to an external excitation. Due to the property of rapid response, it is possible to measure the weight of a vehicle in motion with high speed. In the development of the WIM sensor, the dominant target value was the uniformity of the sensor. To increase the uniformity, We employed shrinkable tube made of rubber to enhance the uniformity, and performed the rolling of the brass tube repeatedly. The uniformity of the sensor was examined experimentally. It was comparable to that of a WIM sensor of the MSI which was the benchmark of this development. This paper also describes the mechanical modeling of the sensor and the suitable charge amplifier for the sensor.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.165-168
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• 2005

The objects, which can be personal digital assistants, electronic rings, doors or even clothes, offer embedded chips with computation facilities and are generally called artifacts. I later realized that this was not so the real problem is actually authentication. Recent results indicate scalability problems for flat ad hoc networks. Sensor network achieves function that handle surrounding information perception through sensor and sensed information to network that is consisted of sensor nodes of large number. Research about new access control techniques and height administration techniques need authentication information persons' certification assurance level classification in sensor network environment which become necessary different view base with authentication information at node for application of AAA technology in USN environment that must do authentication process using information that is collected from various sensor mountings. So, get base authentication information in sensor type and present weight grant model by security strength about authentication information through information who draw. In this paper collected information of sensor nodes model who give weight drawing security reinforcement as authentication information by purpose present be going to. and Must be able to can grasp special quality of each sensor appliances in various side and use this and decide authentication assurance level for value estimation as authentication information elements. Therefore, do to define item that can evaluate Authentication information elements thus and give simple authentication assurance level value accordingly because applying weight. Present model who give authentication assurance level value and weight for quotation according to security strength.

• Current Optics and Photonics
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• v.8 no.5
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• pp.508-514
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• 2024

This paper presents a deep learning-based weight sensor, using optical speckle patterns of multimode fiber, designed for real-time intrusion detection. The weight sensor has been trained to identify 11 distinct speckle patterns, ranging in weight from 0.0 kg to 2.0 kg, with an interval of 200 g between each pattern. The estimation for untrained weights is based on the generalization capability of deep learning. This results in an average weight error of 243.8 g. Although this margin of error precludes accurate weight measurement, the system's ability to detect abrupt weight changes makes it a suitable choice for intrusion detection applications. The weight sensor is integrated with the Google Teachable Machine, and real-time intrusion notifications are facilitated by the ThingSpeak^TM cloud platform, an open-source Internet of Things (IoT) application developed by MathWorks.

• Journal of Sensor Science and Technology
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• v.15 no.6
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• pp.411-416
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• 2006

This paper describes the development of a 3-axis finger force sensor to grasp an unknown object safely in an intelligent robot's hand. In order to safely grasp an unknown object, robot's hand should measure the weight of an object and the force of grasping direction simultaneous. But, in the published papers, the grippers and hands equippd with the force sensor that could only measure the force of grasping direction, and grasped objects using their sensors. These grippers and hands can't safely grasp unknown objects, because they can't measure the weight of it. Thus, it is necessary to develop 3-axis force sensor that can measure the weight of an object and the force of grasping direction for an intelligent gripper. In this paper, 3-axis finger force sensor to grasp an unknown object safely in an intelligent robot's hand was developed. In order to fabricate a 3-axis finger force sensor, the sensing elements were modeled using parallel plate beams, and the theoretical analysis was performed to determine the size of sensing elements, then the 3-axis finger force sensor was fabricated. Also, the characteristic test of the developed 3-axis finger force sensor was performed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.6
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• pp.612-627
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• 2009

The high-level contribution of this paper is to illustrate the effectiveness of using graph theory tree traversal algorithms (pre-order, in-order and post-order traversals) to generate the chain of sensor nodes in the classical Power Efficient-Gathering in Sensor Information Systems (PEGASIS) data aggregation protocol for wireless sensor networks. We first construct an undirected minimum-weight spanning tree (ud-MST) on a complete sensor network graph, wherein the weight of each edge is the Euclidean distance between the constituent nodes of the edge. A Breadth-First-Search of the ud-MST, starting with the node located closest to the center of the network, is now conducted to iteratively construct a rooted directed minimum-weight spanning tree (rd-MST). The three tree traversal algorithms are then executed on the rd-MST and the node sequence resulting from each of the traversals is used as the chain of nodes for the PEGASIS protocol. Simulation studies on PEGASIS conducted for both TDMA and CDMA systems illustrate that using the chain of nodes generated from the tree traversal algorithms, the node lifetime can improve as large as by 19%-30% and at the same time, the energy loss per node can be 19%-35% lower than that obtained with the currently used distance-based greedy heuristic.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1814-1819
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• 2005

This paper describes the development of a Stewart platform-based robot force sensor with distinctive structure of ball joints. The number of ball joints is only a half of the similar style sensors, so it is possible to reduce size and weight of the sensor. The structure of ball joint is described and discussed. Furthermore, we use strain gauges, but not liner voltage differential transformers, as sensing elements, in order to reduce size and weight of the sensor. It is also proposed that beams are replaced with pipes as sensing elements of the sensor. The ball joints and sensing elements with pipes can effectively reduce the error of the sensor. A geometric analysis model is also proposed. The external force and its moment can be measured with this model. Moreover, the performance of this sensor was tested. The test results conducted to evaluate the sensing capability of the sensor is reported and discussed.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.56-60
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• 2021

Recently, technologies for internet of things have been rapidly advanced with development of network. Also interest in smart healthcare that informs about motion information of users has been growing. In this paper, a system that is monitoring the weight on both feet by using aduino and FSR(Force Sensitive Resistor) Sensor is implemented. A 4-channel sensor driver module was implemented to measure a more accurate weight value. It is monitoring the weight on both feet by the using an application for either your PC or mobile device. Mobile applications can contribute to reducing human damage by sending messages along with location in emergency situations, such as injuries caused by falls during outdoor activities.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.229-237
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• 2022

Motion recognition is very useful for implementing an intuitive HMI (Human-Machine Interface). In particular, hands are the body parts that can move most precisely with relatively small portion of energy. Thus hand motion has been used as an efficient communication interface with other persons or machines. In this paper, we design and implement a light-weight ANN (Artificial Neural Network)-based hand motion recognition using a state-of-the-art flex sensor. The proposed design consists of data collection from a wearable flex sensor, preprocessing filters, and a light-weight NN (Neural Network) classifier. For verifying the performance and functionality of the proposed design, we implement it on a low-end embedded device. Finally, our experiments and prototype implementation demonstrate that the accuracy of the proposed hand motion recognition achieves up to 98.7%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.4
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• pp.721-725
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• 2007

A fiber optic based weight sensor has fabricated using a fiber Bragg grating with a weight sensitive. The sensing concept exploits the inherent characteristics of the FBG and is based on the strain effect induced in the fiber Bragg grating through. A direct indication of the weight level is given by the shift of the Bragg wavelength caused by the expansion of the sensing material. A FBG behaves like a spectral filter which has inherent characteristics that render it very sensitive to strain and temperature. The sensing principle is also based on the strain effect induced in the FBG through the caused by the weight. The experimental setup used for the initial investigation to characterize the mass response of the sensor. The transmitted signal from the sensor was monitored using an optical spectrum analyzer with a resolution bandwidth of 0.4nm. In this paper, we presented the spectral characterization and shaping of FBG by scanning a mass element that affects a small grating fraction at a time, without permanent effects on the optical fiber when the various wavelength and strain is removed. That is, destruction when the optical fiber for weight is physically damaged.